20201207Supplement to Application.pdf

December 7, 2020
Ms. Jan Noriyuki
Commission Secretary
Idaho Public Utilities Commission
P.O. Box 83720
Boise, ID 83720-0074
RE: Case No. INT-G-20-06
Dear Ms. Noriyuki:
Attached for consideration by this Commission is an electronic submission of Intermountain Gas
Company’s Supplement to Application for a Determination of 2019 Energy Efficiency Expenses as
Prudently Incurred.
If you should have any questions regarding the attached, please don’t hesitate to contact me at (208)
377-6015.
Sincerely,
Lori A. Blattner
Director, Regulatory Affairs
Intermountain Gas Company
Enclosure
cc: Mark Chiles
Preston Carter
RECEIVED
Monday, December 7, 2020 11:20:44 AM
IDAHO PUBLIC
UTILITIES COMMISSION
INTERMOUNTAIN GAS COMPANY
CASE NO. INT-G-20-06

SUPPLEMENT TO APPLICATION
AND
EXHIBITS

In the Matter of the Application of INTERMOUNTAIN GAS COMPANY
For a Determination of 2019 Energy Efficiency Expenses as Prudently Incurred

SUPPLEMENT TO APPLICATION - 2
Preston N. Carter, ISB No. 8462 Givens Pursley LLP
601 W. Bannock St.
Boise, Idaho 83702 Telephone: [phone]
Attorneys for Intermountain Gas Company
BEFORE THE IDAHO PUBLIC UTILITIES COMMISSION
for a Determination of 2019 Energy
Incurred
Case No. INT-G-20-06
SUPPLEMENT TO APPLICATION
Intermountain Gas Company (“Intermountain” or “Company”), a subsidiary of MDU
Resources Group, Inc. with general offices located at 555 South Cole Road, Boise, Idaho, pursuant
to the Rules of Procedure of the Idaho Public Utilities Commission (“Commission”), submits
additional information pertinent to this case in this Supplement (“Supplement to Application” or
“Supplement”) to the Application for Determination of 2019 Energy Efficiency Expenses as
Prudently Incurred, Case No. INT-G-20-06, filed September 1, 2020 (“Original Application”). This
Supplement includes the final report from Intermountain’s Evaluation, Measurement and
Verification (“EM&V”) study, resulting cost-effectiveness tests, and proposed changes to the
Company’s Residential Energy Efficiency Program (“EE Program”) resulting from the EM&V
study.1 The information included in this Supplement application is additive to the Original
Application, and does not replace, amend, or remove any portion of the Original Application.
Please address communications regarding this Application to:

1 In the Original Application, Intermountain noted that the EM&V study was underway but not yet complete.
Original Application at 9 (“The CPA, along with the soon to be completed Evaluation, Measurement and Verification (“EM&V”) study will be valuable tools in bringing the program back to cost effectiveness in the
future.”). This Supplement provides the study and summarizes its key results.
SUPPLEMENT TO APPLICATION - 3
Preston N. Carter Givens Pursley LLP
601 W. Bannock St.
Boise, Idaho 83702 [e-mail] [e-mail]
and

Lori A. Blattner Director – Regulatory Affairs Intermountain Gas Company Post Office Box 7608
Boise, ID 83707
[e-mail] In support of this Supplement to Application, Intermountain alleges and states as follows.
I. INTRODUCTION
Intermountain is a gas utility, subject to the jurisdiction of the Commission, engaged in the
sale of and distribution of natural gas within the State of Idaho under authority of Commission
Certificate No. 219, issued December 2, 1955, as amended and supplemented by Order No. 6564,
dated October 3, 1962.
Intermountain provides natural gas service to the following Idaho communities and counties
and adjoining areas:
Ada County - Boise, Eagle, Garden City, Kuna, Meridian, and Star; Bannock County - Arimo, Chubbuck, Inkom, Lava Hot Springs, McCammon, and Pocatello; Bear Lake County - Georgetown, and Montpelier;
Bingham County - Aberdeen, Basalt, Blackfoot, Firth, Fort Hall, Moreland/Riverside, and Shelley; Blaine County - Bellevue, Hailey, Ketchum, and Sun Valley; Bonneville County - Ammon, Idaho Falls, Iona, and Ucon; Canyon County - Caldwell, Greenleaf, Middleton, Nampa, Parma, and Wilder;
Caribou County - Bancroft, Grace, and Soda Springs; Cassia County - Burley, Declo, Malta, and Raft River; Elmore County - Glenns Ferry, Hammett, and Mountain Home; Fremont County - Parker, and St. Anthony; Gem County - Emmett;
Gooding County - Bliss, Gooding, and Wendell; Jefferson County - Lewisville, Menan, Rigby, and Ririe; Jerome County - Jerome;
SUPPLEMENT TO APPLICATION - 4
Lincoln County - Shoshone; Madison County - Rexburg, and Sugar City;
Minidoka County - Heyburn, Paul, and Rupert; Owyhee County - Bruneau, Marsing, and Homedale; Payette County - Fruitland, New Plymouth, and Payette; Power County - American Falls;
Twin Falls County - Buhl, Filer, Hansen, Kimberly, Murtaugh, and Twin Falls; Washington County - Weiser.

Intermountain’s properties in these locations consist of transmission pipelines, liquefied
natural gas storage facilities, a compressor station, distribution mains, services, meters and
regulators, and general plant and equipment.
II. BACKGROUND
On September 1, 2020, Intermountain filed the Original Application. The Original
Application discussed the ongoing EM&V study, though the study had not been completed at the
time. See Original Application at 9 (discussing EM&V study). Following that filing, on September
16, 2020, Intermountain held a meeting of its Energy Efficiency Stakeholder Committee (“EESC”).
At that meeting, ADM Associates, Inc. (“ADM”) presented the results of the EM&V study, and
Intermountain presented proposed EE Program changes to address the EM&V study
recommendations. During that meeting Staff noted it would be helpful to have the study included as
part of Case No. INT-G-20-06. The minutes from the EESC meeting are included in Exhibit No. 4
and incorporated by reference.
Intermountain agreed with Staff that it made sense to bring both the EM&V study and
resulting cost effectiveness tests as well as the proposed EE Program changes into Case No. INT-G-
20-06. This Supplement includes these items.
III. EVALUATION, MEASUREMENT & VERIFICATION
Energy efficiency programs follow a three-step feedback loop of program design, program
implementation and program evaluation, measurement and verification. The last step of the
SUPPLEMENT TO APPLICATION - 5
feedback loop, EM&V, provides important data and feedback that serve to continually improve the
performance of an energy efficiency program.
There are three categories of EM&V studies: impact, process and market evaluations.
Respectively, these categories assess the direct and indirect program benefits such as energy and
demand savings, systematic assessments of program operations and customer satisfaction, and
behavior of market participants or market changes that result from a program. The EESC provided
feedback in shaping the depth and scope of the first EM&V study of Intermountain’s EE Program.
Case No. INT-G-20-06, Exhibit No. 2 at 16-17. To get the most value from the study, it was decided
the EM&V study would include both an impact evaluation and process evaluation. As a new
program it was important to conduct a process evaluation to identify potential areas of improvement
in program operations and delivery. The process evaluation reviewed the entire EE Program.
On the other hand, the decision was made to only conduct an impact evaluation on the
Whole Home and Furnace rebates. The impact evaluation analyzes therm savings realized by a
rebate program. These two rebates accounted for more than 95% of all estimated program savings
over the life of the program. Additionally, based on the low uptake of the remaining rebates,
changing codes and standards, and suggestions for new offerings outlined in the Company’s
Conservation Potential Assessment (“CPA”), Intermountain planned to cancel or make revisions to
the remaining rebates. Thus, it did not make sense to perform an impact evaluation on rebates that
would be cancelled or drastically changed. The EESC agreed that it made sense to revise the
smaller rebates and not include them in the EM&V study. Case No. INT-G-20-06, Exhibit No. 2 at
17.
In January 2020, Intermountain commissioned an independent third party to conduct the
EM&V studies on the first two program years. The Company sent a request for proposal (“RFP”) to
SUPPLEMENT TO APPLICATION - 6
29 organizations and posted the RFP on the Association of Energy Services Professionals website.
After reviewing 10 proposals and conducting 4 interviews, ADM was selected to conduct the
EM&V study.
The impact evaluations of both the Furnace and Whole Home rebates used both a billing
analysis and an engineering-based evaluation. In both cases, ADM recommended the Company use
the therm savings found in the engineering-based analysis. Although the billing analysis found
measurable therm savings, “billing analyses include any changes in household behavior, equipment,
or occupancy, and therefore may include factors other than the impact of improved equipment
efficiency.” Exhibit No. 5 at 11-12.
The recommendations resulting from the Furnace impact evaluation included collecting
additional information on the rebate form to facilitate more accurate evaluations in the future.
Additionally, ADM discovered that furnaces tended to be over-sized in Weather Zone 5 which
negatively affects program savings due to repeated shorter run cycles and associated higher use
during heating ramp-up. Providing or encouraging contractor training in Manual J sizing
calculations should help combat this issue. Exhibit No. 5 at 11. The engineering-based evaluation,
Equivalent Full Load Hours for Heating (“EFLH”) study, found Ex-Post therm savings of 134 per
rebate, or a realization rate of 118%. Exhibit No. 5 at 84.
In their review of the Whole Home rebate, ADM found that the average non-program home
in Intermountain’s service territory exceeds amended IECC 2012 Idaho building code. The EM&V
study also found that lower HERS scores do not necessarily equate to higher therm savings. In light
of these findings as well as the recent legislation to adopt the amended 2018 IECC residential code,
ADM recommended imposing specific requirements to directly target natural gas savings. ADM
also recommended removing the ENERGY STAR certification requirement as it seems to be a
SUPPLEMENT TO APPLICATION - 7
barrier to builder participation. The EM&V study found no basis to state that this requirement saves
more energy than current code requirements. Exhibit No. 5 at 12-13. The engineering-based
evaluation, Simulation Analysis, found Ex-Post therm savings of 274 per rebate, or a realization rate
of 134%. Exhibit No. 5 at 85.
The EM&V process evaluation focused on identifying potential program improvements to
increase program efficiency or effectiveness in terms of customer participation and satisfaction.
Surveys, evaluations, conclusions and recommendations from the process evaluation were separated
into two categories: appliance rebates and new construction (Whole Home) rebate.
Overall, ADM concluded the EE Program performed well during the first two years,
effectively engaging various stakeholders and using a broad range of marketing efforts and
community outreach. Recommendations for improvement of the entire EE Program included
creating a standard operating procedures manual, increasing communication with contractors,
builders and HERS raters, exploring opportunities to incorporate new measures into the program,
establishing a builder and contractor approved network, and standardizing tracking database
categories. For the Whole Home rebate specifically, ADM recommended working with builders to
achieve HERS requirements at a lower cost, providing additional marketing materials to promote
the Whole Home rebate, creating company-specific reports and cost-savings estimates that builders
can use with homebuyers, continuing to participate in community events, developing materials that
increase knowledge on the benefits of owning an energy efficient home, and providing additional
education or training opportunities for HERS raters, builders, and real estate agents. In its review of
the appliance rebate category, ADM recommended ensuring contractors are aware of all
participation options, enabling contractors or customers to track the status of their rebate, creating
and distributing promotional materials that can be used by contractors, creating educational
SUPPLEMENT TO APPLICATION - 8
materials for customers, considering an Electronically Commutated Motor for the furnace blower as
a program requirement, collecting data on the condition of the replaced furnace, incorporating
avoided replacement cost into tankless water heater cost-effectiveness calculations, and tracking
additional fields in rebate applications to assist in future energy savings estimates. Exhibit No. 5 at
143 – 146.
The recommendations from the process evaluation provide an excellent list of opportunities
for continued EE Program improvement. Intermountain has already begun to implement many of
the recommendations, such as updates to the rebate application, the creation of standard operating
procedures documents, standardizing database tracking categories, and improvements to promotion,
education and outreach.
Both the impact and process evaluations completed as part of the EM&V study are attached
as Exhibit No. 5 and incorporated by reference.
IV. COST EFFECTIVENESS
With the completion of the impact evaluation, Intermountain used the verified therm savings
to recalculate the cost-effectiveness of the Furnace and Whole Home rebates. In the original cost-
effectiveness tests that were included in this case, the Company used therm savings found in the
CPA study. Exhibit No. 6 updates the cost-effectiveness tests for the Furnace and Whole Home
rebates to instead use the Ex-Post therm savings of 134 for the Furnace rebate and 274 for the
Whole Home rebate. Exhibit No. 5 at 84-85.
Because overhead expenses are allocated to the rebates based on therm savings, increasing
the therm savings for the Furnace and Whole Home rebates shifted costs away from the remaining
rebates. This resulted in the Combi Radiant Heat System, 70% Fireplace, Water Heater, and
Tankless Water Heater rebates all becoming slightly more cost-effective. The overall program
SUPPLEMENT TO APPLICATION - 9
Utility Cost Test (“UCT”) based cost-effectiveness also increased from the 0.87 included in the
2019 Annual Report (Exhibit No. 1 at 9) to 1.3 as shown on Exhibit No. 6, Page 6. UCT based cost-
effectiveness tests resulting from the EM&V study Ex-Post therm savings show the Furnace with a
cost-effectiveness of 1.2 and the Whole Home at 1.5. Exhibit No. 6 at 7-8. Exhibit No. 6 is attached
and incorporated by reference.
V. STAKEHOLDER MEETINGS
Intermountain hosted three EESC meetings to share the EM&V study and discuss resulting
program changes. The minutes from all three of these meetings are included as Exhibit No. 4. The
first meeting on August 5, 2020 was held to review the material that would be included in the 2019
Annual Report that was filed as part of the Original Application in Case No. INT-G-20-06. Exhibit
No. 4 at 1.
At the second meeting on September 16, 2020, ADM presented the findings of the EM&V
study and Intermountain presented proposed Residential EE Program revisions. The EESC had
concerns with several of the changes Intermountain proposed to the Whole Home rebate. Exhibit
No. 4 at 10-11. As a result of the discussions, Intermountain commissioned a follow-up study from
ADM to explore the best options to improve therm savings while encouraging additional builder
participation. The Company also reviewed information from the 2019 Idaho Residential Energy
Code Field Study and the Idaho Code Collaborative.
On October 27, 2020 the EESC met again to review proposed EE Program revisions. The
EESC was supportive of the proposal. A question was raised regarding the appropriate duct leakage
requirement to include in the revised Whole Home rebate. The Company agreed to further review
that requirement before proposing a final rebate. Exhibit No. 4 at 12-15.

SUPPLEMENT TO APPLICATION - 10
VI. PROPOSED RESIDENTIAL PROGRAM
The Company proposes to update its Residential EE Program based on recommendations
from the EM&V study (Exhibit No. 5), input from the Energy Efficiency Stakeholder Committee
(Exhibit No. 4), and other resources including the ADM “Residential Whole Home Modeling
Result” memorandum (Exhibit No. 8), the Idaho Residential Energy Code Field Study (Exhibit No.
8), the Idaho Code Collaborative, and upcoming energy code changes.
Exhibit No. 7 outlines in detail the proposed changes to the Residential EE Program. The
Company proposes to leave the Furnace rebate at $350, while making the changes recommended by
the EM&V study to collect additional data points which will provide a more complete dataset from
which to evaluate savings attributed to the Furnace in the future.
Intermountain proposes to retire the 70% Fireplace rebate. This rebate has had very low
participation over the life of the program. Additionally, the CPA drastically reduced the therm
savings attributed to the fireplace which would result in a minimal rebate to maintain cost-
effectiveness.
The Company proposes to add rebates for a Smart Thermostat, Boiler and a second tier
Tankless Water Heater. The Smart Thermostat has consistently been the most requested rebate and
was a recommended addition in the EM&V study. A residential Boiler rebate will provide a high-
efficient heating option for those customers that do not have a forced air furnace. An additional
recommendation from the EM&V study was to add a tankless water heater at a lower price point
than the current offering. Intermountain is proposing a Tier II Tankless Water Heater with a
minimum efficiency level of 0.87 UEF.
Intermountain proposes revisions to the Storage Water Heater, the Tier I Tankless Water
Heater, the Combination System, and the Whole Home rebates. The Company’s CPA study
SUPPLEMENT TO APPLICATION - 11
estimated therm savings for the Storage Water Heater that were higher than those included in the
preliminary study Intermountain had used to begin the EE Program. As a result, the Company
proposes to increase the rebate for the Storage Water Heater from $50 to $115 to encourage
increased participation for this energy efficient appliance. The CPA also identified an increase in
therm savings and estimated useful life for the 0.91 UEF Tankless Water Heater included in the
Company’s original rebate offering. Intermountain plans to retain this Tier I Tankless Water Heater
rebate and increase the rebate from $150 to $325.
The existing combination system for space and water heating has had low participation and
the equipment requirements of the rebate have often been misunderstood. The Company proposes
to clarify this rebate by requiring a combination boiler, designed for both space and water heat. The
proposed rebate of $800 would require a minimum efficiency rating of 95% AFUE.
Finally, the Company proposes to revise the Whole Home rebate by retiring the ENERGY
STAR certification requirement, creating a two-tier rebate, and allowing the rebate to be combined
with Thermostat and Water Heating rebates. Due to higher efficient energy code being implemented
in January 2021, therm savings for the proposed rebate will be reduced from the current therm
savings identified in the EM&V study. Therefore, the rebate amounts have also been reduced from
the current $1,200 to $900 for the proposed Tier I rebate and $700 for the proposed Tier II rebate.
Removing the added expense to builders of the ENERGY STAR certification requirement and
encouraging the installation of high-efficient water heaters with their associated rebates should
continue to make the Whole Home rebate an attractive incentive to encourage energy efficient
building. The two-tiers of rebate should allow builders to work through incremental improvements
in their build process as they move from qualifying for the Tier II rebate to the Tier I.
SUPPLEMENT TO APPLICATION - 12
As illustrated on Exhibit No. 7, Page 9, the resulting EE Program is cost-effective and each
individual rebate is cost-effective with a UCT of 1.0 or greater. Exhibit No. 7 is attached and
incorporated by reference. Exhibit No. 8 includes the “Residential Whole Home Modeling Results”
memorandum prepared for Intermountain by ADM as well as the Idaho Residential Energy Code
Field Study. Both of these documents were used in developing the proposed Whole Home rebate.
Exhibit No. 8 is attached and incorporated by reference.
VII. PROPOSED TARIFF
The Company proposes to make the tariff changes effective March 1, 2021.
Intermountain’s current Rate Schedule EE showing proposed changes in legislative format is
attached as Exhibit No. 9, Pages 1-3. As outlined in Case No. INT-G-20-04, Intermountain is
proposing to rename Rate Schedule EE to Rate Schedule EE-RS to help avoid confusion with the
proposed Commercial Energy Efficiency Rebate Program. Exhibit No. 9, Pages 4-6, provide the
clean copy of the resulting tariff including the name change. Exhibit No. 9 is attached and
incorporated by reference.
VIII. MODIFIED PROCEDURE
Intermountain requests that this matter be handled under modified procedure pursuant to
Rules 201-204 of the Commission’s Rules of Procedure. Intermountain stands ready for immediate
consideration of this matter.
Because neither the Original Application nor this Supplement propose to change customer
rates, Intermountain has not provided direct notice to customers. See Commission Rule of
Procedure 125.01, IDAPA 3.01.01.125.01.
IX. REQUEST FOR RELIEF
Intermountain respectfully petitions the Idaho Public Utilities Commission as follows:

a. That the Commission provide the relief requested in the Original Application,
SUPPLEMENT TO APPLICATION - 13
b. That the Commission include this Supplement, the attached Exhibits, and the issues
identified in this Supplement and the Exhibits within Case No. INT-G-20-06,
c. That Rate Schedule EE be approved as outlined,

d. That this Application be heard and acted upon without hearing under modified procedure,
and
e. For such other relief as this Commission may determine proper.
DATED: December 7, 2020.

SUPPLEMENT TO APPLICATION - 14
CERTIFICATE OF SERVICE
I certify that on December 7, 2020, a true and correct copy of INTERMOUNTAIN GAS
COMPANY’S SUPPLEMENT TO APPLICATION was served upon all parties of record in this
proceeding via electronic mail as indicated below:
Commission Staff Via Electronic Mail
Idaho Public Utilities Commission
11331 W. Chinden Blvd., Bldg. 8, Suite 201-A Boise, ID 83714
Deputy Attorney General
Idaho Public Utilities Commission 11331 W. Chinden Blvd., Bldg. 8, Suite 201-A
Idaho Conservation League
Benjamin J. Otto Idaho Conservation League 710 N. 6th Street Boise, ID 83702

[e-mail]

Lori A. Blattner

EXHIBIT NO. 4
CASE NO. INT-G-20-06
INTERMOUNTAIN GAS COMPANY

Energy Efficiency Stakeholder Committee Meeting Minutes
(15 pages)

Intermountain Gas Energy Efficiency Stakeholder Committee  
Residential Energy Efficiency Program Updates 

August 5, 2020 at 1:00 PM 
Minutes Recorded by Kody Thompson 

Attendees: 
Brad Iverson‐Long – IPUC 
Heath Chisholm – Building Energy 
Ben Otto – Idaho Conservation League 
Kevin Keyt ‐ IPUC 
Marissa Warren – OEMR 
Mike Morrison – IPUC 
Brian Bennett – The Energy Auditor 
Michael Shepard ‐ Neighborworks 

Guests and Presenters: 
Lori Blattner – Intermountain Gas 
John Fisk – Intermountain Gas 
Kody Thompson – Intermountain Gas 
Kathy Wold – Intermountain Gas 
Meeting Facilitator: Kathy Wold 

1:00 PM – Meeting Convened 
Kathy began the meeting and went over housekeeping items related to holding the meeting 
online. An overview of the topics to be discussed was given.  

Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 1 of 15
1:05 PM – John Fisk & Kathy Wold 
John provided a safety moment for the meeting where he discussed the safe use of electrical 
outlets in an office environment. Kathy then provided an icebreaker and introduction to kick off the 
meeting.  
1:10 PM – Kathy Wold   
Kathy gave an update on the progress made by the Avoided Cost Sub‐Committee. An updated 
methodology has been agreed upon. The Sub‐committee agreed to continue to look for a way to include 
avoided distribution costs in the calculation.  
1:10 PM – Kody Thompson & Kathy Wold 
Kody reviewed program planned revenues and expenses compared to actual revenues and 
expenses, as well as the cost‐effectiveness of the program as a whole and for each individual measure. 
In reviewing each measure both pre‐CPA and post‐CPA cost‐effectiveness tests were presented. 
Committee members asked why Conservation Potential Assessment (CPA) measure inputs were used for 
therm savings, incremental costs and estimated useful life. It was explained that the updated CPA values 
were the best available data in 2019. The Evaluation Measurement and Verification (EM&V) study is 
nearing completion in 2020, this data will be used to guide program changes. A Committee member 
asked whether changes will be made to the whole home incentive. Intermountain staff affirmed that 
changes are anticipated. A Committee member inquired when the CPA and EM&V will be made 
available. Intermountain replied the CPA was filed with the IRP as Exhibit No. 4 and the EM&V is nearly 
complete and will be filed later in the fall of 2020.  
Kathy went over the changes to energy efficiency staff in 2019, including the shift to a full‐time 
energy efficiency manager and the addition of Energy Services Representatives (ESR). With continued 
program growth, the Energy Efficiency Department needed to add staff to effectively promote the 
Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 2 of 15
program. Rather than adding two dedicated EE staff members, the decision was made to hire two 
additional ESRs. Following the addition of the new ESRs, each of the resulting eight ESRs had energy 
efficiency duties added to their job responsibilities.  Energy Efficiency underwrites 25% of each ESR 
position. A committee member asked for clarification regarding the funding of the ESRs and whether or 
not new ones were added to staffing. Intermountain explained that prior to adding energy efficiency 
duties to the ESR job responsibilities and adding the two new ESRs, the Company had 6 ESRs.  The 
Company hired two new positions for a total of 8 ESRs. Energy Efficiency underwrites 25% of the funding 
of each ESR, which would be the same as separately funding two dedicated Energy Efficiency 
department staff members.  ESRs meet with customers to talk about gas service and seamlessly work 
the Energy Efficiency Program options into those discussions. Additionally, the ESRs assist with increased 
energy efficiency outreach and community education in their assigned territories.  
1:35 PM – John Fisk 
John presented an overview of the outreach and education efforts completed in 2019, which 
included recognition by the EPA as a Market Leader Award recipient for the whole homes program. 
Social media was incorporated as an outreach strategy and focused on energy efficiency tips, program 
promotion, and education. John presented samples from the Fall marketing campaign which included a 
customer survey and radio/digital media campaign in conjunction with a customer bill insert. John 
discussed the launch of the Contractor Portal and online rebate application, as well as HVAC training the 
Company hosted for HVAC partners and the HVAC Course subsidy for ENERGY STAR Certified contractor 
training offered by the Company. Stakeholders commented that methods used to reach builders were 
interesting and seemed to be headed in the right direction.  

Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 3 of 15
2:00 PM – Kathy Wold 
Kathy discussed the 2018‐19 partnership with Boise Valley Habitat for Humanity (BVHFH) to 
build an ENERGY STAR Certified home and raise awareness about the role of energy efficiency in keeping 
home operating costs affordable.  Intermountain was able to create an on‐line resource library, now 
housed on the IGC YouTube channel, about the energy efficiency features of an ENERGY STAR home by 
video documenting the build process. At the official ribbon cutting ceremony, BVHFH hosted an open 
house for the community to tour the home for the first time in BVHFH history. Additional partnerships 
were discussed including the BVHFH Playhouse, St. Jude Dream Home (which was an ENERGY STAR 
certified home) and participating as a presenter at the South Central Community Action Partnership 
Weatherization Day, in Twin Falls. 
2:10 PM – Lori Blattner 
Lori gave an overview of the Gas Technology Institute (GTI) and Intermountain’s long history as 
a member of GTI. GTI is a non‐profit gas technology research, development, and training organization 
that addresses energy and environmental challenges. Intermountain has been a member of GTI for 
decades. Lori presented a high‐level overview of the work of GTI working groups like Operation 
Technology Development (OTD) and Utilization Technology Development (UTD). These groups deliver 
new technologies that expand the supply of natural gas and renewable energy, ensure a safe and 
reliable infrastructure, deliver solutions for efficient and environmentally responsible use of energy, 
reduce and manage carbon emissions, and advance energy systems innovations that protect air, land, 
water, and communities while enhancing economic growth.  
The EE program participates in a subcommittee working group of UTD, the Emerging Technology 
Program (ETP), which focuses on the last stage of product development such as hosting demonstrations, 
product deployment, commercial introduction and implementation of product development. The most 
Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 4 of 15
current technology ETP has been working on is gas heat pump technology for water heating and 
combination space and water heating applications. Intermountain is enthusiastic about the market 
transforming potential of this technology. This led to a discussion of the North American Gas Heat Pump 
Collaborative.  
2:20 PM – Kathy Wold 
Kathy provided a high‐level explanation of the illustrative market channel from supplier to 
consumer, and the idea of how new products are moved through this channel from idea to a product on 
the shelf. Kathy presented an overview of the Company’s participation as a charter member in the North 
American Gas Heat Pump Collaborative (Collaborative), along with 14 other utilities from across the 
nation. The reasons for forming the Collaborative include bringing new energy and money saving 
technologies to customers, continuing to provide fuel choice to consumers in their homes and 
businesses, and seeking least cost methods of reducing greenhouse gasses. The Collaborative activities 
designed to accelerate adoption of gas heat pump technologies include emerging technology, pilot 
programs, market research and assessment, supply chain and customer incentives, strategic 
partnerships and changing codes and standards. The Company confirmed investments in the 
Collaborative, but not GTI, were paid through the Energy Efficiency Rider. 
2:30 PM – Question and Answer 
There were no follow up questions. Next meeting date was set for September 16, 2020 to 
review the proposed changes for the residential program.  
2:35 PM – Meeting Adjourned 
Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 5 of 15
Intermountain Gas Energy Efficiency Stakeholder Committee  
Residential Energy Efficiency Program Updates 

September 16, 2020 at 1:00 PM 
Minutes Recorded by Kody Thompson 

Attendees: 
Lori Blattner – Intermountain Gas Company 
Mark Chiles – Intermountain Gas Company 
Kody Thompson – Intermountain Gas Company 
John Fisk – Intermountain Gas Company 
Donn English – IPUC 
Brad Iverson‐Long – IPUC 
Katie Pegan – OEMR 
Heath Chisholm – Building Energy 
Ben Otto – Idaho Conservation League 
Jerry Peterson – Home Energy Services 
Kevin Keyt ‐ IPUC 
Marissa Warren – OEMR 
Mike Morrison – IPUC 
Brian Bennett – The Energy Auditor 
Matthew Vandermeer – Momentum 
Michael Shepard ‐ Neighborworks 
Guests and Presenters: 
Kathy Wold – Intermountain Gas Company 
Adam Thomas – ADM 
Melissa Kosla – ADM 
Meeting Facilitator: Kathy Wold 

1:00 PM – Meeting Convened 
Kathy Wold opened the meeting, welcoming the group to Intermountain Gas Company’s 
meeting to discuss the results of the Evaluation, Measurement, and Verification (EM&V) study, as well 
as proposed updates to the residential rebate program.  
Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 6 of 15
1:15 PM – EM&V Overview – Adam Thomas 
Kathy provided background on the selection of the evaluators, before turning the presentation 
over to Adam Thomas, Principal from ADM Associates, Inc. (ADM).  Adam presented the methods used 
and the results of the impact evaluation of the whole home measure and furnace measure. The 
following key points were discussed:  
 Whole Home Program evaluation 
o Overview of current incentive requirements 
o Billing analysis of 2018 and 2019 homes compared to non‐program homes 
o Reviewed a sample of the home simulation models, and the statistical significance of the 
home matching 
o Whole Home results based on bill analysis estimated 28.21% realization rate, or ex‐post 
savings of 57.54 therms per home. ADM explained the billing analysis is essentially a net 
savings evaluation, which may implicitly or explicitly include the effects of free‐ridership, 
spillover and induced market effects. To provide a more complete picture of energy 
savings and program effects, ADM conducted an additional analysis representative of a 
gross savings approach. 
o The gross savings approach of the Whole Home rebate compared a user defined 
reference home (UDRH) based on 2012 IECC with amendments to the Idaho Residential 
Building code against HERS modeling information, submitted by the energy raters, of 80 
program homes. This analysis resulted in an estimated realization rate of 134%, or 274 
therms per home, and can be found in Appendix B of the report.    
 Space Heating Evaluation 
o Overview of current incentive requirements 
o Billing analysis of 2018 and 2019 homes compared against non‐participants 
Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 7 of 15
o Review of sample of home selection process, and statistical significance of customer 
matching  
o Space Heating Results based on bill analysis estimated 49.37% realization rate, or ex‐
post savings of 56.03 therms per furnace. Like the whole home analysis, ADM explained 
the billing analysis is fundamentally an estimate of net savings, which may implicitly or 
explicitly include the effects of free‐ridership, spillover and induced market effects. To 
provide a more complete picture of energy savings and program effects, ADM 
recommended employing a TRM‐based engineering equation to calculate furnace 
savings, or Equivalent Full Load Hours (EFLH), referenced as Method 3, in Appendix A of 
the report. This approach complies with the International Performance Measurement 
and Verification Protocol (IPMVP) maintained by the Efficiency Valuation Organization 
(EVO) with sponsorship by the U.S. Department of Energy (DOE) and is often used to 
calculate deemed savings for gas furnace retrofits. 
o Furnace savings results based on the EFLH analysis resulted in an estimated 118% 
realization rate, or 134 therms saved per furnace.  
Committee members wondered when the EM&V study would be filed. The Company plans to 
file the EM&V study along with proposed revisions to the Residential program to address the issues 
raised by the EM&V study. Committee members expressed concern with not having the EM&V study 
included with the already filed Request for Determination of Prudency. The Company agreed to discuss 
the best way to include the EM&V information in the Prudency case.  
A Committee member questioned why County assessor’s data for property information was 
used in place of the information IGC collects when installing a line extension. The Company said this is 
due to more detailed information being available from the Ada County assessor database. A Committee 
member wondered why Eastern Idaho wasn’t included in the whole home impact analysis. In general, 
Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 8 of 15
there were far fewer participating whole home program homes in Eastern Idaho over the time period 
studied. This made it more difficult to create a statistically significant matched sample in Eastern Idaho. 
There was a discussion around the use of net versus gross therm savings in cost testing and 
program planning. The general opinion was that both are important to calculate when possible and both 
provide important insights into the program. Attempting to address free‐ridership is important in 
program planning but decrementing current cost‐effectiveness based on something as difficult to 
calculate as free‐ridership may not be fair to the program. Market transformation can often be confused 
for free‐ridership.  
2:30 PM – Proposed Residential Program Updates – Kathy Wold 
Kathy highlighted some of the conclusions from the process evaluation: ADM found the Program 
performed well during the first two program years, customers who participated in the Program were 
satisfied with the Program and contractors were largely satisfied with the design and participation 
process of the Program. Based on process evaluation recommendations, Intermountain has completed 
or is in the process of implementing the following changes:  
 Explore incorporating new measures 
 Standardize the tracking database 
 Develop education materials and training opportunities for raters, builders and add outreach to 
the real estate sector 
 Increase communications, continue to raise program awareness 
 Provide builders/contractors with marketing materials 
Based on impact evaluation recommendations the Company plans to implement:   
 Collecting installation type for each rebate 
Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 9 of 15
o Replace‐on‐burnout, early retirement, new construction 
 Collecting prior efficiency for each early retirement furnace 
 Encouraging contractor training for Manual J sizing calculations 
 Evaluate the Whole Home offering:    
o ENERGY STAR certified home requirement is a barrier to participation 
o Evaluate a more stringent HERS requirement 
o Targeting natural gas savings by adding specific requirements to the whole home rebate 
specifically designed to increase natural gas savings 
Stakeholders recommended collecting the size of the previous furnace in conjunction with the 
efficiency of the unit.  
The Company proposed a revised whole home rebate structure based on a user defined 
reference home requiring a HERS score of 60, and specific energy performance targets of 3 ACH, 2% duct 
leakage to outside and 95% AFUE furnace requirement to target natural gas savings. All these proposed 
additional requirements could be verified on a HERS certificate which would assist in verification. Under 
the proposed changes ENERGY STAR would no longer be a program requirement. Committee members 
expressed concern that builders would not participate under this revised rebate structure due to the 
rebate amount being reduced from $1,200 to $650, and felt the HERS threshold, ACH and duct leakage 
targets were too aggressive. While Committee members agreed the current rebate HERS threshold of 75 
was not aggressive enough, some expressed concern that builders would drop out of the program at the 
proposed levels. One energy rater estimated that of the 5 builders currently participating, only 1 would 
probably continue at the proposed $650 rebate level and with these requirements, others would see it 
as “not worth the time.” Several participants suggested exploring a tiered rebate structure. It was also 
Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 10 of 15
suggested that the Company allow builders to stack the water heater and programmable thermostat 
rebates on top of the whole home rebate.  
Intermountain agreed to do some further research into the whole home rebate to include some 
potential tiers and options on water heaters.  
The Company gave an overview of the proposed changes to the remaining measures in the 
portfolio. The Committee discussed whether measures with a UCT of less than 1.0 should be included. It 
was agreed that all measures in the program should independently pass the UCT with a ratio of 1.0 or 
higher. Although there has been much interest in adding a smart thermostat to the Company’s program 
offering, this was one of the measures that had a UCT of 0.9. In reviewing the measure assumptions, 
Adam Thomas from ADM commented that the 8‐year measure life of the smart thermostat referenced 
in the proposal appeared too low and recommended a minimum 11‐year measure life be used. The 
Committee agreed this would be an appropriate change.  
Because of the great discussions, the end of the scheduled meeting time was reached prior to 
completing the slide deck. The Company proposed sending the presentation to Committee members for 
further review. The Committee members would then provide feedback to the Company. The Committee 
approved this suggestion and agreed to reconvene for further discussion after the Company had time to 
further analyze the whole home rebate proposal.  
3:15 PM Meeting Adjourned 

Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 11 of 15
Intermountain Gas Energy Efficiency Stakeholder Committee  
Meeting 
Residential Program Proposed Updates
October 27, 2020 at 1:00 pm 
Minutes Recorded by Kody Thompson 
Attendees: 
Landon Barber – IGC 
Kody Thompson – IGC 
Kathy Wold – IGC 
John Fisk – IGC 
Kevin Keyt – IPUC 
Melissa Kosla – ADM Associates 
Mike Morrison – IPUC 
Donn English – IPUC 
Mark Chiles – IGC 
Lori Blattner – IGC 
Heath Chisholm – Building Energy 
Jerry Peterson – Home Energy Services 
Ben Otto – Idaho Conservation League 
Brian Bennett – The Energy Auditor 
Katie Pegan – OEMR 
Brad Iverson‐Long – IPUC 
Melissa Culverson – ADM Associates 

Guests and Presenters: 
Kathy Wold 
Melissa Kosla 
Melissa Culverson 
Meeting Facilitator: Kathy Wold 

1:00 PM Meeting Convened 
Kathy welcomed the group to the Stakeholder meeting and discussed the agenda. John Fisk 
provided a safety moment regarding cold weather driving tips. Introductions were offered and an ice 
breaker question was given. Kathy provided a refresher on what was discussed in the previous 
stakeholder meeting, highlighting the feedback that was received. 
Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 12 of 15
1:15 PM Updated Proposed Program Discussed 
Kathy provided background on research that was done in preparing the updated program. This 
included reviewing the 2019 Idaho Residential Code Field Study, consulting with the Idaho Code 
Collaborative, and commissioning ADM Associates, Inc. to model a Whole Home rebate analysis based 
on the results of the impact evaluation. A proposed list of measures and cost test estimates for each 
measure was presented. The Company proposed changes for all rebate offerings except for the furnace 
rebate.  Three new rebates are proposed. The Radiant Combi system naming has been clarified to help 
customers more easily identify qualifying equipment. A Boiler rebate and a Smart Thermostat offering 
will be added. The water heater incentive levels will be increased, along with an additional tankless 
water heater option at a lower efficiency of 0.87 UEF. Feedback was received that the addition of a 
boiler incentive was a good plan. 
Kathy discussed IGC’s intent to retire the 70% FE fireplace. This is due to the reduction of therm 
savings based on the CPA analysis which would necessitate lowering the rebate amount to $40 to 
maintain cost‐effectiveness. Ultimately the appliance is a largely decorative unit that is not typically 
rated for energy efficiency and a large amount of administrative time was used in trying to get 
information on equipment and helping customers understand the difference between FE and steady 
state efficiency.  
The updates to Idaho Energy Code effective January 1, 2021 were discussed to provide a 
reference for the proposed changes to the residential program. A question was asked regarding the 
difference between ERI (Energy Rating Index) and HERS (Home Energy Rating System). There are slight 
differences between the two, but HERS is essentially a copyrighted brand name of an energy rating 
system. Jerry Peterson, from Home Energy Services, clarified that Idaho amended the national IECC 
Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 13 of 15
performance path ERI requirement from 61 to a 68 (less efficient). This was done to make the ERI score 
equivalent to an Idaho Code based home under the 2018 Idaho amended IECC.  
A two‐tier rebate was proposed for the Whole Home rebate with new requirements focused on 
therm savings. The proposed Whole Home rebate revision would require the home be HERS scored and 
require specific energy performance targets for air changes per hour, ceiling insulation, duct leakage, 
and the efficiency of the furnace. The ENERGY STAR Certification requirement will be retired. Builders 
would now be able to layer water heater and thermostat rebates on top of the whole home rebate.  
All appliance rebates are applicable to multi‐family structures, since they are also on the 
residential rate. A separate multi‐family listing was not created.  
A Committee member asked if IGC was going to lower the HERS score. The Company is 
proposing that a specific HERS score threshold not be required since it is possible to achieve a lower 
HERS score by implementing non‐therm saving inputs. The proposed rebate instead requires the home 
receive a HERS score and meet additional requirements. Requiring the home to be HERS scored 
highlights the importance of energy efficiency in home operation and also provides a simple method for 
the Company to verify other requirements are met since they are all noted by the rater during the HERS 
rating process.  
A Committee member suggested that a 2% duct leakage rate would be very difficult to achieve 
and an alternative requirement of ducts and air handlers in conditioned spaces versus a duct leakage 
performance target should be considered. ADM explained HVAC placement is a design element in the 
modeling software, not an energy efficiency measure, so it is difficult to attribute therm savings to this 
kind of requirement. IGC needs to be able to attribute therm savings to that specific performance target. 
While one Committee member agreed duct leakage is the next largest area for potential savings next to 
air infiltration (ACH), it would require a significant amount of builder education, along with the challenge 
Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 14 of 15
of setting standards such as supply and return duct allowances in unconditioned space. While the 
Committee agreed on duct sealing as an appropriate energy saving measure, more study is required 
regarding the therm savings of this measure, and the appropriate application to capture these savings. 
Intermountain agreed to further review the duct leakage requirement before proposing a final rebate. 
Once the whole home rebate specifics are determined, the Company plans to file EM&V results 
and proposed residential changes as an amendment to the prudency filing.  
2:05 PM Meeting Adjourned 

Exhibit No. 4 Case No. INT-G-20-06 Intermountain Gas Company Page 15 of 15

EXHIBIT NO. 5
CASE NO. INT-G-20-06
INTERMOUNTAIN GAS COMPANY

Evaluation, Measurement & Verification Study
(329 pages)

2018-2019 ENERGY EFFICIENCY PROGRAM
IMPACT EVALUATION
(127 pages)
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 1 of 329
i

Intermountain Gas Company 2018-2019
Energy Efficiency Program Impact Evaluation

Submitted to:

Submitted on:
June 11, 2020

Prepared by:
ADM Associates, Inc.
39650 Liberty St. Suite 425
Fremont, CA 94538

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 2 of 329
ii
Prepared by:
Melissa Kosla
Chris Johnson
Mark Ewalt
Adam Thomas

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 3 of 329
iii
Table of Contents
1 Executive Summary .............................................................................................................. 1
1.1 Conclusions and Recommendations ................................................................. 2
2 General Methodology .......................................................................................................... 5
2.1 Glossary of Terminology ................................................................................... 5
2.2 Sampling Methodology ..................................................................................... 5
3 95% AFUE Natural Gas Furnace Rebate ................................................................................ 7
3.1 Measure Description ......................................................................................... 7
3.2 Impact Evaluation Approach ............................................................................. 7
3.3 Conclusions and Recommendations ............................................................... 23
4 Whole Home Rebate .......................................................................................................... 24
4.1 Program Description ....................................................................................... 24
4.2 Impact Evaluation Approach ........................................................................... 24
4.3 Program and Non-Program HERS Index Analysis ............................................ 41
4.4 Incremental HERS Index Score Savings Analysis .............................................. 44
4.5 ENERGY STAR Certification Analysis ................................................................ 53
4.6 Program Home Time-on-Market Analysis ....................................................... 55
4.7 Builder and Rater Savings Profile .................................................................... 58
4.8 Conclusions and Recommendations ............................................................... 65
5 Appendix A: 95% AFUE Natural Gas Alternative Impact EM&V Methods and Results ....... 67
5.1 Billing Analysis with Pre-Post Model ............................................................... 67
5.2 Equivalent Full Load Hours for Heating (EFLHh) .............................................. 70
6 Appendix B: Whole Home Alternative Impact EM&V Methods and Results ...................... 76
6.1 Simulation Analysis ......................................................................................... 76

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 4 of 329
iv
List of Figures
Figure 3-1 PY2017-2018 Average Daily Usage by Season in Treatment and Control Groups
Before Matching ......................................................................................................................... 14
Figure 3-2 PY2017-2018 Average Daily Usage by Season in Treatment and Control Groups After
Matching ..................................................................................................................................... 15
Figure 3-3 PY2019 Average Daily Usage by Season in Treatment and Control Groups Before
Matching ..................................................................................................................................... 16
Figure 3-4 PY2019 Average Daily Usage by Season in Treatment and Control Groups After
Matching ..................................................................................................................................... 16
Figure 3-5 PY2017-2018 Average Daily Therm Usage in Treatment and Control Groups Before
Matching ..................................................................................................................................... 17
Figure 3-6 PY2017-2018 Average Daily Therm Usage in Treatment and Control Groups After
Matching ..................................................................................................................................... 18
Figure 3-7 PY2019 Average Daily Therm Usage in Treatment and Control Groups Before
Matching ..................................................................................................................................... 18
Figure 3-8 Average Daily Therm Usage in Treatment and Control Groups Before Matching ...... 19
Figure 4-1 Average Daily Usage by Group ................................................................................... 30
Figure 4-2 PY2017-2018 Average Daily Usage by Load Type ....................................................... 31
Figure 4-3 PY2017-2018 Home Characteristics Before Matching Treatment and Control Groups
33
Figure 4-4 PY2017-2018 Home Characteristics After Matching Treatment and Control Groups 34
Figure 4-5 PY2019 Home Characteristics Before Matching Treatment and Control Groups ....... 35
Figure 4-6 PY2017-2018 Home Characteristics After Matching Treatment and Control Groups 36
Figure 4-7 PY2017-2018 Average Daily Heating Load for Matched Groups ................................ 38
Figure 4-8 PY2019 Average Daily Heating Load for Matched Groups .......................................... 39
Figure 4-9 HERS Index Score Distribution by Group .................................................................... 43
Figure 4-10 Whole Home to UDRH Therms Savings (Therms/SQFT) ........................................... 46
Figure 4-11 Whole Home to UDRH kWh Savings (kWh/SQFT) .................................................... 47
Figure 4-12 Whole Home to UDRH Percent Therms Savings (%) ................................................. 48
Figure 4-13 Whole Home to UDRH Percent kWh Savings (%) ..................................................... 49
Figure 4-14 Whole Home to UDRH Component Load Percent Savings (%) ................................. 50
Figure 4-15 Average HERS Index Score and Therms Savings per Rebate by Builder .................... 59
Figure 4-16 Average HERS Index Score and Therms Savings per Square Foot by Builder ............ 59
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 5 of 329
v
Figure 4-17 Average Component Load Contribution to Total Savings by Builder ........................ 60
Figure 5-1 Average Daily Usage by Load Type ............................................................................. 73

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 6 of 329
vi
List of Tables
Table 1-1 Summary of Intermountain Gas Company Energy Efficiency Program Measures ......... 1
Table 1-2 Summary of Intermountain Gas Company Energy Efficiency Program Years ................ 1
Table 1-3 PY2017-2018 Summary of Therms Savings .................................................................... 1
Table 1-4 PY2019 Summary of Therms Savings ............................................................................. 2
Table 2-1 Sampled Projects ........................................................................................................... 6
Table 2-2 Whole Home Rebate Sampled Projects ......................................................................... 6
Table 3-1 Matched Control Group Regression Analysis Summary ................................................ 7
Table 3-2 Ex-Ante Therms Savings of Furnaces by Program Year .................................................. 7
Table 3-3: Weather Station Mapping ............................................................................................ 9
Table 3-4 Total Verified Rebates ................................................................................................. 10
Table 3-5 Summary Statistics for Rebated Efficient Furnaces ..................................................... 10
Table 3-6 PSM Customer Matches .............................................................................................. 12
Table 3-7 PSM Covariate Summary ............................................................................................. 12
Table 3-8 Post Matching T-Test of Difference in Usage by Month, PY2017-2018 ....................... 13
Table 3-9 Post Matching T-Test of Difference in Usage by Month, PY2019 ................................ 13
Table 3-10 Method 1 Regression Parameters ............................................................................. 19
Table 3-11 Method 1 Regression Results .................................................................................... 20
Table 3-12 Method 1 Model Fit ................................................................................................... 20
Table 3-13 Number of Premises by 3-Digit Zip Code and Program Year ..................................... 21
Table 3-14 Monthly Savings Summary by Program Year ............................................................. 21
Table 3-15 Ex-Ante and Ex-Post Annual Therms Savings for 95% AFUE Natural Gas Furnace by
Program Year .............................................................................................................................. 22
Table 3-16 Average Rebated Furnace Sizing per Square Foot by Vintage and Weather Zone ..... 22
Table 4-1 Method 1 Ex-Ante and Ex-Post Program Savings ......................................................... 24
Table 4-2 Ex-Ante Therms Savings of Whole Home Rebate by Program Year ............................. 24
Table 4-3 Savings Summary for Billing Analysis ........................................................................... 28
Table 4-4 Program Premises Data Cleaning Steps ....................................................................... 28
Table 4-5 Non-Program Data Cleaning Steps .............................................................................. 28
Table 4-6 Billing Data Cleaning Steps .......................................................................................... 29
Table 4-7 Summer Daily Usage T-test ......................................................................................... 30
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 7 of 329
vii
Table 4-8 PSM Customer Matches .............................................................................................. 32
Table 4-9 PSM Covariate Summary ............................................................................................. 37
Table 4-10 Treatment-Control Equivalency Test ......................................................................... 38
Table 4-11 Whole Home Regression Parameters ........................................................................ 39
Table 4-12 Whole Home Regression Results ............................................................................... 40
Table 4-13 Whole Home Model Fit ............................................................................................. 40
Table 4-14 Monthly Savings by Program Year ............................................................................. 40
Table 4-15 Summary of Annual Program Savings ........................................................................ 41
Table 4-16 Non-Program Homes in HERS Dataset ....................................................................... 42
Table 4-17 Non-Program Home Average Rating ......................................................................... 42
Table 4-18 Whole Home Rebate Home Average Rating .............................................................. 42
Table 4-19 Matched Control Group in HERS Dataset .................................................................. 44
Table 4-20 Matched Control and Treatment Group Average HERS Ratings ................................ 44
Table 4-21 Whole Home to UDRH Therms Savings by HERS Bin ................................................. 45
Table 4-22 Whole Home to UDRH kWh Savings by HERS Bin ...................................................... 46
Table 4-23 Whole Home to UDRH Therms Savings by HERS Bin ................................................. 46
Table 4-24 Whole Home to UDRH kWh Savings by HERS Bin ...................................................... 47
Table 4-25 Whole Home to UDRH Percent Therms Savings by HERS Bin .................................... 48
Table 4-26 Whole Home to UDRH Percent kWh Savings by HERS Bin ......................................... 48
Table 4-27 Whole Home to UDRH Component Load Annual MBtuh Savings .............................. 49
Table 4-28 Whole Home to UDRH Component Load Percent Savings ......................................... 50
Table 4-29 Percent Therms Savings by Prevalent Component Load (%) ..................................... 51
Table 4-30 Pacific Northwest National Laboratory HERS Index Compliance Range .................... 52
Table 4-31 ENERGY STAR to UDRH Average Annual Therms Savings .......................................... 53
Table 4-32 ENERGY STAR to UDRH Average Annual kWh Savings ............................................... 53
Table 4-33 ENERGY STAR to Whole Home Average Annual Therms Savings ............................... 54
Table 4-34 ENERGY STAR to Whole Home Average Annual kWh Savings ................................... 54
Table 4-35 Treatment-Control Equivalency Test ......................................................................... 56
Table 4-36 Whole Home Regression Results ............................................................................... 57
Table 4-37 Time-on-Market Model Fit ........................................................................................ 57
Table 4-38 Simulation Analysis Summary by Builder ................................................................... 58
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 8 of 329
viii
Table 4-39 Component Load Contribution to Total Savings by Builder ....................................... 60
Table 4-40 Average Normalized Therms Savings T-Test by Builder ............................................. 61
Table 4-41 Average Realization Rate T-Test by Builder ............................................................... 62
Table 4-42 Average Absolute Therms Savings T-Test by Builder ................................................. 62
Table 4-43 Average Square Footage T-Test by Builder ................................................................ 63
Table 4-44 Average HERS Index Score T-Test by Builder ............................................................. 63
Table 4-45 Simulation Analysis Summary by Rater ..................................................................... 64
Table 4-46 Average Realization Rate T-Test by Rater .................................................................. 64
Table 5-1 Method 2 Ex-Ante Ex-Post Program Savings Summary ............................................... 67
Table 5-2 Method 3 Ex-Ante Ex-Post Program Savings Summary ............................................... 67
Table 5-3 Method 2 Regression Parameters ............................................................................... 68
Table 5-4 Method 2 Regression Results ...................................................................................... 69
Table 5-5 Method 2 Model Fit ..................................................................................................... 69
Table 5-6 Method 2 Monthly Savings Summary by Program Year .............................................. 69
Table 5-7 Method 2 Ex-Ante Ex-Post Program Savings Summary ............................................... 70
Table 5-8 TMY HDD and Weather Zone by Major City ................................................................ 70
Table 5-9 Calculated EFLH by Weather Zone............................................................................... 73
Table 5-10 Average Monthly Heating Load and EFLHh ............................................................... 74
Table 5-11 Method 3 Ex-Ante Ex-Post Program Savings Summary ............................................. 75
Table 6-1 Method 2 Ex-Ante and Ex-Post Program Savings ......................................................... 76
Table 6-2 UDRH Key Assumptions ............................................................................................... 76
Table 6-3 UDRH and Whole Home Modeled Annual Therms Usage ........................................... 78
Table 6-4 UDRH and Whole Home Modeled Annual kWh Usage ................................................ 78
Table 6-5 Average Modeled Therms Savings per Home .............................................................. 79
Table 6-6 Average Modeled kWh Savings per Home .................................................................. 79
Table 6-7 Method 2 Ex-Ante and Ex-Post Program Savings ......................................................... 79

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 9 of 329
1 Executive Summary
This report is a summary of the evaluation, measurement, and verification (EM&V) effort of the 2018 and
2019 program years (PY2017-2018, PY2019) portfolio of programs for Intermountain Gas Company (IGC).
The evaluation was administered by ADM Associates, Inc (herein referred to as the “Evaluators”).
The Evaluators collected data for the evaluation through review of program materials, acquisition of
program tracking data, interviews with IGC staff members, participating builders, raters, and contractors,
and surveys of participating and nonparticipating customers. Table 1-1 summarizes the measures offered
to residential customers in the IGC service area. Table 1-2 summarizes the program implementation
budgets for each the 2017-2018 and 2019 program years.
Table 1-1 Summary of Intermountain Gas Company Energy Efficiency Program Measures
Measure PY2017-2018
Rebates
PY2019
Rebates
PY2017-2018
Incentives
PY2019
Incentives
Whole Home Rebate 619 1,079 $742,800 $1,294,800
95% AFUE Natural Gas Furnace 1,334 2,066 $466,900 $723,100
90% Radiant Combo 3 11 $3,000 $11,000
80% AFUE Fireplace 0 0 $0 $0
70% FE Fireplace 13 14 $1,300 $1,400
0.67 Water Heater 9 8 $450 $400
0.91 Tankless Water Heater 88 159 $13,200 $23,850
Table 1-2 Summary of Intermountain Gas Company Energy Efficiency Program Years
Program Year Rebate
Payments Labor Program
Delivery
Annual Implementation
Budget
2017-2018 $600,000 $147,000 $30,000 $777,000
2019 $1,250,000 $376,840 $94,120 $1,720,960
Table 1-3 and Table 1-4 provide a summary of evaluated savings of the IGC programs. The table presents
the ex-ante and ex-post Therms savings per rebate; also included are a comparison between ex-ante and
ex-post Therms savings per program.
During PY2017-2018, the 95% AFUE Natural Gas Furnace Rebate and Whole Home Rebate ex-post energy
savings totaled 123,668 Therms, with a 44.1% realization rate. During PY2019, the 95% AFUE Natural Gas
Furnace Rebate and Whole Home Rebate ex-post energy savings totaled 164,434 Therms, with a 36.4%
realization rate.
Table 1-3 PY2017-2018 Summary of Therms Savings
Program
Number
of
Rebates
Ex-Ante
Therms
Savings per
Rebate
Ex-Post
Therms
Savings per
Rebate
Ex-Ante
Therms
Savings
Ex-Post
Therms
Savings
Realization
Rate
95% AFUE Furnace 1,332 112.00 66.27 154,224 88,275 57.24%
Whole Home Rebate 619 204.00 57.18 126,276 35,393 28.03%
Total 280,500 123,668 44.09%

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 10 of 329
Executive Summary 2
Table 1-4 PY2019 Summary of Therms Savings
Program Number of
Rebates
Ex-Ante Therms
Savings per
Rebate
Ex-Post Therms
Savings per
Rebate
E-Ante
Therms
Savings
Ex-Post
Therms
Savings
Realization
Rate
95% AFUE Furnace 2,066 112.00 49.43 231,392 102,123 44.13%
Whole Home Rebate 1,079 204.00 57.75 220,116 62,311 28.31%
Total 451,508 164,434 36.42%
*The Evaluators used number of verified rebates to produce ex-post measure savings
1.1 Conclusions and Recommendations
The Evaluators offers the following conclusions and recommendations for consideration in planning future
program cycles.
1.1.1 95% AFUE Natural Gas Furnace Rebate
1.1.1.1 Conclusions
◼The 95% AFUE Natural Gas Furnace provides measurable savings through billing analysis.
However, the observable energy savings through billing analysis are much lower than expected
equipment savings. Billing analyses include any changes in household behavior, equipment, or
occupancy, and therefore may include factors other than the impact of improved equipment
efficiency.
◼The Evaluators found several deviations in furnace capacity size and AFUE in the tracking data
compared to AHRI certification values. Other than two instances where a tankless water heater
was classified as a furnace erroneously, all rebated units were program-qualifying furnaces.
◼Program tracking data maintained by IGC was very comprehensive and readily facilitated the
evaluation effort.
◼The Evaluators identified a discernable trend in furnace sizing relative to home vintage. Of key
note, sizing in Weather Zone 5 was often larger than for Weather Zone 6, despite Zone 5 being a
warmer climate.
1.1.1.2 Recommendations
◼Collect installation disposition on the project application: replace-on-burnout, early retirement,
or new construction. Program tracking currently distinguishes new construction but does not
capture replace-on-burnout versus early retirement.
◼Verify AHRI certification for all rebated furnaces before approving. All units were program-
qualifying, but many had sizing or efficiency data entry errors.
◼Provide or encourage contractor training for Manual J sizing calculations for rebated furnaces.
Over-sizing in Weather Zone 5 negatively affects program savings due to repeated shorter run
cycles and associated higher use during the heating ramp-up (which is the point of greatest energy
use for a furnace).
◼The Evaluators suggests using engineering-based evaluation approaches that comply with the
International Performance Measurement and Verification Protocol (IPMVP) maintained by the
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 11 of 329
Executive Summary 3
Efficiency Valuation Organization (EVO) with sponsorship by the U.S. Department of Energy
(DOE)1 such as Method 3 described in Appendix A.
1.1.2 Whole Home Rebate
1.1.2.1 Conclusions
◼The Evaluators found that the average non-program home in IGC’s service territory exceeds IECC
2012 with Idaho amendments requirements. This is observed in the billing analysis, comparing
non-program, newly built homes to the newly built program-rebated homes. In addition, the
proportion and distribution of HERS-rated homes in the IGC territory suggest that the market
already demands efficiency homes. The average non-program newly built home that has been
rated receives a HERS Index score of 63. The average program home received a HERS Index score
of 60. This suggests that to some extent the market for new homes in Idaho includes high
efficiency options without program intervention.
◼The Evaluators’ benchmarking research indicates that the energy efficiency standard for a code-
built home is increasing, and that current program requirements may need to increase efficiency
in order to keep up with higher efficiency building codes.
◼The Whole Home Rebate provides measurable savings through billing analysis. However, the
observable energy savings through billing analysis are much lower than expected equipment
savings. Billing analyses include any changes in household behavior, equipment, or occupancy,
and therefore are unable to entirely isolate equipment savings.
◼It is expected that the lower the HERS Index score, the higher the energy savings. However, the
Evaluators found the savings normalized by square footage remains relatively constant across a
20-point HERS Index range. The same lack of relationship is seen when analyzing the electric
energy savings across the HERS Index. Homes with lower HERS Index scores do not equate to
higher Therms savings. The HERS Index score instead depends on the proportional difference
between the RESNET reference home and the rebated home.
◼The Evaluators found higher savings in all component loads for the program-rebated homes than
the simulated home with only the ENERGY STAR certification, except for window and door
component loads. Overall, the program-rebated home displayed 27% higher savings than a home
only completing ENERGY STAR requirements.
◼Homes rebated through the program are shown to sell on average 39 days faster than non-
rebated homes. This information can be forwarded to builders and raters to help increase
program participation.
1.1.2.2 Recommendations
◼The Evaluators recommends imposing a more stringent HERS Index requirement for the program
in light of recent legislation adoption to adopt the 2018 IECC residential code in Idaho. This
adoption enforces a required maximum HERS Index score of 68.
◼The Evaluators recommends removing ENERGY STAR certification requirement, as it seems to be
a barrier to builder participation, and the analysis completed for this study indicates that there is
1 Core Concepts: International Measurement and Verification Protocol. EVO 100000 – 1:2016, October 2016.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 12 of 329
Executive Summary 4
no basis to state that this requirement saves more energy than the current market requirements
of energy efficiency in newly built homes.
◼To directly target natural gas savings, IGC could impose specific requirements in addition to a
HERS requirement. This may include an efficient furnace, tankless water heater, higher insulation
levels (such as R-49 ceiling insulation), or more stringent air sealing and duct sealing requirements.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 13 of 329
2 General Methodology
This chapter details general impact evaluation methodologies by program-type. This chapter will present
full descriptions of:
◼Glossary of terminology;
◼Sampling methodologies; and
◼Process evaluation methodologies.
The following sections contain a glossary of terminology used throughout the report.
2.1 Glossary of Terminology
As a first step to detailing the evaluation methodologies, the Evaluators has provided a glossary of terms
to follow:
◼Ex-ante Savings – Calculated savings used for program and portfolio planning purposes.
◼Ex-post Savings – Savings estimates reported by an evaluator after the energy impact evaluation
has been completed.
◼Deemed Savings – An estimate of an energy savings outcome (gross savings) for a single unit of
an installed energy efficiency measure. This estimate (a) has been developed from data sources
and analytical methods that are widely accepted for the measure and purpose and (b) are
applicable to the situation being evaluated.
◼Gross Savings – The change in energy consumption directly resulting from program-related
actions taken by participants in an efficiency program, regardless of why they participated.
◼Gross Realization Rate – Ratio of Ex-Post Savings / Ex-ante Savings (e.g. If the Evaluators verify
100 Therms per furnace, Gross Realization Rate = 100/112 = 89%).
2.2 Sampling Methodology
This section explains the sampling methodology used for evaluating IGC’s energy efficiency measures.
2.2.1 95% AFUE Furnace
The Evaluators used simple random sampling strategies to evaluate the measure. The sampling strategies
must achieve 10% relative precision at a 90% confidence level (90/10). The required sample size to meet
90/10 requirements is calculated by using the coefficient of variation of savings. The coefficient of
variation (CV) is defined as:
𝐶𝑉(𝑥)= 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 (𝑥)
𝑀𝑒𝑎𝑛(𝑥)
Where (x) represents participant energy savings in each stratum. The required sample size is estimated
at:
𝑛0 =(1.645 ∗𝐶𝑉
𝑅𝑃)
2
Where,
1.645 = Z-score for 90% confidence interval in a normal distribution
CV = Coefficient of variation
RP = Relative precision, 10%
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 14 of 329

General Methodology 6

The 95% AFUE Furnace random sample size is shown in Table 2-1. A sample of 80 measure rebate
documents were verified for accuracy in the tracking database, provided by IGC. The sample displayed a
precision of 4.34% at the 90% confidence interval.
Table 2-1 Sampled Projects
Stratum Sample Size Coefficient of
Variation Precision
95% AFUE Natural Gas Furnace 80 0.24 4.34%
2.2.2 Whole Home Rebate
The Whole Home Rebate sampling methodology is similar to the methodology described in Section 2.2.1
The Evaluators developed a stratified sampling plan to achieve the required relative precision at the
required confidence level. The Evaluators created a sample of 80 homes to verify, stratified by builder. A
total of 68 of the original 80 sampled homes were able to be verified and remain in the sample, as 12 of
the simulation files contained unresolvable software errors. The Evaluators received the sample of energy
models from program HERS raters as well as application materials via IGC. The Whole Home Rebate
stratified random sample is shown in Table 2-2. Builders were stratified by overall contribution to the
number of homes rebated. Two builders retained statistically significant precision under 10%, however,
the overall sample of 68 displayed a precision of 6% at the 90% confidence interval.
Table 2-2 Whole Home Rebate Sampled Projects
Builder
Percent
Contribution
of Homes
Sampled
Homes
Average
Therms Savings
per Home
Standard
Deviation of
Therms Savings
per Home
Coefficient of
Variation
Relative
Precision (90%
Confidence
Interval)
Builder 1 26.0% 14 272.71 89.26 0.33 14.4%
Builder 2 12.2% 10 258.40 64.61 0.25 13.0%
Builder 3 10.2% 7 268.57 48.20 0.18 11.2%
Builder 4 7.8% 6 242.83 90.26 0.37 25.0%
Builder 5 7.4% 5 180.20 58.52 0.32 23.9%
Builder 6 7.3% 6 253.17 43.82 0.17 11.6%
Builder 7* 6.2% 5 324.80 36.68 0.11 8.3%*
Builder 8 4.3% 4 239.25 66.21 0.28 22.8%
Builder 9** 3.5% 1 260.00 - - -
Builder 10 3.1% 3 379.67 51.71 0.14 12.9%
Builder 11 2.7% 2 389.50 111.02 0.29 33.2%
Builder 12** 2.1% 1 350.00 - - -
Builder 13* 2.1% 2 357.50 0.71 0.00 0.2%*
Other Builders 5.1% 2 322.50 104.32 0.32 37.6%
Total 100% 68 273.94 82.48 0.30 6.0%
*Statistically significant precision for individual builder
**The Evaluators was unable to calculate precision for some builders, as the standard deviation was 0 due to one total project
sampled by that builder
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 15 of 329
3 95% AFUE Natural Gas Furnace Rebate
The 95% AFUE Natural Gas Furnace rebate was designed to provide financial incentives to encourage
residential customers to install energy efficient natural gas furnaces. The rebate provides $350 in
incentives for each residential furnace installed that has a thermal efficiency rating of 95% or greater.
3.1 Measure Description
This measure offers incentives for residential customers that purchase and install qualifying high-
efficiency natural gas furnaces. The measure requires the participant to be served on the residential rate
schedule and to use natural gas exclusively for space heating. Eligible project dispositions include new
construction, replacement of existing failed furnaces (“normal replacement” or “replace on burnout”),
replacement of existing furnaces that are still functional (“early replacement) as well as furnaces
converted from an alternate fuel. The furnace must meet a minimum rated efficiency of 95% AFUE.
The Evaluators estimated savings for 95% AFUE Natural Gas Furnace measures using a matched control
group of non-participating residences in IGC’s service territory. Table 3-1 presents overall ex-ante and ex-
post savings by program year. Across both program years, the program achieved a 49.4% realization rate,
with annual ex-post savings per furnace of 56.0 Therms.
Table 3-1 Matched Control Group Regression Analysis Summary
Program
Year
Ex-Ante
Savings per
Rebate
(Therms)
Number of
Valid
Rebates
Ex-Post
Savings per
Rebate
(Therms)
Ex-Ante
Program
Savings
(Therms)
Ex-Post
Program
Savings
(Therms)
Realization
Rate
2017-2018 112 1,332 66.27 154,224 88,275 57.24%
2019 112 2,066 49.43 231,392 102,123 44.13%
All 3,398 56.03 385,616 190,398 49.37%

Table 3-2 shows the number of rebated appliances and ex-ante Therms savings for the 95% AFUE Natural
Gas Furnace in each program year.
Table 3-2 Ex-Ante Therms Savings of Furnaces by Program Year
Program Year Number of
Furnaces
Ex-Ante
Therms
Savings per
unit
Ex-Ante
Therms
Savings
2017-2018 1,377 112 154,224
2019 2,066 112 231,392
Total 3,443 112 385,616
3.2 Impact Evaluation Approach
This section describes the impact evaluation of the 95% AFUE Natural Gas Furnace measure. The
Evaluators performed a billing analysis with a matched control group as part of the Evaluators’ impact
evaluation for 95% AFUE Natural Gas Furnaces. The Evaluators utilized a quasi-experimental method of
producing a post-hoc control group.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 16 of 329

95% AFUE Natural Gas Furnace Rebate 8

While it is not possible to guarantee the possibility of creating a sufficiently matched control group, this
method is preferred because it is likely to have more meaningful results than a treatment-only analysis.
Some examples of outside variables that a control group can sufficiently control for are changes in
economies and markets, large-scale social changes, or impacts from weather-related anomalies such as
flooding or hurricanes.
The Evaluators also conducted the following two additional methods for impact evaluation of this
measure:
◼ Billing Analysis with Pre-Post Model
◼ Equivalent Full Load Hours for Heating (EFLH) Estimation
The results of the alternative impact evaluation methods can be found in Appendix A.
3.2.1.1 Data Collection
IGC provided the following data to support the analysis:
◼ Pre- and post-installation monthly gas billing data for 3,331 participants and 5,000 non-
participants. The data started on June 21, 2016 and ended on February 27, 2020; and
◼ Program tracking data for participants, including participant zip code, date of installation (furnace
firing date), efficient furnace input capacity (BTU/hr.), and furnace annual fuel utilization
efficiency (AFUE).
In addition, the Evaluators acquired:
◼ Heating degree days (HDD) by month and year from the National Oceanic and Atmospheric
Administration (NOAA)2. The monthly HDD weather data were collected from four weather
stations in Idaho by associating 3-digit zip codes with the nearest weather station having available
historical weather data, as shown in Table 3-3. Monthly HDDs are calculated as the sum of daily
average temperature values under 63°F in a given month. The degree day base temperature of
63°F was selected by observing the best fitting model for a range of base temperatures between
55 and 75°F.
◼ HDD by month and year for a Typical Meteorological Year (TMY) for the specified weather
stations, as shown in the table below3.

2 ftp://ftp.ncdc.noaa.gov/pub/data/noaa/
3 https://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/by_state_and_city.html

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 17 of 329

95% AFUE Natural Gas Furnace Rebate 9

Table 3-3: Weather Station Mapping
Major City Weather
Station ID
3-Digit Zip
Code TMY HDD
Boise 726810 836/837 5,188
Twin Falls 725866 833 5,913
Pocatello 725780 832 6,839
Idaho Falls 725785 834 7,588
3.2.1.2 Data Cleaning
The Evaluators performed the following data cleaning steps for participants in preparation for the billing
analyses:
◼ Identified homes in the billing data that participated in the 95% AFUE Natural Gas Furnace Rebate
measure
◼ Excluded homes that also participated in the Whole Home Rebate (did not occur)
◼ Removed homes with multiple furnace install dates.
◼ Removed homes missing any billing data.

3.2.1.3 Data Verification
The Evaluators verified tracking data by reviewing invoices and model numbers on a sample of participant
households. The Evaluators first screened each furnace rebate household to ensure the customer who
received a furnace rebate did not also receive a rebate for the Whole Home measure. There were no
overlaps in participation. Tracking data was reviewed to verify each rebated unit satisfies all program
efficiency requirements. All rebated furnaces were found to pass efficiency requirements.
The Evaluators verified the tracking data provided by IGC. A sample of 80 furnace model input capacity
and AFUE were verified in the AHRI database. The Evaluators used the confirmed, AHRI-verified input
capacities and furnace efficiencies in the calculation of the following savings estimate. Although some
tracked efficiencies were inconsistent with the AHRI database, the Evaluators found no rebates to display
an efficiency lower than the 95% AFUE requirement for the rebate. It should be noted that 448 rebates
were found with prior furnace efficiencies within 15% of efficiency of the newly installed furnace. In
addition, the tracking data revealed 11 furnaces had prior furnace efficiency above the installed furnace
efficiency. Although the Evaluation team applied average savings for these rebates, prior efficiency of
replaced furnaces should be noted in future program years to enable proper savings estimates.
The Evaluators also verified a sample of 80 rebate documents provided by IGC and cross referenced the
tracking data to verify accurate inputs into the database. The Evaluators found one model number for
equipment that was not a furnace in PY2017-2018, removed the rebate from the annual savings estimates,
and did not extrapolate savings for that rebate.
The billing data provided by IGC revealed an additional 45 rebate participants in PY2017-2018 did not
receive a rebate, and therefore the Evaluators removed these participants from the treatment group. The
45 rebates were removed from savings calculations throughout the evaluation. Table 3-1 displays the
original and final number of rebates used in the calculation of the methodologies below.

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95% AFUE Natural Gas Furnace Rebate 10

Table 3-4 Total Verified Rebates
Program Year Original
Rebates
Validated
Rebates
2017-2018 1,377 1,332
2019 2,066 2,066
Total 3,443 3,398
Table 3-5 provides summary statistics for rebate efficient furnaces by program year. Furnace efficiencies
ranged from a low of 95.0 AFUE to a high of 98.7, while input capacity ranged from 26,000 to 150,000
BTU/hr. The table also displays prior efficiency, although prior efficiency was not used in the calculations
in this methodology. The prior efficiency ranged from 50.0 to 98.7 AFUE.
As mentioned in the sections above, the Evaluators found 448 rebates with prior furnace efficiencies
within 15% of efficiency of the newly installed furnace. In addition, the tracking data revealed 11 furnaces
had prior furnace efficiency above the installed furnace efficiency.
Table 3-5 Summary Statistics for Rebated Efficient Furnaces
Program Year Avg
Capacity
Min
Capacity
Max
Capacity
Avg
AFUE
Min
AFUE
Max
AFUE
Avg Prior
AFUE
Min Prior
AFUE
Max Prior
AFUE
2017-2018 74,431 26,000 120,000 95.9 95 98.7 81.0 50 98.7
2019 73,529 40,000 140,000 96.0 95 98.7 81.6 60 98.5
A sample of participants were surveyed via online survey or telephone survey to confirm that the units
were operational prior to replacement. If the units were found to be inoperative prior to replacement,
the Evaluators would re-classify the unit as replace-on-burnout. This would aid in providing more accurate
estimation of annual savings by replacement type. The Evaluators found that none of the 80 surveyed
furnace appliance rebate participants had a non-operational furnace unit at the time of install.
3.2.1.4 Billing Analysis with Matched Control Group
The Evaluators used participant and non-participant billing data in the pre-period (before furnace retrofit)
and participant and non-participant billing data in the post-period (after furnace retrofit) in a difference-
in-difference fixed-effects panel regression model to predict weather-dependent Therms savings, as
detailed in IPMVP Option C.
The control group was created using propensity score matching (PSM), a method that allows the
Evaluators to find the most similar household based on customers’ monthly energy consumption trends
in the pre-period, specifically covariates for average summer, winter, fall, and spring pre-period usage
were used. After matching, a t-test was conducted for each month in the pre-period to help determine
the success of PSM. The Evaluators also performed a joint chi-square test, which tests for whether any of
the covariates used in PSM are still unbalanced after matching. Lastly, the Evaluators computed the
standardized mean difference for each PSM covariate to determine whether any PSM covariates had large
differences between treatment and control after matching.
After creating a PSM control group, the Evaluators fit a difference-in-difference fixed-effects panel
regression model to estimate weather-dependent daily consumption differences between homes with
rebated furnaces and homes without rebated furnaces. The following equation displays the summarizes
the model specifications used for this analysis.

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Equation 3-1: Difference-in-Difference Fixed-Effects Panel Regression Model Specification
𝐷𝑎𝑖𝑙𝑦 𝑇ℎ𝑒𝑟𝑚𝑠𝑖𝑡
=𝛼0 +𝛽1(𝑃𝑜𝑠𝑡)𝑖𝑡+𝛽2(𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡)𝑖𝑡+𝛽3(𝐻𝐷𝐷)𝑖𝑡+𝛽4(𝑃𝑜𝑠𝑡× 𝐻𝐷𝐷)𝑖𝑡
+𝛽5(𝑃𝑜𝑠𝑡× 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡)𝑖𝑡+𝛽6(𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡× 𝐻𝐷𝐷)𝑖𝑡
+𝛽7(𝑃𝑜𝑠𝑡× 𝐻𝐷𝐷× 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡)𝑖𝑡+𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐷𝑢𝑚𝑚𝑦𝑖+𝜀𝑖𝑡
Where,
𝐷𝑎𝑖𝑙𝑦 𝑇ℎ𝑒𝑟𝑚𝑠𝑖𝑡 = Estimated energy usage (dependent variable) in home i during period t
𝑃𝑜𝑠𝑡𝑖𝑡 = Dummy variable indicating whether period t was in pre- or post- retrofit for home i
𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 = Dummy variable indicating whether household i was in the treatment group or control
group
𝐻𝐷𝐷𝑖𝑡 = Average heating degree days (base 63°F) during period t for home i
𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐷𝑢𝑚𝑚𝑦𝑖 = Customer-specific dummy variable for fixed effects
𝜀𝑖𝑡 = Customer-level random error
𝛼0= The model intercept for home i
𝛽1−7 = Coefficients determined via regression
The coefficient 𝛽5 represents the average non-weather-related change in daily consumption between the
groups in the post-period. The coefficient 𝛽7 represents the average change in daily weather-related
consumption between the groups in the post-period. HDD are calculated from local weather data. HDD
was estimated using a range of balance points (55°F to 75°F base temperature) and the HDD result that
yielded the greatest model R-square was used in the final analysis. This accounts for the “dead-band” in
residential heating loads, as there is a range of temperatures in which a residential customer will be
neither heating nor cooling. Typical monthly and annual savings were estimated by extrapolating the 𝛽5
and 𝛽7 coefficients with Typical Meteorological Year (TMY) weather data.
Additional data cleaning steps for combined treatment and control customers included:
◼ Removed bills with missing billing dates
◼ Removed account numbers for homes that do not overlap with the furnace install date
◼ Kept homes with install dates in the first six month of the program year (to allow for sufficient post-
period data)
◼ Restricted to pre-period and post-period data. The post period begins on July 1 of the program year,
while the pre-period begins one year prior to the program year and ends at the start of the program
year
◼ Removed bill duration outliers (<9 or > 60 days duration)
◼ Removed homes with insufficient number of bills (< 12 months)
3.2.2 Impact Evaluation Results
This section provides the propensity score matching and post propensity score matching regression
results.
3.2.2.1 Propensity Score Matching
The results of PSM are summarized below. The Evaluators used nearest neighbor, 2 to 1 ratio matching
and had a considerable pool of control customers to draw upon, as shown in Table 3-6. Customers were
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95% AFUE Natural Gas Furnace Rebate 12

matched on their average pre-period seasonal usage, including winter, spring, summer and fall for each
control and treatment household.
Table 3-6 PSM Customer Matches
Program Year Status Control Treated
2017-2018
All 3,563 388
Matched 776 388
Unmatched 2,787 0
2019
All 3,805 552
Matched 1,104 552
Unmatched 2,701 1
Table 3-7 presents the propensity score covariate summary of pre-period usage for treatment and control
customers before and after matching, for each program year. The standardized mean difference prior to
matching is often over 0.2 for many covariates; however, after matching the absolute value of the
standardized mean difference is less than 0.1, with one season exception in PY2017-2018, which is an
ideal outcome.
Table 3-7 PSM Covariate Summary
Program
Year Variable
Before Matching After Matching
Mean
Treated
Mean
Control
Standardized
Mean
Difference
Mean
Treated
Mean
Control
Standardized
Mean
Difference
2017-2018
Distance 0.106 0.097 0.009 0.106 0.106 0.000
Pre-period Winter Usage 5.249 4.503 0.746 5.249 5.129 0.121
Pre-period Spring Usage 1.998 1.756 0.242 1.998 1.933 0.065
Pre-period Summer Usage 0.567 0.501 0.066 0.567 0.572 -0.006
Pre-period Fall Usage 2.180 1.881 0.300 2.180 2.181 -0.001
2019
Distance 0.137 0.125 0.012 0.137 0.137 0.000
Pre-period Winter Usage 4.469 3.885 0.583 4.469 4.417 0.052
Pre-period Spring Usage 2.037 1.753 0.284 2.037 1.984 0.053
Pre-period Summer Usage 0.613 0.499 0.113 0.613 0.580 0.033
Pre-period Fall Usage 2.278 1.890 0.388 2.278 2.252 0.026
Table 3-8 and Table 3-9 provide results for a t-test which helps determine the success of matching. The
test measures whether there are statistically significant differences in average daily Therms usage
between the treatment and control groups in the pre-period by month. Statistically significant differences
occur when the P-Value is less than 0.05 at the 95% significance level. As shown in both tables, the P-
Value is much greater than 0.05 for each month in both program years. This result further indicates
propensity score matching performed well.
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Table 3-8 Post Matching T-Test of Difference in Usage by Month, PY2017-2018
Month
Average Daily
Therms
Control
Average Daily
Therms
Treatment
T Stat Std Error P-Value Reject Null?
Jan 6.004 6.169 0.940 0.176 0.348 No
Feb 4.349 4.331 -0.121 0.145 0.904 No
Mar 2.660 2.725 0.722 0.090 0.470 No
Apr 1.940 2.015 1.095 0.068 0.274 No
May 1.126 1.175 0.887 0.055 0.375 No
June 0.670 0.672 0.044 0.044 0.965 No
July 0.505 0.502 -0.097 0.030 0.923 No
Aug 0.549 0.529 -0.501 0.041 0.616 No
Sept 1.054 1.033 -0.389 0.054 0.698 No
Oct 2.171 2.145 -0.366 0.071 0.715 No
Nov 3.279 3.331 0.547 0.095 0.585 No
Dec 4.902 5.009 0.786 0.136 0.432 No
Table 3-9 Post Matching T-Test of Difference in Usage by Month, PY2019
Month
Average Daily
Therms
Control
Average Daily
Therms
Treatment
T Stat Std Error P-Value Reject Null?
Jan 4.119 4.191 0.613 0.118 0.540 No
Feb 4.273 4.299 0.236 0.110 0.814 No
Mar 3.260 3.267 0.064 0.110 0.949 No
Apr 1.888 1.978 0.977 0.092 0.329 No
May 0.899 0.964 1.147 0.056 0.252 No
June 0.638 0.676 0.764 0.050 0.445 No
July 0.551 0.549 -0.052 0.042 0.958 No
Aug 0.550 0.609 1.180 0.050 0.238 No
Sept 0.863 0.955 1.201 0.077 0.230 No
Oct 1.997 1.976 -0.346 0.060 0.729 No
Nov 3.857 3.909 0.539 0.097 0.590 No
Dec 4.820 4.873 0.411 0.128 0.681 No

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Figure 3-1 displays the density of each variable employed in propensity score matching, before conducting
matching for PY2017-2018. Figure 3-2 displays the density of each variable employed in propensity score
matching, after conducting matching for PY2017-2018.
Figure 3-1 PY2017-2018 Average Daily Usage by Season in Treatment and Control Groups Before Matching

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Figure 3-2 PY2017-2018 Average Daily Usage by Season in Treatment and Control Groups After Matching

Figure 3-3 through Figure 3-4 display the same home characteristics, before and after matching, for
PY2019.
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Figure 3-3 PY2019 Average Daily Usage by Season in Treatment and Control Groups Before Matching

Figure 3-4 PY2019 Average Daily Usage by Season in Treatment and Control Groups After Matching

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95% AFUE Natural Gas Furnace Rebate 17

Lastly, the joint chi-square test for covariate balance had a p-value of 0.51, meaning the Evaluators failed
to reject the null hypothesis of covariate balance.
Figure 3-5 displays the average daily Therm usage for both the control and treatment group before
matching in PY2017-2018. Figure 3-6 displays the same values after matching. Figure 3-7 and Figure 3-8
display the average daily Therm usage for both the treatment and control groups before and after
matching for PY2019.
Figure 3-5 PY2017-2018 Average Daily Therm Usage in Treatment and Control Groups Before Matching

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95% AFUE Natural Gas Furnace Rebate 18

Figure 3-6 PY2017-2018 Average Daily Therm Usage in Treatment and Control Groups After Matching

Figure 3-7 PY2019 Average Daily Therm Usage in Treatment and Control Groups Before Matching

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95% AFUE Natural Gas Furnace Rebate 19

Figure 3-8 Average Daily Therm Usage in Treatment and Control Groups Before Matching

3.2.2.2 Regression Results After Matching
This section details the regression results after performing propensity score matching for participants with
a 95% AFUE Natural Gas Furnace measure. Table 3-11 summarizes the results of the billing analysis for
participants by program year. A p-value less than 0.05 indicates that the parameter estimate is statistically
significant at the 95% confidence level.
As discussed in the evaluation approach section, savings are determined through parameters B5 and B7,
which are defined again in Table 3-10, along with all the other model parameters. In any given month, per
participant savings are calculated as follows: (B5*Days in Month) + (B7*HDD). In computing HDD for each
month, the Evaluators used Typical Meteorological Year 3 (TMY 3) data for the weather stations
associated with each 3-digit zip code area (per Table 3-3). This extrapolation provides savings under typical
weather conditions. Monthly TMY HDDs were calculated from the TMY data as the sum of daily average
temperature values under 63°F in a given month. Monthly TMY HDDs were weighted by the number of
rebates found in each 3-digit zip code when computing monthly and annual program savings.
Table 3-10 Method 1 Regression Parameters
Variable Parameter Interpretation
Post B1 Average daily usage in the post-period
Treatment B2 Average daily usage for the treatment group
Avg.HDD B3 Average daily usage per HDD
Post*Avg.HDD B4 Average daily usage per HDD in the post-period
Post*Treatment B5 Average daily usage in the post-period for the treatment group
Treatment*Avg.HDD B6 Average daily usage per HDD for the treatment group
Post*Treatment*Avg.HDD B7 Average daily usage per HDD in the post-period for the treatment group
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95% AFUE Natural Gas Furnace Rebate 20

As shown in Table 3-11, the coefficient estimates for Post*Treatment*Avg.HDD (B7) are negative,
indicating lower usage per HDD in the post-period for treatment customers. In addition, these coefficients
are statically significant at the 95% level in both program years. The estimate for Post*Treatment (B5) is
also negative, indicating positive savings that are independent of the weather or HDDs, but is not
statistically significant for PY2017-2018. This is expected for furnace retrofits due to weather dependency.
Table 3-11 Method 1 Regression Results
Program
Year Coefficient Estimate Std Error P Value 5% 95%
2017-2018
Post 0.0370 0.0221 0.0935 0.0007 0.0734
Treatment 1.8486 0.2708 0.0000 1.4031 2.2940
Avg.HDD 0.1395 0.0008 0.0000 0.1381 0.1409
Post*Avg.HDD -0.0006 0.0011 0.6047 -0.0023 0.0012
Post*Treatment -0.0330 0.0380 0.3849 -0.0955 0.0295
Treatment*Avg.HDD 0.0036 0.0014 0.0117 0.0013 0.0060
Post*Treatment*Avg.HDD -0.0095 0.0019 0.0000 -0.0126 -0.0063
2019
Post 0.0654 0.0273 0.0165 0.0205 0.1102
Treatment 5.0537 0.3749 0.0000 4.4370 5.6704
Avg.HDD 0.1383 0.0009 0.0000 0.1368 0.1397
Post*Avg.HDD 0.0014 0.0014 0.3118 -0.0009 0.0036
Post*Treatment -0.0220 0.0473 0.6421 -0.0998 0.0558
Treatment*Avg.HDD 0.0003 0.0015 0.8471 -0.0022 0.0028
Post*Treatment*Avg.HDD -0.0072 0.0024 0.0026 -0.0112 -0.0033
*Per-household fixed-effects coefficients were omitted from this table for brevity
Each of the models showed they were a good fit for the data, as seen by the Adjusted R-square in Table
3-12. Model fit is slightly lower for PY2019, which may be due to fewer observations in the post-period
relative to PY2017-2018.
Table 3-12 Method 1 Model Fit
Program
Year Adjusted R2 F Statistic Number of
Observations
Number of
Treatment
Customers
2017-2018 0.8067 128 34,475 388
2019 0.7700 65 29,024 552
Table 3-14 provides monthly savings estimates per rebate and for all rebates by program year. TMY HDDs
were weighted by the number of rebates from each 3-digit zip code.

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95% AFUE Natural Gas Furnace Rebate 21

Table 3-13 Number of Premises by 3-Digit Zip Code and Program Year
Program
Year
Weather
Station ID
3-Digit Zip
Code
Number of
Premises
2017-2018
725780 832 257
725866 833 71
725785 834 99
726810 836/837 880
Total 1,307
2019
725780 832 369
725866 833 140
725785 834 177
726810 836/837 1,336
Total 2,022

Table 3-14 Monthly Savings Summary by Program Year
Month Weighted
HDD
PY2017-PY2018
Savings/Rebate
(Therms)
PY2019
Savings/Rebate
(Therms)
1 1,118 11.59 8.75
2 815 8.64 6.51
3 666 7.32 5.49
4 387 4.65 3.45
5 251 3.40 2.49
6 112 2.05 1.47
7 35 1.35 0.93
8 79 1.77 1.25
9 163 2.53 1.84
10 406 4.86 3.61
11 757 8.15 6.12
12 945 9.96 7.51
Annual 5,734 66.27 49.43

The ex-ante and ex-post Therms savings of the 95% AFUE Natural Gas Furnace are summarized below by
program year. The number of rebates used to calculate total ex-post Therms savings is the number of
validated furnace rebates, detailed in the verification section above. Table 3-15 displays the total measure
ex-ante and ex-post savings for each program year.
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95% AFUE Natural Gas Furnace Rebate 22

Table 3-15 Ex-Ante and Ex-Post Annual Therms Savings for 95% AFUE Natural Gas Furnace by Program
Year
Program
Year
Ex-Ante
Savings per
Rebate
(Therms)
Number of
Valid
Rebates
Ex-Post
Savings per
Rebate
(Therms)
Ex-Ante
Program
Savings
(Therms)
Ex-Post
Program
Savings
(Therms)
Realization
Rate
2017-2018 112 1,332 66.27 154,224 88,275 57.24%
2019 112 2,066 49.43 231,392 102,123 44.13%
All 3,398 56.03 385,616 190,398 49.37%
3.2.3 Furnace Sizing
The Evaluators took steps to review the furnace sizing of rebated furnaces in the program. Most deemed
savings protocols (such as equivalent full load heating hours) for residential furnaces assume appropriate
sizing for the residence, when the reality is that oversizing by a factor of 33%-48% of HVAC equipment is
a commonly observed practice4. The Evaluators reviewed rebated furnace equipment sizing to square
footage by home vintage. This analysis is meant to provide this feedback to IGC to provide context for any
deviations in savings estimates, and to allow for feedback to HVAC contractors on equipment sizing.
The Evaluators calculated the average furnace sizing per square foot by home vintage and weather zone,
presented in Table 3-16.
Table 3-16 Average Rebated Furnace Sizing per Square Foot by Vintage
and Weather Zone

Year Built
Weather Zone 5
Average Sizing
(BTUH/SQFT)
Weather Zone 6
Average Sizing
(BTUH/SQFT)
Pre-1979 39.32 43.14
1980-1989 37.82 37.39
1990-1999 37.05 32.34
2000-Present 33.51 30.29
Total 35.30 35.75

4 https://www.energy.gov/sites/prod/files/2018/06/f53/bto-ResidentialHVACLitReview-06-2018.pdf, Table 2.
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95% AFUE Natural Gas Furnace Rebate 23

3.3 Conclusions and Recommendations
This section presents conclusions and recommendations for the 95% AFUE Natural Gas Furnace measure.
3.3.1 Conclusions
◼ The 95% AFUE Natural Gas Furnace provides measurable savings through billing analysis.
However, the observable energy savings through billing analysis are much lower than expected
equipment savings. Billing analyses include any changes in household behavior, equipment, or
occupancy, and therefore may include factors other than the impact of improved equipment
efficiency.
◼ The Evaluators found several deviations in furnace capacity size and AFUE in the tracking data
compared to AHRI certification values. Other than two instances where a tankless water heater
was classified as a furnace erroneously, all rebated units were program-qualifying furnaces.
◼ Program tracking data maintained by IGC was very comprehensive and readily facilitated the
evaluation effort.
◼ The Evaluators identified a discernable trend in furnace sizing relative to home vintage. Of key
note, sizing in Weather Zone 5 was often larger than for Weather Zone 6, despite Zone 5 being a
warmer climate.
3.3.2 Recommendations
◼ Collect installation disposition on the project application: replace-on-burnout, early retirement,
or new construction. Program tracking currently distinguishes new construction but does not
capture replace-on-burnout versus early retirement.
◼ Verify AHRI certification for all rebated furnaces before approving. All units were program-
qualifying, but many had sizing or efficiency data entry errors.
◼ Provide or encourage contractor training for Manual J sizing calculations for rebated furnaces.
Over-sizing in Weather Zone 5 negatively affects program savings due to repeated shorter run
cycles and associated higher use during the heating ramp-up (which is the point of greatest energy
use for a furnace).
◼ The Evaluators suggests using engineering-based evaluation approaches that comply with the
International Performance Measurement and Verification Protocol (IPMVP) maintained by the
Efficiency Valuation Organization (EVO) with sponsorship by the U.S. Department of Energy
(DOE)5 such as Method 3 described in Appendix A.

5 Core Concepts: International Measurement and Verification Protocol. EVO 100000 – 1:2016, October 2016.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 32 of 329
4 Whole Home Rebate
The Whole Home measure offers incentives to home builders and buyers for new construction, single-
family residential homes that meet energy efficiency standards of the program. The Whole Home measure
requires homes must be certified to meet the requirements of the Environmental Protection Agency (EPA)
ENERGY STAR certified homes program. In addition, the home must achieve a Residential Energy Services
Network (RESNET) Home Energy Rating System (HERS) score of 75 or less with a certificate completed by
a certified HERS rater. The Whole Home Rebate cannot be combined with any other appliance rebates in
IGC’s Energy Efficiency Program.
4.1 Program Description
The objective of the Whole Home Rebate is to encourage homebuilders to include energy efficient
measures in the construction of new homes built within IGC’s service area. This measure provides a $1,200
rebate for new construction residential ENERGY STAR certified homes with a RESNET Home Energy Rating
Score (HERS) of 75 or less.
The Evaluators verified savings for the Whole Home Rebate measure using a billing analysis including a
matched control group. Table 4-1 presents the overall ex-ante and ex-post savings by program year.
Across all program years, the Evaluators found a 28.2% realization rate, with an annual ex-post savings
per rebate of 57.5 Therms.
Table 4-1 Method 1 Ex-Ante and Ex-Post Program Savings
Program
Year
Ex-Ante Savings
per Rebate
(Therms)
Number of
Rebates
Ex-Post Savings
per Rebate
(Therms)
Ex-Ante Program
Savings (Therms)
Ex-Post
Program
Savings
(Therms)
Realization
Rate
2017-2018 204 619 57.18 126,276.00 35,393 28.03%
2019 204 1,079 57.75 220,116.00 62,311 28.31%
All 1,698 57.54 346,392.00 97,704 28.21%
Table 4-2 shows the number of completed projects and ex-ante Therms savings for the Whole Home
Rebate by program year.
Table 4-2 Ex-Ante Therms Savings of Whole Home Rebate by Program Year
Program Year Number of
Homes
Ex-Ante
Therms
Savings per
unit
Ex-Ante
Therms
Savings
2017-2018 619 204 126,276
2019 1,079 204 220,116
Total 1,698 204 346,392
The Evaluators also presents a HERS Index analysis between the program and non-program group, as
well as between the matched control and treatment group used in the billing analysis.
4.2 Impact Evaluation Approach
The following section presents the methodology that was used for estimating energy impacts resulting
from the Whole Home Rebate measure.
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Whole Home Rebate 25

The Evaluators performed a billing analysis with a matched control group as part of the impact evaluation
for Whole Home Rebate measure. While it is not possible to guarantee the possibility of creating a
sufficiently matched control group, this method is preferred because it is likely to have more meaningful
results than a treatment-only analysis. Some examples of outside variables that a control group can
sufficiently control for are changes in economies and markets, large-scale social changes, or impacts from
weather-related anomalies such as flooding or hurricanes.
The Evaluators also conducted a whole building simulation analysis as an alternative method for impact
evaluation of this measure. The results of the alternative impact evaluation methods can be found in
Appendix B. Each of these approaches comply with the International Performance Measurement and
Verification Protocol (IPMVP) maintained by the Efficiency Valuation Organization (EVO) with sponsorship
by the U.S. Department of Energy (DOE)6.
4.2.1.1 Data Collection
IGC provided the following data to support the analysis:
◼ Monthly gas billing data for 1,698 program homes and 10,433 non-program homes. The data started
on November 28, 2016 and ended on March 30, 2020;
◼ Program tracking data for program homes, including household zip code, year built, HERS rating, and
equipment information (appliance firing date, capacity, efficiencies);
◼ Ada County Assessor data for program and non-program premises; and
◼ REM/Rate files and HERS certification documents from HERS Raters for a sample of 80 Whole Home-
rebated premises.
In addition, the Evaluators acquired:
◼ Heating degree days (HDD) by month and year from the National Oceanic and Atmospheric
Administration (NOAA)7. The monthly HDD weather data were collected from the Boise (KBOI)
weather station. Monthly HDDs are calculated as the sum of daily average temperature values under
63°F in a given month. The degree day base temperature was selected by observing the best fitting
model for a range of base temperatures between 55°F and 75°F.
◼ HDD by month and year for a Typical Meteorological Year (TMY) for the specified weather station is
shown in the table below8.
4.2.1.2 Data Cleaning
The Evaluators began impact evaluation work by reviewing the data tracking systems for completeness
and found them to be satisfactory for use in assessing program impacts, regulatory reporting and other

6 Core Concepts: International Measurement and Verification Protocol. EVO 100000 – 1:2016, October 2016.
7 ftp://ftp.ncdc.noaa.gov/pub/data/noaa/
8 TMY Source: https://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/by_state_and_city.html

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Whole Home Rebate 26

requirements. The Evaluators also reviewed the tracking system for overlaps in redemption of Appliance
rebates and a Whole Home Rebate. The Evaluators found there to be no home that received both a Whole
Home Rebate and any of the Appliance rebates.
The following steps were taken to prepare data for regression analysis:
1. Gather billing data for homes that participated in the Whole Home Rebate (program homes).
2. Gather billing data for new, occupied homes that did not participate in the Whole Home Rebate
measure (non-program homes).
3. Exclude program and non-program homes that also participated in the 95% AFUE Natural Gas
Furnace Program (no occurrences).
4. Connect county records associated with non-program homes for household characteristics.
5. Exclude homes that started occupation after July in the program year (to ensure analysis of the
same post-period in the treatment and control groups).
6. Exclude homes missing winter-season or summer-season billing history.
7. Exclude bills with consumption indicated to be outliers.
8. Exclude bills that contain less than ten days of valid usage out of the month.
The following additional billing restrictions were used:
1. PY2017-2018 required all program and non-program premises have at minimum eight of the nine
heating months (October through April) and at minimum nine months total.
2. PY2019 required all program and non-program premises have at minimum four of the nine
heating months (October through April) and at minimum six months total.
4.2.1.3 Data Verification
The Evaluators screened each Whole Home Rebate household to ensure the customer did not also receive
a rebate for the 95% AFUE Natural Gas Furnace Rebate measure. There were no occurrences of double
participation. Tracking data and ENERGY STAR and HERS certificates were reviewed to verify each
household satisfies all program efficiency requirements.
The Evaluators created a sample of 80 homes to verify documentation and simulation files, stratified by
builder. The Evaluators developed a sampling plan to achieve the required relative precision at the
required confidence level. The Evaluators received the sample of energy models from program HERS
raters as well as application materials via IGC. All data was reviewed for consistency and accuracy.
IGC provided 80 of the requested files on behalf of the HERS raters. The Evaluators encountered several
barriers with the model files provided by the HERS raters and was unable to resolve the errors in 12 of the
80 exported files IGC provided the Evaluators. Therefore, 68 of the original 80 home sample was used to
develop savings estimates from this Method. The final sample plan is summarized in Section 2.2.2.
4.2.1.4 Billing Analysis with Matched Control Group
The Evaluators conducted a linear regression billing analysis using billing data from Whole Home-rebated
homes and newly built non-program homes. The Evaluators successfully created a statistically similar
control group using propensity score matching (PSM). The control group consists of similar newly
constructed residential homes without the energy efficiency standards required in the Whole Home
Rebate.
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Whole Home Rebate 27

The Evaluators fit a simple differences panel linear regression model with random effects to estimate
weather-dependent daily consumption differences between efficient homes and baseline homes. Under
the random effects model the goal is not to estimate one true effect, but to estimate the mean of a
distribution of effects. The Evaluators included the independent variables Heating Degree Days and square
footage to use for forecasting and to understand the relative change between treatment and control
groups, magnitude, and direction of consumption differences in heating months. A household is
considered a treatment household if it received a Whole Home Rebate. It is considered a control
household if the household did not receive a Whole Home Rebate.
The following equation displays the model specifications the Evaluators used for this analysis.
Equation 4-1 Simple Difference Regression Model Specification
𝐷𝑎𝑖𝑙𝑦 𝐻𝑒𝑎𝑡𝑖𝑛𝑔 𝐿𝑜𝑎𝑑 𝑇ℎ𝑒𝑟𝑚𝑠/𝑆𝑄𝐹𝑇𝑖𝑡=𝛽1(𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡)𝑖+𝛽2(𝐻𝐷𝐷)𝑖𝑡+𝛽3(𝐻𝐷𝐷× 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡)𝑖𝑡
Where,
𝐷𝑎𝑖𝑙𝑦 𝐻𝑒𝑎𝑡𝑖𝑛𝑔 𝐿𝑜𝑎𝑑 𝑇ℎ𝑒𝑟𝑚𝑠/𝑆𝑄𝐹𝑇𝑖𝑡 = Estimated heating energy usage (dependent variable) in home
I during period t per square footage of home i
𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 = Dummy variable indicating whether household i was in the treatment group or control
group
𝐻𝐷𝐷𝑖𝑡 = Average heating degree days (base 63°F) during period t at home i
𝛽1−3 = Coefficients determined via regression
𝛽1 and 𝛽3 represent the average change in daily heating load in the post-period between the treatment
and control group, and the change in weather-related daily consumption in the post-period between the
groups respectively. HDD was estimated using a range of balance points (55°F to 75°F base temperature)
and the HDD result that yielded the greatest model R-square was used in the final analysis. This accounts
for the “dead-band” in residential heating loads, as there is a range of temperatures in which a residential
customer will be neither heating nor cooling. Typical monthly and annual savings is estimated by
extrapolating the 𝛽1 and 𝛽3 coefficients with Typical Meteorological Year (TMY) weather data.
4.2.2 Impact Evaluation Results
The following section details the Evaluators’ impact evaluation findings for the Whole Home Rebate under
each of the methods described above.
The Evaluators estimated savings for Whole Home Rebate measure using a matched control group. Table
4-3 presents the overall ex-ante and ex-post savings by program year. Across all program years, the
program achieved a 28.2% realization rate, with annual ex-post savings per Whole Home Rebate of 57.5
Therms.
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Table 4-3 Savings Summary for Billing Analysis
Program
Year
Ex-Ante Savings
per Rebate
(Therms)
Number
of Rebates
Ex-Post Savings
per Rebate
(Therms)
Ex-Ante Program
Savings (Therms)
Ex-Post Program
Savings (Therms)
Realization
Rate
2017-2018 204.00 619 57.18 126,276.00 35,393 28.03%
2019 204.00 1,079 57.75 220,116.00 62,311 28.31%
All 1,698 57.54 346,392.00 97,704 28.21%

4.2.2.1 Data Cleaning
The following tables summarize the number of customers filtered out due to the above criteria, for each
program year and for each of the treatment and control groups. For each program year, the separate
program year-specific restrictions were conducted on the same control group. Therefore, PY2017-2018
and PY2019 each started with the full non-program group to match against.
Table 4-4 Program Premises Data Cleaning Steps
Criteria
PY2017-2018
Program
Homes
PY2019
Program
Homes
Total received billing data 619 1,079
After removing premises still owned by builders 611 948
After removing premises without Ada County Assessor data 539 807
After removing premises without a valid occupancy date and
with billing data occurring after occupancy date 531 751

Table 4-5 Non-Program Data Cleaning Steps
Criteria
Non-
Program
Premises
Total received billing data 12,277
After removing premises with an associated Appliance or Whole Home Rebate 10,430
After removing premises without Ada County Assessor data (No premises removed) 10,430
After removing premises without a valid occupancy date and with billing data
occurring after occupancy date 9,101
After removing premises with more than one number of dwellings in County
Assessor Data 9,081
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Table 4-6 Billing Data Cleaning Steps
Criteria
PY2017-2018
Program
Homes
PY2019
Program
Homes
PY2017-2018
Non-Program
Premises
PY2019 Non-
Program
Premises
Total number of premises after household-level
cleaning steps 531 751 9081 9,081
After calendarizing 531 751 9081 9,081
After removing non-program premises with year built
outside of treatment customer year builds 531 751 7476 5,333
After removing non-program premises without Ada
County Assessor data 531 751 7470 5,328
After removing billing months without sufficient
number of days occupied 531 738 7470 5,327
After removing bill outliers 532 739 7445 5,298
After removing premises without sufficient post-
period for each program year 324 403 3294 2,599
After removing premises without sufficient summer
months to calculate baseload 322 380 3240 2,310
The typical treatment period of an analysis contains at least twelve months of billing data in the post-
period. Although the data used in this analysis contains sufficient winter-period billing data for PY2019, it
contains seven months of total post-period data. Results from less than one full year of a treatment period
are not fully indicative of 12-month savings that may be anticipated going forward. The analysis for
PY2017-2018 allows for a sufficient number of post-period months. It is recommended that future
program year impact analyses allow for collection of 12 months of post-period data.
4.2.2.2 Heating Load Analysis
The Evaluators found there to be statistically significant differences in treatment and control summer
Therms usage. In the months of June, July, and August, variation in usage is expected to be very low
between households, as space heating is no longer required in the summer months. The statistically
significant difference in heating load usage in summer months is indicative of differences in number of
occupants between the treatment group and the control group. This is because the majority of gas usage
in summer months comes from water heating and gas cooking appliances. Higher usage from these
sources indicates larger demand for hot water and kitchen appliances, indicating a larger number of
people living in the home. Figure 4-1 visually displays the differences between treatment and control daily
usage per square foot by treatment and control group. The treatment group displays a higher daily usage
per square foot than the control group in the summer months (June, July, and August).
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Figure 4-1 Average Daily Usage by Group

Table 4-7 displays the statistically significant differences in daily energy consumption per square foot
between the treatment and control groups for each program year. A p-value of less than 0.05 indicates
strong evidence against the null hypothesis at the 95% confidence interval. It means there is less than a
5% probability that the groups display similar behaviors to usage in the summer months.
Table 4-7 Summer Daily Usage T-test
Program
Year Month
Treatment
Average Daily
Therms per
SQFT
Control
Average
Daily Therms
per SQFT
P-value
2018
June 0.00034 0.000296 0.000134
July 0.000283 0.000247 0.000402
August 0.000284 0.00025 0.000818
2019 July* 0.000287 0.000241 0.000206
August 0.000273 0.000241 0.001382
*PY2019 does not include June in the analysis due to post-period restrictions between the treatment group and control group.
With this knowledge, the Evaluators regressed on daily heating load per square foot rather than daily
usage per square foot to filter out differences in summer occupancy. The Evaluators performed the
following calculations as part of the daily heating load per square foot estimation:
◼ Calculated average daily baseload usage per square foot for each premise, where baseload is
average summer usage in June, July, and August.
◼ Calculated average daily heating load for each premise by taking the difference between average
daily usage per square foot and daily baseload usage per square foot.
◼ Set any negative heating loads to zero (assumed to be deviations from average baseload usage).
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Whole Home Rebate 31

Figure 4-2 displays PY2017-2018 treatment group’s average daily usage, average daily baseload in the
summer months, and the calculated average daily heating load as an example of heating load distribution
throughout the year. The Evaluators removed the daily baseload usage from the daily overall usage, with
daily heating load remaining.
Figure 4-2 PY2017-2018 Average Daily Usage by Load Type

4.2.2.3 Propensity Score Matching
The Evaluators used propensity score matching to create a statistically similar group of control households
for each program year. Customers were matched on the following household-level characteristics:
◼ Zip code
◼ Year built
◼ Number of bedrooms
◼ Number of bathrooms
◼ Number of fireplaces
◼ Square footage of home
◼ Square footage of upper floor
◼ Square footage of pool
◼ Square footage of attic
◼ Square footage of garage
The Evaluators had a considerable pool of control customers to draw upon, as shown in Table 4-8. The
Evaluators used nearest neighbor for all the above characteristics, and exact matching for zip code with a
1:1 ratio for matching. It is important the homes being compared are in as similar geography as possible.
Therefore, during the matching process, the 5-digit zip code must match exactly to the control household,
with all other variables matched at the nearest household. This accounts for small geographic differences
between the groups.
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Table 4-8 PSM Customer Matches
Program
Year Status Control Treated
2017-2018
All 3,240 322
Matched 322 322
Unmatched 2,918 0
2019
All 2,310 380
Matched 380 380
Unmatched 1,930 0
Figure 4-3 displays the home characteristics for the treatment and control groups before matching. Figure
4-4 displays the home characteristics for the treatment and control groups after matching. Home
characteristics for PY2019 are also displayed in Figure 4-5 and Figure 4-6, before and after matching.

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Figure 4-3 PY2017-2018 Home Characteristics Before Matching Treatment and Control Groups

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Figure 4-4 PY2017-2018 Home Characteristics After Matching Treatment and Control Groups

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Figure 4-5 PY2019 Home Characteristics Before Matching Treatment and Control Groups

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Figure 4-6 PY2017-2018 Home Characteristics After Matching Treatment and Control Groups

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Table 4-9 presents the propensity score covariate summary of pre-period usage for treatment and control
customers before and after matching, for each program year.
Table 4-9 PSM Covariate Summary
Program
Year Variable
Before Matching After Matching
Mean
Treated
Mean
Control
Mean
Difference
Mean
Treated
Mean
Control
Mean
Difference
2017-2018
Distance 0.234 0.076 0.157 0.234 0.227 0.007
Year Built 2017.898 2017.508 0.389 2017.898 2017.879 0.019
Number of Bedrooms 2.605 2.597 0.007 2.605 2.613 -0.009
Number of Bathrooms 3.463 3.595 -0.132 3.463 3.528 -0.065
Number of Fireplaces 0.882 0.720 0.162 0.882 0.929 -0.047
Square Footage of home 2418.683 2435.734 -17.051 2418.683 2456.186 -37.503
Square footage of upstairs 525.016 500.323 24.693 525.016 530.491 -5.475
Square footage of attic 81.186 48.649 32.537 81.186 80.506 0.680
Square footage of garage 778.183 793.360 -15.176 778.183 772.112 6.071
Square footage of pool 2.795 4.704 -1.909 2.795 1.590 1.205
Zip Code* 0.109 0.176 -0.067 0.109 0.109 0.000
2019
Distance 0.365 0.104 0.261 0.365 0.299 0.066
Year Built 2018.634 2018.181 0.453 2018.634 2018.503 0.132
Number of Bedrooms 2.570 2.535 0.036 2.570 2.595 -0.024
Number of Bathrooms 3.379 3.519 -0.140 3.379 3.426 -0.047
Number of Fireplaces 0.918 0.627 0.291 0.918 0.937 -0.018
Square Footage of home 2390.366 2341.966 48.400 2390.366 2450.600 -60.234
Square footage of upstairs 383.789 500.965 -117.175 383.789 429.903 -46.113
Square footage of attic 59.166 40.342 18.823 59.166 46.858 12.308
Square footage of garage 790.266 739.175 51.091 790.266 808.389 -18.124
Square footage of pool 2.842 2.461 0.381 2.842 3.053 -0.211
Zip Code* 0.089 0.138 -0.049 0.089 0.089 0.000
*Zip Code displays a single zip as opposed to all 30. The mean difference after matching for all 30 zip codes is 0 due to exact
matching for this variable.

Each matched control group passed the chi square equivalency test, a test that estimates the goodness of
fit between two groups. A p-value of less than 0.05 indicates there is a statistically significant difference
between the treatment and control group in terms of the matching criteria. The chi square test accepted
the null hypothesis, as the p-value is over 0.05 for each program year, indicating there are no statistically
significant differences between the treatment and control groups household-level characteristics. Table
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Whole Home Rebate 38

4-10 summarizes the results of the chi square test between the two matched groups for each program
year.
Table 4-10 Treatment-Control Equivalency Test
Program
Year
Chi
Square
Value
Degrees of
Freedom P-value
2018 14 22 0.903
2019 20.1 22 0.575

Figure 4-7 and Figure 4-8 displays the average daily heating load usage for the matched treatment and
control groups for each program year. The post-period for PY2019 contained the months of July 2019
through February 2020, due to data cleaning restrictions and therefore the figure displays only these
months.
Figure 4-7 PY2017-2018 Average Daily Heating Load for Matched Groups

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Whole Home Rebate 39

Figure 4-8 PY2019 Average Daily Heating Load for Matched Groups

4.2.2.4 Regression Results After Matching
This section details the regression results after performing propensity score matching for program
premises. The Evaluators associated 3-digit zip codes with the nearest weather station with historical
weather data and found all homes in the program to match with Boise weather station. As discussed in
the evaluation approach section, savings are determined through parameters B1 and B3, which are
defined again in Table 4-11, along with all the other model parameters. In any given month, per program
home savings are calculated as follows: (B1*Days in Month*Square footage of Home) + (B3*HDD*Square
footage of home). In computing HDD for each month, the Evaluators used TMY3 data for the Boise
International Airport weather station. Monthly HDDs were calculated from the TMY data as the sum of
daily average temperature values under 63°F in a given month.
Table 4-11 Whole Home Regression Parameters
Variable Parameter Interpretation
Treatment B1 Average daily heating load usage per square
foot for the treatment group
Daily HDD B2 Average daily heating load usage per square
foot per HDD
Treatment*Daily HDD B3 Average daily heating load usage per square
foot per HDD for the treatment group
Table 4-12 summarizes the results of the billing analysis for program participant households by program
year. A p-value less than 0.05 indicates that the parameter estimate is statistically significant at the 95%
confidence level. The coefficient estimates for Treatment*Daily HDD (B3) are negative, indicating lower
usage per HDD in the post-period for treatment customers. In addition, these coefficients are statically
significant at the 95% level in both program years.
The estimate for Treatment (B1) is positive for both, indicating negative savings that are independent of
the weather or HDDs. This coefficient is not statistically significant for PY2019 but is statistically significant
for PY2017-2018. This indicates that for PY2017-2018 homes, the treated homes display a statistically
significant difference in heating load usage, independent of weather. This behavior difference was not
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Whole Home Rebate 40

able to be controlled with a valid control group matched on home characteristics and indicates some bias
may remain between the groups for this program year.
Table 4-12 Whole Home Regression Results
Program
Year Variable Estimate Std Error P-Value 5% CI 95% CI
2017-2018
(Intercept) -1.45E-04 6.57E-06 <2e-16 -1.56E-04 -1.35E-04
Treatment 1.88E-05 9.29E-06 0.0430 3.52E-06 3.41E-05
Daily HDD 5.74E-05 3.42E-07 <2e-16 5.69E-05 5.80E-05
Treatment*Daily HDD -5.74E-06 4.84E-07 <2e-16 -6.54E-06 -4.94E-06
2019
(Intercept) -1.10E-04 9.23E-06 2.69E-32 -1.25E-04 -9.47E-05
Treatment 1.29E-05 1.29E-05 0.3179 -8.37E-06 3.42E-05
Daily HDD 5.81E-05 4.58E-07 0.0000 5.73E-05 5.88E-05
Treatment*Daily HDD -5.57E-06 6.54E-07 2.06E-17 -6.65E-06 -4.50E-06
Each of the models showed they were a good fit for the data, as seen by the high Adjusted R-square and
F values in Table 4-13. Model fit is slightly lower for PY2019, which is likely due to fewer observations in
the post-period.
Table 4-13 Whole Home Model Fit
Program
Year Adjusted R2 F Statistic Number of
Observations
Number of
Premises
2017-2018 0.8594 17,020 8,355 322
2019 0.8388 9,680 5,583 380
Table 4-14 summarizes monthly savings for each program year, extrapolated from TMY Heating Degree
Days per month with a degree day base temperature of 63°F. The degree day base temperature was
selected by observing the best fitting model for a range of base points between 55 and 75°F.
Table 4-14 Monthly Savings by Program Year
Month HDD
PY2017-2018
Savings per
Rebate
(Therms)
PY2019
Savings per
Rebate
(Therms)
January 1,054 13.65 13.07
February 732 9.17 8.87
March 625 7.50 7.36
April 343 3.50 3.63
May 216 1.64 1.91
June 92 0.09 0.30
July 22 -1.14 -0.67
August 72 -0.43 0.00
September 122 0.35 0.70
October 341 3.42 3.57
November 712 8.80 8.55
December 857 10.82 10.44
Total 5,188 57.18 57.75
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Whole Home Rebate 41

Method 1 using billing analysis provides the Whole Home Rebate measure an overall realization rate of
28% with 97,704 Therms savings across both program years. Table 4-15 presents the ex-ante, ex-post, and
realization rate for each program year.
Table 4-15 Summary of Annual Program Savings
Program
Year
Verified Program
Savings (Therms)
Ex-Ante Program
Savings (Therms)
Realization
Rate
2018 35,393 126,276 28.03%
2019 62,311 220,116 28.31%
Total 97,704 346,392 28.21%
It is important to note that this billing analysis compares homes in the program-rebated homes to non-
program homes built in the past few years. This analysis suggests that newer homes are being built at
standards greater than the current residential building code. Simulated modeling with REM/Rate
compares these homes to a code-built home and shows typical Therms savings values. Although new
homes are built to meet current building codes, they are often more energy efficient, and can incorporate
more recent technology. The Evaluators presents the results of an alternative approach to impact
measurement for the program using calibrated simulation in Appendix B.
4.3 Program and Non-Program HERS Index Analysis
The Evaluators performed an exploratory analysis on the HERS Index scores between the treatment and
control groups. IGC provided a dataset of HERS-rated homes in the territory. The Evaluators matched the
supplied newly built non-program homes to the HERS dataset and found a number of non-program homes
to be rated. The HERS dataset provided to the Evaluators did not contain any homes rated in 2019.
Therefore, all non-program homes built in 2019 were removed from this analysis.
When comparing HERS Index scores between the program homes and non-program homes, it is important
to remember that non-program homes would likely only get HERS-rated if the builder, homeowner, or
rater believes that it will be energy-efficient enough to provide a satisfactory HERS rating. Otherwise, the
builder, homeowner, or rater would likely not invest the additional cost of getting the house rated.
Therefore, the HERS Index scores across non-program households is likely not indicative of the efficiency
of non-rated households. However, it is still informative to compare the efficiency of the rated, non-
program households, as they provide information about the changing market towards higher efficiency
homes, even without incentives provided.
4.3.1 Total Program and Non-Program Households
The Evaluators found a total of 12% of non-program homes were built by participating builders in the IGC
territory, and 15% of non-program homes received a HERS rating. The following table summarizes the
percent of non-program homes built by participating builders and the percent of non-program homes
rated. Of the 1,160 homes found in the HERS dataset, 902 (78%) were built by participating builders, but
did not apply for a Whole Home Rebate. This represents 12% of all newly built homes in the IGC territory
that IGC delivered to the Evaluators.
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Table 4-16 Non-Program Homes in HERS Dataset
Year Built Number of
Homes
Homes in
HERS
Dataset
Built by
Program
Builder
% Built by
Program
Builder
Rated % Rated
2016 15 15 15 100% 15 100%
2017 3,775 697 579 15% 697 18%
2018 3,813 448 308 8% 448 12%
2019 2,850 0 0 0% 0 0%
Total* 7,603 1,160 902 12% 1,160 15%
*Total estimates do not contain homes built in 2019
Of the non-program homes that were rated, the average HERS Index score was 63. The Whole Home
Rebate homes displayed an average HERS Index score of 60 and 62 for program years 2018 and 2019,
respectively. Table 4-17 displays the HERS Index score distribution for non-program homes built in 2016
through 2018. Table 4-18 displays the HERS Index score distribution for Whole Home Rebate participating
homes for each program year.
Table 4-17 Non-Program Home Average Rating
Year Built
Number
of Rated
Homes
Average
HERS Index
Score
Median
HERS Index
Score
Minimum
HERS Index
Score
Maximum
HERS Index
Score
2016 15 63.3 62 54 83
2017 697 63.0 64 28 79
2018 448 63.8 63 47 80
Total 1,160 63.3 64 28 83
Table 4-18 Whole Home Rebate Home Average Rating
Year Built
Number
of Rated
Homes
Average
HERS Index
Score
Median
HERS Index
Score
Minimum
HERS Index
Score
Maximum
HERS Index
Score
2017-2018 619 60.1 61 35 71
2019 1,079 62.4 63 50 72
Total 1,698 61.6 62 35 72

Figure 4-9 displays the distribution of HERS Index scores between all program and non-program premises
found in the HERS dataset.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 51 of 329

Whole Home Rebate 43

Figure 4-9 HERS Index Score Distribution by Group

This analysis shows that there are a number of homes outside of the program that are already being built
to program standards. As noted prior, it should be noted that the homes in the HERS dataset are biased
to be efficient because only homes that are being built efficiently will go through the effort to get a HERS
rating from a certified HERS rater. However, this shows that the market for above-code, energy efficient
homes is significant in the IGC territory and that homes have been and are still currently being built to
above-code standards outside of the program.
4.3.2 Matched Program and Non-Program Households
The tables above display the statistics for all program homes and all non-program homes provided by IGC.
The following tables display the statistics for the matched treatment and control group used in the billing
analysis. These tables provide insight on a portion of the control group and how those homes compare
against the treatment group, normalized by home characteristics matched during PSM.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 52 of 329

Whole Home Rebate 44

Table 4-19 Matched Control Group in HERS Dataset
Program
Year
Number
of
Matched
Homes
Homes in
HERS
Dataset
Built by
Program
Builder
% Built by
Program
Builder
Rated % Rated
2017-2018 322 46 32 10% 46 14%
2019 380 17 7 2% 17 4%
Total* 702 63 39 6% 63 9%
Table 4-20 Matched Control and Treatment Group Average HERS Ratings
Program
Year Group
Number
of Rated
Homes
Average
HERS
Index
Score
Median
HERS
Index
Score
Minimum
HERS
Index
Score
Maximum
HERS
Index
Score
2017-2018 Treatment 322 60 61 35 71
Control 46 63 63 54 69
2019 Treatment 380 63 63 52 72
Control 17 63 62 57 74
Total Treatment 702 62 62 35 72
Control 63 63 63 54 74
4.4 Incremental HERS Index Score Savings Analysis
In addition to estimating measure impact through this analysis, the Evaluators utilized the results of the
simulation analysis detailed in Appendix B to determine if the HERS Index threshold of 75 or less is
adequate enough to supply sufficient Therms savings in relation to incentive levels offered by this
measure. This was accomplished by exploring how savings estimates change with respect to efficient
home HERS ratings.
This is a parametric analysis of savings versus cost at different HERS levels and is based on estimated
energy usage from the M&V sample reflecting various score levels (in 5-point increments on the HERS
scale).
The HERS Index was developed by RESNET as a nationally recognized system for inspecting and calculating
a home’s energy performance9. The HERS Index is calculated on a per-home basis by comparing the
home’s data against a reference home – a designed model home of the same size and shape as the actual
home. Therefore, the score is relative to the size of the home. Some of the components included in an
energy rating are:
◼ Exterior walls (above and below grade);
◼ Floors over unconditioned spaces;

9 https://www.hersindex.com/hers-index/what-is-the-hers-index/
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 53 of 329

Whole Home Rebate 45

◼ Ceilings and roofs;
◼ Attics, foundations and crawlspaces;
◼ Windows and doors, vents and ductwork; and
◼ HVAC systems, water heating systems, and thermostats.
RESNET defines their reference home based on the 2006 International Energy Conservation Code (IECC)10.
Therefore, a home built to the 2006 building energy code would receive a HERS Index of 100. A Home
Energy Rater performs an energy analysis of the home and assigns a HERS Index score to the home, based
on how efficient it compares to the reference home. The home’s score reduces by 1 point for every 1%
more efficient it is simulated to be against the reference home. Therefore, if a home is 10% more efficient
than the 2006 IECC, the home would receive a score of 90. The lower the score, the more efficient a home.
The Therms savings presented in this section is calculated by comparing the program-rebated homes to a
User Defined Reference Home (UDRH) with 2012 IECC with Idaho amendments, instead of the RESNET
reference home using 2006 IECC. The Evaluators used the 2012 IECC with Idaho amendments in order to
define savings based on current residential code. Further details on the specifications of the UDRH is
provided in Appendix B.
4.4.1 Savings by HERS Index Score
The Evaluators performed a comparative savings analysis using the 2012 IECC UDRH on a sample of homes
using REM/Rate across homes receiving different HERS Index scores. The Evaluators combined homes into
groups by 5-point increment HERS Index bracket. A number of variables were explored in this analysis in
order to understand how savings vary as the HERS Index score reduces. The Evaluators compared Therms
savings normalized by home size.
This represents the average difference in household usage between a home simulated as the Whole Home
rebated household and the User Defined Reference Home (UDRH) built to the same physical specifications
of the program homes. The following tables display the average Therms and kWh savings by 5-point
increments in HERS Index.
Table 4-21 Whole Home to UDRH Therms Savings by HERS Bin
HERS Index Number of
Homes
Space
Heating
(Therms)
Water
Heating
(Therms)
Lights &
Appliances
(Therms)
Total
(Therms)
51 to 55 4 280.25 12.50 0.00 292.75
56 to 60 20 258.25 12.75 0.00 271.10
61 to 65 36 252.39 14.42 0.00 266.94
66 to 70 8 288.63 14.25 0.00 303.13

10 https://www.resnet.us/raters/hers-raters/
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 54 of 329

Whole Home Rebate 46

Table 4-22 Whole Home to UDRH kWh Savings by HERS Bin
HERS Index Number of
Homes
Space
Heating
(kWh)
Cooling
(kWh)
Lights &
Appliances
(kWh)
Total (kWh)
51 to 55 4 145 160 1,116 1,446
56 to 60 20 162 125 952 1,239
61 to 65 36 134 120 886 1,139
66 to 70 8 138 218 982 1,338
The absolute savings between homes scoring 70 and homes scoring 50 ranges between 11 and 37 Therms
and between 108 and 307 kWh. The savings do not show a trend of increasing across the HERS Index bins.
Instead of comparing absolute savings between the HERS brackets, the Evaluators compared the savings
normalized by home square footage to discover if there is a trend in fuel per square footage across the
brackets. Table 4-23 displays the savings differences between each HERS Index score normalized by home
square footage. Normalizing the savings by square footage allows comparison between differing
households. A visual representation of the values is presented in Figure 4-10.
Table 4-23 Whole Home to UDRH Therms Savings by HERS Bin
HERS
Index
Number of
Homes
Space Heating
(Therms/SQFT)
Water Heating
(Therms/SQFT)
Lights &
Appliances
(Therms/SQFT)
Total
(Therms/SQFT)
51 to 55 4 0.1147 0.0051 0 0.1197
56 to 60 20 0.1062 0.0052 0 0.1114
61 to 65 36 0.1157 0.0066 0 0.1224
66 to 70 8 0.1335 0.0064 0 0.1400
Figure 4-10 Whole Home to UDRH Therms Savings (Therms/SQFT)

0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
55 60 65 70
Th
e
r
m
s

S
a
v
i
n
g
s
/
S
Q
F
T
HER Index Score
Total
Space Heating
Water Heating
Lights & Appliances
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 55 of 329

Whole Home Rebate 47

It is expected that the lower the HERS Index score, the higher the energy savings. However, the Evaluators
found the savings normalized by square footage remains relatively constant across a 20-point HERS Index
range. The same lack of relationship is seen when analyzing the electric savings across the HERS Index.
Table 4-24 and Figure 4-11 display the electric savings across the HERS Index.
Table 4-24 Whole Home to UDRH kWh Savings by HERS Bin
HERS
Index
Number of
Homes
Space
Heating
(kWh/SQFT)
Cooling
(kWh/SQFT)
Lights &
Appliances
(kWh/SQFT)
Total
(kWh/SQFT)
51 to 55 4 0.0588 0.0652 0.4536 0.5892
56 to 60 20 0.0681 0.0529 0.3820 0.5030
61 to 65 36 0.0616 0.0545 0.4167 0.5322
66 to 70 8 0.0634 0.0982 0.4698 0.6317
Figure 4-11 Whole Home to UDRH kWh Savings (kWh/SQFT)

The lack of trend is apparent when inspecting ten homes that were given a HERS Index score of 60.
Between the 10 homes, total normalized Therms savings ranged between 0.05 Therms per square foot
and 0.18 Therms per square foot. That amounts to an equivalent of a range of 125 Therms to 450 Therms
for a 2,500 square foot home.
4.4.2 Percent Savings by HERS Index Score
The following section presents the proportion of energy saved against the 2012 IECC with amendments
UDRH by HERS Index. The following tables and figures show that as the HERS Index score lowers (improves
efficiency), total percent savings in both gas and electric end uses increase, from 28% to 32% for gas usage
and from 15% to 17% for electric usage. The majority of savings for both fuels stems from increased
efficiency in space heating. A more efficient home on the HERS Index saves as much as 39% Therms in
space heating against the UDRH, while a less efficient home on the HERS Index saves 32%. The source of
the increased proportional savings can be pinpointed by evaluating how the component load changes
across HERS Index, detailed in the section below.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
55 60 65 70
kW
h

S
a
v
i
n
g
s
/
S
Q
F
T
HER Index Score
Total
Space Heating
Cooling
Lights & Appliances
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 56 of 329

Whole Home Rebate 48

Table 4-25 Whole Home to UDRH Percent Therms Savings by HERS Bin
HERS
Index
Number of
Homes
Space Heating
(%)
Water Heating
(%)
Lights &
Appliances (%) Total (%)
51 to 55 4 39.49% 8.16% 0.00% 32.31%
56 to 60 20 34.85% 8.65% 0.00% 29.89%
61 to 65 36 33.14% 9.79% 0.00% 28.74%
66 to 70 8 32.28% 8.87% 0.00% 28.26%
Table 4-26 Whole Home to UDRH Percent kWh Savings by HERS Bin
HERS
Index
Number of
Homes
Space Heating
(%)
Water Heating
(%)
Lights &
Appliances (%) Total (%)
51 to 55 4 29.74% 16.14% 15.89% 17.01%
56 to 60 20 23.44% 13.55% 14.28% 15.05%
61 to 65 36 19.31% 11.62% 13.52% 13.89%
66 to 70 8 19.40% 17.51% 13.65% 14.48%
Figure 4-12 Whole Home to UDRH Percent Therms Savings (%)

0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55 60 65 70
Pe
r
c
e
n
t
HERS Index Score
Total
Space Heating
Water Heating
Lights & Appliances
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 57 of 329

Whole Home Rebate 49

Figure 4-13 Whole Home to UDRH Percent kWh Savings (%)

4.4.3 Component Load Savings by HERS Index Score
To identify energy efficient variables that contribute the most to energy savings in a new home, the
Evaluators compared the sampled home’s as-built heating component loads against its reference home
heating component loads. The component load consists of the main categories compared in the simulated
analysis, using inputs from the HERS raters and the 2012 IECC. The Evaluators found the infiltration
reduction in the program home displayed the largest component load reductions. About two thirds of the
total component load reductions stem from infiltration reduction. Other measures that contribute to
savings are: energy efficient windows, duct sealing, and ceiling insulation. Table 4-27 displays the energy
savings by component load between the program homes and the UDRH in the REM/Rate analysis.
Table 4-27 Whole Home to UDRH Component Load Annual MBtuh Savings
HERS
Index
Ce
i
l
i
n
g
s
/
R
o
o
f
s

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s

Fo
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t
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n

W
a
l
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s

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r
s

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n
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p
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ti
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n

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c
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t
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l
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r

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n
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o
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t
a
l

51 to 55 2.20 0.13 1.35 -0.02 0.55 2.13 0.00 0.00 -1.63 11.28 0.00 2.13 0.00 0.00 -2.30 0.00 15.75
56 to 60 1.26 0.21 0.71 0.25 0.66 3.27 0.04 -0.08 -1.81 8.96 -0.01 1.97 0.00 0.00 -1.99 0.00 13.41
61 to 65 1.07 0.18 0.24 0.50 0.64 3.29 -0.02 -0.12 -1.45 8.28 -0.02 1.69 0.00 0.00 -1.71 0.00 13.28
66 to 70 1.76 0.19 0.66 -0.35 0.65 3.80 -0.20 0.00 -0.10 7.49 0.00 1.94 0.00 0.00 -1.94 0.00 13.94
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55 60 65 70
Pe
r
c
e
n
t
HERS Index Score
Total
Space Heating
Cooling
Lights & Appliances
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 58 of 329

Whole Home Rebate 50

Table 4-28 Whole Home to UDRH Component Load Percent Savings
HERS
Index
Ce
i
l
i
n
g
s
/
R
o
o
f
s

Ri
m
/
B
a
n
d

J
o
i
s
t
s

Ab
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v
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G
r
a
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a
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l
s

Fo
u
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d
a
t
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o
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W
a
l
l
s

Do
o
r
s

Wi
n
d
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w
/
S
k
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l
i
g
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t
s

Fr
a
m
e

F
l
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o
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s

Cr
a
w
l

S
p
a
c
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Sl
a
b

F
l
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o
r
s

In
f
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a
t
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n

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c
h
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l

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e
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l
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t
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n

Du
c
t
s

Ac
t
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S
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l
a
r

Su
n
s
p
a
c
e

In
t
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r
n
a
l

G
a
i
n
s

Wh
o
l
e

H
o
u
s
e

V
e
n
t
i
l
a
t
i
o
n

51 to 55 30% 7% 11% 6% 34% 28% 0% 0% -26% 77% 0% 71% 0% 0% 14% 0%
56 to 60 17% 12% 6% 9% 36% 34% 2% -3% -29% 63% 0% 37% 0% 0% 13% 0%
61 to 65 14% 11% 2% 11% 34% 30% 0% -3% -22% 60% 0% 24% 0% 0% 12% 0%
66 to 70 20% 10% 5% -3% 34% 25% -2% 0% -2% 47% 0% 26% 0% 0% 12% 0%
The components leading to the highest magnitude savings is the increased efficiency in ceiling insulation,
windows, infiltration, and duct sealing. These categories represent the largest differences between the
UDRH and the rebated homes in the sample. The increase in proportional savings for these components
can be seen in Figure 4-14.
Figure 4-14 Whole Home to UDRH Component Load Percent Savings (%)

4.4.4 Summary of Incremental HERS Index Score Savings Analysis
The HERS Index places value on the percent change in annual household energy usage between the as-
built home and the 2006 IECC reference home rather than the absolute energy usage of the household.
This means that a household that has 5 bedrooms, 5 bathrooms, and is 4,000 square feet has the potential
to receive the same score as a household that has 1 bedroom, 1 bathroom, and is 1,000 square feet large.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
55 60 65 70
Pe
r
c
e
n
t
HERS Index Score
Total Ceilings/Roofs Infiltration Ducts Windows
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 59 of 329

Whole Home Rebate 51

This is because the HERS Index depends on the proportional difference between the as-built home and
the reference home is the same percent, rather than the magnitude of the reference home annual usage.
Therefore, the Evaluators is unable to recommend an incremental savings value for each HERS Index
bracket. The Therms savings per household varies widely based on the baseline annual energy usage of
each household. The results of this analysis suggest that lowering the HERS Index requirement of the
Whole Home measure does not guarantee higher Therms savings per rebate. The HERS Index is not
designed to evaluate absolute energy savings of a household, but rather used as a comparison against
other homes with very similar physical characteristics, such as number of bedrooms, bathrooms, and
square footage.
To improve savings, IGC should consider more specific requirements in the construction of the home:
◼ 95% AFUE furnaces
◼ Tankless water heaters
◼ R-49 attic insulation
◼ Increased air sealing and duct sealing
4.4.5 Benchmarking HERS Index Score Requirements
Although the Evaluators is unable to determine incremental savings between HERS Index scores, a
benchmarking analysis was completed on current building codes and reports.
The Evaluators summarized the total percent Therms savings for each HERS Index present in the sample
of 68 households, displayed in Table 4-29. According to the HERS Index calculation method, a home
scoring 60 should be 40% more efficient than the reference home. However, because RESNET’s reference
home uses an outdated building code from 2006, the percent savings comparing the program homes to
the 2012 IECC with Idaho’s amendments UDRH is lower. The percent savings a home scoring 60 displays
in the Evaluators’ analysis is 19% more efficient than the reference home.
Table 4-29 Percent Therms Savings by Prevalent Component Load (%)
HERS
Index
Score
Total
53 30%
54 28%
55 25%
56 27%
57 29%
58 23%
59 22%
60 19%
61 22%
62 21%
63 27%
64 18%
65 16%
66 23%
67 17%
68 17%
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 60 of 329

Whole Home Rebate 52

The U.S. Department of Energy estimates the 2012 IECC is expected to decrease energy consumption in
homes by 30% when compared to the 2006 IECC11. Therefore, a home built to 2012 IECC code would
achieve a HERS Index score of 70. IGC requires a HERS Index score of 75; this is less efficient than 2012
IECC and thus is an insufficient requirement to provide incremental savings above code.
RESNET reported 1,164 households were HERS-rated in 2018 with an average HERS rating of 6112. The
average Idaho rating is 1 point higher than the average HERS rating across all program-rebated homes. As
mentioned before, this comparison includes the caveat that HERS-rated homes will likely only get rated if
the builder, homeowner, or rater believes it will receive a satisfactory HERS Index score. However, this
does indicate that the housing market is progressing towards higher efficiency homes, as a higher
proportion of homes are receiving HERS Index scores in the IGC territory.
In addition, the 2020 Idaho Legislature has recently adopted the 2018 IECC with amendments, effective
January 1, 202113. The 2018 IECC includes a new requirement that the home’s Energy Rating Index (ERI)
score must be 68 or less. The HERS Index is one type of ERI and qualifies as a suitable index for this
requirement. According to this new legislature, the Whole Home rebate HERS Index score requirement
would be less efficient than effective residential building code, starting at the beginning of 2021.
The Pacific Northwest National Laboratory analyzed the relationship between the RESNET HERS Index and
the traditional simulation-based Performance Path used in the IECC14. The analysis evaluates the ranges
of HERS Index values that would imply compliance with the 2012 IECC. The report summarized the range
of HERS Index scores that would be required to comply with current building codes, for each weather
zone. The following table summarizes largest range of HERS Index requirements presented in the report,
for weather zones 5 and 6, the weather zones in Idaho and in the IGC service territory15.
Table 4-30 Pacific Northwest National Laboratory HERS Index Compliance Range
Zone Lowest Highest Range
Zone 5, Moist 53 64 11
Zone 5, Dry 53 65 12
Zone 6, Moist 48 63 15
Zone 6, Dry 51 65 14
It is important to note that, as described above, score of the home does not relate to the absolute Therms
savings per household. Therefore, homes may still achieve measurable Therms savings while still receiving

11 http://www.resnet.us/wp-content/uploads/archive/resblog/2014/06/EnergyRatings_FactSheet1_Final.pdf
12 https://www.resnet.us/articles/demand-for-hers-continues-to-grow-over-236000-homes-hers-rated-in-2018/
13 https://www.idahoenergycode.com/2018-iecc-a-quick-look
14https://www.energycodes.gov/sites/default/files/documents/HERSandIECCPerformancePath_TechnicalReport.pdf
15 The values presented in this table represent the aggregated lowest and highest CHI for each weather zone, and therefore the
range differs compared to the disaggregated value ranges in the report.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 61 of 329

Whole Home Rebate 53

a high HERS Index score. This benchmarking study does, however, indicate that the HERS Index score
standard for a code-built home is decreasing, and that program requirements may need to increase in
order to keep up with building codes.
4.5 ENERGY STAR Certification Analysis
In addition to calculating the average savings between a UDRH and the Whole Home-rebated home, the
Evaluators conducted a comparative analysis between a UDRH and an ENERGY STAR certified home. This
was accomplished by using the home model simulation with adjusted inputs to reflect a home satisfying
ENERGY STAR certification requirement. The ENERGY STAR certification requirements are included in the
software REM/Rate and were readily available for each modeled home in the sample of 68 households.
This analysis provides insight into one of the two requirements for the Whole Home Rebate: the ENERGY
STAR certification. The outcome of this analysis can guide the decision to make adjustments to program
requirements in the future.
4.5.1 ENERGY STAR Compared Against UDRH
The Evaluators compiled the annual energy savings for each of the 68 households. The average Therms
savings between the UDRH and an ENERGY STAR-certified home are summarized in Table 4-31 by program
year. In addition, the average kWh savings is presented in Table 4-32.
Table 4-31 ENERGY STAR to UDRH Average Annual Therms Savings
Program Year
Space
Heating
(Therms)
Water
Heating
(Therms)
Lights &
Appliances
(Therms)
Total
(Therms)
2017-2018 263.71 -54.33 1.04 210.88
2019 241.11 -49.50 -0.18 191.20
Total 249.09 -51.21 0.23 198.15

Table 4-32 ENERGY STAR to UDRH Average Annual kWh Savings
Program Year
Space
Heating
(kWh)
Cooling
(kWh)
Lights &
Appliances
(kWh)
Total
(kWh)
2017-2018 172.46 -529.96 -337.25 -696.88
2019 136.05 -436.61 473.93 173.18
Total 148.90 -469.56 187.63 -133.90
The average ENERGY STAR-certified home saves nearly 200 Therms compared to the UDRH. The savings
originate from the increase in space heating efficiency. However, the ENERGY STAR certified homes
display a negative savings for water heating. In terms of electric usage, the ENERGY STAR requirements
show larger annual electric usage than the baseline UDRH. The negative savings originates from the
cooling components of the household.
As a reference, the average program home saved nearly 274 Therms compared to the UDRH. Therefore,
this analysis suggests the ENERGY STAR certification results in 27% lower savings than a home that meets
other IGC requirements but is not ENERGY STAR-certified. However, it is important to note that the
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 62 of 329

Whole Home Rebate 54

Evaluators is unable to separate the Whole Home requirement from the ENERGY STAR requirement. The
ENERGY STAR requirement may therefore supplement the program’s HERS Index score requirement,
rather than being included in it.
4.5.2 ENERGY STAR Homes Compared Against Program Homes
As a supplement, the Evaluators also compared the ENERGY STAR home to the as-built Whole Home. This
analysis may provide insight into the incremental savings between a rebated home and an ENERGY STAR
certified home. The average savings between the 68 sampled Whole Home and the equivalent modeled
ENERGY STAR certified homes are presented in Table 4-33 and Table 4-34.
Table 4-33 ENERGY STAR to Whole Home Average Annual Therms Savings
Program Year
Space
Heating
(Therms)
Water
Heating
(Therms)
Lights &
Appliances
(Therms)
Total
(Therms)
2017-2018 -18.58 -66.88 1.04 -84.25
2019 -6.75 -63.98 -0.59 -71.18
Total -10.93 -65.00 -0.01 -75.79
Table 4-34 ENERGY STAR to Whole Home Average Annual kWh Savings
Program Year
Space
Heating
(kWh)
Cooling
(kWh)
Lights &
Appliances
(kWh)
Total
(kWh)
2017-2018 1.04 -689.33 -1,170.79 -1,858.83
2019 7.82 -558.39 -509.50 -1,062.68
Total 5.43 -604.60 -742.90 -1,343.68
This analysis estimates that an ENERGY STAR certified home, on average, uses an additional 75 Therms
per year when compared against a rebated home. In addition, an ENERGY STAR certified home uses 1,300
more kWh per year than a rebated home. The increased energy usage towards space heating and cooling
stems from lower duct sealing and window efficiency requirements for ENERGY STAR homes than the
rebated homes currently being built in Idaho. In addition, the rebated homes have much more efficient
lighting and appliances than an ENERGY STAR certified home. The ENERGY STAR certified home shows
lower efficiency in all household load components except doors and windows. This comparison shows
that the ENERGY STAR certification alone leads to lower savings than the Whole Home requirements of
both the ENERGY STAR certification and the HERS Index score requirement.
4.5.3 ENERGY STAR Certification Analysis Summary
The Evaluators compared simulated energy usage between the UDRH, the rebated home, and simulated
ENERGY STAR certified home. Although the Evaluators are unable to separate the savings from the
ENERGY STAR certification and the HERS requirement, the average differences in savings between each
scenario provides insight into the combination of requirements. This analysis revealed that the ENERGY
STAR certification alone amounts to approximately 27% lower annual Therms savings than the rebated
homes. The rebated homes display higher efficiency in all load components except doors and windows.
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Whole Home Rebate 55

4.6 Program Home Time-on-Market Analysis
The Evaluators conducted an analysis to determine if there is a statistically significant difference in the
amount of time it took to sell a participating house and a non-participating housed. Over the course of
the of the Energy Efficiency programs, IGC was instrumental in the increase from two builders to the
current 18 builders in the service territory to become 100% ENERGY STAR certified. The information from
this analysis may be useful in encouraging more builders to become 100% ENERGY STAR certified as well.
The Evaluators performed a linear regression on the amount of time a Whole Home rebated home
remained on the market compared to the amount of time nonparticipant households remained on the
market. This analysis can reveal tangible benefits of participating in the Whole Home Rebate with the
connection of IGC’s efficiency approval to active homebuyers.
4.6.1 Time-on-Market Analysis Methodology
The billing data provided sufficient information to carry out this analysis. IGC provided premise
information including earliest billed date and occupancy date, unique to each premise. Using the
information above, the Evaluators conducted the following data cleaning steps:
◼ Removed observations with unknown occupancy dates (104 occurrences)
◼ Calculated number of days between earliest bill date and occupancy date
◼ Removed observations with invalid earliest bill dates (15 occurrences)
◼ Removed observations with occupancy date equal to or earlier than earliest bill date (18
occurrences)
◼ Removed program and non-program households outside of previously matched groups from
Method 1.
The Evaluators selected a random sample of 200 of the remaining treatment premises after the
cleaning steps above. The Evaluators then matched to 200 nonparticipating homes, following the
above cleaning steps. The matching criteria is the same matching criteria used in the Method 1 billing
analysis for Whole Home-rebated homes. Summarized here again, those criteria were:
◼ Zip code
◼ Year built
◼ Number of bedrooms
◼ Number of bathrooms
◼ Number of fireplaces
◼ Square footage of home
◼ Square footage of upper floor
◼ Square footage of pool
◼ Square footage of attic
◼ Square footage of garage
The Evaluators used nearest neighbor for all the above characteristics, and exact matching for zip code
with a 1:1 ratio for matching. It is important the homes being compared are in as similar geography
as possible. Therefore, during the matching process, the 5-digit zip code must match exactly to the
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 64 of 329

Whole Home Rebate 56

control household, with all other variables matched at the nearest household. This will account for
small geographic differences between the groups. This analysis combined both program years into
one treatment group and therefore only one regression was completed.
The matched control group passed the chi square equivalency test, a test that estimates the goodness
of fit between two groups. A p-value of less than 0.05 indicates there is a statistically significant
difference between the treatment and control group in terms of the matching criteria. The chi square
test accepted the null hypothesis, as the p-value is over 0.05 for each program year, indicating there
are no statistically significant differences between the treatment and control groups household-level
characteristics. Table 4-35 summarizes the results of the chi square test between the treatment and
control group.
Table 4-35 Treatment-Control Equivalency Test
Chi Square
Value
Degrees of
Freedom P-value
8.53 21 0.992

The Evaluators collected assessed home value from the Ada County Assessor database to include in the
regression model to correct for differences in costs for program- and non-program homes.
Equation 4-2: Random-Effects Panel Regression Model Specification
𝐷𝑎𝑦𝑠 𝑜𝑛 𝑀𝑎𝑟𝑘𝑒𝑡𝑖
=𝛽1(𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡)𝑖𝑡+𝛽2(𝑆𝑄𝐹𝑇)𝑖𝑡+𝛽3(𝐵𝑒𝑑𝑟𝑜𝑜𝑚𝑠)𝑖𝑡+𝛽4(𝐵𝑎𝑡ℎ𝑟𝑜𝑜𝑚𝑠)𝑖𝑡
+𝛽5(𝐻𝐸𝑅𝑆 𝐷𝑢𝑚𝑚𝑦)𝑖𝑡+𝛽6(𝑃𝑜𝑜𝑙 𝐷𝑢𝑚𝑚𝑦)𝑖𝑡+𝛽7(𝑍𝑖𝑝 𝐶𝑜𝑑𝑒)𝑖𝑡
+𝛽8(𝑀𝑜𝑛𝑡ℎ 𝐿𝑖𝑠𝑡𝑒𝑑)𝑖𝑡+𝛽9(𝑌𝑒𝑎𝑟 𝐵𝑢𝑖𝑙𝑡)𝑖𝑡+𝛽10(𝐻𝑜𝑚𝑒 𝑉𝑎𝑙𝑢𝑒)𝑖𝑡
Where,
𝐷𝑎𝑦𝑠 𝑜𝑛 𝑀𝑎𝑟𝑘𝑒𝑡𝑖 = Number of days between earliest premise bill and occupancy in home i
𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 = Dummy variable indicating whether household i was in the treatment group or control
group
𝑆𝑄𝐹𝑇𝑖 = Square footage of home i
𝐵𝑒𝑑𝑟𝑜𝑜𝑚𝑠𝑖 = Number of bedrooms of home i
𝐵𝑎𝑡ℎ𝑟𝑜𝑜𝑚𝑠𝑖 = Number of bathrooms of home i
𝐻𝐸𝑅𝑆 𝐷𝑢𝑚𝑚𝑦𝑖 = Dummy variable indicating whether household i has a HERS rating
𝑃𝑜𝑜𝑙 𝐷𝑢𝑚𝑚𝑦𝑖 = Dummy variable indicating whether household i has a pool
𝑍𝑖𝑝 𝐶𝑜𝑑𝑒𝑖 = The 5-digit zip code of household i
𝑀𝑜𝑛𝑡ℎ 𝐿𝑖𝑠𝑡𝑒𝑑𝑖 = The month of the earliest bill date for household i
𝑌𝑒𝑎𝑟 𝐵𝑢𝑖𝑙𝑡𝑖 = The year household i was built
𝐻𝑜𝑚𝑒 𝑉𝑎𝑙𝑢𝑒𝑖 = The home value of household i from Ada County Assessor database
𝛽1−10 = Coefficients determined via regression
The coefficient 𝛽1 represents the average change in days on market between the groups.
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Whole Home Rebate 57

4.6.2 Time-on-Market Analysis Results
This section details the time-on-market regression results after performing propensity score matching
for program homes with Whole Home Rebate. As discussed in the methodology section, the
difference in time on market between groups is determined through parameter B1, which is the
average change in days on market for the treatment group.
Table 4-36 summarizes the results of the analysis. A p-value less than 0.05 indicates that the
parameter estimate is statistically significant at the 95% confidence level. The coefficient estimate for
Treatment (B1) is negative, indicating the treatment group is on the market for a lower number of
days than the control group. In addition, this coefficient is statically significant at the 95% level.
Table 4-36 Whole Home Regression Results
Variable Estimate Std Error P-Value 5% CI 95% CI
Intercept 123.0540 21.8329 3.48E-08 87.0509 159.0570
Treatment -38.6892 11.9828 1.36E-03 -58.4491 -18.9293
SQFT 0.0176 0.0080 2.79E-02 0.0044 0.0307
Bedrooms 0.5149 4.6818 9.12E-01 -7.2056 8.2353
Bathrooms -0.1644 7.5745 9.83E-01 -12.6549 12.3260
HERS Dummy 33.7273 12.0843 5.53E-03 13.7999 53.6547
Pool Dummy 19.9998 39.1337 6.10E-01 -44.5327 84.5324
Zip Code 0.9241 20.2587 9.64E-01 -32.4831 34.3312
Month Listed 8.3444 12.7904 5.15E-01 -12.7472 29.4361
Year Built -33.0853 12.6134 9.08E-03 -53.8851 -12.2854
Home Value -6.91E-05 2.25E-05 2.25E-03 -1.06E-04 -3.21E-05
The following table represents the model fit for this analysis. The adjusted R-squared value is 0.1775,
indicating that the model accounts for 17.8% of the variation present in the data. Although this
adjusted R-squared value is low, the model contains many of the primary covariates present in
determining the marketability of a household. Further addition of covariates could lead to overfitting
the model, where the coefficients would not apply to homes outside of the dataset.
Table 4-37 Time-on-Market Model Fit
Adjusted R2 F Statistic Number of
Observations
Number of
Premises
0.17747 3.939 366 396
The regression analysis suggests the Whole Home-rebated house sell 39 days faster than a similar
non-Whole Home rebated house. This statistic is statistically significant at the 95% confidence
interval. The median number of days a non-program home remains on market is 90, similar to current
realtor estimates in the United States. This analysis suggests that, on average, a non-program builder
may be able to reduce the amount of time invested to sell a home by 43%. Because the control group
was matched by home characteristics, location, and price, this suggests the Whole Home Rebate
measure requirements and IGC’s mark of approval from the program results in program homes selling
more quickly than non-program homes. This information can be communicated to builders as an
incentive to building homes that meet program requirements of home energy efficiency.

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Whole Home Rebate 58

4.7 Builder and Rater Savings Profile
Realization rates were examined for commonalities among home builders and HERS raters to inform IGC
if any program partner demonstrated a statistically significant increased likelihood of association with low
or high realization rates. The Evaluators then reviewed the home results in further detail to identify root-
cause of statistically significant differences.
The tracking data for PY2017-2018 and PY2019 included 27 unique builders total. The Evaluators randomly
selected 80 homes, stratified by builder and program year in order to ensure representativeness. This
sample included 15 unique builders and two unique raters. The following sections presents the findings
from examination of each builder’s and each rater’s simulation analysis results.
4.7.1 Builder Savings Profile
The Evaluators created a profile for each builder’s estimated savings through simulation analysis. The
average HERS Index scores, absolute Therms savings, normalized Therms savings, and realization rates are
summarized below for each of the 15 sampled builders. Builder 5 has the lowest realization rate of all the
builders at 88%, while Builder 10 and Builder 14 have the highest realization rates, at 186%. Figure 4-15
displays each builder’s average HERS Index score and Therms savings per rebate. Figure 4-16 displays each
builder’s average HERS Index score and normalized Therms savings by square footage. These figures
display that HERS Index score is not representative of Therms savings, absolute and normalized.
Table 4-38 Simulation Analysis Summary by Builder
Builder Sampled
Homes
Average
HERS Index
Score
Average
Therms
Savings per
Rebate
Average
Therms
Savings per
Square
Foot
Average
Realization
Rate (%)
Builder 1 14 63.4 272.71 0.1291 133.68%
Builder 2 10 60.3 258.40 0.1088 126.67%
Builder 3 7 62.0 268.57 0.1169 131.65%
Builder 4 6 60.5 242.83 0.1110 119.04%
Builder 5 5 62.8 180.20 0.1096 88.33%
Builder 6 6 62.7 253.17 0.1266 124.10%
Builder 7 5 62.8 324.80 0.1160 159.22%
Builder 8 4 57.5 239.25 0.1359 117.28%
Builder 9 1 63.0 260.00 0.1183 127.45%
Builder 10 3 57.7 379.67 0.1081 186.11%
Builder 11 2 64.5 389.50 0.1294 190.93%
Builder 12 1 55.0 350.00 0.1229 171.57%
Builder 13 2 56.5 357.50 0.1316 175.25%
Builder 14 1 68.0 380.00 0.1084 186.27%
Builder 15 1 67.0 265.00 0.0778 129.90%
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 67 of 329

Whole Home Rebate 59

Figure 4-15 Average HERS Index Score and Therms Savings per Rebate by Builder

Figure 4-16 Average HERS Index Score and Therms Savings per Square Foot by Builder

To assist with the examination of the 15 builders, the Evaluators also summarized the average proportion
of savings for the four major component loads (ceilings/roof sealing, window efficiency, infiltration, and
duct sealing) for each builder. Table 4-39 and Figure 4-17 portray the component load contribution to
total savings by builder.
Builders that achieve higher realization rates tend to have reduced infiltration, duct leakage, and increase
ceiling/roof insulation. These home’s infiltration reduction provides the most energy savings (60-78% of
the total savings). Duct sealing and ceiling efficiency gains provide about 10-17% of total savings.
50
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Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 68 of 329

Whole Home Rebate 60

Table 4-39 Component Load Contribution to Total Savings by Builder
Builder
Average
Realization
Rate (%)
Ceilings/Roofs Window/Skylights Infiltration Ducts
Builder 1 133.68% 11.18% 26.97% 52.92% 14.61%
Builder 2 126.67% 7.05% 30.60% 77.60% 14.64%
Builder 3 131.65% 6.75% 23.94% 68.44% 14.36%
Builder 4 119.04% 8.37% 25.26% 79.52% 10.31%
Builder 5 88.33% 8.13% 15.66% 92.17% 15.36%
Builder 6 124.10% 5.96% 19.75% 46.24% 10.46%
Builder 7 159.22% 14.20% 33.33% 46.05% 16.54%
Builder 8 117.28% 11.74% 28.34% 57.29% 14.57%
Builder 9 127.45% 8.27% 42.11% 34.59% 13.53%
Builder 10 186.11% 11.80% 9.98% 77.61% 10.89%
Builder 11 190.93% 4.59% 18.38% 78.11% 12.70%
Builder 12 171.57% 17.32% 13.41% 60.34% 16.76%
Builder 13 175.25% 10.77% 17.80% 66.98% 9.37%
Builder 14 186.27% 6.78% 15.25% 67.23% 15.25%
Builder 15 129.90% 12.00% 32.00% 42.00% 15.00%

Figure 4-17 Average Component Load Contribution to Total Savings by Builder

In addition to summary statistics for each builder, the Evaluators examined the existence of statistically
significant differences between builders in terms of realization rate, absolute Therms savings, normalized
Therms savings, square footage of homes built, and HERS Index scores. The Evaluators conducted t-tests
0%
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Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 69 of 329

Whole Home Rebate 61

for each category, with the builder in question against the other 14 builders. The t-test was unable to be
conducted on Builders 9, 12, 14, and 15 due to there only being one home sampled from each of these
builders. A p-value of less than 0.05 indicates that there is a statistically significant difference between
the groups. The tables below also display the 90% confidence intervals, along with p-value and mean
difference between the groups being compared.
The Evaluators found there to be no statistically significant differences in normalized Therms savings.
However, some builders display statistically significant differences in realization rate, absolute Therms
savings, square footage, and HERS Index scores. The results of the t-tests for normalized Therms savings
are displayed in Table 4-40.
Table 4-40 Average Normalized Therms Savings T-Test by Builder
Builder Mean
Difference P-Value 5% CI 95% CI
Builder 1 0.0141 0.4576 -0.0251 0.0532
Builder 2 -0.0117 0.5117 -0.0495 0.0261
Builder 3 -0.0039 0.7640 -0.0321 0.0243
Builder 4 -0.0003 0.9910 -0.0731 0.0724
Builder 5 -0.0102 0.6618 -0.0678 0.0474
Builder 6 0.0088 0.6166 -0.0314 0.0491
Builder 7 -0.0058 0.6224 -0.0323 0.0206
Builder 8 0.0164 0.5621 -0.0602 0.0929
Builder 9 NA NA NA NA
Builder 10 -0.0153 0.3642 -0.0614 0.0309
Builder 11 0.0147 0.8016 -0.5274 0.5568
Builder 12 NA NA NA NA
Builder 13 0.0002 0.9919 -0.1599 0.1603
Builder 14 NA NA NA NA
Builder 15 NA NA NA NA

The results of the t-tests for realization rates are displayed in Table 4-41. The Evaluators found there to
be statistically significant differences in realization rates for Builder 5, Builder 7, and Builder 13. Builders
7 and 13 display higher than average realization rates, while Builder 5 displays lower than average
realization rates. The t-test revealed Builders 7 and 13 display, on average, 27% and 42% higher realization
rates than the rest of the group, respectively; a difference that is statistically significant.

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 70 of 329

Whole Home Rebate 62

Table 4-41 Average Realization Rate T-Test by Builder
Builder Mean
Difference P-Value 5% CI 95% CI
Builder 1 -0.01 0.9538 -0.28 0.26
Builder 2 -0.09 0.4464 -0.33 0.15
Builder 3 -0.03 0.8392 -0.35 0.29
Builder 4 -0.17 0.4082 -0.63 0.30
Builder 5 -0.50 0.0090* -0.81 -0.18
Builder 6 -0.11 0.3035 -0.34 0.12
Builder 7 0.27 0.0230* 0.05 0.49
Builder 8 -0.18 0.3538 -0.67 0.31
Builder 9 NA NA NA NA
Builder 10 0.54 0.2484 -0.87 1.96
Builder 11 0.58 0.3680 -4.00 5.17
Builder 12 NA NA NA NA
Builder 13 0.42 0.0000* 0.32 0.52
Builder 14 NA NA NA NA
Builder 15 NA NA NA NA
*T-test indicates statistically significant difference between groups at the 90% confidence interval
The results of the t-tests for absolute Therms savings are displayed in Table 4-42. The Evaluators found
there to be statistically significant differences in average absolute Therms savings per household for
Builders 5, 7, and 13 as well.
Table 4-42 Average Absolute Therms Savings T-Test by Builder
Builder Mean
Difference P-Value 5% CI 95% CI
Builder 1 -1.54 0.9538 -56.61 53.52
Builder 2 -18.22 0.4464 -67.88 31.44
Builder 3 -5.99 0.8392 -71.69 59.72
Builder 4 -34.12 0.4082 -128.49 60.26
Builder 5 -101.18 0.0090* -165.51 -36.85
Builder 6 -22.78 0.3035 -69.90 24.33
Builder 7 54.90 0.0230* 9.81 99.98
Builder 8 -36.86 0.3538 -137.32 63.60
Builder 9 NA NA NA NA
Builder 10 110.61 0.2484 -178.24 399.45
Builder 11 119.06 0.3680 -816.04 1054.16
Builder 12 NA NA NA NA
Builder 13 86.09 0.0000* 65.81 106.37
Builder 14 NA NA NA NA
Builder 15 NA NA NA NA
*T-test indicates statistically significant difference between groups at the 90% confidence interval
The results of the t-tests for square footage are displayed in Table 4-43. The Evaluators found there to be
statistically significant differences in average square footage per household for Builders 5, 6, 7, and 8.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 71 of 329

Whole Home Rebate 63

Builders 5, 6, and 8 are shown to build homes that range between 414 and 721 square feet smaller than
the rest of the group, whereas Builder 7 builds homes 469 square feet larger than the rest of the group.
Table 4-43 Average Square Footage T-Test by Builder
Builder Mean
Difference P-Value 5% CI 95% CI
Builder 1 -165.19 0.3375 -512.71 182.33
Builder 2 174.08 0.4901 -359.99 708.14
Builder 3 -126.33 0.4372 -467.98 215.32
Builder 4 -138.37 0.6315 -806.76 530.03
Builder 5 -721.15 0.0410* -1398.51 -43.78
Builder 6 -414.02 0.0208* -751.28 -76.76
Builder 7 468.53 0.0595* -25.98 963.03
Builder 8 -643.95 0.0004* -917.30 -370.61
Builder 9 NA NA NA NA
Builder 10 1199.95 0.1334 -867.44 3267.33
Builder 11 618.36 0.3726 -3987.69 5224.41
Builder 12 NA NA NA NA
Builder 13 607.03 0.3895 -4208.14 5422.20
Builder 14 NA NA NA NA
Builder 15 NA NA NA NA
*T-test indicates statistically significant difference between groups at the 90% confidence interval
The Evaluators found there to be statistically significant differences in HERS Index score for Builders 2, 8,
11, and 13. The results of the t-tests for HERS Index scores are displayed in Table 4-44. Builders 2, 8, and
13 receive HERS scores that are, on average, 1.5, 4.4, and 5.3 points lower than the rest of the group,
respectively. Builder 11 receives HERS Index scores that are, on average, 3 points higher than the rest of
the group.
Table 4-44 Average HERS Index Score T-Test by Builder
Builder Mean
Difference P-Value 5% CI 95% CI
Builder 1 2.28 0.0117 0.55 4.01
Builder 2 -1.54 0.0485* -3.08 -0.01
Builder 3 0.43 0.5047 -0.87 1.73
Builder 4 -1.23 0.1788 -3.12 0.67
Builder 5 1.28 0.4427 -2.72 5.27
Builder 6 1.15 0.2427 -0.94 3.24
Builder 7 1.28 0.4121 -2.42 4.97
Builder 8 -4.38 0.0396* -8.40 -0.35
Builder 9 NA NA NA NA
Builder 10 -4.13 0.4268 -21.90 13.63
Builder 11 2.97 0.0221* 0.83 5.11
Builder 12 NA NA NA NA
Builder 13 -5.27 0.0053* -7.44 -3.10
Builder 14 NA NA NA NA
Builder 15 NA NA NA NA
*T-test indicates statistically significant difference between groups at the 90% confidence interval
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 72 of 329

Whole Home Rebate 64

In summary, Builder 5 displayed a lower than average realization rate, as well as lower than average
absolute Therms savings, both statistically significant. However, it is seen that Builder 5 builds homes that
are smaller. Although this builder displayed the largest percent savings in infiltration, and average percent
savings in all other component loads, the total Therms savings are lower than the rest of the builders,
because the baseline home already used a lower amount of Therms than the rest, and therefore absolute
Therms savings is lower as well.
Builder 7 displays higher realization rates, higher absolute Therms savings, and higher average square
footage than the rest of builders. Although this builder displayed lower than average savings in infiltration,
the overall savings were still large enough due to the larger baseline home and therefore larger
opportunity for absolute Therms savings.
Builder 13 displayed realization rate and absolute savings that were higher than the rest of sampled
homes, and statistically significant, as well as HERS Index scores that were lower. Examination of
component load savings displayed this builder’s homes were two stories or over, and therefore saved
much more in duct sealing and infiltration than the rest of builders.
The above relationships between realization rate, absolute savings, HERS Index scores, square footage,
and component load savings are repeated throughout the builders with statistically significant differences
in each.
4.7.2 Rater Savings Profile
In addition to examining builder profiles, the Evaluators examined the raters for statistically significant
differences. The sample of 68 homes included two unique raters. One rater had rated the majority (63) of
the 68 sampled homes. The following table displays the summary of homes for each rater. The Evaluators
found there to be statistically significant differences in each rater’s realization rates, as seen in Table 4-46.
Rater 1 displays a higher realization rate than Rater 2. Rater 1 displays double the savings in insulation
and ceiling components than Rater 2, leading Rater 1 to display 51% higher average realization rate.
However, the Evaluators found no statistically significant differences between the two Rater’s projects in
terms of absolute savings and HERS Index score.
Table 4-45 Simulation Analysis Summary by Rater
Rater Sampled
Homes
Average
HERS Index
Score
Average Therms
Savings per
Rebate
Average Therms
Savings per
Square Foot
Average
Realization
Rate (%)
Rater 1 5 57.2 371 0.117473 182%
Rater 2 63 62.0 266 0.118981 131%

Table 4-46 Average Realization Rate T-Test by Rater
Rater Mean
Difference P-Value 5% CI 95% CI
Rater 1 0.5125 0.0498 0.0007 1.0243
Rater 2 -0.5125 0.0498 -1.0243 -0.0007
*T-test indicates statistically significant difference between groups at the 90% confidence interval
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 73 of 329

Whole Home Rebate 65

4.8 Conclusions and Recommendations
4.8.1 Conclusions
◼ The Evaluators found that the average non-program home in IGC’s service territory exceeds IECC
2012 with Idaho amendments requirements. This is observed in the billing analysis, comparing
nonparticipating newly built homes to the newly built program-rebated homes. In addition, the
proportion and distribution of HERS-rated homes in the IGC territory suggest that the market
already demands high-efficiency homes. The average non-program newly built home that has
been rated receives a HERS Index score of 63. The average participating home receives a HERS
Index score of 60. This suggests that a proportion of the homes rebated through the program may
be free-riders. A free-rider is a program household that would have been built at the same
efficiency even if the program and its incentives were not present.
◼ The Evaluators’ benchmarking research indicates that the energy efficiency standard for a code-
built home is increasing, and that current program requirements may need to increase efficiency
in order to keep up with higher efficiency building codes.
◼ The Whole Home Rebate provides measurable savings through billing analysis. However, the
observable energy savings through billing analysis are much lower than expected equipment
savings. Billing analyses include any changes in household behavior, equipment, or occupancy,
and therefore are unable to entirely isolate equipment savings.
◼ It is expected that the lower the HERS Index score, the higher the energy savings. However, the
Evaluators found the savings normalized by square footage remains relatively constant across a
20-point HERS Index range. The same lack of relationship is seen when analyzing the electric
savings across the HERS Index. Homes with lower HERS Index scores do not equate to higher
Therms savings. The HERS Index score instead depends on the proportional difference between
the RESNET reference home and the rebated home.
◼ The Evaluators found higher savings in all component loads for the program-rebated homes than
the simulated home with only the ENERGY STAR certification, except for window and door
component loads. Overall, the program-rebated home displayed 27% higher savings than a home
only completing ENERGY STAR requirements.
◼ Homes rebated through the program are shown to sell on average 39 days faster than non-
rebated homes. This information can be forwarded to builders and raters to help increase
program participation.
4.8.2 Recommendations
◼ The Evaluators recommends imposing a more stringent HERS Index requirement for the program
in light of recent legislation adoption to adopt the 2018 IECC residential code in Idaho. This
adoption enforces a required maximum HERS Index score of 68.
◼ The Evaluators recommends removing ENERGY STAR certification requirement, as it seems to be
a barrier to builder participation, and the analysis completed for this study indicates that there is
no basis to state that this requirement saves more energy than the current market requirements
of energy efficiency in newly built homes.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 74 of 329

Whole Home Rebate 66

◼ To directly target natural gas savings, IGC could impose specific requirements in addition to a
HERS requirement. This may include an efficient furnace, tankless water heater, higher insulation
levels (such as R-49 ceiling insulation), or more stringent air sealing and duct sealing requirements.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 75 of 329
5 Appendix A: 95% AFUE Natural Gas Alternative Impact
EM&V Methods and Results
The following sections present alternative EM&V methodology for estimating the 95% AFUE Natural Gas
Furnace rebate impact for program years 2017-2018 and 2019.
As a secondary analysis, the Evaluators estimated savings for 95% AFUE Natural Gas Furnace measures
using a pre-post treatment only model. This method will be referred to as “Method 2.” This model does
not have the advantage of a control group and is therefore potentially subject to greater bias. Table 5-1
presents overall ex-ante and ex-post savings by program year. Across all program years, the program
achieved a 26.0% realization rate, with annual ex-post savings per furnace of 29.4 Therms.
Table 5-1 Method 2 Ex-Ante Ex-Post Program Savings Summary
Program
Year
Ex-Ante
Savings per
Rebate
(Therms)
Number of
Valid
Rebates
Ex-Post
Savings per
Rebate
(Therms)
Ex-Ante
Program
Savings
(Therms)
Ex-Post
Program
Savings
(Therms)
Realization
Rate
2017-2018 112.00 1,332 42.62 154,224.00 56,764.03 36.81%
2019 112.00 2,066 20.95 231,392.00 43,285.80 18.71%
All 3,398 29.44 385,616.00 100,049.82 25.95%
A third method the Evaluators used to estimate savings is calculating Equivalent Full Load Hours for
Heating (EFLH) from monthly consumption bills and employing a TRM-based engineering equation to
calculate average furnace savings. This method will be referred to as “Method 3.” This is a widely used
equation for calculating gas furnace hours of use. Table 5-2 summarizes the overall ex-ante and ex-post
savings by program year. Across all program years, this method results in a 118.0% realization rate, with
annual ex-post savings per furnace of 133.9 Therms.
Table 5-2 Method 3 Ex-Ante Ex-Post Program Savings Summary
Program Year
Ex-Ante
Savings per
Rebate
(Therms)
Number of
Valid
Rebates
Ex-Post
Savings per
Rebate
(Therms)
Ex-Ante
Program
Savings
(Therms)
Ex-Post
Program
Savings
(Therms)
Realization
Rate
2017-2018 112.00 1,332 134.64 154,224.00 179,344.07 116.29%
2019 112.00 2,066 133.37 231,392.00 275,533.86 119.08%
All 3,398 133.87 385,616.00 454,877.93 117.96%
The following sections detail the alternative methods and results for impact evaluation of the 95% AFUE
Natural Gas Furnace rebate.
5.1 Billing Analysis with Pre-Post Model
In addition to estimation of savings via PSM, the Evaluators estimated savings using pre- and post-
program participant billing data. The assumption of this model is that program participants’ pre-period
data can provide a sufficient baseline in which to compare post-period data, once normalized on weather
differences in each period.
5.1.1 Pre-Post Model Methodology
The pre-post model estimated weather-dependent energy consumption differences between the pre-
retrofit period and post-retrofit period for program participants only in a fixed-effects panel regression
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 76 of 329

Appendix A: 95% AFUE Natural Gas Alternative Impact EM&V Methods and Results 68

model. The Evaluators merged the natural gas billing data with historic weather conditions and program
tracking data in both the pre-retrofit and post-retrofit periods for each home. The following equation
summarizes the model specifications used for this analysis.
Equation 5-1: Fixed-Effects Panel Regression Model Specification
𝐷𝑎𝑖𝑙𝑦 𝑇ℎ𝑒𝑟𝑚𝑠𝑖𝑡=𝛼0 +𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟_𝐷𝑢𝑚𝑚𝑦𝑖+𝛽1(𝑃𝑜𝑠𝑡)𝑖𝑡+𝛽2(𝐻𝐷𝐷)𝑖𝑡+𝛽3(𝑃𝑜𝑠𝑡× 𝐻𝐷𝐷)𝑖𝑡+𝜀𝑖𝑡
Where,
𝐷𝑎𝑖𝑙𝑦 𝑇ℎ𝑒𝑟𝑚𝑠𝑖𝑡 = Estimated energy usage (dependent variable) in home i during period t
𝑃𝑜𝑠𝑡𝑖𝑡 = Dummy variable indicating whether period t was in pre- or post- retrofit
𝐻𝐷𝐷𝑖𝑡 = Average heating degree days (base 63°F) during period t at home i
𝜀𝑖𝑡 = Customer-level random error
𝛼0= The model intercept for home i
𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟_𝐷𝑢𝑚𝑚𝑦𝑖 = A vector of dummy variables measuring fixed effects for each customer i
𝛽1−3 = Coefficients determined via regression
𝛽1, 𝛽2, and 𝛽3 represent the average change in daily baseload in the post-period, daily Therms per HDD,
and daily Therms per HDD in the post-period, respectively.
Additional data cleaning steps for program participants included:
◼ Removed bills with missing billing dates
◼ Removed account numbers for homes that do not overlap with the furnace install date
◼ Removed bill duration outliers (<9 or > 60 days duration)
◼ Removed homes with insufficient number of bills (< 12 months)
5.1.2 Pre-Post Model Results
This section details the regression results from estimating savings for program participants only with a
pre- post-model. Table 5-4 summarizes the results of the billing analysis for program participants by
program year. A p-value less than 0.05 indicates that the parameter estimate is statistically significant at
the 95% confidence level. Not shown in the tables below are the coefficients for each treatment program
participant (dummy variables) for the sake of brevity.
Savings are determined through parameters B1 and B3, which are defined in Table 5-3. In any given
month, per program participant savings are calculated as follows: (B1*Days in Month) + (B3*HDD). In
computing HDD for each month, the Evaluators used TMY3 data for the weather stations associated with
each 3-digit zip code (per Table 3-3). This extrapolation provides savings under typical weather conditions.
Monthly TMY HDDs were calculated from the TMY data as the sum of daily average temperature values
under 63°F in a given month. Monthly TMY HDDs were weighted by the number of rebates found in each
weather station area defined by the first 3 digits of the 5-digit zip code when computing monthly and
annual program savings (see Table 3-13 for rebate counts).
Table 5-3 Method 2 Regression Parameters
Variable Parameter Interpretation
Post B1 Average daily usage in the post-period
Avg.HDD B2 Average daily usage per HDD
Post*Avg.HDD B3 Average daily usage per HDD in the post-period
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Appendix A: 95% AFUE Natural Gas Alternative Impact EM&V Methods and Results 69

As shown in Table 5-4, the coefficient estimates for Post*Avg.HDD (B3) are negative, indicating lower
usage per HDD in the post-period for treatment customers. In addition, these coefficients are statically
significant at the 95% level in both program years. The estimate for Post (B1) is positive, indicating
negative savings that are independent of the weather or HDDs, but these coefficients are not statistically
significant. This result is what one would expect for furnace measures, which are a weather dependent
savings measure.
Table 5-4 Method 2 Regression Results
Program Year Coefficient Estimate Std Error P Value 5% 95%
2017-2018
Post 0.009 0.018 0.624 -0.021 0.038
Avg.HDD 0.121 0.001 0.000 0.119 0.123
Post*Avg.HDD -0.008 0.001 0.000 -0.009 -0.007
2019
Post 0.014 0.021 0.524 -0.022 0.049
Avg.HDD 0.119 0.002 0.000 0.116 0.121
Post*Avg.HDD -0.005 0.001 0.000 -0.006 -0.003
*Customer-specific fixed effects coefficients are omitted for brevity
Each of the models showed they were a good fit for the data, as seen by the high Adjusted R-square in
Table 5-5.
Table 5-5 Method 2 Model Fit
Program Year Adjusted R2 F Statistic Number of
Observations
2017-2018 0.83 179 28,700
2019 0.81 159 31,947
Table 5-6 provides monthly savings estimates per rebate and for all rebates by program year. HDDs were
weighted by the number of rebates from each of the associated weather stations.
Table 5-6 Method 2 Monthly Savings Summary by Program Year
Month Weighted
HDD
PY2017-PY2018
Savings/Rebate
(Therms)
PY2019
Savings/Rebate
(Therms)
Jan 1,118 8.66 4.64
Feb 815 6.27 3.30
Mar 666 5.05 2.59
Apr 387 2.83 1.34
May 251 1.74 0.71
June 112 0.63 0.10
July 35 0.01 (0.27)
Aug 79 0.36 (0.07)
Sept 163 1.04 0.33
Oct 406 2.97 1.41
Nov 757 5.78 3.01
Dec 945 7.28 3.85
Annual 5,734 42.62 20.95
The above methodology results in an ex-post savings of 29.4 Therms per rebate and with 100,049.8
Therms savings across both program years with a realization rate of 26.0%. The summary of savings is
displayed in Table 5-7.
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Appendix A: 95% AFUE Natural Gas Alternative Impact EM&V Methods and Results 70

Table 5-7 Method 2 Ex-Ante Ex-Post Program Savings Summary
Program
Year
Ex-Ante
Savings per
Rebate
(Therms)
Number of
Valid
Rebates
Ex-Post
Savings per
Rebate
(Therms)
Ex-Ante
Program
Savings
(Therms)
Ex-Post
Program
Savings
(Therms)
Realization
Rate
2017-2018 112.00 1,332 42.62 154,224.00 56,764.03 36.81%
2019 112.00 2,066 20.95 231,392.00 43,285.80 18.71%
All 3,398 29.44 385,616.00 100,049.82 25.95%

5.2 Equivalent Full Load Hours for Heating (EFLHh)
The Evaluators estimated Equivalent Full Load Hours for Heating (EFLHh) for gas furnace participants in
the post-period. An EFLHh estimate provides a way to estimate Therms savings for each furnace in each
household using TRM-based engineering equations, under various scenarios for pre- and post- efficiency
factors and furnace capacities.
This approach complies with the International Performance Measurement and Verification Protocol
(IPMVP) maintained by the Efficiency Valuation Organization (EVO) with sponsorship by the U.S.
Department of Energy (DOE)16. It is often used to calculate deemed savings for gas furnace retrofits.
5.2.1 Data Collection
IGC provided the Evaluators with the necessary data to compute EFLH estimates specific two weather
zones: 5 and 6, present in the IGC service territory. The EFLH was estimated separately for weather zone
because furnace runtimes vary depending on weather zone and associated heating degree days (HDDs).
For instance, Table 5-8 shows that Boise and Twin Falls are in weather zone five where HDDs average less
than 6,000 per year, while Pocatello and Idaho Falls are in weather zone six where HDDs average over
6,500 per year in a typical year.
Table 5-8 TMY HDD and Weather Zone by Major City
Major City Weather
Zone
Weather
Station ID
3-Digit Zip
Code TMY HDD
Boise 5 726810 836/837 5,188
Twin Falls 5 725866 833 5,913
Pocatello 6 725780 832 6,839
Idaho Falls 6 725785 834 7,588

The information required to conduct this analysis included:
◼ Efficient furnace capacity

16 Core Concepts: International Measurement and Verification Protocol. EVO 100000 – 1:2016, October 2016.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 79 of 329

Appendix A: 95% AFUE Natural Gas Alternative Impact EM&V Methods and Results 71

◼ Efficient furnace AFUE
◼ Monthly billing data for 95% Furnace rebate participants
◼ Heating Degree Days from local weather stations
5.2.2 EFLH Methodology
Traditionally, the TRM defines the EFLH to be used in the engineering equation below. This EFLH estimate
is calculated using a large dataset of primary consumption and on-site measurements tailored to each
geographic region. The Evaluators performed the following calculations as part of the EFLH estimation
process:
◼ Calculated post-period baseload usage (Therms) for each participant, where baseload is average
summer usage in June, July, and August.
◼ Calculated average daily heating load for each participant in the post-period by taking the difference
between average daily usage (Therms) and baseload usage.
◼ Set any negative heating loads to zero (assumed to be deviations from average baseload usage).
◼ Calculated average input capacity for furnaces (BTU/hr).
◼ Calculated average post-install furnace efficiency factor in terms of Annual Fuel Utilization Efficiency
(AFUE).
The components listed above are used in the following equation for EFLHh estimation:
𝐸𝐹𝐿𝐻ℎ=𝐻𝑒𝑎𝑡𝑙𝑜𝑎𝑑 (𝑇ℎ𝑒𝑟𝑚𝑠)∗𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝐹𝑎𝑐𝑡𝑜𝑟𝐼𝑛𝑝𝑢𝑡 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦∗𝐴𝐹𝑈𝐸⁄
Where,
𝐻𝑒𝑎𝑡𝑙𝑜𝑎𝑑 (𝑇ℎ𝑒𝑟𝑚𝑠) = Average daily heating load usage for participants in the post period
𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝐹𝑎𝑐𝑡𝑜𝑟 = 100,000 BTU/Therms
𝐼𝑛𝑝𝑢𝑡 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 = Average furnace input capacity (BTU/hr) for participant in the post period
𝐴𝐹𝑈𝐸 = Annual Fuel Utilization Efficiency (AFUE) factor, which measures the furnace efficiency ratio in
terms of output to input Therms usage.
Once the EFLH values are defined for each of the two weather zones, the following TRM engineering
equation is used to estimate the annual Therms savings for each furnace retrofit measure, using the actual
input capacity and actual post-retrofit AFUE collected from each furnace rebate. The default 80%
efficiency value is used for prior furnace efficiency.
The equation for estimating gas furnace savings with EFLH in puts is shown below17:

17Indiana TRM V1: Residential Market Sector: Condensing Furnace-Residential, pg. 159
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 80 of 329

Appendix A: 95% AFUE Natural Gas Alternative Impact EM&V Methods and Results 72

𝐴𝑛𝑛𝑢𝑎𝑙 𝐹𝑢𝑟𝑛𝑎𝑐𝑒 𝑆𝑎𝑣𝑖𝑛𝑔𝑠 (𝑇ℎ𝑒𝑟𝑚𝑠)
=𝐼𝑛𝑝𝑢𝑡 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (𝐵𝑇𝑈
ℎ𝑟)∗𝐸𝐹𝐿𝐻ℎ∗(1
𝐴𝐹𝑈𝐸 𝑏𝑎𝑠𝑒
−1
𝐴𝐹𝑈𝐸 𝑒𝑓𝑓)/(𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝐹𝑎𝑐𝑡𝑜𝑟)
Where,
𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝐹𝑎𝑐𝑡𝑜𝑟 = 100,000 BTU/Therms;
𝐼𝑛𝑝𝑢𝑡 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 = Average furnace input capacity (BTU/hr);
𝐸𝐹𝐿𝐻ℎ = Equivalent full load hours for heating;
𝐴𝐹𝑈𝐸 𝑏𝑎𝑠𝑒 = Pre-retrofit AFUE; and
𝐴𝐹𝑈𝐸 𝑒𝑓𝑓 = Post-retrofit AFUE.
The Evaluators chose to include only new construction rebated furnaces in the estimation of EFLH.
Customers replacing older, less efficient furnaces through early retirement and replace-on-burnout often
observe a “snapback” in energy usage. Snapback is a common user behavior in which a customer adopts
less efficient energy usage behaviors due to the fact that they installed more efficient equipment. The
Evaluators aimed to separate the snapback phenomenon from the EFLH calculation by including new
construction rebated furnaces only.
The Evaluators parsed out new construction furnaces using billing data and tracking data. Furnaces were
only included in the EFLH estimation if the install date occurred during the same year as the home’s built
year and if they did not portray more than 3 months of bills before the date of installation. In addition,
furnaces were removed from EFLH estimation that did not have sufficient bills to calculate average
summer time baseload. Only post-period (post-installation) billing data was used in this estimation.
5.2.3 EFLH Results
The Evaluators calculated daily heating load after filtering for new construction furnaces. Figure 5-1
displays the average load by end use for the new construction furnaces. The heating load was calculated
by subtracting the average daily baseload from the average daily Therm usage.
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Appendix A: 95% AFUE Natural Gas Alternative Impact EM&V Methods and Results 73

Figure 5-1 Average Daily Usage by Load Type

The Evaluators estimated EFLH for heating by city and weather zone18. The results of the EFLHh estimation
for each the 5 and 6 weather zone is summarized in the table below.
Table 5-9 Calculated EFLH by Weather Zone
Weather
Zone EFLH Heating
5 846.85
6 936.61
Table 5-10 provides average monthly heating load and EFLHh by month. Participants averaged 1.76
Therms/day across the entire year, with usage varying from a low of 0.05 and 0.06 Therms/day in July to
a high of 3.65 and 3.49 Therms/day in February.

18Weather zone was obtained for each city using the following source:
https://www.energy.gov/sites/prod/files/2015/10/f27/ba_climate_region_guide_7.3.pdf
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 82 of 329

Appendix A: 95% AFUE Natural Gas Alternative Impact EM&V Methods and Results 74

Table 5-10 Average Monthly Heating Load and EFLHh
Month
Weather Zone 5
Average Heating
Load
(Therms/Day)
Weathe6
Zone 5
Average
Heating Load
(Therms/Day)
Weather
Zone 5
Monthly
EFLH Heating
Weather
Zone 6
Monthly
EFLH Heating
Jan 3.49 3.33 159.33 168.63
Feb 3.65 3.49 151.91 161.06
Mar 2.33 2.34 106.52 118.59
Apr 0.99 1.08 43.85 52.92
May 0.41 0.60 18.82 30.19
June 0.10 0.10 4.43 4.76
July 0.05 0.06 2.11 3.07
Aug 0.06 0.09 2.92 4.58
Sept 0.30 0.48 13.40 23.69
Oct 1.47 1.69 67.14 85.68
Nov 2.69 2.39 118.96 117.49
Dec 3.45 3.27 157.47 165.94
Annual 1.58 1.58 846.85 936.61
This Method uses the default 80% AFUE baseline efficiency to calculate savings. Using the actual capacity
and efficient AFUE from each rebated furnace in each program year, the Evaluators applied the TRM-
based engineering equation for annual furnace savings referenced in the impact evaluation approach. The
Evaluators assumed baseload efficiency factor of 80% AFUE.
As a concrete example of the TRM-based savings calculation, assuming a furnace has an input capacity of
75,000 (BTU/hr.), an AFUE of 0.98, and is installed in Boise, Idaho the estimated annual savings is as
follows:
Annual Savings (Therms) = 75,000 (BTU/hr.)*868 (EFLH)*(1/0.80 – 1/0.98)/100,000 = 149.5 Therms
The TRM-based methodology for estimating gas furnace savings using EFLHh is shown again below for
reference purposes19:
𝐴𝑛𝑛𝑢𝑎𝑙 𝐹𝑢𝑟𝑛𝑎𝑐𝑒 𝑆𝑎𝑣𝑖𝑛𝑔𝑠 (𝑇ℎ𝑒𝑟𝑚𝑠)
=𝐼𝑛𝑝𝑢𝑡 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (𝐵𝑇𝑈
ℎ𝑟)∗𝐸𝐹𝐿𝐻ℎ∗(1
𝐴𝐹𝑈𝐸 𝑏𝑎𝑠𝑒
−1
𝐴𝐹𝑈𝐸 𝑒𝑓𝑓)/(𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝐹𝑎𝑐𝑡𝑜𝑟)
Where,
𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝐹𝑎𝑐𝑡𝑜𝑟 = 100,000 BTU/Therms;

19Indiana TRM V1: Residential Market Sector: Condensing Furnace-Residential, pg. 159
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Appendix A: 95% AFUE Natural Gas Alternative Impact EM&V Methods and Results 75

𝐼𝑛𝑝𝑢𝑡 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 = Average furnace input capacity (BTU/hr.);
𝐸𝐹𝐿𝐻ℎ = Equivalent full load hours for heating;
𝐴𝐹𝑈𝐸 𝑏𝑎𝑠𝑒 = Pre-retrofit AFUE; and
𝐴𝐹𝑈𝐸 𝑒𝑓𝑓 = Post-retrofit AFUE.
The above methodology results in an ex-post savings of 133.9 Therms per rebate and with 454,878 Therms
savings across both program years with a realization rate of 117.96%. The summary of savings is displayed
in Table 5-11.
Table 5-11 Method 3 Ex-Ante Ex-Post Program Savings Summary
Program
Year
Ex-Ante
Savings per
Rebate
(Therms)
Number of
Valid
Rebates
Ex-Post per
Rebate
(Therms)
Ex-Ante
Program
Savings
(Therms)
Ex-Post
Program
Savings
(Therms)
Realization
Rate
2017-2018 112.00 1,332 134.64 154,224.00 179,344.07 116.29%
2019 112.00 2,066 133.37 231,392.00 275,533.86 119.08%
All 3,398 133.87 385,616.00 454,877.93 117.96%
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 84 of 329
6 Appendix B: Whole Home Alternative Impact EM&V
Methods and Results
The following sections present alternative EM&V methodology for estimating the Whole Home Rebate
impact for program years 2017-2018 and 2019.
6.1 Simulation Analysis
As a secondary analysis, the Evaluators estimated savings for the Whole Home Rebate measure using
calibrated simulation. This method will be referred to as “Method 2.” Method 2 does not employ the use
of a control group. It instead compares the HERS Rater’s modeling files to a User Defined Reference Home
(UDRH) specific to Idaho’s residential building code requirements. Table 6-1 presents overall ex-ante and
ex-post savings by program year. Across all program years, the program achieved a 134.28% realization
rate, with annual ex-post savings per furnace of 273.94 Therms. In addition, the Evaluators estimated
electric savings of 1,210 kWh per year per rebate.
Table 6-1 Method 2 Ex-Ante and Ex-Post Program Savings
Program
Year
Ex-Ante
Savings per
Rebate
(Therms)
Number
of
Rebates
Ex-Post
Savings per
Rebate
(Therms)
Ex-Ante
Program
Savings
(Therms)
Ex-Post Program
Savings (Therms) Realization Rate
2017-2018 204.00 619 295.13 126,276.00 182,682.38 144.67%
2019 204.00 1,079 262.39 220,116.00 283,114.89 128.62%
All 1,698 273.94 346,392.00 465,152.12 134.28%

6.1.1 UDRH Baseline Homes
The Evaluators incorporated a User Defined Reference Home (UDRH) into the energy models provided
by the HERS raters. The UDRH reflects Idaho’s code minimum house, based on the 2012 IECC with Idaho-
specific amendments. Some of the key UDRH assumption are shown in Table 6-2.
Table 6-2 UDRH Key Assumptions
Input UDRH
Assumption Source
Infiltration 7 ACH50 2012 IECC with Idaho Amendments
Duct Leakage 0.04 CFM25/CFA 2012 IECC with Idaho Amendments
Percent Efficiency Lighting 50 2012 IECC with Idaho Amendments
Gas Instant Water Heater (%) 82 2012 IECC with Idaho Amendments
Attic Insulation (U-factor) 0.03 2012 IECC with Idaho Amendments
Wall Insulation (U-factor) 0.57 2012 IECC with Idaho Amendments
Window SHGC 0.4 2012 IECC with Idaho Amendments
Window U-factor 0.35 2012 IECC with Idaho Amendments
Furnace AFUE 80 Federal minimum efficiency
6.1.2 Simulation Analysis Methodology
In addition to estimating impact savings for the Whole Home Rebate using matched treatment and control
billing analysis, the Evaluators estimated savings using Method 2, an alternative approach. This alternative
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 85 of 329

Appendix B: Whole Home Alternative Impact EM&V Methods and Results 77

approach involves the comparison of program-participating homes with a User Defined Reference Home
(UDRH). The Evaluators used the simulation models to compare a sample of 80 participating homes with
a User Defined Reference Home (UDRH), an agreed upon set of efficiency standards built to represent the
baseline residential home in the state of Idaho.
The Evaluators utilized the whole building energy modeling program REM/Rate during the review and
verification efforts in the impact evaluation for this measure. REM/Rate is software used by organizations
to conduct HERS Ratings. REM/Rate files were reviewed for a sample of 80 program-participating homes.
The sampling approach stratify the sample by home builder to ensure representativeness of the 18
builders participating in the program. The REM/Rate data was subset by the builders in the sample and
used to develop a profile for each builder by program year. Energy savings was calculated per-home with
the following calculation:
Equation 6-1 Simulation Savings
Tℎ𝑒𝑟𝑚 𝑆𝑎𝑣𝑖𝑛𝑔𝑠=𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑈𝐷𝑅𝐻−𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝐸𝑁𝐸𝑅𝐺𝑌 𝑆𝑇𝐴𝑅
Where,
𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑈𝐷𝑅𝐻 = Simulated energy consumption values from REM/Rate for a household under the
UDRH efficient code standards
𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝐸𝑁𝐸𝑅𝐺𝑌 𝑆𝑇𝐴𝑅 = Simulated energy consumption from REM/Rate for a household built
referencing the HERS certification values
The UDRH represents a home built to meet the state of Idaho’s current minimum energy efficiency code
requirements. Idaho currently uses the residential 2012 International Energy Conservation Code (IECC)
with amendments20 for newly constructed residential homes. The Evaluators used the residential 2012
IECC with Idaho-specific amendments efficiency values when creating the UDRH in REM/Rate. This
comparison provides an accurate simulation of a newly constructed minimum efficient code residential
home to compare against efficiency, program-participating homes.
6.1.3 Simulation Analysis Results
Energy models of the as-built house were created by HERs raters to model the energy use of the actual
house. This model was compared to the User Defined Reference Home (UDRH). The UDRH reflects the
2012 International Energy Conservation Code with Idaho’s amendments. The UDRH was developed by
inspecting building codes, HVAC equipment codes, and appliance codes. Most of the energy saving in the
as-built home comes from the home’s sealing of its envelope and ducts. Furthermore, the as-built home’s
windows and HVAC systems are more efficient than the code minimum house. The primary goal of the
desk review verification effort is to verify as much data as possible using supporting documentation. The
Evaluators can verify the following metrics through this type of desk review:
◼ Efficiency of furnaces and water heaters;
◼ Size, layout, shape, and location of the home

20 https://www.energycodes.gov/adoption/states/idaho
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 86 of 329

Appendix B: Whole Home Alternative Impact EM&V Methods and Results 78

Table 6-3 and Table 6-4 displays the average annual UDRH and rebated home Therms and kWh values for
space heating, water heating, lighting, and appliances in each program year. These values represent the
results of the simulation model prior to billing calibrations.
Table 6-3 UDRH and Whole Home Modeled Annual Therms Usage
Program
Year
UDRH Program Homes
Space
Heating
(Therms)
Water
Heating
(Therms)
Lights &
Appliances
(Therms)
Total
(Therms)
Space
Heating
(Therms)
Water
Heating
(Therms)
Lights &
Appliances
(Therms)
Total
(Therms)
2017-2018 779.38 144.67 14.71 939.13 497.08 132.13 14.71 644.00
2019 743.95 153.84 18.16 915.59 496.09 139.36 17.75 653.20
Total 756.46 150.60 16.93 923.90 496.44 136.81 16.68 649.96

Table 6-4 UDRH and Whole Home Modeled Annual kWh Usage
Program
Year
UDRH Program Homes
Space
Heating
(kWh)
Cooling
(kWh)
Lights &
Appliances
(kWh)
Total
(kWh)
Space
Heating
(kWh)
Cooling
(kWh)
Lights &
Appliances
(kWh)
Total
(kWh)
2017-2018 723.46 1,033.67 6,527.63 8,282.21 552.04 874.29 5,694.08 7,120.25
2019 636.41 957.86 6,809.20 8,403.64 508.18 836.09 5,825.77 7,167.77
Total 667.13 984.62 6,709.82 8,360.78 523.66 849.57 5,779.29 7,151.00

Table 6-5 and Table 6-6 displays the average of the per-home annual usage differences between UDRH
and Whole Home-rebated homes for each program year. Both program years display a zero savings value
for lighting and appliances. The simulated participating homes in PY2017-2018 saved an average of 295
Therms per home and an average of 262 Therms per home in PY2019. The average savings per home
across both program years is 274 Therms.
The occupancy and home characteristics between the UDRH modeled simulation and the as-built modeled
simulation remain constant, which means the as-built home, with all else constant, uses less energy to
heat the space of the home than the code-built efficient home, leading to savings for those categories.
The majority of the gas savings (95%) are displayed in space heating due to higher home sealing and more
efficient HVAC equipment. The majority of electric savings (77%) for electric usage is from lighting and
appliances.
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Appendix B: Whole Home Alternative Impact EM&V Methods and Results 79

Table 6-5 Average Modeled Therms Savings per Home
Program
Year
Space
Heating
(Therms)
Water
Heating
(Therms)
Lights &
Appliances
(Therms)
Total
(Therms)
2017-2018 282.29 12.54 - 295.13
2019 247.86 14.48 0.41 262.39
Total 260.01 13.79 0.25 273.94
Table 6-6 Average Modeled kWh Savings per Home
Program
Year
Space
Heating
(kWh)
Cooling
(kWh)
Lights &
Appliances
(kWh)
Total
(kWh)
2017-2018 171.42 159.38 833.54 1,161.96
2019 128.23 121.77 983.43 1,235.86
Total 143.47 135.04 930.53 1,209.78
This method used REM/Rate simulation models to provide the Whole Home Rebate measure an overall
realization rate of 134.28% with 465,152 Therms savings across both program years. In addition, this
method allowed the Evaluators to estimate annual electric savings of 1,210 kWh per home.
Table 6-7 Method 2 Ex-Ante and Ex-Post Program Savings
Program Year
Ex-Ante
Therms
Savings
Ex-Post Therms
Savings
Therms
Savings
Realization
Rate
Ex-Post kWh
Savings
2017-2018 126,276 182,682 144.67% 1,163
2019 220,116 283,115 128.62% 1,237
Total 346,392 465,152 134.28% 1,211

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 88 of 329

Appendix C: Impact Evaluation Scripts 80

7 Appendix C: Impact Evaluation Scripts
The following section displays the R code utilized to produce the impact evaluation results presented in
this report.
7.1 95% AFUE Natural Gas Furnace Rebate Script

# install and initialize packages
# install.packages("easypackages")
library(easypackages)

# list of required packages to complete analysis
package_list <- c("doBy", "RItools", "cowplot", "MatchIt", "openxlsx", "dplyr", "lazyeval",
"lubridate", "ggplot2", "broom", "readr", "lme4")

# install and load the list of required packages
packages(package_list)

# functions
# function to compute weather variables for each billing interval
weather_for_interval <- function(x, noaa_dta){
f_dta <- data.frame(Bill.St = x$Bill.St, Bill.End = x$Bill.End, USAF = x$USAF)
obs_in_range <- noaa_dta$Date >= x$Bill.St & noaa_dta$Date <= x$Bill.End & x$USAF ==
noaa_dta$USAF
# sequence of temps to calculate HDD/CDD with diff base temps
seq <- seq(from = 55, to = 75, by = 1)
for (i in seq) {
f_dta[,paste("HDD.",i,sep="")] <- sum(noaa_dta[obs_in_range, paste("HDD.",i,sep="")],
na.rm = T)
}
return(f_dta)
}

# function that checks a condition is true or false and halts execution if false
assert <- function(x) {
if(x == TRUE) print("condition is true")
else stop("condition is false")
}

# function that checks if a data frame is unique to a variable
isid <- function(idvars, data) {
length_st <- length(data[, 1])
length_end <- length(as.data.frame(unique(data[, idvars]))[, 1])
if(length_st != length_end) stop("dataset not unique by id variables")
else print("dataset is unique by id variables")
}

# parameters
st_dt_cutoff <- as.Date("2016-01-01")

# set up program paths and create directories
vers <- "script"
wd <- getwd()
code_dir <- paste(wd, "Code", sep = "/")
sub_dir <- paste(wd, "Code", "Subroutines", sep = "/")
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Appendix C: Impact Evaluation Scripts 81

raw_dir <- paste(wd, "Raw", sep = "/")
noaa_dir <- paste(wd, "Raw/Weather", sep = "/")
inter <- paste(wd, "Inter", vers, sep = "/")
results <- paste(wd, "Results", vers, sep = "/")

# create directories
dir.create(code_dir, recursive = T)
dir.create(raw_dir, recursive = T)
dir.create(inter, recursive = T)
dir.create(results, recursive=T)

# read in the raw files
# import billing data
fl_name <- "IGC INDIVIDUAL REBATE BILLING DATA (1).xlsx"
pct <- read.xlsx(paste(raw_dir, fl_name, sep = "/"), sheet = 4, startRow=2, detectDates =
TRUE)
save(pct, file = paste(inter, "pct.RData", sep = "/"))

fl_name <- "IGC NONPARTICIPANT DATA (REVISED).xlsx"
non_pct <- read.xlsx(paste(raw_dir, fl_name, sep = "/"), sheet = 4,startRow=2, detectDates =
TRUE)
save(non_pct, file = paste(inter, "non_pct.RData", sep = "/"))

# import nonparticipant site info
fl_name <- "IGC NONPARTICIPANT DATA (REVISED).xlsx"
site <- read.xlsx(paste(raw_dir, fl_name, sep = "/"), sheet = 3,startRow=2, detectDates =
TRUE)
save(site, file = paste(inter, "site.RData", sep = "/"))

# import tracking data
fl_name <- "All Appliances.xlsx"
track <- read.xlsx(paste(raw_dir, fl_name, sep = "/"), sheet = 3) # files must be closed
save(track, file = paste(inter, "track.RData", sep = "/"))

# import rebate file
fl_name <- "IGC INDIVIDUAL REBATE BILLING DATA (1).xlsx"
rebate_raw <- read.xlsx(paste(raw_dir, fl_name, sep = "/"), sheet = 3, startRow=2, detectDates
= TRUE)
save(rebate_raw, file = paste(inter, "rebate_raw.RData", sep = "/"))

# import whole home rebate file to check for nonparticipant overlaps
fl_name <- "IGC NEWLY BUILT DATA.xlsx"
whole_homes_raw <- read.xlsx(paste(raw_dir, fl_name, sep = "/"), sheet = 3, startRow=2,
detectDates = TRUE)
save(whole_homes_raw, file = paste(inter, "whole_homes_raw.RData", sep = "/"))

fl_name <- "IGC WHOLE HOME DATA.xlsx"
whole_homes_bills <- read.xlsx(paste(raw_dir, fl_name, sep = "/"), sheet = 3, startRow=2,
detectDates = TRUE)

fl_name <- "Whole Home Tracking Data Aggregate.xlsx"
whole_homes_tracking <- read.xlsx(paste(raw_dir, fl_name, sep = "/"), sheet = 2, detectDates =
TRUE)

# check there are no appliance rebate participants in whole home participants
table(whole_homes_raw$ENERGY_STAR, whole_homes_raw$INDIVIDUAL_REB)
whole_homes_part <- whole_homes_raw[whole_homes_raw$ENERGY_STAR==1,]
rebate_raw[rebate_raw$PREMISE_ID %in% whole_homes_part$PREM_ID,] # 0
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Appendix C: Impact Evaluation Scripts 82

rebate_raw[rebate_raw$PREMISE_ID %in% whole_homes_bills$PREM_ID,] # 0
rebate_raw[rebate_raw$ADDRESS1_UPR %in% whole_homes_part$ADDRESS1_UPR,] # 0
rebate_raw[rebate_raw$ADDRESS1_UPR %in% whole_homes_bills$SITE_ADDRESS,] # 0

# check there are no appliance rebate nonparticipants in whole homes participants
nonpart_in_whole_home <- site[site$PREM_ID %in% whole_homes_part$PREM_ID,] # 0
nonpart_in_whole_home <- unique(rbind(nonpart_in_whole_home, site[site$ADDRESS1_UPR %in%
whole_homes_part$ADDRESS1_UPR,])) # 0
nonpart_in_whole_home_tracking <- site[site$ADDRESS1_UPR %in%
toupper(whole_homes_tracking$Site.Address),] # 0
nonpart_in_whole_home_bills <- site[site$PREM_ID %in% whole_homes_bills$PREMISE_ID,] # 0
nonpart_in_whole_home_bills <- site[site$ADDRESS1_UPR %in%
toupper(whole_homes_bills$SITE_ADDRESS),] # 0

non_pct_in_whole_home <- non_pct[non_pct$PREM_ID %in% whole_homes_part$PREM_ID,] # 0
non_pct_in_whole_home <- unique(rbind(non_pct_in_whole_home, non_pct[non_pct$ADDRESS1_UPR %in%
whole_homes_part$ADDRESS1_UPR,])) # 0
non_pct_in_whole_home_tracking <- non_pct[non_pct$ADDRESS1_UPR %in%
toupper(whole_homes_tracking$Site.Address),] # 0
non_pct_in_whole_home_bills <- non_pct[non_pct$PREM_ID %in% whole_homes_bills$PREMISE_ID,] # 0
non_pct_in_whole_home_bills <- non_pct[non_pct$ADDRESS1_UPR %in%
toupper(whole_homes_bills$SITE_ADDRESS),] # 0

# Pulled weather data from ADM Shiny App
# 3 digit zip assignments:
# Boise: 836, 837
# Twin Falls: 833
# Pocatello: 832
# Idaho Falls: 834

# USAF Codes
# Boise: 726810
# Twin Falls: 725866
# Pocatello: 725780
# Idaho Falls: 725785

list.files <- list.files(path=noaa_dir)
head(list.files)

# import all weather station data
import_noaa <- function(x) {
df <- read_csv(paste(noaa_dir,list.files[x],sep="/"))
df$USAF <- substr(list.files[x],1,6)
df <- as.data.frame(df)
return(df)
}

# combine weather station data into one data frame
noaa <- lapply(1:length(list.files), import_noaa)
noaa <- do.call("rbind",noaa)
noaa$datetime <- as.POSIXct(noaa$date_UTC)

# keep only necessary columns
keep_cols <- c("date_UTC", "date_local", "temp", "USAF", "datetime")
noaa <- noaa[,keep_cols]

# get year
noaa$year <- year(noaa$datetime)
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Appendix C: Impact Evaluation Scripts 83

# convert to date fmt
noaa$Date <- as.Date(as.character(noaa$datetime), "%Y-%m-%d")
# some duplicate datetimes occured
noaa <- noaa[!duplicated(noaa),]

# sequence of temps to calculate HDD/CDD with diff base temps
seq <- seq(from = 55, to = 75, by = 1)
for (i in seq) {
# data must be hourly
noaa[,paste("HDD.",i,sep="")] <- pmax((i-noaa$temp),0)/24
}
assert(sum(noaa$HDD!=noaa$HDD.65, na.rm=TRUE)==0)
save(noaa, file = paste(inter, "noaa.RData", sep = "/"))
rm(noaa)

# prepare data for regression modeling
# prepare participant rebate and tracking data to merge with monthly billing data
load(paste(inter, "rebate_raw.RData", sep = "/"))
rebate_raw <- rebate_raw[, colnames(rebate_raw)[colnames(rebate_raw) != "IGC_ACCOUNT_NO"]]
load(paste(inter, "track.RData", sep = "/"))
table(track$Source)
track <- track[,c("Rebate.Type", "Rebate.ID", "Source",
"Site.Zip","Owner.First.Name","Owner.Last.Name","ADM.Verified.Furnace.Size.(BTUH)","ADM.Verifi
ed.AFUE",
"Furnace.Size.(BTUH)","AFUE" )]
track <- track[!is.na(track$Rebate.ID),]

# check for duplicates
sum(duplicated(track))
# checks data is unique
isid(idvars = c("Rebate.ID"), d = track)
table(track$Source, track$Rebate.Type)

track$Name <- paste0(track$Owner.Last.Name,", ",track$Owner.First.Name)
track$Cap.Input <-
suppressWarnings(as.numeric(ifelse(!is.na(track$`ADM.Verified.Furnace.Size.(BTUH)`),track$`ADM
.Verified.Furnace.Size.(BTUH)`, track$`Furnace.Size.(BTUH)`)))
track$AFUE <-
suppressWarnings(as.numeric(ifelse(!is.na(track$ADM.Verified.AFUE),track$ADM.Verified.AFUE,tra
ck$AFUE)))

track <- track[!is.na(track$Rebate.ID),]
track <- track %>% dplyr::filter(Rebate.Type == "95% Furnace")
track <- track[,c("Source", "Rebate.ID", "Site.Zip", "Name","Cap.Input","AFUE")]
names(track)[names(track)=="Site.Zip"] <- "Zip"

not_found <- rebate_raw[!rebate_raw$REBATE_ID %in% track$Rebate.ID,]
table(not_found$REBATE_TYPE)

# get zip and name from tracking data
rebate_raw <- merge(rebate_raw, track,by.x="REBATE_ID",by.y="Rebate.ID")
table(rebate_raw$REBATE_TYPE, rebate_raw$Source)

# check for duplicates
sum(duplicated(rebate_raw))
rebate <- rebate_raw
rm(rebate_raw)

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Appendix C: Impact Evaluation Scripts 84

# let install date equal date furnace fired
rebate$Install.Dt <- ymd(as.Date(rebate$DATE_FIRED, origin="1899-12-30"))
# define install year
rebate$Install.Year <- year(rebate$Install.Dt)

# reorganize and rename
rebate <- rebate[, c("Source", "PREMISE_ID", "REBATE_TYPE",
"REBATE_ID","Install.Dt","Install.Year",
"ADDRESS1_UPR","SITE_CITY","Name","Zip","Cap.Input","AFUE")]
names(rebate) <- c("Source", "PREM_ID","Install.Type","Rebate_ID",
"Install.Dt","Install.Year","Address","City","Name","Zip","Cap.Input","AFUE")
trmt_counts <- data.frame(Detail="Starting
Count",PY18Count=n_distinct(rebate$PREM_ID[rebate$Source == "2017-2018"]), PY19Count =
n_distinct(rebate$PREM_ID[rebate$Source == "2019"])) # count participants at the start

# trmt counts prior to any restrictions (use at the end to get total program savings)
trmt_by_year <- rebate %>%
dplyr::group_by(Install.Year) %>%
dplyr::summarize(n.cust = n_distinct(PREM_ID)) %>% as.data.frame()
write.csv(trmt_by_year, paste(results, "trmt_by_program_year.csv", sep = "/"))
rm(trmt_by_year)
rebate$Install.Year <- NULL

# check for customers with multiple addresses for same account number (will remove)
multi.address <- rebate %>%
dplyr::group_by(PREM_ID) %>%
dplyr::summarise(n.address = n_distinct(Address)) %>%
dplyr::filter(n.address>1) %>%
as.data.frame
# none found
table(multi.address$n.address)

# remove customers with multiple addresses
rebate <- rebate[!(rebate$PREM_ID %in% multi.address$PREM_ID),]
trmt_counts <- rbind(trmt_counts, data.frame(Detail="Remove for multiple
addresses",PY18Count=n_distinct(rebate$PREM_ID[rebate$Source == "2017-2018"]), PY19Count =
n_distinct(rebate$PREM_ID[rebate$Source == "2019"]))) # count participants

# check for duplicates
sum(duplicated(rebate))
# check for multiple rows for a given measure
dup.installs <- rebate %>%
dplyr::group_by(PREM_ID) %>%
dplyr::mutate(ndups = n_distinct(Install.Dt)) %>%
dplyr::filter(ndups >1) %>%
dplyr::slice(1) %>%
as.data.frame
# 7 customers with multiple install dates. drop them.
n_distinct(dup.installs$PREM_ID)
rebate <- rebate[!(rebate$PREM_ID %in% dup.installs$PREM_ID),]
trmt_counts <- rbind(trmt_counts, data.frame(Detail="Remove for multiple rebate
premises",PY18Count=n_distinct(rebate$PREM_ID[rebate$Source == "2017-2018"]), PY19Count =
n_distinct(rebate$PREM_ID[rebate$Source == "2019"]))) # count participants

# some customers have multiple rebate IDs. remove.
mult.rebate <- rebate %>%
dplyr::group_by(PREM_ID) %>%
dplyr::summarise(n=n_distinct(Rebate_ID)) %>%
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Appendix C: Impact Evaluation Scripts 85

dplyr::filter(n>1) %>%
dplyr::select(PREM_ID)

rebate <- rebate %>% dplyr::filter(!PREM_ID %in% mult.rebate$PREM_ID)
table(rebate$Source)
# check data is unique by customer
isid(idvars = c("PREM_ID"), d = rebate)

trmt_counts <- rbind(trmt_counts, data.frame(Detail="Remove homes with multiple furnace
rebates",PY18Count=n_distinct(rebate$PREM_ID[rebate$Source == "2017-2018"]), PY19Count =
n_distinct(rebate$PREM_ID[rebate$Source == "2019"]))) # count participants

# missing install dates (0 occurences)
sum(is.na(rebate$Install.Dt))
save(rebate, file = paste(inter, "rebate.RData", sep = "/"))
str(rebate)
rm(dup.installs,multi.address,track)

# Prep Non-Participant Billing Data
load(paste(inter, "non_pct.RData", sep = "/"))
load(paste(inter, "site.RData", sep = "/"))
non_pct$treatment <- 0

# merge corrected bills
str(non_pct)
non_pct <- non_pct %>%
dplyr::group_by(PREM_ID, ACCT_ID, BSEG_START_DT, BSEG_END_DT,treatment) %>%
dplyr::summarise(CONSUMPTION = sum(CONSUMPTION)) %>% as.data.frame()

nrow(unique(non_pct[, c("PREM_ID", "ACCT_ID")]))
length(unique(non_pct$PREM_ID))

str(site)
site <- site[,c("PREM_ID", "ADDRESS1_UPR","POSTAL", "CITY_UPR","ENTITY_NAME")]
names(site) <- c("PREM_ID", "Address","Zip","City","Name")

# get site info for non-participants
non_pct <- merge(non_pct,site,by="PREM_ID",all.x=T)
non_pct <- non_pct[,c("PREM_ID", "ACCT_ID",
"Name","Address","City","Zip","BSEG_START_DT","BSEG_END_DT","CONSUMPTION","treatment")]
rm(site)

# prep participant billing data
load(paste(inter, "pct.RData", sep = "/"))
str(pct)

names(pct)[names(pct)=="PREMISE_ID"] <- "PREM_ID"
pct$treatment <- 1

# merge corrected bills
pct <- pct %>%
dplyr::group_by(PREM_ID, ACCT_ID, BSEG_START_DT, BSEG_END_DT, treatment) %>%
dplyr::summarise(CONSUMPTION = sum(CONSUMPTION))
nrow(unique(pct[, c("PREM_ID", "ACCT_ID")]))
length(unique(pct$PREM_ID))

# check that rebated customers are not in non-participant billing data (did not occur)
sum(unique(pct$PREM_ID) %in% unique(non_pct$PREM_ID))
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Appendix C: Impact Evaluation Scripts 86

str(pct)

# get site info for participants and restrict bills to treatment customers
pct <- pct[,c("PREM_ID", "ACCT_ID", "BSEG_START_DT","BSEG_END_DT","CONSUMPTION","treatment")]
pct <- merge(pct,rebate,by="PREM_ID")
sum(is.na(pct$Install.Dt))
# all treatement customers have billing data
assert(n_distinct(pct$PREM_ID)==n_distinct(rebate$PREM_ID))

trmt_counts <- rbind(trmt_counts, data.frame(Detail="Missing Billing
Data",PY18Count=n_distinct(rebate$PREM_ID[rebate$Source == "2017-2018"]), PY19Count =
n_distinct(rebate$PREM_ID[rebate$Source == "2019"]))) # count participants
write.csv(trmt_counts, paste(results, "trmt_counts.csv", sep = "/"))
rm(trmt_counts)

sum(is.na(pct$Install.Type))
pct <- pct[,c("PREM_ID", "ACCT_ID",
"Name","Address","City","Zip","BSEG_START_DT","BSEG_END_DT","CONSUMPTION","treatment")]

# combine participant and non-participant billing data
mo_dta <- rbind(pct,non_pct)
mo_dta <- merge(mo_dta,rebate[,c("Source",
"PREM_ID","Install.Type","Install.Dt","Cap.Input","AFUE")],by="PREM_ID",all.x=T)

clean_counts <- data.frame(Detail="Starting Count",
Non_Pct_2017_2018 = n_distinct(mo_dta[mo_dta$treatment==0
,"PREM_ID"]),
Non_Pct_2019 = n_distinct(mo_dta[mo_dta$treatment==0
,"PREM_ID"]),
Pct_2017_2018 = n_distinct(mo_dta[mo_dta$treatment==1 &
mo_dta$Source == "2017-2018","PREM_ID"]),
Pct_2019 = n_distinct(mo_dta[mo_dta$treatment==1 &
mo_dta$Source == "2019","PREM_ID"]))

rm(pct,non_pct,rebate)
save(mo_dta, file = paste(inter, "mo_dta.RData", sep = "/"))

# prep combined billing data
load(paste(inter, "mo_dta.RData", sep = "/"))

mo_dta$Bill.St <- ymd(as.Date(mo_dta$BSEG_START_DT, origin="1899-12-30"))
mo_dta$Bill.End <- ymd(as.Date(mo_dta$BSEG_END_DT, origin="1899-12-30"))
mo_dta$Therms <- mo_dta$CONSUMPTION

mo_dta$BSEG_END_DT <- NULL
mo_dta$BSEG_START_DT <- NULL
mo_dta$CONSUMPTION <- NULL

# order by customer id and st dt
mo_dta <- mo_dta[order(mo_dta$PREM_ID, mo_dta$ACCT_ID, mo_dta$Bill.St), ]

# customers with empty billing info
sum(is.na(mo_dta$BillEnd) | is.na(mo_dta$Bill.St))
# drop customers with empty billing info
mo_dta <- mo_dta[!(is.na(mo_dta$Bill.End) | is.na(mo_dta$Bill.St)),]

clean_counts <- rbind(clean_counts,data.frame(Detail="Remove if billing dates are NA",
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Appendix C: Impact Evaluation Scripts 87

Non_Pct_2017_2018 =
n_distinct(mo_dta[mo_dta$treatment==0 ,"PREM_ID"]),
Non_Pct_2019 =
n_distinct(mo_dta[mo_dta$treatment==0 ,"PREM_ID"]),
Pct_2017_2018 =
n_distinct(mo_dta[mo_dta$treatment==1 & mo_dta$Source == "2017-2018","PREM_ID"]),
Pct_2019 =
n_distinct(mo_dta[mo_dta$treatment==1 & mo_dta$Source == "2019","PREM_ID"])))

# make sure no bills have bill.end less than bill.st
sum(mo_dta$Bill.End<mo_dta$Bill.St)

# estimate billed use during month
mo_dta$Billed.Use <- mo_dta$Therms

# calculate bill duration
mo_dta$Bill.Duration <- as.numeric(mo_dta$Bill.End - mo_dta$Bill.St + 1)

# billing midpoint
mo_dta$Midpoint <- mo_dta$Bill.St + (mo_dta$Bill.Duration/2)

# assign month for billing interval as billing midpoint
mo_dta$Month <- month(mo_dta$Midpoint)

# assign the year based on billing midpoint
mo_dta$Year <- year(mo_dta$Midpoint)

mo_dta$Yr_Mo <- paste(mo_dta$Year,mo_dta$Month,sep="_")

# average daily billed use for billing interval (for regression)
mo_dta$therms.day <- mo_dta$Billed.Use / mo_dta$Bill.Duration

# dummy for winter months
mo_dta$Winter <- ifelse(mo_dta$Month %in% c(11,12,1,2,3,4), 1, 0)
sum(is.na(mo_dta$PREM_ID))

str(mo_dta$Zip)
str(mo_dta$PREM_ID)

# adhoc fixing missing/incorrect customer Zip data (used google street/city)
mo_dta$Zip[mo_dta$PREM_ID==1711033471] <- "83607"
mo_dta$Zip[mo_dta$PREM_ID==7307503980] <- "83686"
mo_dta$Zip[mo_dta$PREM_ID== 2055203847] <- "83221"

# define 3 digit zip
mo_dta$zip_3d <- substr(mo_dta$Zip,1,3)
table(mo_dta$zip_3d)

# assign install date to control group
# control group's install date will be the average install date of the treatment group, if
they installed in the first 6 months of the PY
trmt <- mo_dta[mo_dta$treatment == 1,]
custs <- unique(trmt[, c("PREM_ID", "ACCT_ID", "treatment", "Source", "Install.Dt")])
install_dates <- data.frame(Source = c("2017-2018", "2019"),
treatment = c(0,0),
Install.Dt = c(mean(custs$Install.Dt[custs$Source == "2017-2018" &
year(custs$Install.Dt) == 2018 & month(custs$Install.Dt) %in% c(1:6)], na.rm = T),
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Appendix C: Impact Evaluation Scripts 88

mean(custs$Install.Dt[custs$Source == "2019" &
year(custs$Install.Dt) == 2019 & month(custs$Install.Dt) %in% c(1:6)], na.rm = T)),
stringsAsFactors = F)

ctrl_18 <- mo_dta[mo_dta$treatment == 0,]
ctrl_19 <- mo_dta[mo_dta$treatment == 0,]
ctrl_18$Source <- "2017-2018"
ctrl_19$Source <- "2019"
ctrl <- rbind(ctrl_18, ctrl_19) %>% dplyr::select(-Install.Dt)
ctrl <- merge(ctrl, install_dates, by = c("Source", "treatment"))
mo_dta <- rbind(trmt, ctrl)
rm(ctrl_18,ctrl_19,trmt,ctrl)

custs <- unique(mo_dta[, c("PREM_ID", "ACCT_ID", "treatment", "Source", "treatment")])
table(custs$Source, custs$treatment)

# remove accoutns that dont overlap with intervention date
cust_bills <- mo_dta %>%
dplyr::group_by(PREM_ID, ACCT_ID, treatment, Source, Install.Dt) %>%
dplyr::summarise(min_bill = min(Bill.St),
max_bill = max(Bill.End)) %>%
dplyr::mutate(install_cust = ifelse(min_bill <= Install.Dt & max_bill >= Install.Dt,1,0))
%>%
dplyr::filter(install_cust == 1) %>%
dplyr::select(PREM_ID, ACCT_ID, treatment, Source)
mo_dta <- merge(cust_bills, mo_dta, by = c("PREM_ID", "ACCT_ID", "treatment", "Source"), all.x
= T)

custs <- unique(mo_dta[, c("PREM_ID", "ACCT_ID", "treatment", "Source", "treatment")])
table(custs$Source, custs$treatment)

clean_counts <- rbind(clean_counts,data.frame(Detail="Keep only account numbers that overlaps
with install date", # CRJ also removed control accounts
Non_Pct_2017_2018 =
n_distinct(mo_dta[mo_dta$treatment==0 & mo_dta$Source == "2017-2018","PREM_ID"]),
Non_Pct_2019 =
n_distinct(mo_dta[mo_dta$treatment==0 & mo_dta$Source == "2019","PREM_ID"]),
Pct_2017_2018 =
n_distinct(mo_dta[mo_dta$treatment==1 & mo_dta$Source == "2017-2018","PREM_ID"]),
Pct_2019 =
n_distinct(mo_dta[mo_dta$treatment==1 & mo_dta$Source == "2019","PREM_ID"])))
clean_counts

# clean pre- and post-periods
# define install year
mo_dta$Install.Year <- year(mo_dta$Install.Dt)

# check distribution of billing data by trmt and year_mo
table(mo_dta$treatment,mo_dta$Yr_Mo, mo_dta$Source)

#check how many customers had trmt in first half of the year
mo_dta$first.half.yr <- ifelse(month(mo_dta$Install.Dt) %in% c(1,2,3,4,5,6), 1,0)
uniq_trmt <- unique(mo_dta[mo_dta$treatment==1 &
mo_dta$first.half.yr==1,c("PREM_ID","Install.Year", "treatment", "Source")])
table(uniq_trmt$Install.Year, uniq_trmt$Source)

# summarize customers by install year
x <- unique(mo_dta[, c("PREM_ID", "Install.Year", "Source", "treatment")])
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Appendix C: Impact Evaluation Scripts 89

table(x$Install.Year, x$treatment, x$Source)

# define post and pre period
mo_dta$pre <- ifelse(mo_dta$Bill.End < mo_dta$Install.Dt,1,0)
mo_dta$post <- ifelse(mo_dta$Bill.St > mo_dta$Install.Dt,1,0)
mo_dta$Install.Year <- year(mo_dta$Install.Dt)

#calculate Baseload average for each customer to compute Avg daily heating load
# number of customers before restrictions
n_distinct(mo_dta$PREM_ID)
mo_dta <- mo_dta %>%
dplyr::group_by(PREM_ID) %>%
dplyr::mutate(Baseload.Avg = mean(ifelse(Month %in% c(6,7,8), therms.day, NA), na.rm =
TRUE)) %>%
as.data.frame()
# 187 customers missing summer months
n_distinct(mo_dta[is.na(mo_dta$Baseload.Avg),"PREM_ID"])

# average daily heating load
mo_dta$Heatload.Avg <- pmax(0, mo_dta$therms.day - mo_dta$Baseload.Avg)
save(mo_dta, file = paste(inter, "mo_dta.RData", sep = "/"))

# check for and remove problem obs
load(paste(inter, "mo_dta.RData", sep = "/"))
summary(mo_dta[, c("Month", "Bill.Duration", "therms.day", "Winter", "Bill.St",
"Bill.End","Midpoint")]) # summarise numeric/date variables
mo_dta <- mo_dta[mo_dta$Bill.Duration >= 9 & mo_dta$Bill.Duration <= 60, ] # remove if
original billing duration too short/too long

clean_counts <- rbind(clean_counts,data.frame(Detail="Bill Duration Outliers (<9 or >60 days",
Non_Pct_2017_2018 =
n_distinct(mo_dta[mo_dta$treatment==0 & mo_dta$Source == "2017-2018","PREM_ID"]),
Non_Pct_2019 =
n_distinct(mo_dta[mo_dta$treatment==0 & mo_dta$Source == "2019","PREM_ID"]),
Pct_2017_2018 =
n_distinct(mo_dta[mo_dta$treatment==1 & mo_dta$Source == "2017-2018","PREM_ID"]),
Pct_2019 =
n_distinct(mo_dta[mo_dta$treatment==1 & mo_dta$Source == "2019","PREM_ID"])))

nrow(mo_dta)
n_distinct(mo_dta$PREM_ID)
# remove outliers in terms of therms usage (none)
mo_dta <- mo_dta[mo_dta$therms.day < 200,]
# remove bills occuring prior to this date (no weather data available from degreedays); did
not occur
mo_dta <- mo_dta[mo_dta$Bill.St >= as.Date(st_dt_cutoff),]
n_distinct(mo_dta$PREM_ID)

# customer restrictions
test <- mo_dta %>%
group_by(PREM_ID, ACCT_ID, Source, treatment) %>%
summarise(n_bills_pre = sum(pre),
n_bills_post = sum(post),
avg.therms = mean(Billed.Use))
table(test$n_bills_pre, test$treatment, test$Source)
table(test$n_bills_post, test$treatment, test$Source)

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Appendix C: Impact Evaluation Scripts 90

x <- unique(test[test$Source == "2017-2018", c("PREM_ID", "ACCT_ID", "treatment",
"n_bills_pre", "n_bills_post")])
x <- x %>% dplyr::group_by(n_bills_pre, n_bills_post, treatment) %>% summarise(n=n()) %>%
dplyr::filter(n_bills_pre > 6 & n_bills_post > 3)

# require avg.therms is positive and more than 12 bills
mo_dta <- mo_dta %>%
group_by(PREM_ID, ACCT_ID, Source, treatment) %>%
mutate(n_post_restriction = ifelse(Source == "2017-2018",9,6),
n_bills_pre = sum(pre),
n_bills_post = sum(post),
avg.therms = mean(Billed.Use),
keep = (avg.therms > 0 & n_bills_pre >= 12 & n_bills_post >= n_post_restriction)) %>%
as.data.frame

sapply(mo_dta[, c("n_bills_pre","avg.therms")], quantile, na.rm=TRUE, probs = seq(from = 0, to
= 1, by = 0.025))
sapply(mo_dta[, c("n_bills_post","avg.therms")], quantile, na.rm=TRUE, probs = seq(from = 0,
to = 1, by = 0.025))
# percent of customers to be dropped
n_distinct(mo_dta[mo_dta$keep==0,"PREM_ID"])/n_distinct(mo_dta$PREM_ID)*100

mo_dta$keep <- as.numeric(mo_dta$keep)
sumry <- unique(mo_dta[,c("PREM_ID","keep","treatment")])
sumry <- table(sumry$treatment,sumry$keep)
missing_bills <- data.frame(treatment = c(0,1),drops = c(sumry[1],sumry[2]), keep =
c(sumry[3],sumry[4]))
# percent of control customers with insufficient data
sumry[1]/(sumry[1]+sumry[3])*100
# percent of treatment customers with insufficient data
sumry[2]/(sumry[2]+sumry[4])*100
rm(sumry)

# remove customers with insufficient data
mo_dta <- mo_dta[mo_dta$keep==1,]
mo_dta$keep <- NULL

clean_counts <- rbind(clean_counts,data.frame(Detail="Insufficient bill data (<12 months of
bills)",
Non_Pct_2017_2018 =
n_distinct(mo_dta[mo_dta$treatment==0 & mo_dta$Source == "2017-2018","PREM_ID"]),
Non_Pct_2019 =
n_distinct(mo_dta[mo_dta$treatment==0 & mo_dta$Source == "2019","PREM_ID"]),
Pct_2017_2018 =
n_distinct(mo_dta[mo_dta$treatment==1 & mo_dta$Source == "2017-2018","PREM_ID"]),
Pct_2019 =
n_distinct(mo_dta[mo_dta$treatment==1 & mo_dta$Source == "2019","PREM_ID"])))
clean_counts

# Still premises with more than one account? only keep the one with the most data
keep <- mo_dta %>% dplyr::group_by(PREM_ID, treatment, Source) %>% dplyr::summarise(n =
n_distinct(ACCT_ID)) %>% filter(n>1)
keep <- mo_dta %>% dplyr::group_by(PREM_ID, treatment, Source) %>% mutate(n_months = n()) %>%
arrange(desc(n_months)) %>% slice(1) %>% select(PREM_ID, ACCT_ID, treatment, Source)
mo_dta <- merge(keep, mo_dta, by = c("PREM_ID", "ACCT_ID", "treatment", "Source"))

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Appendix C: Impact Evaluation Scripts 91

clean_counts <- rbind(clean_counts,data.frame(Detail="Keep acct with most bill data from a
premise",
Non_Pct_2017_2018 =
n_distinct(mo_dta[mo_dta$treatment==0 & mo_dta$Source == "2017-2018","PREM_ID"]),
Non_Pct_2019 =
n_distinct(mo_dta[mo_dta$treatment==0 & mo_dta$Source == "2019","PREM_ID"]),
Pct_2017_2018 =
n_distinct(mo_dta[mo_dta$treatment==1 & mo_dta$Source == "2017-2018","PREM_ID"]),
Pct_2019 =
n_distinct(mo_dta[mo_dta$treatment==1 & mo_dta$Source == "2019","PREM_ID"])))
clean_counts
# data is now unique by premise ID

# get weather
load(paste(inter, "noaa.RData", sep = "/"))
str(noaa)
str(mo_dta$zip_3d)

table(mo_dta$zip)
mo_dta$USAF <- ""
mo_dta$USAF[mo_dta$zip_3d %in% c("836","837")] <- "726810"
mo_dta$USAF[mo_dta$zip_3d %in% c("833")] <- "725866"
mo_dta$USAF[mo_dta$zip_3d %in% c("832")] <- "725780"
mo_dta$USAF[mo_dta$zip_3d %in% c("834")] <- "725785"
table(mo_dta$USAF)

unique(noaa$USAF)
unique(mo_dta$USAF)

# get the unique combination of billing intervals
uniq_bills <- unique(mo_dta[c("Bill.St", "Bill.End","USAF")])
nrow(uniq_bills)

# source file that gets weather for billing intervals
source(paste(sub_dir, "weather_for_interval.R", sep = "/"), echo = TRUE)
# get weather for each unique billing interval
weather <- lapply(split(uniq_bills, 1:nrow(uniq_bills)),
FUN = weather_for_interval, noaa_dta = noaa
)
# convert from list to dataframe
weather <- do.call("rbind", weather)
head(weather)

n_distinct(mo_dta$PREM_ID)
mo_dta <- merge(mo_dta, weather, all.x = TRUE)
mo_dta <- mo_dta[order(mo_dta$PREM_ID, mo_dta$Bill.St), ]
n_distinct(mo_dta$PREM_ID)

# ensure no missing or NAN values for weather data
assert(is.finite(sum(mo_dta$HDD.55)))

mo_dta$HDD.day <- mo_dta$HDD.63 / mo_dta$Bill.Duration # use 63 for the setpoint for HDD
(from optimal testing)
save(mo_dta, file = paste(inter, "mo_dta.RData", sep = "/"))

#check weather is correct (adhoc)
sum(noaa[noaa$Date>=weather[1,1] & noaa$Date <= weather[1,2] & noaa$USAF
=="726810","HDD.56"])==weather$HDD.56[1]
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Appendix C: Impact Evaluation Scripts 92

rm(noaa,weather,uniq_bills)

# PSM only (method 1) restrictions
psm_dta <- mo_dta

# only keep treatment customers that installed their furnace in the first 6 months of the year
trmt <- psm_dta %>%
dplyr::mutate(inst_in_py = ifelse(Source == "2017-2018" & Install.Year ==
2018,1,ifelse(Source == "2019" & Install.Year == 2019,1,0))) %>%
dplyr::filter(treatment == 1 & month(Install.Dt) %in% 1:6 & inst_in_py == 1) %>%
dplyr::select(-inst_in_py)

ctrl <- psm_dta %>% dplyr::filter(treatment == 0)

psm_dta <- rbind(trmt, ctrl)
x <- unique(psm_dta[, c("PREM_ID", "treatment", "Source", "Install.Year")])
table(x$Install.Year, x$Source)

clean_counts <- rbind(clean_counts,data.frame(Detail="Keep only trmt accounts with installs in
first 6 months of PY",
Non_Pct_2017_2018 =
n_distinct(psm_dta[psm_dta$treatment==0 & psm_dta$Source == "2017-2018","PREM_ID"]),
Non_Pct_2019 =
n_distinct(psm_dta[psm_dta$treatment==0 & psm_dta$Source == "2019","PREM_ID"]),
Pct_2017_2018 =
n_distinct(psm_dta[psm_dta$treatment==1 & psm_dta$Source == "2017-2018","PREM_ID"]),
Pct_2019 =
n_distinct(psm_dta[psm_dta$treatment==1 & psm_dta$Source == "2019","PREM_ID"])))

# restrict to pre period before program year and post period starting on July 1st of program
year (alternate definition)
psm_dta <- psm_dta %>% mutate(PY = ifelse(Source == "2017-2018", 2018,2019)) %>%
dplyr::filter(Midpoint < as.Date(paste0(as.character(PY),"-01-01")) | Midpoint >=
as.Date(paste0(as.character(PY),"-07-01")))
table(year(psm_dta$Midpoint), psm_dta$pre, psm_dta$PY)

# restrict to pre period only in year before install (year install -1)
psm_dta <- psm_dta %>% dplyr::filter((pre == 1 & year(Midpoint) == PY-1) | post == 1)
clean_counts <- rbind(clean_counts,data.frame(Detail="Keep only pre and post period data.
Post-period starts the July of the PY",
Non_Pct_2017_2018 =
n_distinct(psm_dta[psm_dta$treatment==0 & psm_dta$Source == "2017-2018","PREM_ID"]),
Non_Pct_2019 =
n_distinct(psm_dta[psm_dta$treatment==0 & psm_dta$Source == "2019","PREM_ID"]),
Pct_2017_2018 =
n_distinct(psm_dta[psm_dta$treatment==1 & psm_dta$Source == "2017-2018","PREM_ID"]),
Pct_2019 =
n_distinct(psm_dta[psm_dta$treatment==1 & psm_dta$Source == "2019","PREM_ID"])))

save(psm_dta, file = paste(inter, "psm_dta.RData", sep = "/"))
rm(psm_dta,trmt,ctrl)

write.csv(missing_bills, file = paste(results,"customers_missing_full_year_bills.csv", sep =
"/"))
write.csv(clean_counts, file = paste(results, "clean_counts.csv", sep = "/"))
save(missing_bills, file = paste(results, "missing_bills.RData", sep = "/"))
save(mo_dta, file = paste(inter, "mo_dta.RData", sep = "/"))
rm(missing_bills,mo_dta)
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Appendix C: Impact Evaluation Scripts 93

rm(mult.rebate,clean_counts,custs,install_dates,keep,not_found,test,uniq_trmt,x)

# prepare for propensity score matching
py <- c(2018, 2019)
py_prog <- c("2017-2018", "2019")

load(paste(inter, "psm_dta.RData", sep = "/"))
mo_dta <- psm_dta

for (i in 1:2) {

comb <- mo_dta[mo_dta$Source == py_prog[i],]

# restrict to the pre period for PSM
score_dta <- comb[comb$pre==1,]
# count how many customers are left with pre period data
counts <- score_dta %>%
dplyr::group_by(treatment,Install.Year) %>%
dplyr::summarize(n.cust = n_distinct(PREM_ID)) %>% as.data.frame()
counts
write.csv(counts, file = paste(results,
paste0(py[i],"Met_enrollment_and_pre_period_requirements.csv"), sep = "/"))
rm(counts)

score_dta <- score_dta %>% dplyr::mutate(zip_5 = as.factor(substr(Zip,1,5)))

# calculate average usage in pre period
score_dta <- score_dta %>%
dplyr::group_by(PREM_ID, ACCT_ID, treatment, Source, zip_5) %>%
dplyr::summarise(pre.fall.therms = mean(therms.day[month(Midpoint) %in% c(9,10,11)], na.rm
= T),
pre.winter.therms = mean(therms.day[month(Midpoint) %in% c(12,1,2)],
na.rm = T),
pre.spring.therms = mean(therms.day[month(Midpoint) %in% c(3,4,5)], na.rm
= T),
pre.summer.therms = mean(therms.day[month(Midpoint) %in% c(6,7,8)], na.rm
= T))

# check how many customers have complete pre-period data
score_dta$is.complete <- as.numeric(!is.na(rowSums(score_dta[,grepl("therms",
colnames(score_dta))])))
pre_period_completes <- score_dta %>%
dplyr::group_by(treatment) %>%
dplyr::summarize(nobs = n(),
completes = sum(is.complete),
pct.complete = completes/nobs*100) %>%
as.data.frame()
pre_period_completes
save(pre_period_completes, file = paste(inter, paste0(py[i],".pre_period_completes.RData"),
sep = "/"))
write.csv(pre_period_completes, file = paste(results,
paste0(py[i],".pre_period_completes.csv"), sep = "/"))
rm(pre_period_completes)

# remove customers with incomplete pre-period data
score_dta$prog.year <- py[i]
score_dta <- score_dta[score_dta$is.complete==1,]

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Appendix C: Impact Evaluation Scripts 94

# plot densities before matching
score_dta <- score_dta %>% dplyr::mutate(Group = ifelse(treatment == 1, "Treatment",
"Control"))
str(score_dta)
score_dta$Group <- as.factor(score_dta$Group)
pre.fall <- ggplot(score_dta, aes(x=pre.fall.therms, color=Group,fill=Group ))
+geom_density(alpha=0.2)+scale_fill_manual(values = c("#0574b0", "#92c5de"))
+scale_colour_manual(values = c("#0574b0", "#92c5de")) + ylab("Density") + xlab("Pre-Period
Winter Therms/Day") + ylim(0,0.6) + xlim(0,20)
pre.winter <- ggplot(score_dta, aes(x=pre.winter.therms, color=Group,fill=Group ))
+geom_density(alpha=0.2)+scale_fill_manual(values = c("#0574b0", "#92c5de"))
+scale_colour_manual(values = c("#0574b0", "#92c5de")) + ylab("Density") + xlab("Pre-Period
Spring Therms/Day") + ylim(0,0.4) + xlim(0,45)
pre.spring <- ggplot(score_dta, aes(x=pre.spring.therms, color=Group,fill=Group ))
+geom_density(alpha=0.2)+scale_fill_manual(values = c("#0574b0", "#92c5de"))
+scale_colour_manual(values = c("#0574b0", "#92c5de")) + ylab("Density") + xlab("Pre-Period
Summer Therms/Day") + ylim(0,0.6) + xlim(0,25)
pre.summer <- ggplot(score_dta, aes(x=pre.summer.therms, color=Group,fill=Group ))
+geom_density(alpha=0.2)+scale_fill_manual(values = c("#0574b0", "#92c5de"))
+scale_colour_manual(values = c("#0574b0", "#92c5de")) + ylab("Density") + xlab("Pre-Period
Fall Therms/Day") + ylim(0,1.75) + xlim(0,20)
zip <- ggplot(score_dta, aes(x=as.factor(zip_5), color=Group, fill = Group)) +
geom_bar(aes(y = (..count..)/sum(..count..)), position=position_dodge())
+scale_fill_manual(values = c("#0574b0", "#92c5de")) +scale_colour_manual(values =
c("#0574b0", "#92c5de"))+
xlab("Zip Code") + ylab("Density") +theme(axis.text.x = element_text(angle = 90, hjust =
1)) + ylim(0,0.06)

ggsave(paste(results, paste0("zip_before_matching_", py[i], ".png"), sep = "/"),zip)

# write to png file
plot <- plot_grid(pre.winter,pre.spring,pre.summer,pre.fall,labels = "AUTO")
ggsave(paste(results, paste0("plot_grid_before_matching_", py[i], ".png"), sep = "/"),plot)
rm(pre.fall,pre.winter,pre.spring,pre.summer,plot)

save(score_dta, file = paste(inter, paste0(py[i],".score_dta.RData"), sep = "/"))
rm(score_dta,comb)

}

set.seed(101)
py <- c(2018, 2019)
py_prog <- c("2017-2018", "2019")

for(i in 1:2) {
load(paste(inter, paste0(py[i],".score_dta.RData"), sep = "/"))
dta <- score_dta

# propensity score matching
# define matching method
psm_ratio <- 2
psm_method <- "nearest"

dta <- dta %>% as.data.frame()

# match
dta <- ungroup(dta)
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nearest <- matchit(treatment ~ pre.winter.therms + pre.spring.therms + pre.summer.therms
+ pre.fall.therms,
data = dta,
method = psm_method,
ratio = psm_ratio,
replace = FALSE)

# summary
a <- summary(nearest)

# save
psm_ratio <- data.frame(".PSM Matching Ratio" = psm_ratio)
psm_method <- data.frame(".PSM Matching Method" = psm_method)
save(psm_ratio, file = paste(inter, paste0(py[i], ".psm_ratio.RData"), sep = "/"))
save(psm_method, file = paste(inter, paste0(py[i],".psm_method.RData"), sep = "/"))

# save matched output
df.match <- match.data(nearest)
table(df.match$treatment)

# extract matched account numbers
matches <- data.frame(nearest$match.matrix)
str(matches)

# save matched df
save(df.match, file = paste(inter, paste0(py[i],".df.match.RData"), sep = "/"))

# compute balance after matching
treated <- (df.match$treatment==1)
df.match$zip_num <- as.numeric(as.character(df.match$zip_5))
cov1 <- df.match[,c(colnames(df.match)[grepl("therms", colnames(df.match))], "zip_num")] #
restrict to propensity covariates
std.diff <- apply(cov1,2,function(x) 100*(mean(x[treated])-
mean(x[!treated]))/(sqrt(0.5*(var(x[treated]) + var(x[!treated]))))) # computes standardized
difference
std.diff <- abs(std.diff) # should be less than 25
write.csv(std.diff, file = paste(results, paste0(py[i],".standardized_difference.csv"), sep
= "/"))
rm(treated,cov1,std.diff)

df.match$treatment_num <- as.integer(df.match$treatment)
chi.test <- xBalance(treatment_num ~ pre.fall.therms + pre.winter.therms + pre.spring.therms
+ pre.summer.therms, data=df.match, report = c("chisquare.test"))
chi.output <- capture.output(chi.test) # if p-value is < 0.05, then imbalance occurs in one
of the covariates
chi.output
write.csv(chi.output, file = paste(results, paste0(py[i],".chi_square_test.csv"), sep =
"/"))
df.match$treatment <- as.factor(df.match$treatment)
rm(chi.output,chi.test)

# output summaries
a$nn
a$sum.all[c(1,2,3,4)]
a$sum.matched[c(1,2,3,4)]

# print stats
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Appendix C: Impact Evaluation Scripts 96

table_before <- data.frame(a$sum.all)
table_after <- data.frame(a$sum.matched)

# save output summaries and stats
match_summary <- a$nn

save(match_summary, file = paste(inter, paste0(py[i],".summary_nn.RData"), sep = "/"))
save(table_before, file = paste(inter, paste0(py[i],".summary_all.RData"), sep = "/"))
save(table_after, file = paste(inter, paste0(py[i],".summary_matched.RData"), sep = "/"))

write.csv(match_summary, file = paste(results, paste0(py[i],".summary_nn.csv"), sep = "/"))
write.csv(table_before, file = paste(results, paste0(py[i],".summary_all.csv"), sep = "/"))
write.csv(table_after, file = paste(results, paste0(py[i],".summary_matched.csv"), sep =
"/"))

df.match <- df.match %>% dplyr::mutate(Group = ifelse(treatment == 1, "Treatment",
"Control"))

str(df.match)
df.match$Group <- as.factor(df.match$Group)
pre.fall <- ggplot(df.match, aes(x=pre.fall.therms, color=Group,fill=Group ))
+geom_density(alpha=0.2)+scale_fill_manual(values = c("#0574b0", "#92c5de"))
+scale_colour_manual(values = c("#0574b0", "#92c5de")) + ylab("Density") + xlab("Pre-Period
Winter Therms/Day") + ylim(0,0.6) + xlim(0,20)
pre.winter <- ggplot(df.match, aes(x=pre.winter.therms, color=Group,fill=Group ))
+geom_density(alpha=0.2)+scale_fill_manual(values = c("#0574b0", "#92c5de"))
+scale_colour_manual(values = c("#0574b0", "#92c5de")) + ylab("Density") + xlab("Pre-Period
Spring Therms/Day") + ylim(0,0.4) + xlim(0,45)
pre.spring <- ggplot(df.match, aes(x=pre.spring.therms, color=Group,fill=Group ))
+geom_density(alpha=0.2)+scale_fill_manual(values = c("#0574b0", "#92c5de"))
+scale_colour_manual(values = c("#0574b0", "#92c5de")) + ylab("Density") + xlab("Pre-Period
Summer Therms/Day") + ylim(0,0.6) + xlim(0,25)
pre.summer <- ggplot(df.match, aes(x=pre.summer.therms, color=Group,fill=Group ))
+geom_density(alpha=0.2)+scale_fill_manual(values = c("#0574b0", "#92c5de"))
+scale_colour_manual(values = c("#0574b0", "#92c5de")) + ylab("Density") + xlab("Pre-Period
Fall Therms/Day") + ylim(0,1.75) + xlim(0,10)
zip <- ggplot(df.match, aes(x=as.factor(zip_5), color=Group, fill = Group)) +
geom_bar(aes(y = (..count..)/sum(..count..)), position=position_dodge())
+scale_fill_manual(values = c("#0574b0", "#92c5de")) +scale_colour_manual(values =
c("#0574b0", "#92c5de"))+
xlab("Zip Code") + ylab("Density") +theme(axis.text.x = element_text(angle = 90, hjust =
1)) + ylim(0,0.06)

ggsave(paste(results, paste0("zip_after_matching_", py[i], ".png"), sep = "/"),zip)

# save plots
plot <- plot_grid(pre.winter,pre.spring,pre.summer,pre.fall,labels = "AUTO")
ggsave(paste(results, paste0("plot_grid_after_matching_", py[i], ".png"), sep = "/"),plot)
rm(pre.fall,pre.winter,pre.spring,pre.summer,plot)
}

py <- c(2018, 2019)
py_prog <- c("2017-2018", "2019")

load(paste(inter, "psm_dta.RData", sep = "/"))
mo_dta <- psm_dta

# combine program year regression and matched data
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Appendix C: Impact Evaluation Scripts 97

reg_dta <- data.frame()

for(i in 1:2) {

# monthly billing data
comb <- mo_dta[mo_dta$Source == py_prog[i],]
# customer level matched
load(paste(inter, paste0(py[i],".df.match.RData"), sep = "/"))
n_distinct(df.match$PREM_ID)
# keep matched customers
comb <- merge(df.match,comb, by=c("PREM_ID", "ACCT_ID", "treatment", "Source"))
n_distinct(comb$PREM_ID)
reg_dta <- rbind(reg_dta,as.data.frame(comb))
}
rm(comb,df.match)
head(reg_dta)
table(reg_dta$prog.year)

reg_dta <- reg_dta %>% select(-zip_5, -Group)

# get weather
load(paste(inter, "noaa.RData", sep = "/"))
str(noaa)
str(reg_dta$zip_3d)

table(reg_dta$zip)
reg_dta$USAF <- ""
reg_dta$USAF[reg_dta$zip_3d %in% c("836","837")] <- "726810"
reg_dta$USAF[reg_dta$zip_3d %in% c("833")] <- "725866"
reg_dta$USAF[reg_dta$zip_3d %in% c("832")] <- "725780"
reg_dta$USAF[reg_dta$zip_3d %in% c("834")] <- "725785"
table(reg_dta$USAF)

unique(noaa$USAF)
unique(reg_dta$USAF)

# get the unique combination of billing intervals
uniq_bills <- unique(reg_dta[c("Bill.St", "Bill.End","USAF")])
nrow(uniq_bills)

# get weather for each unique billing interval
weather <- lapply(split(uniq_bills, 1:nrow(uniq_bills)),
FUN = weather_for_interval, noaa_dta = noaa
)
# convert from list to dataframe
weather <- do.call("rbind", weather)

reg_dta <- merge(reg_dta, weather, all.x = TRUE)
reg_dta <- reg_dta[order(reg_dta$PREM_ID, reg_dta$Bill.St), ]

# ensure no missing or NAN values for weather data
assert(is.finite(sum(reg_dta$HDD.55)))

# use 63 for the setpoint for HDD (from optimal testing)
reg_dta$HDD.day <- reg_dta$HDD.63 / reg_dta$Bill.Duration
save(reg_dta, file = paste(inter, "reg_dta.RData", sep = "/"))

# check weather is correct (adhoc)
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Appendix C: Impact Evaluation Scripts 98

sum(noaa[noaa$Date>=weather[1,1] & noaa$Date <= weather[1,2] & noaa$USAF
=="726810","HDD.56"])==weather$HDD.56[1]
rm(noaa,weather,uniq_bills)

# TMY by weather station (HDD setpoint=63 from optimal testing)
TMY <-rbind(data.frame(USAF = "725780",HDD.TMY=6838.6),
data.frame(USAF = "726810",HDD.TMY=5188.1),
data.frame(USAF = "725866",HDD.TMY=5912.6),
data.frame(USAF = "725785",HDD.TMY=7588.5))

# run regression
for(i in 1:2) {

load(paste(inter, "reg_dta.RData", sep = "/"))

#restrict to program year
reg_dta <- reg_dta[reg_dta$prog.year==py[i],]

#run regression with matched PS group (method 1)
model <- lm(therms.day ~ post + treatment + HDD.day + post*HDD.day +
post*HDD.day*treatment+as.factor(PREM_ID), data = reg_dta)
summary(model)
model_output <- capture.output(summary(model))
coeff <- as.data.frame(summary(model)$coeff)
coeff <- coeff[!grepl("PREM_ID", rownames(coeff)),]

write.csv(coeff, paste(results, paste0(py[i], ".coeff.csv"), sep = "/"))
write.csv(model_output, paste(results, paste0(py[i], ".model_output.csv"), sep = "/"))
rm(coeff,model,reg_dta)

}
rm(TMY)

7.2 Whole Home Rebate Script

# install and load required packages for analysis
install.packages("easypackages")
library(easypackages)
packages("readxl", "dplyr", "lubridate", "zoo", "ggplot2", "cowplot", "MatchIt", "stringr",
"data.table", "RItools", "broom")

# parameters
setpoint <- 63

# functions
# tracks treatment customers
tracking_trmt <- function(string, count18, count19) {
tracking_dta <- data.frame(Detail = string,
`PY2018 Count` = count18,
`PY2019 Count` = count19, stringsAsFactors = F)
return(tracking_dta)
}

# tracks control customers
tracking_ctrl <- function(string, count) {
tracking_dta <- data.frame(Detail = string,
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Appendix C: Impact Evaluation Scripts 99

`Ctrl Count` = count, stringsAsFactors = F)
return(tracking_dta)
}

# tracks treatment and control customers
tracking <- function(string, count18, count19, countctrl18, countctrl19) {
tracking_dta <- data.frame(Detail = string,
"2017-2018 Trmt Count" = count18,
"2019 Trmt Count" = count19,
"2017-2018 Ctrl Count" = countctrl18,
"2019 Ctrl Count" = countctrl19, stringsAsFactors = F)
return(tracking_dta)
}

# calendarization functions
func_fd <- function(x) {
return(ymd(paste(year(x),
month(x),
01)))
}

func_month_between <- function(end_date, start_date) {
dif_year <- year(end_date) - year(start_date)
dif_month <- month(end_date) - month(start_date)
12 * dif_year + dif_month
}

func_month_fd <- function(input.date, n.month) {
date <- as.character(input.date)
y <- as.integer(substr(date, 1, 4))
m <- as.integer(substr(date, 6, 7)) + n.month - 1
r <- m %% 12
q <- m %/% 12
y <- ifelse(r == 0, y + q - 1, y + q)
m <- ifelse(r == 0, 12, r)
new.date <- paste(y, m, 1, sep = "-")
ymd(new.date)
}

# set up program paths
vers <- "script"
wd <- getwd()
code_dir <- paste(wd, "Code", sep = "/")
raw_dir <- paste(wd, "Raw", sep = "/")
inter <- paste(wd, "Inter", vers, sep = "/")
results <- paste(wd, "Results", vers, sep = "/")

# create directories
suppressWarnings(dir.create(code_dir, recursive = T))
suppressWarnings(dir.create(raw_dir, recursive = T))
suppressWarnings(dir.create(inter, recursive = T))
suppressWarnings(dir.create(results, recursive=T))

# read in raw data files
# whole homes tracking data
fl_info <- "Whole Home Tracking Data Aggregate.xlsx"
tracking_raw <- read_xlsx(paste(raw_dir, fl_info, sep = "/"), sheet = 2, skip = 0, col_types =
"text") # NEED TO SPECIFY AS CHARACTER
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Appendix C: Impact Evaluation Scripts 100

save(tracking_raw, file = paste(inter, "tracking_raw.RData", sep = "/"))

# whole homes participant billing data ---
fl_info <- "IGC WHOLE HOME DATA.xlsx"
homes_participant_info_raw <- read_xlsx(paste(raw_dir, fl_info, sep = "/"), sheet = 3, skip
= 1)
homes_participant_billing_raw <- read_xlsx(paste(raw_dir, fl_info, sep = "/"), sheet = 4, skip
= 1)
save(homes_participant_info_raw, file = paste(inter, "homes_participant_info_raw.RData",
sep = "/"))
save(homes_participant_billing_raw, file = paste(inter, "homes_participant_billing_raw.RData",
sep = "/"))

# nonparticipant whole homes data
# this dataset includes households that received a whole home rebate or an appliance rebate,
as well as new homes that are nonparticipant.
# need to first filter out any participants
fl_info <- "IGC NEWLY BUILT DATA.xlsx"
all_info_raw <- read_xlsx(paste(raw_dir, fl_info, sep = "/"), sheet = 3, skip = 1)
all_billing_raw <- read_xlsx(paste(raw_dir, fl_info, sep = "/"), sheet = 4, skip = 1)
all_billing_housing_char <- read_xlsx(paste(raw_dir, fl_info, sep = "/"), sheet = 5, skip = 2)
save(all_info_raw, file = paste(inter, "all_info_raw.RData", sep = "/"))
save(all_billing_raw, file = paste(inter, "all_billing_raw.RData", sep = "/"))
save(all_billing_housing_char, file = paste(inter, "all_billing_housing_char.RData", sep =
"/"))

# appliance rebate tracking data
fl_info <- "All Appliances.xlsx"
rebate_tracking_raw <- read_xlsx(paste(raw_dir, fl_info, sep = "/"), sheet = 3, skip = 0,
col_types = "text") # NEED TO SPECIFY AS CHARACTER
save(rebate_tracking_raw, file = paste(inter, "rebate_tracking_raw.RData", sep = "/"))

# appliance rebate participant billing data
fl_info <- "IGC INDIVIDUAL REBATE BILLING DATA (1).xlsx"
rebate_participant_info_raw <- read_xlsx(paste(raw_dir, fl_info, sep = "/"), sheet = 3,
skip = 1)
rebate_participant_billing_raw <- read_xlsx(paste(raw_dir, fl_info, sep = "/"), sheet = 4,
skip = 1)
save(rebate_participant_info_raw, file = paste(inter, "rebate_participant_info_raw.RData",
sep = "/"))
save(rebate_participant_billing_raw, file = paste(inter,
"rebate_participant_billing_raw.RData", sep = "/"))

# HERS dataset
fl_info <- "ESRI_HERS.xlsx"
hers <- read_xlsx(paste(raw_dir, fl_info, sep = "/"), sheet = 1, skip = 0, col_types = "text")
# NEED TO SPECIFY AS CHARACTER
save(hers, file = paste(inter, "hers.RData", sep = "/"))

# load data ---
load(paste(inter, "homes_participant_info_raw.RData", sep = "/"))
load(paste(inter, "homes_participant_billing_raw.RData", sep = "/"))
load(paste(inter, "tracking_raw.RData", sep = "/"))
load(paste(inter, "all_billing_housing_char.RData", sep = "/"))
load(paste(inter, "all_info_raw.RData", sep = "/"))

# make sure all rebates are found in tracking data and vice versa
tracking_raw$`Rebate ID`[!tracking_raw$`Rebate ID` %in% homes_participant_info_raw$REBATE_ID]
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# tracking count start
part_tracking_dta <- tracking_trmt("Starting premises", nrow(tracking_raw[tracking_raw$Source
== "2017-2018",]), nrow(tracking_raw[tracking_raw$Source == "2019",]))

n_before <- length(unique(homes_participant_info_raw$PREMISE_ID))
homes_participant_info_raw$REBATE_ID[!homes_participant_info_raw$REBATE_ID %in%
tracking_raw$`Rebate ID`]
n_after <- length(unique(homes_participant_info_raw$PREMISE_ID))
if(n_before != n_after) {stop("MISSING REBATES")}

# participant data
# testing
# check if IGC_ACCOUNT_NO is the most recent customer's account number or the original rebate
account number
part_raw <- merge(homes_participant_billing_raw, homes_participant_info_raw, by =
c("PREMISE_ID"))
part_raw <- merge(part_raw, tracking_raw, by.x = "REBATE_ID", by.y = "Rebate ID")
part_raw_earliest <- part_raw %>%
dplyr::arrange(PREMISE_ID,BSEG_START_DT) %>%
dplyr::group_by(PREMISE_ID) %>%
slice(1)
# Note all original rebate account ids
orig_acct_ids <- part_raw$IGC_ACCOUNT_NO

# merge billing data with customer household info by premise ID
part <- merge(homes_participant_info_raw, homes_participant_billing_raw, by = c("PREMISE_ID"))

# merge billing data and customer household info with tracking data by rebate ID
part <- merge(part, unique(tracking_raw[, c("Source", "Rebate ID", "IGC Account No", "Site
Address", "HERS Score")]), by.x = "REBATE_ID", by.y = "Rebate ID")

# tracking
part_tracking_dta <- rbind(part_tracking_dta, c("Fully merged premises",
length(unique(part$PREMISE_ID[part$Source == "2017-2018"])),
length(unique(part$PREMISE_ID[part$Source == "2019"]))))

# make sure all participating homes have HERS scores (from builder tracking data and from ada
assessor data)
part %>% dplyr::select(PREMISE_ID, `HERS Score`, Source) %>% dplyr::distinct() %>%
dplyr::filter(!is.na(`HERS Score`)) %>% dplyr::group_by(Source) %>%
dplyr::summarise(mean_score = mean(as.numeric(`HERS Score`), na.rm = T), n = n())
part %>% dplyr::select(PREMISE_ID, HERS_SCORE, Source) %>% dplyr::distinct() %>%
dplyr::filter(!is.na(HERS_SCORE)) %>% dplyr::group_by(Source) %>% dplyr::summarise(mean_score
= mean(as.numeric(HERS_SCORE), na.rm = T), n = n())

# keep this dataset to do sold time analysis
sell_analysis_trmt <- part
sell_analysis_trmt$trmt <- 1
sell_analysis_trmt <- sell_analysis_trmt[, !grepl("^4", colnames(sell_analysis_trmt))]
save(sell_analysis_trmt, file = paste(inter, "sell_analysis_trmt.RData", sep = "/"))

# find and remove unoccupied homes (homes still owned by builders)
names <- part %>%
dplyr::select(ENTITY_NAME, SITE_ADDRESS) %>%
distinct() %>%
dplyr::group_by(ENTITY_NAME) %>%
dplyr::summarise(n=n())
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 110 of 329

Appendix C: Impact Evaluation Scripts 102

confirmed_builder_names <- c("Hubble Homes", "Brighton Homes", "Todd Campbell Construction",
"Brighton Living",
"Clyde Development", "Tresidio Corporation", "Berkeley Building
Company", "Eaglewood Homes Inc",
"Riverwood Homes", "Shadow Mountain Homes", "Avimor Partners
Corp", "Gardner Homes Idaho, LLC",
"Titan Homes LLC", "Arden Lynn Aker Trust", "Brandt Agency",
"Foothill Construction",
"Hale Developement", "SDH Construction", "St Marks Church",
"Sustainable Homes LLC", "Vega Holdings V, LLC",
"Douglass, Patrick")

# remove all original rebate account ids (these are the builders)
part <- part[part$ACCT_ID != part$IGC_ACCOUNT_NO,]
# remove all remaining builder names found
part <- part[!part$ENTITY_NAME %in% confirmed_builder_names,]
# make sure there are no builder IDS
part <- part[!part$ACCT_ID %in% orig_acct_ids,]
# remove all business identifiers
part <- part[part$PER_OR_BUS_FLG == "P",]
part <- part %>%
dplyr::select(Source, PREMISE_ID, PER_ID, ACCT_ID, PARCEL, SITE_ADDRESS, SITE_CITY,
ENTITY_NAME,
BSEG_START_DT, BSEG_END_DT, CONSUMPTION, `HERS Score`, BUILDER_SQUARE_FOOTAGE,
YEAR_BUILT) %>%
dplyr::rename(BUILDER_YEARBUILT = YEAR_BUILT)

# tracking
part_tracking_dta <- rbind(part_tracking_dta, c("After removing homes still owned by
builders", length(unique(part$PREMISE_ID[part$Source == "2017-2018"])),
length(unique(part$PREMISE_ID[part$Source == "2019"]))))

# merge billing data with ada county assessor household data by premise ID
ada_info <- merge(all_billing_housing_char, all_info_raw, by = c("PREM_ID", "PARCEL"))
# remove the 3 hoursholds that have different year built
ada_info$equal <- ada_info$YEARBUILT.x == ada_info$YEARBUILT.y
ada_info <- ada_info %>%
dplyr::filter(equal == TRUE) %>%
dplyr::select(-YEARBUILT.y) %>%
dplyr::rename(YEARBUILT = YEARBUILT.x)
# how many premises are repeated with different info in ada info?
multiple_ada_info <- ada_info %>%
dplyr::group_by(PREM_ID, PARCEL) %>%
dplyr::summarise(n = n()) %>%
filter(n > 1)
table(multiple_ada_info$n)
# remove the ones with multiple rows
ada_info <- ada_info %>% anti_join(multiple_ada_info)
# save
save(ada_info, file = paste(inter, "ada_info.RData", sep = "/"))
# keep matching variables
ada_info <- ada_info %>%
dplyr::select(PREM_ID, PARCEL, OCCDATE, YEARBUILT, BEDROOMS, BATHROOMS, FIREPLACE, HEATING,
AC,
ASSESSOR_SQUARE_FOOTAGE, HERS_SCORE,
ENERGY_STAR, INDIVIDUAL_REB, EARLIEST_BILL_DATE, ADDRESS1_UPR, CITY_UPR,
POSTAL)
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 111 of 329

Appendix C: Impact Evaluation Scripts 103

part <- merge(part, ada_info , by.x = c("PREMISE_ID", "PARCEL"), by.y = c("PREM_ID",
"PARCEL")) # Merge

# tracking
part_tracking_dta <- rbind(part_tracking_dta, c("After matching premise ID and parcel with Ada
County Assessor info (required for matching)", length(unique(part$PREMISE_ID[part$Source ==
"2017-2018"])), length(unique(part$PREMISE_ID[part$Source == "2019"]))))

# make sure all have HERS scores
part %>% dplyr::select(PREMISE_ID, `HERS Score`, Source) %>% dplyr::distinct() %>%
dplyr::filter(!is.na(`HERS Score`)) %>% dplyr::group_by(Source) %>%
dplyr::summarise(mean_score = mean(as.numeric(`HERS Score`), na.rm = T), n = n())
part %>% dplyr::select(PREMISE_ID, HERS_SCORE, Source) %>% dplyr::distinct() %>%
dplyr::filter(!is.na(HERS_SCORE)) %>% dplyr::group_by(Source) %>% dplyr::summarise(mean_score
= mean(as.numeric(HERS_SCORE), na.rm = T), n = n())

# find differences between builder and assessor square footages
x <- part %>%
dplyr::mutate(YEARBUILT = as.numeric(YEARBUILT)) %>%
dplyr::select(PREMISE_ID, Source, BUILDER_YEARBUILT, YEARBUILT, BUILDER_SQUARE_FOOTAGE,
ASSESSOR_SQUARE_FOOTAGE, SITE_ADDRESS, ADDRESS1_UPR) %>%
dplyr::distinct() %>%
dplyr::mutate(yearbuilt_diff = BUILDER_YEARBUILT - YEARBUILT,
sqft_diff = BUILDER_SQUARE_FOOTAGE - ASSESSOR_SQUARE_FOOTAGE)
table(x$yearbuilt_diff)
# use builder values instead of assessor values. note that they are very different
summary(x$sqft_diff)
# make sure none of the whole homes also get appliance rebates
table(part$INDIVIDUAL_REB)

# keep homes with occupancy date, and keep bills that occur only after occupancy date
part <- part %>%
dplyr::mutate(BSEG_START_DT = as.Date(BSEG_START_DT),
BSEG_END_DT = as.Date(BSEG_END_DT),
OCCDATE = as.Date(OCCDATE)) %>%
dplyr::filter(OCCDATE > as.Date("2015-01-01")) %>%
dplyr::filter(BSEG_START_DT > OCCDATE)

# tracker
part_tracking_dta <- rbind(part_tracking_dta, c("After only keeping households with billing
data occurring after occupancy date", length(unique(part$PREMISE_ID[part$Source == "2017-
2018"])), length(unique(part$PREMISE_ID[part$Source == "2019"]))))

# see how many months are available
n_months <- part %>%
dplyr::group_by(PREMISE_ID, ACCT_ID) %>%
dplyr::summarise(n_months = n())
table(n_months$n_months)

# test how many unique accounts per premise
x <- part %>%
dplyr::group_by(PREMISE_ID, YEARBUILT, BUILDER_YEARBUILT) %>%
dplyr::summarise(n_acct = n_distinct(ACCT_ID))
table(x$n_acct)

# see year built distribution
table(x$YEARBUILT)
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 112 of 329

Appendix C: Impact Evaluation Scripts 104

table(x$BUILDER_YEARBUILT)

# save fully merged participant data
part_tracking_dta <- rbind(part_tracking_dta, c("Saved full info",
length(unique(part$PREMISE_ID[part$Source == "2017-2018"])),
length(unique(part$PREMISE_ID[part$Source == "2019"]))))
part_full <- part
# save current data to match to customer later, if needed
save(part_full, file = paste(inter, "part_full.RData", sep = "/"))

# keep needed variables
part <- part %>%
dplyr::select(-PER_ID, -SITE_ADDRESS, -SITE_CITY, -ENTITY_NAME, -ENERGY_STAR, -
INDIVIDUAL_REB, -HERS_SCORE, -ASSESSOR_SQUARE_FOOTAGE, -YEARBUILT) %>%
dplyr::rename(START_DT = BSEG_START_DT,
END_DT = BSEG_END_DT,
YEARBUILT = BUILDER_YEARBUILT,
SQFT = BUILDER_SQUARE_FOOTAGE,
HERS_SCORE = `HERS Score`) %>%
# assign treatment
dplyr::mutate(trmt = 1,
HERS_SCORE = as.numeric(HERS_SCORE))
part_tracking_dta <- rbind(part_tracking_dta, c("Saved required info",
length(unique(part$PREMISE_ID[part$Source == "2017-2018"])),
length(unique(part$PREMISE_ID[part$Source == "2019"]))))

# save participant dataset
save(part, file = paste(inter, "part.RData", sep = "/"))
save(part_tracking_dta, file = paste(inter, "part_tracking_dta.RData", sep = "/"))

# are there still premises with acct_id in original rebate application? no.
test <- part[part$ACCT_ID %in% orig_acct_ids,]

# nonparticipant data
# start tracking
nonpart_tracking_dta <- tracking_ctrl("All new homes data", length(unique(ada_info$PREM_ID)))

# merge nonparticipant billing to ada info, with HERS score and assessor SQFT (we don't have
builder HERS or SQFT)
load(paste(inter, "ada_info.RData", sep = "/"))
ada_info <- ada_info %>%
dplyr::select(PREM_ID, PARCEL, OCCDATE, YEARBUILT, BEDROOMS, BATHROOMS, FIREPLACE, HEATING,
AC, ASSESSOR_SQUARE_FOOTAGE, NOOFDWELLI, FIREPLACE,
HERS_SCORE, ENERGY_STAR, INDIVIDUAL_REB, EARLIEST_BILL_DATE, ADDRESS1_UPR,
CITY_UPR, POSTAL)

# find and remove nonparticipant households that participate in appliance rebate program
load(paste(inter, "all_billing_raw.RData", sep = "/"))
load(paste(inter, "rebate_participant_info_raw.RData", sep = "/"))
load(paste(inter, "rebate_participant_billing_raw.RData", sep = "/"))
load(paste(inter, "rebate_tracking_raw.RData", sep = "/"))

# remove based on IGC-provided identifier
nonpart_info <- ada_info[ada_info$ENERGY_STAR == 0 & ada_info$INDIVIDUAL_REB == 0,]

# tracking
nonpart_tracking_dta <- rbind(nonpart_tracking_dta, c("New homes that didnt have a rebate
identifier", length(unique(nonpart_info$PREM_ID))))
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 113 of 329

Appendix C: Impact Evaluation Scripts 105

# remove on premise ID and address in whole homes rebate tracking data
# none found
nrow(nonpart_info[nonpart_info$PREM_ID %in% unique(homes_participant_info_raw$PREMISE_ID),])
# none found
nrow(nonpart_info[nonpart_info$ADDRESS1_UPR %in%
toupper(homes_participant_info_raw$SITE_ADDRESS),])

# remove on premise ID and address in appliance rebate tracking data
# none found
nrow(nonpart_info[nonpart_info$PREM_ID %in% rebate_participant_info_raw$PREMISE_ID,])
# 3 found
nrow(nonpart_info[nonpart_info$ADDRESS1_UPR %in%
toupper(rebate_participant_info_raw$SITE_ADDRESS),])
# none found
nrow(nonpart_info[nonpart_info$ADDRESS1_UPR %in% toupper(tracking_raw$`Site Address`),])
# 3 found, same as above 3
nrow(nonpart_info[nonpart_info$ADDRESS1_UPR %in% toupper(rebate_tracking_raw$`Site
Address`),])

# check that participant whole homes aren't also participant appliance homes
nrow(part[part$PREMISE_ID %in% rebate_participant_info_raw$PREMISE_ID,])
nrow(part[part$ADDRESS1_UPR %in% toupper(rebate_participant_info_raw$SITE_ADDRESS),])
nrow(part[part$ADDRESS1_UPR %in% toupper(rebate_tracking_raw$`Site Address`),])

# remove the 3 nonparticipants that were found in rebate info
nonpart_info_valid <- nonpart_info[!nonpart_info$ADDRESS1_UPR %in%
toupper(rebate_participant_info_raw$SITE_ADDRESS),]
nonpart_tracking_dta <- rbind(nonpart_tracking_dta, c("After removing additional homes found
in rebate data", length(unique(nonpart_info_valid$PREM_ID))))

# merge validated nonparticipant data onto full billing data
nonpart <- merge(nonpart_info_valid, all_billing_raw, by = "PREM_ID")
nonpart_tracking_dta <- rbind(nonpart_tracking_dta, c("After merging with billing data",
length(unique(nonpart$PREM_ID))))

# keep this dataset for selling analysis
sell_analysis_ctrl <- nonpart
sell_analysis_ctrl$trmt <- 0
save(sell_analysis_ctrl, file = paste(inter, "sell_analysis_ctrl.RData", sep = "/"))

# keep only bills that occur after occupancy date
length(unique(nonpart$PREM_ID))
nonpart <- nonpart %>%
dplyr::mutate(BSEG_START_DT = as.Date(BSEG_START_DT),
BSEG_END_DT = as.Date(BSEG_END_DT),
OCCDATE = as.Date(OCCDATE)) %>%
dplyr::filter(OCCDATE > as.Date("2015-01-01")) %>%
dplyr::filter(BSEG_START_DT > OCCDATE)
length(unique(nonpart$PREM_ID))
nonpart_tracking_dta <- rbind(nonpart_tracking_dta, c("After keeping only billing data after
occupancy date", length(unique(nonpart$PREM_ID))))

# only keep households with 1 NOOFDWELLI
nonpart <- nonpart[nonpart$NOOFDWELLI == 1,]
nonpart_tracking_dta <- rbind(nonpart_tracking_dta, c("After keeping only households with
NOOFDWELLI == 1", length(unique(nonpart$PREM_ID))))

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 114 of 329

Appendix C: Impact Evaluation Scripts 106

# test unique accounts per premise ID
x <- nonpart %>%
dplyr::group_by(PREM_ID, YEARBUILT) %>%
dplyr::summarise(n_acct = n_distinct(ACCT_ID))
table(x$n_acct)

# keep variables we need
nonpart <- nonpart %>%
dplyr::select(-BSEG_ID, -BILL_ID, -SA_ID, -ACCOUNTING_DATE, -ENERGY_STAR, -INDIVIDUAL_REB, -
NOOFDWELLI) %>%
dplyr::rename(PREMISE_ID = PREM_ID,
START_DT = BSEG_START_DT,
END_DT = BSEG_END_DT,
SQFT = ASSESSOR_SQUARE_FOOTAGE) %>%
dplyr::mutate(trmt = 0) # assign treatment

# save nonparticipant billing data
save(nonpart, file = paste(inter, "nonpart.RData", sep = "/"))
save(nonpart_tracking_dta, file = paste(inter, "nonpart_tracking_dta.RData", sep = "/"))

# save tracking tables
write.csv(part_tracking_dta, paste(results, "part_tracking_dta.csv", sep = "/"))
write.csv(nonpart_tracking_dta, paste(results, "nonpart_tracking_dta.csv", sep = "/"))

# load data
load(paste(inter, "part.RData", sep = "/"))
load(paste(inter, "part_tracking_dta.RData", sep = "/"))
load(paste(inter, "nonpart.RData", sep = "/"))
load(paste(inter, "nonpart_tracking_dta.RData", sep = "/"))

# combine part and nonpart into one dataset to find zip codes
nonpart$Source <- "2017-2018"
bills <- rbind(part, nonpart)
nonpart$Source <- "2019"
bills <- rbind(bills, nonpart)

bills$zip_3 <- substr(bills$POSTAL, 0, 3)
table(bills$zip_3)

# Boise: 836, 837
# USAF Code: 726810

# read in NOAA weather data
weather_2017 <- read.csv(paste(raw_dir, "726810-2017.csv", sep = "/"), stringsAsFactors =
F)
weather_2018 <- read.csv(paste(raw_dir, "726810-2018.csv", sep = "/"), stringsAsFactors =
F)
weather_2019 <- read.csv(paste(raw_dir, "726810-2019.csv", sep = "/"), stringsAsFactors =
F)
weather_2020 <- read.csv(paste(raw_dir, "726810-2020.csv", sep = "/"), stringsAsFactors =
F)
weather_dta <- rbind(weather_2017, weather_2018, weather_2019, weather_2020)

# change hourly weather data to monthly HDD and CDD
weather_dta <- weather_dta %>%
dplyr::select(date_local, temp) %>%
dplyr::rename(datetime = date_local) %>%
dplyr::mutate(date = as.Date(datetime),
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 115 of 329

Appendix C: Impact Evaluation Scripts 107

hour = hour(datetime),
hdd = ifelse((setpoint-temp)/24>0,(setpoint-temp)/24,0),
cdd = ifelse((temp-setpoint)/24>0,(temp-setpoint)/24,0)) %>%
dplyr::select(-datetime) %>%
dplyr::group_by(date) %>%
dplyr::summarise(temp = mean(temp, na.rm = T),
hdd = mean(hdd, na.rm = T)*24,
cdd = mean(cdd, na.rm = T)*24)

test <- weather_dta %>%
dplyr::mutate(month = month(date),
year = year(date)) %>%
dplyr::group_by(month,year) %>%
dplyr::summarise(hdd = sum(hdd, na.rm = T),
cdd = sum(cdd, na.rm = T))

# check annual HDD and CDD
annual <- test %>%
dplyr::group_by(year) %>%
dplyr::summarise(hdd = sum(hdd),
cdd = sum(cdd))

# save weather data
save(weather_dta, file = paste(inter, "weather_dta.RData", sep = "/"))

# load files
load(paste(inter, "part.RData", sep = "/"))
load(paste(inter, "part_tracking_dta.RData", sep = "/"))
load(paste(inter, "nonpart.RData", sep = "/"))
load(paste(inter, "nonpart_tracking_dta.RData", sep = "/"))
load(paste(inter, "weather_dta.RData", sep = "/"))

# combine participant and nonparticipant into one dataset
nonpart$Source <- "2017-2018"
bills <- rbind(part, nonpart)
nonpart$Source <- "2019"
bills <- rbind(bills, nonpart)

# get weather data for all unique billing intervals
intervals <- unique(bills[, c("START_DT", "END_DT")])
intervals_hdd_cdd <- data.frame()
for (i in 1:nrow(intervals)) {
st <- intervals$START_DT[i]
end <- intervals$END_DT[i]

return_dta <- weather_dta %>%
dplyr::filter(date >= st & date <= end) %>%
dplyr::summarise(temp = mean(temp, na.rm = T),
hdd = sum(hdd, na.rm = T),
cdd = sum(cdd, na.rm = T)) %>%
dplyr::mutate(START_DT = st,
END_DT = end)
intervals_hdd_cdd <- rbind(intervals_hdd_cdd, return_dta)
}

# merge bills with weather data
bills <- merge(bills, intervals_hdd_cdd, by = c("START_DT", "END_DT"))

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 116 of 329

Appendix C: Impact Evaluation Scripts 108

# take into account the corrections
nrow(bills)
bills <- bills %>%
dplyr::mutate(hdd = ifelse(CONSUMPTION<0, -1*hdd, hdd),
cdd = ifelse(CONSUMPTION<0, -1*cdd, cdd)) %>%
dplyr::group_by(PREMISE_ID, PARCEL, ACCT_ID, Source, HERS_SCORE, SQFT, YEARBUILT, OCCDATE,
BEDROOMS, BATHROOMS, FIREPLACE, HEATING, AC, EARLIEST_BILL_DATE, ADDRESS1_UPR, CITY_UPR,
POSTAL, trmt, temp, START_DT, END_DT) %>%
dplyr::summarise(CONSUMPTION = sum(CONSUMPTION, na.rm = T),
hdd = sum(hdd, na.rm = T),
cdd = sum(cdd, na.rm = T)) %>%
ungroup()
nrow(bills)

# tracker
# starting cleaning steps
tracking_dta <- tracking("Starting",
nrow(unique(bills[bills$Source == "2017-2018" & bills$trmt == 1,
c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" & bills$trmt == 1,
c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2017-2018" & bills$trmt == 0,
c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" & bills$trmt == 0,
c("PREMISE_ID", "Source", "trmt")])))

# already removed the bills that overlap with occupancy date
# calendarize
raw_merge <- bills %>%
mutate(START_DT = START_DT+1) %>%
arrange(START_DT) %>%
group_by(PREMISE_ID, ACCT_ID, Source, PARCEL, OCCDATE, YEARBUILT, BEDROOMS, BATHROOMS,
FIREPLACE, HEATING, AC, SQFT, HERS_SCORE, EARLIEST_BILL_DATE, ADDRESS1_UPR, CITY_UPR, POSTAL,
trmt) %>%
mutate(bill_date = START_DT+as.numeric(END_DT-START_DT),
last_ym = lag(bill_date),
duration = as.numeric(END_DT-START_DT)+1) %>%
ungroup() %>%
mutate(month_bt = func_month_between(bill_date, last_ym),
month_bt = ifelse(is.na(month_bt), 0, month_bt),
year_mon = func_fd(bill_date),
avg_therm = CONSUMPTION/ duration,
avg_hdd = hdd/duration,
avg_cdd = cdd/duration)

summary(raw_merge$month_bt)
raw_temp <- as.data.frame(raw_merge)
raw_merge <- data.frame()

i <- 1
while(i > 0) {
raw_temp <- raw_temp %>%
mutate(day_bt = bill_date - year_mon + 1,
day_bt = ifelse(day_bt < duration, day_bt, duration))

raw_merge <- rbindlist(list(raw_merge, raw_temp), fill = T)

raw_temp <- raw_temp %>%
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Appendix C: Impact Evaluation Scripts 109

mutate(bill_date = bill_date - day_bt,
duration = duration - day_bt,
year_mon = func_fd(bill_date),
month_bt = month_bt - 1) %>%
filter(month_bt >= 0)

i <- nrow(raw_temp)

}

rm(i, raw_temp)

raw_merge <- raw_merge %>%
arrange(PREMISE_ID, ACCT_ID, Source, PARCEL, OCCDATE, YEARBUILT, BEDROOMS, BATHROOMS,
FIREPLACE, HEATING, AC, SQFT, HERS_SCORE, EARLIEST_BILL_DATE, ADDRESS1_UPR, CITY_UPR, POSTAL,
trmt, START_DT) %>%
mutate(daily_usage = avg_therm * day_bt,
daily_hdd = avg_hdd * day_bt,
daily_cdd = avg_cdd * day_bt) %>%
group_by(PREMISE_ID, ACCT_ID, Source, PARCEL, OCCDATE, YEARBUILT, BEDROOMS, BATHROOMS,
FIREPLACE, HEATING, AC, SQFT, HERS_SCORE, EARLIEST_BILL_DATE, ADDRESS1_UPR, CITY_UPR, POSTAL,
trmt, year_mon) %>%
summarise(days = sum(day_bt),
monthly_usage = sum(daily_usage),
daily_usage = monthly_usage/days,
monthly_hdd = sum(daily_hdd),
monthly_cdd = sum(daily_cdd)) %>%
ungroup()

nrow(unique(raw_merge[, c("PREMISE_ID", "PARCEL", "Source", "ACCT_ID")]))
nrow(unique(bills [, c("PREMISE_ID", "PARCEL", "Source", "ACCT_ID")]))

keep_colnames <- c("PREMISE_ID", "PARCEL", "Source", "ACCT_ID",
"OCCDATE", "YEARBUILT", "BEDROOMS", "BATHROOMS", "FIREPLACE",
"HEATING", "AC", "SQFT", "HERS_SCORE", "EARLIEST_BILL_DATE",
"ADDRESS1_UPR", "CITY_UPR", "POSTAL", "trmt")
merge_df <- unique(bills[, keep_colnames])
bills <- merge(merge_df, raw_merge, by = c("PREMISE_ID", "PARCEL", "Source", "ACCT_ID",
"OCCDATE", "YEARBUILT", "BEDROOMS",
"BATHROOMS", "FIREPLACE",
"HEATING", "AC", "SQFT", "HERS_SCORE",
"EARLIEST_BILL_DATE",
"ADDRESS1_UPR", "CITY_UPR", "POSTAL", "trmt"))
bills <- bills %>% ungroup()

# tracker
tracking_dta <- rbind(tracking_dta, c("After calendarizing",
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")]))))

# see how housing char range between groups
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Appendix C: Impact Evaluation Scripts 110

test_var_full <- unique(bills[, c("PREMISE_ID", "BEDROOMS", "BATHROOMS", "FIREPLACE",
"HERS_SCORE", "YEARBUILT", "trmt", "Source")])
test_var_full <- test_var_full %>% dplyr::mutate(trmt = as.factor(trmt))

# keep homes within year build range for each program year
year_built_py <- bills %>%
dplyr::filter(trmt == 1) %>%
dplyr::group_by(Source) %>%
dplyr::summarize(min_year_built = min(YEARBUILT),
max_year_built = max(YEARBUILT))
bills <- merge(bills, year_built_py, by = "Source")
bills <- bills[bills$YEARBUILT >= bills$min_year_built & bills$YEARBUILT <=
bills$max_year_built,]

# tracker
tracking_dta <- rbind(tracking_dta, c("After removing ctrl customers with year built before
min or after max trmt year built for each PY",
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")]))))

# remove households without square footage value or doesn't have heating and cooling, like our
entire trmt group
nrow(unique(bills[is.na(bills$SQFT) | bills$SQFT == 0, c("PREMISE_ID", "ACCT_ID", "trmt",
"Source")]))

bills <- bills %>%
dplyr::filter(!is.na(SQFT) & SQFT>0 & HEATING == "Y" & AC == "Y")

# tracker
tracking_dta <- rbind(tracking_dta, c("After removing customers that don't have heating and
cooling and SQFT",
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")]))))

# make variables
bills <- bills %>% ungroup()
# bill month assigned from bill midpoint
bills$month <- month(bills$year_mon)
# bill year assigned from bill midoint
bills$year <- year(bills$year_mon)
bills$heating_months <- as.integer(bills$month %in% c(10,11,12,1,2,3,4))

# remove if number billing days too few or too many
bills <- bills[bills$days >= 10 & bills$days <= 31,]

# tracker
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Appendix C: Impact Evaluation Scripts 111

tracking_dta <- rbind(tracking_dta, c("After removing months with less than 1 or more than 31
days of data",
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")]))))

# calculate median usage
median_usage <- bills %>%
filter(trmt == 1) %>%
dplyr::group_by(month) %>%
dplyr::summarise(median_usage = median(daily_usage, na.rm = T))

bills <- merge(bills, median_usage, by = c("month"), all.x = T)

bills_removed = bills[bills$daily_usage > bills$median_usage*10, ]
bills <- bills[bills$daily_usage <= bills$median_usage*10, ]

# kept this lower bound restriction to filter out unoccupied households
bills_removed = bills[bills$daily_usage < bills$median_usage/10, ]
bills <- bills[bills$daily_usage >= bills$median_usage/10, ]

# remove outliers with unrealistically high usage
bills <- bills[bills$daily_usage <= 50, ]

# tracker
tracking_dta <- rbind(tracking_dta, c("After removing outliers",
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")]))))

# before we restrict on sufficient post-period bills, summarize usage and average square
footage
usage_smry <- bills %>%
dplyr::filter(year_mon >= as.Date("2019-01-01")) %>%
dplyr::group_by(Source, trmt, month) %>%
dplyr::summarise(avg.daily.usage = mean(daily_usage,na.rm=T),
avg.daily.usage.sqft = mean(daily_usage/SQFT, na.rm = T)) %>%
as.data.frame()
write.csv(usage_smry, paste(results, "usage.smry.before.match.csv", sep = "/"))

sqft_smry <- bills %>%
dplyr::filter(year_mon >= as.Date("2019-01-01")) %>%
# make unique by household, so we dont give greater weight to the homes with more bills
dplyr::select(PREMISE_ID, ACCT_ID, YEARBUILT, SQFT, Source, trmt) %>%
dplyr::distinct() %>%
dplyr::group_by(Source, trmt) %>%
dplyr::summarise(avg.sqft = mean(SQFT, na.rm = T))
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Appendix C: Impact Evaluation Scripts 112

write.csv(sqft_smry, paste(results, "sqft.smry.before.match.csv", sep = "/"))

# restrict earliest bill date for each groups, select a clean post period between each group
and make sure each group has
# sufficient number of customers with sufficiently full post-periods per house

# first test restrictions
# 2019
# see spread
bill_spread_before <- table(bills$year_mon, bills$trmt, bills$Source)
bills_test <- bills %>% dplyr::group_by(PREMISE_ID, ACCT_ID, Source, trmt) %>%
dplyr::mutate(min_bill = min(year_mon))

# check these restrictions
bills_test <- bills_test %>% dplyr::filter(Source == "2019" & year_mon >= as.Date("2019-07-
01") & year_mon <= as.Date("2020-02-01")) %>%
# these are the min date ranges of the trmt customers with the above bill restrictions. Keep
same for ctrl cust
dplyr::filter(min_bill >= as.Date("2018-12-01") & min_bill <= as.Date("2019-09-01"))
table(bills_test$year_mon, bills_test$trmt, bills_test$Source)

# check these min month restrictions
x <- bills_test %>% dplyr::group_by(PREMISE_ID, ACCT_ID, Source, trmt) %>% summarise(n_months
= n(), n_heating_months = sum(heating_months))
# 404 trmt customers have 6 months or more of post-period data here with at least 4 months of
heating months
table(x$n_months, x$n_heating_months, x$trmt)
# only 405 trmt customers have 6 months or more of post-period data here
table(x$n_heating_months, x$trmt)

# how many would we keep?
bills_test_keep <- bills_test %>% dplyr::group_by(PREMISE_ID, ACCT_ID, Source, trmt) %>%
dplyr::mutate(n_months = n(), n_heating_months = sum(heating_months)) %>%
dplyr::filter(n_months >= 6 & n_heating_months >= 4) %>% select(PREMISE_ID, ACCT_ID, Source,
trmt) %>% distinct()
table(bills_test_keep$min_bill, bills_test_keep$trmt)

bills_test <- merge(bills_test_keep, bills_test, by = c("PREMISE_ID", "ACCT_ID", "Source",
"trmt"))
x <- bills_test %>% dplyr::group_by(PREMISE_ID, ACCT_ID, Source, trmt) %>% summarise(min_bill
= min(min_bill), n_months = n(), n_heating_months = sum(heating_months))
table(x$n_months, x$n_heating_months, x$trmt)
table(x$n_heating_months, x$trmt)
table(x$trmt)
table(x$min_bill, x$trmt)

# set 2019 bill restrictions
bill_restrictions <- data.frame(Source = "2019",
min_min_bill = as.Date("2018-12-01"),
max_min_bill = as.Date("2019-09-01"),
min_bill = as.Date("2019-07-01"),
max_bill = as.Date("2020-02-01"),
min_months = 6,
min_heating_months = 4)
keep_2019 <- bills_test_keep

# 2018 ---
# see spread
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Appendix C: Impact Evaluation Scripts 113

bill_spread <- table(bills$year_mon, bills$trmt, bills$Source)
bills_test <- bills %>% dplyr::group_by(PREMISE_ID, ACCT_ID, Source, trmt) %>%
dplyr::mutate(min_bill = min(year_mon))

# check these restrictions
bills_test <- bills_test %>%
dplyr::filter(Source == "2017-2018" &
year_mon >= as.Date("2019-01-01") &
year_mon <= as.Date("2020-01-01")) %>%
dplyr::filter(min_bill >= as.Date("2018-01-01") &
# keep only customers that moved in in 2018. keep same for ctrl cust
min_bill <= as.Date("2018-12-01"))
# this way we have a clean 2019 post-period
table(bills_test$year_mon, bills_test$trmt, bills_test$Source)

# check these min month restrictions
x <- bills_test %>% dplyr::group_by(PREMISE_ID, ACCT_ID, Source, trmt) %>% summarise(n_months
= n(), n_heating_months = sum(heating_months))
table(x$n_months, x$n_heating_months, x$trmt)
table(x$n_heating_months, x$trmt)

# how many would we keep?
bills_test_keep <- bills_test %>%
dplyr::group_by(PREMISE_ID, ACCT_ID, Source, trmt) %>%
dplyr::mutate(n_months = n(),
n_heating_months = sum(heating_months)) %>%
dplyr::filter(n_months >= 9 &
n_heating_months >= 8) %>%
select(PREMISE_ID, ACCT_ID, Source, trmt) %>%
distinct()
table(bills_test_keep$min_bill, bills_test_keep$trmt)

bills_test <- merge(bills_test_keep, bills_test, by = c("PREMISE_ID", "ACCT_ID", "Source",
"trmt"))
x <- bills_test %>%
dplyr::group_by(PREMISE_ID, ACCT_ID, Source, trmt) %>%
summarise(min_bill = min(min_bill),
n_months = n(),
n_heating_months = sum(heating_months))
table(x$n_months, x$n_heating_months, x$trmt)
# 324 trmt customers meet min requirements
table(x$n_heating_months, x$trmt)
table(x$trmt)
# all ctrl and trmt cust have the same earliest bill date ranges
table(x$min_bill, x$trmt)

# set 2018 bill restrictions
bill_restrictions <- rbind(bill_restrictions,
data.frame(Source = "2017-2018",
min_min_bill = as.Date("2018-01-01"),
max_min_bill = as.Date("2018-12-01"),
min_bill = as.Date("2019-01-01"),
max_bill = as.Date("2020-01-01"),
min_months = 9,
min_heating_months = 8))
write.csv(bill_restrictions, file = paste(results, "bill_restrictions.RData", sep = "/"))

# keep the customers and bills we tested and approved
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Appendix C: Impact Evaluation Scripts 114

bills <- merge(bills, bill_restrictions, by = c("Source"))
bills <- bills %>% dplyr::group_by(PREMISE_ID, ACCT_ID, trmt, Source) %>%
dplyr::mutate(earliest_bill = min(year_mon)) %>%
# make bill restrictions after computing earliest bill
dplyr::filter(year_mon >= min_bill &
year_mon <= max_bill &
# make earliest bill restrictions after computing earliest bill
earliest_bill >= min_min_bill &
earliest_bill <= max_min_bill) %>%
dplyr::ungroup()

bills <- bills %>% dplyr::group_by(PREMISE_ID, ACCT_ID, trmt, Source) %>%
dplyr::mutate(n_months = n(),
n_heating_months = sum(heating_months)) %>%
# make month restrictions after filtering bills
dplyr::filter(n_heating_months >= min_heating_months &
n_months >= min_months)

test <- bills %>% dplyr::select(PREMISE_ID, ACCT_ID, trmt, Source) %>% dplyr::distinct()
table(test$trmt, test$Source)

bill_spread_after <- table(bills$year_mon, bills$trmt, bills$Source)

# tracker
tracking_dta <- rbind(tracking_dta, c("Restrict post-period and move-in period in both groups
(This makes sure the post-period is similar between groups)",
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")]))))

# keep only one account ID per premise
test <- bills %>% dplyr::group_by(PREMISE_ID, trmt, Source) %>%
dplyr::summarise(n_ids = n_distinct(ACCT_ID)) %>%
dplyr::filter(n_ids > 1)
# only 112 premises with more than 1 account, all in 2019

acct_most_dta <- bills %>%
dplyr::select(PREMISE_ID, ACCT_ID, n_months, n_heating_months, trmt, Source) %>%
dplyr::distinct() %>%
dplyr::arrange(desc(n_heating_months)) %>%
dplyr::group_by(PREMISE_ID, trmt, Source) %>%
dplyr::slice(1)

bills <- merge(acct_most_dta, bills, by = c("PREMISE_ID", "ACCT_ID", "n_months",
"n_heating_months", "trmt", "Source"), all.x = T)
# TRACKER ---
tracking_dta <- rbind(tracking_dta, c("After keeping one account per premise id, the one with
the most number of heating months",
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
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Appendix C: Impact Evaluation Scripts 115

nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")]))))

# calculate baseload and usage per SQFT
bills <- bills %>% dplyr::group_by(PREMISE_ID, Source, trmt) %>%
dplyr::mutate(daily_usage_sqft = daily_usage/SQFT,
daily_hdd = monthly_hdd/days,
daily_hdd_sqft = daily_hdd/SQFT,
daily_therms_baseload = mean(ifelse(month %in% c(6, 7, 8), daily_usage, NA),
na.rm = T),
daily_heating_load = pmax(0, daily_usage-daily_therms_baseload),
daily_heating_load_sqft = daily_heating_load/SQFT,
daily_therms_heating_hdd_sqft = daily_heating_load/daily_hdd/SQFT,
daily_therms_baseload_hdd_sqft = daily_therms_baseload/daily_hdd/SQFT)
# none are missing summer month data
x <- unique(bills[is.na(bills$daily_heating_load), c("PREMISE_ID", "Source", "trmt")])
table(x$Source, x$trmt)

premises_without_baseload <- unique(bills[is.na(bills$daily_heating_load), c("PREMISE_ID",
"Source", "trmt")])
table(premises_without_baseload$Source, premises_without_baseload$trmt)
bills <- bills %>% anti_join(premises_without_baseload)

# tracker
tracking_dta <- rbind(tracking_dta, c("removing customers without sufficient summer months",
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 1, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2017-2018" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")])),
nrow(unique(bills[bills$Source == "2019" &
bills$trmt == 0, c("PREMISE_ID", "Source", "trmt")]))))
tracking_dta

# full data
# make sure no addresses overlap by treatment
x <- bills %>%
dplyr::group_by(PREMISE_ID) %>%
dplyr::summarise(n_trmt = n_distinct(trmt))
table(x$n_trmt)

# add last variables
bills <- bills %>%
ungroup() %>%
dplyr::mutate(zip_3 = as.factor(substr(POSTAL, 0, 3)),
zip_5 = as.factor(substr(POSTAL,0,5)),
daily_hdd = monthly_hdd/days,
YEARBUILT = as.numeric(YEARBUILT),
trmt = as.factor(trmt),
OCCDATE = as.Date(OCCDATE),
PREMISE_ID = as.factor(PREMISE_ID),
PARCEL = as.factor(PARCEL),
n_heating_months = as.numeric(n_heating_months),
ACCT_ID = as.factor(ACCT_ID))

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Appendix C: Impact Evaluation Scripts 116

# save
write.csv(tracking_dta, paste(results, "tracking_dta.csv", sep = "/"))
save(bills, file = paste(inter, "bills.RData", sep = "/"))

# keep only these columns to match on
load(paste(inter, "bills.RData", sep = "/"))
to_match_dta <- unique(bills[, c("PREMISE_ID", "PARCEL", "ACCT_ID", "trmt", "YEARBUILT",
"BEDROOMS", "BATHROOMS", "FIREPLACE",
"OCCDATE", "SQFT", "n_heating_months", "zip_3", "zip_5",
"Source")])
# merge with ada county data to get upper level sqft
load(file = paste(inter, "ada_info.RData", sep = "/"))
ada_info <- ada_info %>%
dplyr::mutate(PREM_ID = as.factor(PREM_ID),
PARCEL = as.factor(PARCEL),
has_grfl = as.factor(ifelse(GRFLSQFT>0,1,0)),
has_upstairs = as.factor(ifelse(UPFLSQFT > 0,1,0)),
has_porch = as.factor(ifelse(PORCHSQFT>0,1,0)),
has_patio = as.factor(ifelse(PATIOSQFT>0,1,0)),
has_pool = as.factor(ifelse(POOLSQFT > 0,1,0)),
has_attic = as.factor(ifelse(ATTICSQFTF > 0,1,0)),
has_patio = as.factor(ifelse(PATIOSQFT > 0,1,0)),
has_garage = as.factor(ifelse(CARSTORAGE > 0,1,0)),
PATIOCOVER = as.factor(PATIOCOVER))

to_match_dta <- merge(to_match_dta, ada_info[, c("PREM_ID", "PARCEL", "BEDROOMS", "BATHROOMS",
"FIREPLACE",
"has_upstairs", "UPFLSQFT",
"has_pool", "POOLSQFT",
"has_attic","ATTICSQFTU", "ATTICSQFTF",
"has_garage", "CARSTORAGE")],
by.x = c("PREMISE_ID", "PARCEL", "BEDROOMS", "BATHROOMS", "FIREPLACE"),
by.y = c("PREM_ID", "PARCEL", "BEDROOMS", "BATHROOMS", "FIREPLACE"),
all.x = T)

# keep only full data
full <- to_match_dta[complete.cases(to_match_dta),]
paste0(round(nrow(full)/nrow(to_match_dta)*100,0), "% of premises have full matching
variables")

# save
save(to_match_dta, file = paste(inter, "to_match_dta.RData", sep = "/"))
load(paste(inter, "to_match_dta.RData", sep = "/"))

set.seed(101)
py <- c("2017-2018", "2019")
for(i in 1:2) {

dta <- to_match_dta[to_match_dta$Source == py[i],]
write.csv(dta, file = paste(results, paste0(py[i], ".to_match_variables.csv"), sep = "/"))

# propensity score matching
# define matching methods
psm_ratio <- 1
psm_method <- "nearest"

# match
nearest <- matchit(trmt ~ YEARBUILT +
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Appendix C: Impact Evaluation Scripts 117

zip_5 +
BEDROOMS +
BATHROOMS +
FIREPLACE +
SQFT+
UPFLSQFT +
POOLSQFT+
ATTICSQFTF+
CARSTORAGE ,
data = dta,
exact = c("zip_5"),
method = psm_method, ratio = psm_ratio, replace = FALSE)

# summary
a <- summary(nearest)

# save
psm_ratio <- data.frame(".PSM Matching Ratio" = psm_ratio)
psm_method <- data.frame(".PSM Matching Method" = psm_method)
save(psm_ratio, file = paste(inter, paste0(py[i], ".psm_ratio.RData"), sep = "/"))
save(psm_method, file = paste(inter, paste0(py[i],".psm_method.RData"), sep = "/"))

# save matched output
df.match <- match.data(nearest)
table(df.match$trmt)

# extract matched premise IDs
matches <- data.frame(nearest$match.matrix)

# reformat
df.match <- df.match %>%
dplyr::mutate(PREMISE_ID = as.character(PREMISE_ID),
PARCEL = as.character(PARCEL),
ACCT_ID = as.character(ACCT_ID),
trmt = as.factor(trmt),
OCCDATE = as.Date(OCCDATE),
YEARBUILT = as.numeric(YEARBUILT),
BEDROOMS = as.numeric(BEDROOMS),
BATHROOMS = as.numeric(BATHROOMS),
FIREPLACE = as.numeric(FIREPLACE),
SQFT = as.numeric(SQFT),
n_heating_months = as.numeric(n_heating_months),
zip_3 = as.factor(zip_3),
zip_5 = as.factor(zip_5),
Source = as.character(Source))

# save matched dataset
save(df.match, file = paste(inter, paste0(py[i],".df.match.RData"), sep = "/"))

# output summaries
a$nn
a$sum.all[c(1,2,3,4)]
a$sum.matched[c(1,2,3,4)]

# print stats
table_before <- data.frame(a$sum.all)
table_after <- data.frame(a$sum.matched)
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Appendix C: Impact Evaluation Scripts 118

# save output summary and stats
match_summary <- a$nn

save(match_summary, file = paste(inter, paste0(py[i],".summary_nn.RData"), sep = "/"))
save(table_before, file = paste(inter, paste0(py[i],".summary_all.RData"), sep = "/"))
save(table_after, file = paste(inter, paste0(py[i],".summary_matched.RData"), sep = "/"))

write.csv(match_summary, file = paste(results, paste0(py[i],".summary_nn.csv"), sep = "/"))
write.csv(table_before, file = paste(results, paste0(py[i],".summary_all.csv"), sep = "/"))
write.csv(table_after, file = paste(results, paste0(py[i],".summary_matched.csv"), sep =
"/"))

# reformat
plot_groups <- df.match %>%
dplyr::mutate(PREMISE_ID = as.logical(PREMISE_ID),
PARCEL = as.logical(PARCEL),
zip_5 = as.numeric(as.character(zip_5)),
zip_3 = as.numeric(as.character(zip_3)),
has_upstairs = as.logical(ifelse(UPFLSQFT > 0,1,0)),
has_pool = as.logical(ifelse(POOLSQFT > 0,1,0)),
has_attic = as.logical(ifelse(ATTICSQFTF > 0,1,0)),
has_garage = as.logical(ifelse(CARSTORAGE > 0,1,0)))

# check propensity score matches
# compute covariate balance after matching
plot_groups$trmt <- as.numeric(as.character(plot_groups$trmt))
treated <- (plot_groups$trmt==1)
cov1 <- plot_groups[,c("YEARBUILT", "zip_5", "zip_3", "BEDROOMS", "BATHROOMS", "FIREPLACE",
"SQFT",
"UPFLSQFT", "ATTICSQFTF", "CARSTORAGE",
"has_pool", "has_upstairs", "has_attic", "has_garage")]
std.diff <- apply(cov1,2,function(x) 100*(mean(x[treated])-
mean(x[!treated]))/(sqrt(0.5*(var(x[treated]) + var(x[!treated]))))) # computes standardized
difference
std.diff <- abs(std.diff)
write.csv(std.diff, file = paste(results, paste0(py[i],".standardized_difference.csv"), sep
= "/"))

plot_groups <- plot_groups %>%
dplyr::mutate(PREMISE_ID = as.factor(PREMISE_ID),
PARCEL = as.factor(PARCEL),
zip_5 = as.factor(zip_5),
has_upstairs = as.factor(ifelse(UPFLSQFT > 0,1,0)),
has_pool = as.factor(ifelse(POOLSQFT > 0,1,0)),
has_attic = as.factor(ifelse(ATTICSQFTF > 0,1,0)),
has_garage = as.factor(ifelse(CARSTORAGE > 0,1,0)))
plot_groups$trmt <- as.integer(plot_groups$trmt)

chi.test <- xBalance(trmt ~ YEARBUILT +
zip_5 +
BEDROOMS +
BATHROOMS +
FIREPLACE +
SQFT+
UPFLSQFT +
POOLSQFT+
ATTICSQFTF+
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 127 of 329

Appendix C: Impact Evaluation Scripts 119

CARSTORAGE +
has_upstairs + has_pool + has_attic,
data=plot_groups, report = c("chisquare.test"))
chi.output <- capture.output(chi.test)
write.csv(chi.output, file = paste(results, paste0(py[i],".chi_square_test.csv"), sep =
"/"))

}

# load data
load(paste(inter, "bills.RData", sep = "/"))

py <- c("2017-2018", "2019")
for(i in 1:2) {

# load matched data, merge to billing data
load(paste(inter, paste0(py[i],".df.match.RData"), sep = "/"))
reg_dta <- df.match
reg_dta <- merge(reg_dta, bills, by = c("PREMISE_ID", "PARCEL", "ACCT_ID", "trmt", "Source",
"YEARBUILT", "BEDROOMS",
"BATHROOMS", "FIREPLACE", "OCCDATE", "SQFT",
"n_heating_months", "zip_5", "zip_3"))

# save final modeled data
reg_dta <- reg_dta %>%
dplyr::select(PREMISE_ID, ACCT_ID, Source, trmt, zip_5, year_mon, month,
daily_usage_sqft,days, daily_usage, daily_hdd, SQFT, YEARBUILT, monthly_usage, monthly_hdd,
daily_hdd_sqft, daily_therms_baseload, daily_heating_load, daily_heating_load_sqft) %>%
as.data.frame()
write.csv(reg_dta, file = paste(results, paste0(py[i], ".reg_dta.csv"), sep = "/"))

# compute variables
avg_heating_load <- reg_dta %>%
dplyr::mutate(daily_therms_baseload_hdd_sqft = daily_therms_baseload/daily_hdd/SQFT,
daily_therms_heating_hdd_sqft = daily_heating_load/daily_hdd/SQFT) %>%
dplyr::group_by(Source, trmt) %>%
dplyr::summarise(mean_daily_therms_heating_hdd_sqft = mean(daily_therms_heating_hdd_sqft),
mean_daily_therms_baseload_hdd_sqft =
mean(daily_therms_baseload_hdd_sqft))
write.csv(avg_heating_load, file = paste(results, paste0(py[i], ".avg_heating_load.csv"),
sep = "/"))

# regression model
model_heating <- lm(daily_heating_load_sqft ~ trmt + daily_hdd + trmt*daily_hdd, data =
reg_dta)

# model output
coefs_heating <- summary(model_heating)$coefficients
write.csv(coefs_heating, paste(results, paste0(py[i], ".coefs_heating.csv"), sep = "/"))
model_output_heating <- capture.output(summary(model_heating))
write.csv(model_output_heating, paste(results, paste0(py[i], ".model_output_heating.csv"),
sep = "/"))

}

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 128 of 329

2018-2019 ENERGY EFFICIENCY PROGRAM
PROCESS EVALUATION

(200 pages)
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 129 of 329
IGC Energy Efficiency Program Evaluation
1

Intermountain Gas Company 2018-2019
Energy Efficiency Program Process
Evaluation

Submitted to:

Submitted on:
June 19, 2020

Prepared by:
ADM Associates, Inc.
39650 Liberty St. Suite 425
Fremont, CA 94538

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 130 of 329
IGC Energy Efficiency Program Evaluation
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Prepared by:
Theresa Bohannan
Priya Cortes
Sara Gallego
Melissa Kosla
Mike Soszynski
Adam Thomas

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 131 of 329
IGC Energy Efficiency Program Evaluation
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Table of Contents
PREPARED BY: ........................................................................................................................................................ 2
TABLE OF CONTENTS ..................................................................................................................................... 3
LIST OF TABLES................................................................................................................................................ 5
LIST OF FIGURES .............................................................................................................................................. 7
1 EXECUTIVE SUMMARY .................................................................................................................................. 9
1.1 PROCESS EVALUATION RESULTS .............................................................................................................................. 9
1.2 CONCLUSIONS ................................................................................................................................................... 10
1.2.1 Cross-Cutting Findings ......................................................................................................................... 10
1.2.2 Whole Home Measure ......................................................................................................................... 11
1.2.3 Appliance Rebates................................................................................................................................ 13
1.3 RECOMMENDATIONS .......................................................................................................................................... 14
1.3.1 Cross-Cutting Recommendations ......................................................................................................... 14
1.3.2 Whole Home Measure ......................................................................................................................... 15
1.3.3 Appliance Rebates................................................................................................................................ 16
2 INTRODUCTION AND PURPOSE OF STUDY .................................................................................................. 18
2.1 PROGRAM OFFERINGS AND DESCRIPTIONS .............................................................................................................. 18
2.1.1 Whole Home Measure ......................................................................................................................... 18
2.1.2 Appliance Rebates................................................................................................................................ 19
3 PROCESS EVALUATION ............................................................................................................................... 21
3.1 PROCESS EVALUATION OBJECTIVES ........................................................................................................................ 21
3.2 PROCESS EVALUATION APPROACH ......................................................................................................................... 21
3.2.1 Key Researchable Issues ....................................................................................................................... 22
3.3 PROCESS EVALUATION FINDINGS ........................................................................................................................... 22
3.3.1 Program Staff Interviews ..................................................................................................................... 22
3.3.2 HERS Rater Interviews .......................................................................................................................... 26
3.3.3 IGC Contractor Interviews .................................................................................................................... 29
3.3.4 Participating Builder Interviews ........................................................................................................... 35
3.3.5 Nonparticipant Builder Interviews ....................................................................................................... 39
3.3.6 Whole Home Measure Participant Survey ........................................................................................... 42
3.3.7 Appliance Rebate Measure Participant Survey .................................................................................... 52
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3.3.8 Nonparticipant Survey ......................................................................................................................... 65
3.3.9 Survey Respondent Attitude and Satisfaction Comparison .................................................................. 78
3.3.10 Tracking Database Review ................................................................................................................... 80
3.3.11 Rebate Rejection Analysis .................................................................................................................... 82
4 CONCLUSIONS AND RECOMMENDATIONS.................................................................................................. 84
4.1 CONCLUSIONS ................................................................................................................................................... 84
4.1.1 Cross-Cutting Findings ......................................................................................................................... 84
4.1.2 Whole Home Measure ......................................................................................................................... 85
4.1.3 Appliance Rebates................................................................................................................................ 87
4.2 RECOMMENDATIONS .......................................................................................................................................... 88
4.2.1 Cross-Cutting Recommendations ......................................................................................................... 88
4.2.2 Whole Home Measure ......................................................................................................................... 89
4.2.3 Appliance Rebates................................................................................................................................ 90
5 APPENDIX ................................................................................................................................................... 92
5.1 SURVEY INSTRUMENTS ........................................................................................................................................ 93
5.1.1 Whole Home Measure Participant Survey ........................................................................................... 93
5.1.2 Appliance Rebate Measure Participant Survey .................................................................................... 99
5.1.3 Nonparticipant Survey ....................................................................................................................... 112
5.2 INTERVIEW GUIDES ........................................................................................................................................... 122
5.2.1 IGC Program Staff Interview Guide .................................................................................................... 122
5.2.2 HERS Raters Interview Guide ............................................................................................................. 126
5.2.3 Builders Interview Guide .................................................................................................................... 129
5.2.4 Contractors Interview Guide .............................................................................................................. 135
5.3 TABULATIONS .................................................................................................................................................. 140
5.3.1 Whole Home Measure Participant Survey Results ............................................................................. 140
5.3.2 Appliance Rebate Measure Participant Survey Results...................................................................... 153
5.3.3 Nonparticipant Survey Results ........................................................................................................... 178

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IGC Energy Efficiency Program Evaluation
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List of Tables
Table 1 IGC’s Energy Efficiency Program Offerings ..................................................................................... 18
Table 2 Appliance Rebate Offerings ............................................................................................................ 19
Table 3 Process Evaluation Activities .......................................................................................................... 22
Table 4 Contractors’ Roles or Titles ............................................................................................................ 29
Table 5 Contractors’ Past Experience ......................................................................................................... 30
Table 6 Furnace Contractor Replacement Project Types............................................................................ 32
Table 7 Reasons Contractors Did Not Submit Rebate Application ............................................................. 33
Table 8 Contractor Recommendations ....................................................................................................... 35
Table 9 Home Buyer Awareness ................................................................................................................. 38
Table 10 Nonparticipant Builder Locations ................................................................................................. 40
Table 11 Information about the Home Provided by Builders or Real Estate Agents .................................. 44
Table 12 Perception of Energy Efficiency Comprehension ......................................................................... 46
Table 13 Home Demographics .................................................................................................................... 49
Table 14 Participant Demographics ............................................................................................................ 50
Table 15 Survey Responses by Application Type ........................................................................................ 52
Table 16 When Respondents Learned About the Rebate .......................................................................... 53
Table 17 Appliance Status at Time of Replacement ................................................................................... 55
Table 18 Appliance Rebate Project Type .................................................................................................... 55
Table 19 Equipment Age at Time of Replacement ..................................................................................... 56
Table 20 Year Home was Built .................................................................................................................... 62
Table 21 Square Footage of Homes ............................................................................................................ 62
Table 22 Appliance Rebate Respondent Home Characteristics & Demographics ...................................... 63
Table 23 Home Improvements in the Past Three Years ............................................................................. 66
Table 24 How Customers Would Find Out Ways to Reduce Their Monthly Energy Bills ........................... 69
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Table 25 How Trustworthy is Intermountain Gas Company as a Source of Energy-Saving Information ... 72
Table 26 Interest in Receiving Energy Saving Tips and Rebates Offered by Intermountain Gas Company 73
Table 27 Home Characteristics ................................................................................................................... 73
Table 28 Year Home was Built .................................................................................................................... 74
Table 29 Square Footage of Homes ............................................................................................................ 74
Table 30 Age of Heating System ................................................................................................................. 75
Table 31 Age of Water Heating System ...................................................................................................... 75
Table 32 Survey Participants’ Demographics .............................................................................................. 76
Table 33 Rejection Rates for PY2017-2018 and PY2019 ............................................................................. 82
Table 34 Reason for Rejection .................................................................................................................... 83
Table 35 IGC’s Energy Efficiency Program Offerings ................................................................................... 83

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List of Figures
Figure 1 Satisfaction with Technical Support .............................................................................................. 27
Figure 2 Contractors’ Sources of Program Awareness ............................................................................... 31
Figure 3 Contractor Assistance with Application Process ........................................................................... 34
Figure 4 Builders by Territory ..................................................................................................................... 36
Figure 5 Leadership Position ....................................................................................................................... 37
Figure 6 List of Home Buying Stakeholder Support .................................................................................... 43
Figure 7 Primary Source of Information Regarding Home Being ENERGY STAR® Certified ........................ 44
Figure 8 Factors that Influenced Decision on Purchasing the Home .......................................................... 45
Figure 9 Participants Understanding of Home’s Energy Efficiency Features .............................................. 46
Figure 10 Attitudes towards Energy Efficiency ........................................................................................... 47
Figure 11 Willingness to take Energy Efficiency Actions in the Home ........................................................ 48
Figure 12 Satisfaction with IGC as Participant’s Gas Service Provider ........................................................ 48
Figure 13 Age Distribution of Survey Respondents Compared to Idaho Census Data ............................... 51
Figure 14 Educational Attainment of Survey Respondents and Idaho Residents ...................................... 52
Figure 15 Source of Awareness of IGC's Appliance Rebate ........................................................................ 53
Figure 16 Reason for Purchasing Appliance Model or Type ........................................................................ 54
Figure 17 Agreement with Statements regarding Contractor .................................................................... 57
Figure 18 Which Features Contractors Emphasize and Matters Most to Customers ................................. 58
Figure 19 Satisfaction with Rebated Appliance .......................................................................................... 59
Figure 20 Appliance Rebate Program Satisfaction ..................................................................................... 60
Figure 21 Respondent Agreement with Statements Regarding Energy Efficiency ..................................... 61
Figure 22 Age Distribution of Survey Respondents Compared to Idaho Census Data ................................ 64
Figure 23 Educational Attainment of Survey Respondents and Idaho Residents ....................................... 65
Figure 24 Types of Rebate Survey Respondents Recalled .......................................................................... 67
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Figure 25 Customers’ Self-Reported Knowledge of Ways to Save Energy ................................................. 67
Figure 26 Customer Engagement in Lowering Monthly Energy Bills .......................................................... 68
Figure 27 Customer Interest in Home Improvements ................................................................................ 68
Figure 28 Likelihood Customers Would Install High Efficiency Equipment or Other Measures ................. 70
Figure 29 Likelihood Customers Would do Low- or No-Cost Activities to Reduce Monthly Utility Bill ...... 71
Figure 30 Respondent Agreement with Statements Regarding Energy Efficiency ..................................... 72
Figure 31 Age Distribution of Survey Respondents Compared to Idaho Census Data ............................... 77
Figure 32 Educational Attainment of Survey Respondents and Idaho Residents ...................................... 77
Figure 33 Respondent Agreement with Statements Regarding Energy Efficiency ..................................... 79

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 137 of 329
IGC Energy Efficiency Program Evaluation
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1 Executive Summary
This report presents the process evaluation of the Intermountain Gas Company (IGC) 2018-2019 Energy
Efficiency Program as specified in ADM’s response to the Request for Proposals (RFP) for Energy Efficiency
Process Evaluation. IGC’s Energy Efficiency Program offered rebates for seven measures to residential
customers in PY2017-2018 and PY2019.
IGC’s Energy Efficiency Program also includes Appliance Rebates for residential customers that purchase
and install qualifying high-efficiency appliances. Qualifying appliances include:
 95% AFUE Natural Gas Furnace
 90% Radiant Combo
 80% AFUE Fireplace
 70% FE Fireplace
 0.67 Water Heater
 0.91 Tankless Water Heater
In addition, IGC offers a Whole Home Rebate, an incentive to home builders and buyers for new
construction single-family residential homes that meet energy efficiency standards of the program. The
Whole Home rebate requires homes must be certified to meet the requirements of the Environmental
Protection Agency (EPA) ENERGY STAR® certified homes program. In addition, the home must achieve a
RESNET Home Energy Rating System (HERS) score of 75 or less with a certificate completed by a certified
HERS rater.
The Evaluators conducted a process evaluation to gain deliver information to IGC staff about opportunities
for program improvement through understanding of demographics, understanding of program barriers,
and opportunities to streamline marketing and outreach. The focus of the process evaluation activities is
to gain a better understanding of program operations, assess the overall effectiveness of program
operations, and identify areas for program improvement. The key elements for the evaluation are to:
 Subtask 1 - Review current program materials;
 Subtask 2 - Review current program tracking methods;
 Subtask 3 - Collect data from staff and contractors; and
 Subtask 4 - Collect survey data from participating and non-participating customers.
1.1 Process Evaluation Results
The process evaluation examined program operations and results for the Intermountain Gas Company
Energy Efficiency Program between the 2017-2018 and 2019 program years. This portion of the evaluation
is designed to identify potential program improvements that may prospectively increase program
efficiency or effectiveness in terms of customer participation and satisfaction levels. Conclusions and
recommendations have been developed based on interview and survey findings, and overall assessment
of program processes. The process evaluation findings, as well as conclusions and recommendations, are
detailed by each rebate (Appliance Rebate and Whole Home) and may provide strategic advantage during
future program cycles.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 138 of 329
IGC Energy Efficiency Program Evaluation
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1.2 Conclusions
1.2.1 Cross-Cutting Findings
 IGC launched the Energy Efficiency program in PY2017-2018 with limited design or operational
changes in PY2019. IGC received approval to launch the Appliance Rebate and Whole Home
measures starting in PY2017-2018. The initial goal for the Energy Efficiency program was to
increase customer awareness and participation. When IGC staff started implementing the
program, they were focused on determining their logistical partners, leveraging existing
relationships, and refining the rebate process.
 IGC’s Energy Efficiency program performed well during first two program years. According to
IGC staff, both measures (Appliance Rebate and Whole Home) performed well in the first two
years of implementation and were on an “upward trend.” The Appliance Rebate program saw a
60% increase in the number of appliances rebated and the Whole Home program saw a 70%
increase in the number of homes between PY2017-2018 and PY2019. Staff also believed there
was increased customer awareness and improving relations with key program stakeholders (HERS
raters and HVAC contractors). The Appliance Rebate program launched online application forms
and a portal for HVAC contractors.
 Lessons learned between PY2017-2018 and PY2019 included how to effectively engage various
stakeholders. IGC staff indicated they learned various lessons about program implementation and
operation since they launched. Staff stated they learned how to effectively engage with the
community and to target events where program staff can reach the appropriate audience.
Program staff indicated that recruitment of builders and contractors can be most effective either
by phone or in-person, when possible. They also learned that attending events where builders are
in attendance can be one of the most effective ways to discuss the program with them, specifically
golf tournaments held by builder associations. Another lesson learned is to ensure that
participating contractors are licensed with the state to perform install. Staff have kept a list of
contractors’ licenses with their expiration date to compare against installation. This ensures that
installations are done in accordance with state permitting requirements.
 Program changes anticipated in PY2020 and beyond. IGC program staff indicated they do
anticipate some changes to the Energy Efficiency program for the remainder of 2020 and possibly
for the upcoming program year. A conservation potential assessment was conducted, and staff
anticipated changes to their offerings based on the results of that study. Staff anticipated a
commercial offering and wanted to add smart thermostats to their list of eligible measures.
Program staff also discussed whether the HERS rating of 75 was appropriate for the Whole Home
measure. Staff was concerned increasing the stringency of the HERS score as a requirement for
participating homes and theorized it may be a barrier for builders who are not accustomed to
building homes beyond code. Staff believed a tiered incentive system might be a future addition
to the Whole Home measure. Staff would also like to increase customer education efforts about
energy efficiency.
 Broad range of marketing efforts and community outreach were used. IGC staff utilized a broad
approach to promote the availability of the Appliance Rebates and the Whole Homes measures.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 139 of 329
IGC Energy Efficiency Program Evaluation
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Staff indicated they focused on three areas for outreach and promotion which included customers
and communities, home builders, and HVAC contractors. Staff indicated that HVAC contractors
are key to promoting the program to customers because they are seen as trusted experts.
Marketing activities included bill inserts, digital advertising, radio campaigns, and attending
community events. To promote the Whole Home program, IGC staff participated in the Parade of
Homes sponsored by the Builder Contractors’ Association. Promotional activities with builders
included attending association meetings, expositions, and other events. IGC does not provide
marketing collateral or cobranding opportunities for contractors or builders.
1.2.2 Whole Home Measure
 Participating and nonparticipating builders both reported an increase in customer demand for
energy efficient homes. The majority (88% of participating and 83% of nonparticipating builders)
believed that homebuyer demand and expectations for energy efficient homes have increased
over the past few years. Both participating and nonparticipating builders indicated the strong
demand for new homes in Idaho requires little to no marketing to sell.
 Builder participation in the Whole Home measure increased due to recruitment efforts of staff.
There was an increase in the number of participating builders from 18 to 24 between PY2017-
2018 and PY2019. IGC program staff stated they are typically recruited at BCA meeting as they
offer an opportunity to educate builders about the program. Staff also stated that community
outreach and home buyers about the benefits of ENERGY STAR® can help drive more builders to
build energy efficient homes. According to IGC, the feedback from builders has been positive and
that they are grateful to receive the rebates within 1 to 2 weeks. All the participating builders who
were interviewed indicated they have experience with the Whole Home measure starting in either
2018 or 2019
 Non-HVAC measures were noted as the least costly way to help participating builders build
homes with a HERS score of less than 75. According to the builders, the least costly ways to
ensure a home has a HERS score lower than 75 include implementing insulation, air sealing, and
having a conditioned crawl space. Other strategies that builders discussed included implementing
90-95% AFUE furnaces, meeting all the ENERGY STAR® requirements, and following the building
codes. However, these other strategies were not listed as lower cost, and the 90-95% AFUE
furnaces is seen as one of the more costly ways to achieve the HERS score requirement.
 Differentiation in the market and the program financial incentive were two factors important
to builders’ participation. All participating builders stated that program financial incentive and
ability to differentiate themselves in the market were the factors that went into deciding to build
their homes to program-building standards in 2019.
 All Whole Home participating builders were satisfied with their HERS rater that they worked
with. In fact, from the interviews, it was found that 50% of the HERS raters are the parties involved
with the rebate application process.
 In general, HERS raters find IGC’s Energy Efficiency program to be one of the most effective in
the region. All three raters have been participating in IGC’s Energy Efficiency program since 2018.
Raters have also heard positive feedback from builders about the Energy Efficiency program.
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 140 of 329
IGC Energy Efficiency Program Evaluation
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Raters indicated that program requirements seem reasonable and attainable. According to one
HERS rater, IGC’s payment turn-around is efficient and dependable.
 HERS raters indicated the rebate incentive was the best marketing tool for reaching most
builders. According to the HERS raters, the upfront additional costs for builders continues to be a
barrier for participation. By marketing the incentive, builders were more likely try and become
eligible for the rebate(s).
 Raters use direct and indirect messaging to recruit builders to the program. Word-of-mouth was
their best direct messaging strategy, while having builders recommend the program to other
builders as their indirect messaging. All raters expect the number of constructions of new homes
for 2020 to remain about the same from 2019. Due to the economic downturn, raters may not
see the increase in construction as they had anticipated for 2020. All HERS raters have worked as
a rater in Idaho for more than two years.
 Program requirements are broadly understood by participants and would benefit from further
explanation. HERS raters indicated that IGC could improve their outreach to builders, realtors,
and homeowners about buying energy-efficient homes. Participating in events, using more
marketing collateral, or sponsoring educational events may help increase program participation,
according to HERS raters.
 Nonparticipating builders were generally aware of energy efficiency practices but not the
Whole Home measure. Among the nonparticipating builders who were interviewed, they
indicated they have a general understanding of energy efficiency practices and incorporate them
in their new homes. Most nonparticipating builders were not aware of the IGC Whole Home
measure but voiced interest in learning more and potentially participating. The builders noted
experience with energy efficiency and HERS raters. Some voiced disinterest in participating in
certification programs because of the time and resources required and the low perceived payoff.
 The decision about buying a new home varied among participants. Although many indicated that
energy efficiency was important to them (86%), most stated the home’s price to be of greater
importance (94%). Other non-energy-efficient factors that greatly influenced the home sale
include the fact that it was new construction and location.
 Participants worked with different stakeholders when deciding on buying their homes. Fifty
percent of Whole Home survey respondents stated they worked with their own real estate agent,
while 24% worked with the builder’s in-house realtor. Some participants also mentioned they
either worked in real estate or had worked with more than one of the stakeholders during this
process.
 Most survey respondents were aware that their homes were ENERGY STAR® certified but not
necessarily part of the Whole Home measure. Seventy-nine percent of Whole Home survey
respondents were aware that their home was ENERGY STAR® certified, while 16% said they did
not know. Of those who knew, 69% indicated they learned this information from the builder and
18% learned from a real estate agent. Participants also indicated that knowing their home had an
ENERGY STAR® rating and had been inspected by a HERS rater influenced their decision on
whether to buy the home. However, according to the builders interviewed, home buyers were
generally unaware of the Whole Home measure. Five out of eight builders (62.5%) stated that
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 141 of 329
IGC Energy Efficiency Program Evaluation
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they either were unsure about the number of customers that knew about the Whole Home
measure or that none of the customers knew about the measure.
 Respondents could not recall all the home’s energy efficiency features. Thirty-eight percent of
respondents stated the home’s energy efficient characteristics had been clearly explained by the
person who helped them purchase the home, while 25% indicated it had not been clear.
Participants also reported they were unaware of the differences between homes promoted by
IGC (57%) versus the other homes on the market at the time of their purchase.
 Overall, participants indicated they were satisfied with the program. Respondents were either
satisfied or very satisfied with the energy efficiency measures in their homes (92%) and the
ENERGY STAR® certified home promoted by IGC (84%). Ninety percent of respondents indicated
they were either satisfied or very satisfied with IGC as their gas service provider.
 Eight of 1,698 Whole Home Rebates were rejected across both program years. The Evaluators
found that less than 1% of the Whole Home Rebate applications were rejected. The majority (75%)
of rejections were because the rebate had previously been paid out for the address in the
application.
1.2.3 Appliance Rebates
 Contractors play a crucial role in the Appliance Rebate measure. Most survey respondents
learned about the program from a contractor and selected the rebated equipment because of a
contractor recommendation. Nearly half received their rebate form from their contractor.
Contractors not required to be officially registered with IGC. IGC does not have an approved trade
ally network but does require that contractors who participate in the program to be licensed with
the state.
 IGC provides training and educational opportunities for contractors. IGC program staff generally
communicates with contractors via email and phone. There was one training provided by IGC staff
where there was information about the HVAC portal and online application. If contractors were
not able to attend, IGC staff sent emails with videos of the training along with online resources.
IGC also offered a $300 reimbursement for contractors to participate in an ENERGY STAR®
certification course. IGC staff indicated they have received positive feedback from contractors
(e.g., they like the “straight forwardness of the program” and the responsiveness of staff).
 All contractors had experience with other energy efficiency programs and most indicated they
were largely satisfied with the design and participation process. Among the contractors
interviewed, most reported general satisfaction with the Appliance Rebate measure. Contractors’
experience included working with Idaho Power, Rocky Mountain Power, Idaho Department of
Environmental Quality, and Bonneville Power. Interviewed contractors had largely positive things
to say about IGC’s Energy Efficiency program in comparison to their experience with other
programs, citing the responsiveness of program staff and the quick turnaround time for rebates.
 Contractors are an important source of program awareness for customers. Most contractors
reported that only a small portion of their customers know about the program before they inform
them. Most contractors interviewed indicated they encourage the sale of high efficiency
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 142 of 329
IGC Energy Efficiency Program Evaluation
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equipment. Most of the contractors said their only method of encouraging the sale of high
efficiency equipment was discussions with customers.
 Customers experienced high satisfaction with IGC overall as well as the Appliance Rebate
measure. Most respondents were satisfied with their IGC rebated equipment’s installation and
performance. Satisfaction was also high in respect to other aspects of the program including
interactions with IGC staff, amount of time to receive the rebate, and the overall quality of the
contractor’s work. Most respondents said their application was accepted as submitted and said
they received their rebate check in a prompt and timely manner.
 The energy efficiency and warranty/reliability of the equipment mattered most to customers.
Contractors emphasized these features as well. Though nearly half of contractors also emphasized
the rebate amount, a smaller portion of customers noted that the rebate amount mattered most
to them.
 Of the rebate applications, 16% and 11% were rejected in PY2017-2018 and PY2019,
respectively. The Evaluators found the highest rejection rates for the 80% AFUE Fireplace, the
95% AFUE Natural Gas Furnace, and the 0.67 Water Heater measures. The reason applications
were rejected most often included not meeting the minimum efficiency requirements, unsigned
signature pages, lacking information on final invoice or application, missing permit numbers,
and/or the eligibility for application submission had passed.
1.3 Recommendations
1.3.1 Cross-Cutting Recommendations
 Codify quality assurance and control processes in a standard operating procedures (SOP)
manual. The Evaluators recommend that IGC program staff develop and operationalize all quality
assurance and quality control (QA/QC) procedures. By doing so, this will help future staff
understand the procedures for ensuring rebates are processed correctly and identifying gaps.
 Increase communication opportunities with contractors, builders, and HERS raters. Some
interviewed stakeholders were not aware of the online application forms and other components
to the Energy Efficiency program. The Evaluators recommend that IGC program staff to host
listening sessions with each respective stakeholder group to gain insight into the challenges that
they face with the Energy Efficiency program and market conditions.
 Explore opportunities to incorporate new measures (i.e., smart thermostats) in IGC’s Energy
Efficiency program offerings. Some tankless water heater contractors believe cost can be a
barrier for customers and estimated a 0.91 UEF system was two to three times more expensive
than a standard efficiency water heater. The Evaluators recommend exploring additional
opportunities for IGC to add new measures to the Appliance Rebate and Whole Home current
offerings. Specifically, the Evaluators recommend examining adding 0.82 efficiency tankless water
heaters and smart thermostats to the list of available measures. IGC could work with builders to
add smart thermostats to new homes. Staff could also work with contractors to use smart
thermostats as a marketing tool (e.g., limited-time offers for free smart thermostats coupled with
new furnace installation).
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 Consider establishing a builder and contractor approved network. By creating an IGC-approved
network of builders and contractors this could help ensure the appliances are being installed at a
sufficient standard of quality and that the homes built exceed program requirements. The
approved network of builders and contractors could be added to the website, which would
provide legitimacy and marketing for those who are participating in the network, as well as
provide assistance to customers on resources for their future home upgrades.
 Consider standardizing the tracking database categorization of rebate status and rebate
rejection reasoning. The tracking database collected and maintained information on the status of
rebate approval as well as reasoning for rebate rejection. However, the inputs for these fields
were often detailed and high-level insights were difficult to parse. The Evaluators recommend
setting in place a standard categorization for both the rebate status as well as rebate rejection
reason in order to assist in tracking and prioritization of current rebates as well as assist in analysis
of barriers to rebate completion.
1.3.2 Whole Home Measure
 Work with builders to achieve HERS requirements at a lower cost. Builders provided
recommendations on how to achieve a lower HERS scores in a cost-effective manner. The
Evaluators recommend working closely with builders who are successfully achieving the HERS
requirements to identify additional opportunities to reduce overall costs (e.g., implementing non-
HVAC measures such as air sealing, duct sealing, and insulation).
 Provide builders with additional marketing materials to promote the Whole Home measure.
Builders suggested providing marketing materials such as flyers or a logo to use on their website
that will help promote the Whole Home measure to home buyers. Marketing materials can be
utilized to promote awareness of the Whole Home measure and to educate homebuyers about
energy efficiency. The Evaluators recommend exploring all opportunities to generate marketing
collateral that can be used by builders and IGC program staff at community events. Program staff
could also consider co-branding marketing collateral with participating contractors who would be
interested in additional materials.
 Engage builders with company-specific reports and cost-saving estimates that builders can use
with homebuyers. Builders indicated they were reluctant to provide cost-saving estimates to
homebuyers, citing liability, but believed it would be useful information to help sell and promote
program-qualifying homes. The Evaluators recommend working with builders to develop generic
cost-saving estimates that can be used by builders when promoting the benefits of purchasing an
energy efficient home. These cost-saving estimates could also provide an educational opportunity
to engage homebuyers with energy saving tips. The Evaluators also recommend creating material
that will help engage participating builders, such as quarterly or biannual reports that are builder
specific or quarterly newsletters with Whole Home measure statistics and upcoming events that
IGC will be in attendance.
 Continue participating in community events to recruit additional builders and generate
awareness about the Whole Home measure. Many participating builders first learned of the
Energy Efficiency program through IGC-attended events where builders and HERS raters were
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provided with information about the Whole Home measure. The Evaluators recommend
researching additional outreach opportunities to reach nonparticipating builders with
promotional activities (e.g., offer a free HERS rating to builders who have not received a rebate in
the past two years).
 Develop materials that increase knowledge of the benefits of owning an energy efficient home
among IGC customers. The homeowners who do value the idea of energy efficiency are
consumers who are actively researching the latest technology or are interested in the incentives.
Homeowners who do not actively seek this type of information would benefit from knowing about
the incentives from a knowledgeable realtor or other stakeholders. The Evaluators also
recommend developing educational materials that can be used at events where homebuyers
would be in attendance (e.g., home and garden shows). Consider how program participation could
improve by engaging in outreach geared at increasing homeowners’ knowledge of energy
efficiency.
 Provide additional education or training opportunities for HERS raters, builders, and real estate
agents. According to the raters, builders have a significant learning curve to overcome to fully
understand the benefits of energy efficiency. Builders reported they were already building above-
code homes there were energy efficient and were not sure the benefits of participating. The
Evaluators recommend developing trainings that can be either in person or online that builders
and HERS raters can attend to increase their knowledge of the Energy Efficiency program,
specifically the Whole Home measure. IGC staff could also explore creating a training or
presentation for realtors. By offering continuing education credits through the Idaho Board of
Realtors, real estate agents could educate homeowners about the differences between an
ENERGY STAR® certified home and a HERS ratings.
1.3.3 Appliance Rebates
 Ensure that contractors are aware of the different participation options. A portion of contractors
said that participating in the program was time consuming and burdensome. By increasing
awareness of the program’s online form submission option IGC could improve contractor program
experience and expedite customer rebate receipt. Ensuring familiarity with participation
processes may ease the burden of participation and encourage more frequent participation. The
Evaluators recommend conducting direct outreach to current and past program participants (e.g.,
invite contractors to an online webinar or schedule individual one-on-one sessions to explain the
participation process). IGC program staff should investigate and remedy barriers to participation.
 Consider enabling contractors or customers to track the status of their rebate. Allowing the
tracking of rebate status could potentially reduce the number of customer inquiries to IGC.
 Consider creating and distributing IGC Appliance Rebate promotional materials to contractors
and/or customers directly. The contractors suggested that IGC collateral could help them to
promote the program and a large portion of customers noted that they had learned about the
program from contractors. The Evaluators recommend sending contractors program material
directly that could assist with their outreach and promotion of the Appliance Rebate measure.
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 Develop new educational materials and continue community outreach. IGC program staff
indicated there is not a set education budget but they have created educational materials to
increase awareness of the Appliance Rebate measures and general understanding of energy
efficiency. The Evaluators recommend looking for partnership opportunities to create online
instructional videos and other educational materials that can be viewed by customers. The
Evaluators also recommend continuing community outreach efforts with a focus on increasing
customer knowledge and general understanding of energy efficiency.
 If furnaces have difficulty meeting cost-effectiveness requirements, consider the addition of an
Electronically Commutated Motor (ECM) for the furnace blower as a program requirement.
ECMs result in significant kWh savings when installed alongside a new furnace, and this additional
requirement could be used to enhance the cost-effectiveness of a retrofit furnace.
 Consider collecting application data on the condition of the replaced furnace. The Evaluators
found that 72% of survey respondents indicated that their preexisting furnace was functional. This
could be used to increase energy savings estimates via early retirement (in which savings are
estimated based on preexisting equipment efficiency rather than current minimum code).
 Incorporate Avoided Replacement Cost (ARC) into tankless water heater cost-effectiveness
calculations. Tankless water heaters have a 20-year measure life, while storage tank water
heaters have an 11 year measure life. The net present value (NPV) of the purchase of a second
water heater starting in year 12 should be deducted from incremental cost estimates used for this
measure, improving measure cost-effectiveness.
 Consider tracking additional fields in rebate application process to assist in future energy
savings estimates. The tracking database sufficiently collects customer information, household
information, and contractor information. However, additional quality assurance steps while
collecting some variables can be improved upon, such as year built, square footage, appliance
model brand, appliance model number, appliance serial number, and equipment size and
efficiency. In addition, the Evaluators recommend adding the following information as
requirements for rebate application to assist in quality control and future energy savings
calculations: equipment AHRI certification number, reason for replacement (replace-on-burnout,
early retirement, new construction), prior equipment input size, prior equipment efficiency, and
prior equipment age. The above fields are either required or optional values as inputs to
engineering equations to estimate annual energy savings for these measures.

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2 Introduction and Purpose of Study
This report presents an evaluation of the performance of the Energy Efficiency Program offered by IGC in
PY2017-2018 and PY2019. The purpose of the process evaluation is to gather information from a variety
of sources, including program staff, contractors, program participants, and non-participants (collectively
referred to as market actors) to aid in program improvement and development in future program years.
To increase the validity of the findings, it is necessary to gather data from multiple sources and then
“triangulate” the data or compare it across multiple groups. This methodology increases the overall
validity of the findings. The Evaluators addressed a variety of issues, including:
 Program design;
 Program administration;
 Program implementation and delivery;
 Customer satisfaction; and
 Market response.
2.1 Program Offerings and Descriptions
IGC’s Energy Efficiency Program includes the Whole Home and Appliance Rebate measures. The rebate
offerings are summarized in Table 1 and sections below.
Table 1 IGC’s Energy Efficiency Program Offerings
Program Channel
Appliance
Rebate
95% AFUE Natural Gas Furnace 1,334 2,066 $466,900 $723,100
90% Radiant Combo 3 11 $3,000 $11,000
80% AFUE Fireplace 0 0 $0 $0
70% FE Fireplace 13 14 $1,300 $1,400
0.67 Water Heater 9 8 $450 $400
0.91 Tankless Water Heater 88 159 $13,200 $23,850
Whole Home 619 1,079 $742,800 $1,294,800
2.1.1 Whole Home Measure
The Whole Home measure offers incentives to home builders and buyers for new construction, single-
family residential homes that meet energy efficiency standards of the program. The Whole Home measure
requires homes must be certified to meet the requirements of the Environmental Protection Agency (EPA)
ENERGY STAR certified homes program. In addition, the home must achieve a Residential Energy Services
Network (RESNET) Home Energy Rating System (HERS) score of 75 or less with a certificate completed by
a certified HERS rater. The Whole Home Rebate cannot be combined with any other appliance rebates in
IGC’s Energy Efficiency Program.
The objective of the Whole Home Rebate is to encourage homebuilders to include energy efficient
measures in the construction of new homes built within IGC’s service area. This measure provides a $1,200
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rebate for new construction residential ENERGY STAR certified homes with a RESNET Home Energy Rating
Score (HERS) of 75 or less.
The following are also requirements for submitting the application rebate form:
 Must be a residential customer of IGC;
 Homes must be certified by an Energy Star verifier; and
 Energy Star home rebate applications must be submitted and postmarked within 90 days
following Energy Star verification.
2.1.2 Appliance Rebates
The Appliance Rebates were designed to provide financial incentives to encourage residential customers
to install energy efficient appliances. This measure offers incentives for residential customers that
purchase and install qualifying high-efficiency appliances including natural gas furnaces, radiant
combination heating systems, fireplaces, water heaters, and tankless water heaters.
The measure requires the participant to be served on the residential rate schedule and to use natural gas
exclusively for space heating. Eligible project dispositions include new construction, replacement of
existing failed equipment (“normal replacement” or “replace on burnout”), replacement of existing
equipment that are still functional (“early replacement) as well as equipment converted from an alternate
fuel. Customers receive rebate incentives at the amounts displayed below if appliances must meet the
minimum rated efficiency. Table 2 summarizes the Appliance Rebate offerings.
Table 2 Appliance Rebate Offerings
Channel Description Amount
95% AFUE Natural Gas Furnace 95% or greater thermal efficiency rating $350
90% Radiant Combo $1,000
80% AFUE Fireplace Discontinued -
70% FE Fireplace 70% fuel efficient rating or greater $100
0.67 Water Heater 0.67 energy factor or greater $50
0.91 energy factor or greater $150
The 80% AFUE fireplace rebate was discontinued due to lack of participation. The Evaluators completed
process evaluation for the above rebates in the form of contractor interviews and participant surveys.
The following are also requirements for submitting the application rebate form:
 Must be a residential customer of IGC;
 Rebate applications must be submitted and postmarked within 90 days of installation;
 Fuel for the home’s heat and water heating needs must be exclusively provided by IGC for
applicable space heat and water heating rebates;
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 A heat pump must not be used for space heating, even if a natural gas furnace is utilized as
backup. If a heat pump is installed in the home, it must be wired to the “Cool Only” setting;
 Energy-savings equipment must meet the requirements of Intermountain Gas’ Energy Efficiency
Rebate Program tariff and associated Terms and Conditions effective at the date the equipment
was installed; and,
 All equipment must be installed according to current code and approved by local or state
inspection with the signed, approved permit attached to the newly installed equipment.
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3 Process Evaluation
This chapter presents the findings of the process evaluation for Intermountain Gas Company’s (IGC)
Energy Efficiency Program.
3.1 Process Evaluation Objectives
The Evaluators conducted a process evaluation for each of the measures offered in the Energy Efficiency
Program, which:
 Measures and finds opportunities to improve level of customer satisfaction;
 Improve understanding of target population to tailor marketing materials towards customer
segments
 Refine and refocus marketing strategies and increase program effectiveness;
 Identify barriers and suggest changes to reduce barriers to program participation;
 Provide recommendations for changing the program’s structure, management, administration,
design, delivery, operations, or target; and
 Help program designers and managers structure programs to achieve cost-effective savings.
The process evaluations focus on documenting the effects that the program activity had on encouraging
installations of the energy efficiency measure or influencing the customer to make an energy-efficiency
decision. The key program metrics that have been documented in these process evaluations are:
 Documenting overall awareness as well as awareness of the program and its measures;
 Verifying installations of measures through customer surveys;
 Determining if there are significant differences between and among participant groups;
 Assessing customer satisfaction with the utility staff and the overall program;
 Identifying reasons for not participating;
 Determining if the program has led to lasting changes in customer behavior regarding energy
efficiency or influencing customer decision-making; and
 Identifying areas for program improvement.
3.2 Process Evaluation Approach
The Evaluators designed the process evaluation to ensure that best practices and lessons learned could
be shared and incorporated across the entire program portfolio. The findings are intended to serve to:
 Guide changes in program delivery mechanisms and marketing materials to be more effective to
participant demographic;
 Identify barriers to program participation;
 Identify progress made towards achieving the objectives as required by IGC or the Public Utilities
Commission; and
 Identify any issues that may need to be explored more fully in future program evaluations.
The process evaluation data collection activities included interviews with program staff, HERS raters,
Energy Service Representatives (ESRs), contractors, participating builders, non-participant builders as well
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as surveys of IGC customers (program participants and nonparticipants). Table 3 summarizes the number
of participants that responded to each interview or survey.
Table 3 Process Evaluation Activities
Activity
Program staff interviews (including ESRs) 5
HERS rater interviews 3
Contractor interviews 23
Participating builder interviews 8
Nonparticipating builder interviews 6
Whole Home participant survey 80
Appliance Rebate participant survey 138
Nonparticipant survey 160
3.2.1 Key Researchable Issues
The process evaluation focused on documenting the effects program activity had on encouraging
installations of energy efficiency measures and influencing customers to make energy-efficient decisions.
The key research topics that the Evaluators investigated in the process evaluation included:
 Overall awareness of the Energy Efficiency program and its measures;
 Customer satisfaction with the utility staff and the overall program;
 Program influence and lasting changes in customer behavior regarding energy efficiency or
customer decision-making;
 Understand differences between and among participant groups;
 Reasons for customers not participating in the program to understand participation barriers; and
 Areas for program improvement.
3.3 Process Evaluation Findings
3.3.1 Program Staff Interviews
This section summarizes the findings of interviews conducted with IGC staff for the purposes of learning
of any recent changes to the program goals, program structure, and assessing the extent to which there
are future opportunities for program improvement. The Evaluators conducted interviews with two energy
efficiency analysts, an energy efficiency manager, and two energy service representatives to gain insights
into the 2017-2018 (PY2017-2018) and 2019 (PY2019) program years.
IGC has not made significant changes to design or operations of the Energy Efficiency program since its
implementation in PY2017-2018. The program includes the Appliance Rebate and Whole Home Rebate,
which exceeded program staff’s expectations for the first two years of implementation. IGC program staff
were focused on customer participation, community outreach and engagement, and streamlining
processes for the rebates in PY2017-2018 and PY2019.
The following details the key findings of the interviews.
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 IGC launched the Energy Efficiency program in PY2017-2018 with few changes to design or
operations in PY2019. IGC received approval to launch the Appliance Rebate and Whole Home
measures starting in PY2018. After approval, program staff were hired to implement the Energy
Efficiency Program whose design and parameters were set prior to launch. The initial goals for the
Energy Efficiency Program was to increase customer awareness and participation. When IGC staff
started implementing the program they were focused on determining their logistical partners,
leveraging existing relationships, and the rebate process. During the Appliance Rebate program
planning phase, 80% efficient fireplace inserts were included in with other eligible measures, but
staff learned the retrofit was prohibitive due to venting and condensation requirements and was
later removed.
 Energy Efficiency program goals were focused on assessing measure participation and
performance in the first two program years. Staff stated that their program offerings would be
modest in the first years of implementation, that could be “easily understood by customers” and
easy to implement and manage. IGC staff stated in lieu of measuring the Energy Efficiency
program against a specific goal, the program is assessing trends in participation to monitor
program performance and gauge customers engagement and interest. While IGC staff indicated
there were no savings goals for Appliance Rebates or Whole Home measures, they did indicate
there is a 5-year target for demand-side management savings target and they examine
performance trends for customer participation and participating builders. They also assess market
barriers that are specific to measures offered through the programs and identifying areas where
participation could increase.
 Energy Efficiency program’s performance and participation exceeded expectations in the first
two program years. According to IGC staff, both the Appliance Rebate and Whole Home measures
performed well in the first two years of implementation and were on an “upward trend.” The
furnace and Whole Home measures accounted for the largest share of the rebates. The Appliance
Rebate measures saw a 60% increase in the number of appliances rebated and the Whole Home
measure saw a 70% increase in the number of homes between PY2017-2018 and PY2019. There
were additional participating builders and contractors. Staff also believed there was increased
customer awareness and improving relations with key program stakeholders (HERS raters and
HVAC contractors). The average HERS score for participating homes increased slightly from
PY2017-2018 to PY2019, indicating the average participating home decreased in efficiency.
 Online application forms and HVAC portal were developed in the first two years of
implementation. The Appliance Rebate measure launched online application forms and a portal
for HVAC contractors. There have been 250 online applications completed since its launch in
October 2018. In addition, a training on the portal was offered to contractors.
 Home sales grew in Idaho during PY2017-2018 and PY2019 indicating strong demand for new
homes. IGC staff stated that there is a strong housing market in Idaho. They indicated that as a
result, builders do not need to highlight energy efficiency attributes to sell homes and this could
be a barrier to participant among nonparticipating builders. Staff believe that home buyers may
need additional education about energy efficiency when purchasing a new home. There is not a
dedicated budget for education.
 Lessons learned between PY2017-2018 and PY2019 included how to effectively engage various
stakeholders. IGC staff indicated they learned various lessons about program implementation and
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operation since they launched. One lesson learned was how to effectively engage with the
community and to target events where program staff can reach the appropriate audience.
Program staff indicated that recruitment of builders and contractors can be most effective either
by phone or in-person, when possible. They also learned that attending events where builders are
in attendance can be one of the most effective ways to discuss the program with them, specifically
golf tournaments held by builder associations. Another lesson learned is to ensure that
participating contractors are licensed with the state to perform install. Staff have kept a list of
contractors’ licenses with their expiration date to compare against installation. This ensures that
installations are done in accordance with state permitting requirements. IGC staff also indicated
that not exceeding the rebate budget was another important lesson learned in the first two years.
 Energy service representatives (ESRs) actively promote the Energy Efficiency programs. ESRs
main role is to assist customers who inquire about new gas services, retrofits, and new
construction projects. ESRs work with existing and new residential customers, builders, and
contractors and educate them about the Energy Efficiency programs offered by IGC. ESRs provide
customers with brochures and other marketing material to provide them with information about
the rebated measures available. Both ESRs who were interviewed believed PY2017-2018 and
PY2019 were successful years and indicated efficient furnaces were the easiest measure to
promote to customers. ESRs reported that other measures, such as tankless water heaters and
fireplaces, were more challenging to convince customers to adopt. ESRs also indicated they attend
builder event (e.g., Building Contractors Association meetings), sent custom letters to builders
and contractors, and met with stakeholders in-person to establish relationships. ESRs have a few
meetings per year with the IGC Energy Efficiency program staff and mostly communicate through
phone and email.
 Continuing partnerships with Division of Building Safety and Habitat for Humanity to increase
education and understanding of energy efficiency for customers. IGC program staff partnered
with Division of Building Safety (DBS) and Boise Habitat for Humanity to develop additional
educational videos about energy efficiency in homes. In PY2019, IGC created an educational video
about the benefits of air sealing during new home construction. The video provides a
demonstration of air sealing practices and the process for properly sealing a home during
construction. The video is located on the IGC YouTube channel. There also videos about the
benefits of installing a programmable thermostat, ceiling fans, and general information about
natural gas.
 Program changes anticipated in PY2020 and beyond. IGC program staff indicated they anticipate
some changes to their energy efficiency programs for the remainder of 2020 and possibly for the
upcoming program year. Staff anticipated changes to their offerings based on the results cost
potential assessment that was conducted. Staff anticipated a commercial offering and are
interested in adding smart thermostats to their list of eligible measures. Program staff also
discussed whether the HERS rating of 75 was appropriate for the Whole Home measure. Staff was
concerned increasing the stringency of the HERS score as a requirement for participating homes
and theorized it may be a barrier for builders who are not accustomed to building homes beyond
code. Staff believed a tiered incentive system might be a future addition to the Whole Home
measure. Staff would also like to increase customer education efforts about energy efficiency.
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 Communication is adequate and frequent. There were weekly update meetings in 2018 and
2019. Meeting topics included project status, rebate status, trend identification, community
outreach and promotion efforts, and other program issues. Staff also indicated they communicate
on an as-needed basis. All staff indicated the level of communication was appropriate and
meeting the needs of the program.
 Data management is adequate. ICG program staff indicated they utilize Microsoft Excel to track
program data. Staff upload all data from rebate applications and use batch numbers for issuing
checks. They also track community outreach efforts and contractors’ license information. For the
Whole Home measure, staff tracks the HERS certificate and if the home has been ENERGY STAR®
certified. Currently, there is not a registry to check if a home is a certified ENERGY STAR® home
so program staff rely on submitted application with a HERS rater statement. Staff also indicated
they are using software called ThoughtSpot to run queries and reports. The reports can be queried
by service district. Staff believed data management has improved since they launched in 2018 and
that it is kept current enough to effectively monitor program activity and trends. There is not a
formal process for gathering feedback from participants. Staff did administer a short survey in
PY2019 to gather information about participants’ experience, knowledge of furnace ratings, and
program awareness.
 Broad marketing efforts and community outreach were used to increase awareness of the
programs. IGC staff utilized a broad approach to promote the availability of the Appliance Rebates
and the Whole Homes measures. Staff indicated they focused on three areas for outreach and
promotion: customers and communities, home builders, and HVAC contractors. Staff indicated
that HVAC contractors are key to promoting the program to customers because they are trusted
experts. Marketing activities included bill inserts, digital advertising, radio campaigns, and
attending community events. To promote the Whole Home measure, IGC staff participated in the
Parade of Homes sponsored by the Builder Contractors’ Association. Promotional activities with
builders included attending association meetings, expositions, and other events. IGC does not
provide marketing collateral or cobranding opportunities for contractors or builders.
 Social media and digital platforms are used broadly. IGC staff indicated they utilize social media
(e.g., Facebook, Instagram, and YouTube) and other digital platforms to increase awareness of the
energy efficiency programs. Some social media promotions have been paid. Staff stated there are
plans to update the website once the programs have been changed and expanded.
 Development of codified QA/QC procedures for the program. At the time of the interviews, IGC
staff did not indicate there were codified quality assurance or control procedures. Although staff
reviews applications and home rebates, the program lacks standardized quality assurance
guidance. Program staff indicated they review applications to ensure there are not duplicate
rebates submitted by checking serial numbers. IGC requires that the final invoice include the
model name, brand, and serial number and that the installation goes through a permit process.
The permit process will vary depending on the jurisdiction. ICG staff search for the permit to
ensure it passed final inspection.
 Builder participation increased due to recruitment efforts of staff. IGC staff indicated there was
an increase in the number of participating builders from 18 to 24 over the two-year period (2018-
2019). Staff stated they are typically recruited at BCA meeting as they offer an opportunity to
educate builders about the program. Program staff engaged in community and home buyer
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outreach to inform them of the benefits of ENERGY STAR certified new homes. The intent of that
outreach was to increase market interest in the hopes of affecting builder interest in the program.
According to IGC, the feedback from builders has been positive and that they are grateful to
receive the rebates within 1 to 2 weeks.
 HERS raters are an integral component to the Whole Home measure. The number of
participating homes increased between PY2018-2018 and PY2019. IGC staff stated that HERS
raters assist with outreach to builders and recruitment efforts. There were no additional HERS
raters recruited into the program in PY2019. There were no training opportunities or
informational sessions for HERS raters in PY2018 or PY2019. IGC staff generally communicates
with HERS raters via email and phone. Program staff indicated they have received positive
feedback about the Whole Home measure from HERS raters and that they like the quick
turnaround for rebates.
 IGC program staff uses various communication tools to engage with contractors. Contractors
who install program-qualifying equipment are not required to be officially registered with IGC.
IGC does not have an approved trade ally network but does require that contractors who
participate in the program to be licensed with the state. IGC program staff generally
communicates with contractors via email and phone. There was one training provided by IGC staff
where there was information about the HVAC portal and online application. If contractors were
not able to attend, IGC staff sent emails with videos of the training along with online resources.
The online contractor portal training is available on IGC’s YouTube channel. IGC also offered a
$300 reimbursement for contractors to participate in an ENERGY STAR® certification course. IGC
staff indicated they have received positive feedback from contractors (e.g., they like the
“straightforwardness of the program” and the responsiveness of staff).
 IGC would like to see more electronic submissions of applications and increasing awareness of
Energy Efficiency program. IGC staff stated that they would like to see an increase the number of
applications submitted electronically by contractors as they believe this would help streamline
their application process. Staff would also like to get rid of the HERS rater statement in which the
HERS rater must verify and confirm that the home is HERS certified. They would instead like to
have another way to certify if the home meets the ENERGY STAR® requirements for the Whole
Home measure. Program staff would also like to improve customer education and engagement.
They are exploring adding online tools to their website, along with self-directed resources for
customers to increase their energy efficiency knowledge. Increasing community outreach and
trying new approaches were also mentioned as ways to improve the program and increase
awareness.
3.3.2 HERS Rater Interviews
This section summarizes the findings of HERS rater interviews conducted for the purposes of learning of
the HERS raters’ experience with the program, feedback, and assessing the extent to which there are
future opportunities for program improvement. The Evaluators conducted the interview with HERS raters
to gain insights into the PY2017-2018 and PY2019 program years.
All three interviewees have worked as a rater in Idaho for more than a few years and all three stated they
have participated in the Whole Home Rebate since inception. Each rater has worked with a different
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number of builders (range of 3 up to 70 builders total). In addition to rating homes, their companies also
offer services in HVAC design, code testing, or general home auditing. Additionally, they offer ENERGY
STAR® home rating consultation and training services.
The HERS raters were generally satisfied with the Energy Efficiency Program. According to the raters, the
incentives are paid in a timely manner and program requirements are attainable. Raters also indicated
the communication with program staff is sufficient but could improve in some areas. All HERS raters
believe that offering more educational trainings and events could greatly increase understanding about
energy efficiency among builders, realtors, and homeowners. More awareness of energy efficiency
benefits could increase program participation and market competitiveness.
The following details key findings of the in-depth interviews.
 Overall, the raters indicated being satisfied with IGC’s Energy Efficiency Program. Raters
received positive feedback from builders about energy efficiency programs and indicated that the
program’s requirements seem reasonable and attainable. According to one HERS rater, IGC’s
payment turn-around is efficient and dependable.
 In general, raters find IGC’s energy efficiency programs to be one of the most effective in the
region. All the raters indicated they have participated in multiple energy efficiency programs
sponsored by other utility companies. One HERS rater praised IGC as being the best in paying the
incentives in a timely manner and working with other stakeholders to improve program
implementation.
 HERS raters were mostly satisfied with IGC’s technical support and few issues were reported.
IGC staff were primarily responsible for providing information to HERS raters. According to the
raters, most of the information about the program is provided by IGC staff. Two of the three
raters gave a score of four out of five regarding program technical support. The other rater gave
the rating a three out of five because of the issues they have had with the data processing system
(see Figure 1). The rater who rated the technical support a three stated they had experienced
many IT problems when trying to file reports and input important data for IGC.
Figure 1 Satisfaction with Technical Support

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 The perceived sufficiency of communication between HERS raters and IGC varied. According to
the raters, communication with IGC program staff varies depending on the rater. One rater
described how IGC has invited raters in the past to participate in the planning of program
implementation. The collaboration was effective because the program addressed concerns and
issues that HERS raters normally face when participating in these types of incentive programs.
Additionally, the other raters indicated they would be interested in having more communication
with program staff than just receiving emails.
 Raters use direct and indirect messaging to recruit builders to the program. The most successful
strategy for recruiting builders has been word-of-mouth. The HERS raters indicated they have
informed the builders and real estate agents about the different rebate programs. Raters have
also learned that other builders or contractors have been encouraging other builders to join.
 Program requirements are broadly understood by participants and would benefit from further
explanation. The raters stated that IGC’s incentive programs are easy to understand because they
are based primarily on the ENERGY STAR® criteria. However, they have noticed that builders do
not understand the importance of energy efficiency. According to the raters, builders have a
significant learning curve to overcome. One rater suggested that IGC create a detailed checklist
of the specific objectives the builders need to meet. The document should also include the
amount of money it could cost the builder to meet those requirements.
 According to the HERS raters, some builders perceive the costs outweigh the benefits of
participating in the Whole Home program. The HERS raters noted that some builders consider
the costs of upgrading to more energy efficient standards to be burdensome. Other builders did
not find the requirements a barrier because they already build to this standard. According to the
raters, the different perceptions among builders depend on local building codes as well as local
market demand. For example, one rater said that some builders who only needed to make minor
changes to become eligible for the incentive were more likely to make the upgrades and apply for
the rebate.
 HERS raters indicated the rebate incentive as the best marketing tool for reaching most builders.
Currently, builders know what they need to accomplish to obtain the incentive but lack the
understanding of why they should meet the requirements. According to one rater, builders should
be aware that the initial costs may be higher, but the cost decreases once the energy efficiency
upgrades become part of the builder’s services. Raters reported that former program builder
participants have seen an improvement in the quality of their plumbing and insulation installation.
 Program budget could become more cost effective if staff uses a pay-to-performance method
(pay scale). One HERS rater suggested that IGC pay builders according to the actual energy savings
they achieve. In some counties, builders in Idaho are scoring a 53 or 52, when the minimum score
for the Whole Home measure rebate is significantly higher. According to one HERS rater, these
builders should be paid more than the builders in other counties or from other companies who
may be meeting the minimum requirements. He stated that by paying according to actual savings,
program staff could see a more effective use of their program funding.
 Program training for realtors may increase participation. Raters indicated that realtors may be
the key stakeholder to improving program participation among builders because they have direct
contact with homeowners. By developing a course that offers continuing education credits
through the Idaho Board of Realtors, real estate agents could better educate homeowners on the
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benefits of owning an ENERGY STAR® certified home. They could market the homes that are HERS
certified and increase competition within the housing industry.
 Raters indicated that IGC could improve their outreach to homeowners about buying energy-
efficient homes. Although more homeowners are inquiring over certified energy-efficient homes,
the majority do not understand the value of ENERGY STAR® certified homes. Homeowners who
do value the idea of energy efficiency are consumers who are actively researching the latest
technology or are interested in the incentives, according to one rater.
 HERS raters expect the number of new homes for 2020 will remain consistent from 2019. Raters
noted that construction depended on changes in the market. The sudden economic downturn
due to the pandemic has significantly affected company profits for 2020. Earlier calculations in
the year, however, had indicated an increase in home construction for all the HERS raters, which
would have surpassed PY2019 numbers.
3.3.3 IGC Contractor Interviews
This section summarizes the findings of interviews conducted with Idaho contractors. The purpose of the
interviews was to explore contractors’ business and energy efficiency practices, program experience,
satisfaction, and opportunities for program improvement. The Evaluators conducted the interview with
23 contractors that have participated in IGC’s Appliance Rebate measure to gain insights into the 2017-
2018 (PY2017-2018) or 2019 (PY2019) program years.
The Evaluators contacted 119 contractors through 92 phone calls and 145 emails. Of the 23 interviewed
contractors the Evaluators interviewed 20 participated for furnace rebates, 9 for tankless water heater,
and 2 for fireplace rebates. Seven of the contractors interviewed participated for the furnace and tankless
water heater rebate programs. Table 4 displays contractor interviewees’ roles or titles.
Table 4 Contractors’ Roles or Titles
Role or Title n
Owner 12
Sales Manager/Coordinator 5
Office Manager/Admin 3
General Manager 2
President 1
The contractors stated that their residential work was primarily with single family homes, though seven
contractors said that 10% or more of their residential work was at multifamily properties. Nineteen of the
contractors mentioned that their company also did commercial work, while seven stated that they did
very little commercial work.
Interviews indicated that the participating contractors had experience selling high efficiency equipment
through various programs and noted that the IGC Appliance Rebate measure was easy and
straightforward. Most contractors reported that there is sufficient support from IGC and that the Energy
Efficiency program was running smoothly.
The following details key findings of the in-depth interviews.
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 All participating contractors encourage the sale of high efficiency equipment. Most of the
contractors said their only method of encouraging the sale of high efficiency equipment was
through discussions with customers. Four contractors mentioned other methods of encouraging
the sale of high efficiency equipment including social media, newspaper, radio, and television ads.
 Higher cost of energy efficient equipment is occasionally a challenge for some contractors.
Thirteen of the contractors (57%) interviewed said the higher cost of higher energy efficiency
equipment made selling it a challenge. Two contractors mentioned that customers’
understanding of the benefits and the documentation for higher efficiency equipment was
another challenge to selling higher efficiency measures. Ten contractors (43%) said there were no
challenges when trying to sell high efficiency equipment.
 Contractors primarily utilize in-person discussions with customers to promote energy efficiency
equipment. Sixteen of the twenty-three contractors (70%) said that discussing the reduction in
monthly energy use or utility bills was a successful strategy for encouraging customers to choose
high efficiency equipment over standard efficiency equipment. Two contractors said that they do
not offer lower energy efficiency equipment in their bids or proposals to customers. Two
interviewees could not identify successful strategies to selling high efficiency equipment. One
contractor stated that customers care most about the upfront cost and whether an incentive will
cover the difference between standard and higher efficiency equipment. The final contractor
stated that simply explaining the options to customers and educating them on the differences is
enough to encourage the sale of high efficiency equipment.
 All contractors had experience with energy efficiency programs. Thirteen of the contractors said
they had previous experience with Idaho Power and their rebate programs for energy saving
measures such as heat pumps and thermostats. Four contractors mentioned working with Rocky
Mountain Power’s energy efficiency rebate programs. Four contractors mentioned other
experiences including ENERGY STAR® ratings for new construction, the Idaho Department of
Environmental Quality (DEQ)’s rebate program for wood fireplace replacement, working with
Bonneville Power, and other energy efficiency programs that incentivized heat pumps. Five
contractors either could not recall the details of their past participation in energy efficiency
programs or were unable to elaborate on their experience. Table 5 displays interviewees’
experience with energy efficiency programs (some contractors reported having experience with
more than one program).
Table 5 Contractors’ Past Experience
Experience n
Idaho Power 13
Rocky Mountain Power 4
Other Experience 4
Cannot Recall 5

 Contractor program awareness comes from a variety of sources, but primarily from IGC. Sixteen
contractors recalled learning of the Appliance Rebate measure from IGC through direct contact
with a representative or an email, flyer, or promotion sent in the mail from IGC. Other sources of
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program awareness included customers, occupational trainings or seminars, past participation, or
through internet research (see Figure 2). Three contractors could not recall how they learned
about the program. Nineteen of the contractors reported having participated in the program since
2018; four said they had participated for a year or less.
Figure 2 Contractors’ Sources of Program Awareness

 Customer awareness of the IGC Appliance Rebate program is generally low. On average,
contractors estimated that less than 20% of their customers know about the program before they
tell them about it. Two contractors estimated that about half of their customers knew about the
program before they informed them.
 Marketing and program promotion vary among contractors. Sixteen of the 23 contractors (70%)
said they actively marketed the program to their customers through in-person interactions. Three
of these contractors stated that they included the rebate in their customers’ quotes or project
proposals. Two contractors mentioned other forms of promoting the program including
advertising on their website, emails, and in newspapers.
 Most contractors reported that they had not received marketing collateral, though some
thought it might be helpful. Most contractors (19 of the 23 or 83%) reported that they had not
received marketing collateral from IGC. Eleven of the contractors (48%) that had not received IGC
promotional collateral said that promotional assistance or marketing materials may be helpful.
One contractor suggested that IGC provide a canned ad to insert into their own TV ads. Three of
the four contractors that had received marketing collateral noted that they did not find it to be
helpful; the fourth contractor said they would appreciate additional materials.
 Strong program communications and sufficient contractor support. Eighteen of the contractors
(78%) said program communications were adequate, that they were sufficiently able you relay
information to their customers, or they did not think there was any need to improve program
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communication. Seven of the contractors (30%) noted that their company had received program
training in either 2018 or 2019. Four contractors had in-person training and three had online
training or attended webinars regarding the program. All seven contractors that had program
training said that it had been helpful. Five of the contractors suggested that there was room for
IGC to improve communication through additional emails, flyers, newsletters, or training for
contractors explaining the program.
 Furnace contractors typically replace non-functioning furnaces and encourage the installation
of high-efficiency equipment. The Evaluators asked nineteen furnace rebate program
participants specific questions designed to better understand furnace projects. One furnace
rebate contractor did not answer these questions as he was unable to complete the survey.
Furnace rebate contractors provided estimated proportions of their projects that were either
replacing fully functional, near failure, or non-functional furnaces. Table 6 displays the results
from the nineteen furnace rebate contractors.
Table 6 Furnace Contractor Replacement Project Types
Operational Status Percent
(n = 19)
Non-functioning 41%
Near failure 37%
Fully Functional 22%
 Replacement projects are driven by both air conditioners and furnaces. All nineteen of the
furnace contractors interviewed said some percentage of their customers are simultaneously
replacing a furnace and air conditioner. Most (79%) of the interviewed furnace contractors
indicated that in 50% or more of their projects they are simultaneously replacing a furnace and
air conditioner. When the Evaluators asked for the primary reason of the simultaneous
replacement projects, eight contractors said the reason for replacement was equally driven by air
conditioners and furnaces, six of these contractors said most of the replacements are driven by
furnaces, and two said replacements were primarily driven by air conditioners. Some contractors
believed they are more likely to replace the entire HVAC unit in the summer compared to the
winter months. Three contractors did not know which replacement was the primary driver of
simultaneous replacements or referred to how seasonality affects their replacement projects.
 Condensing furnaces typically do not cause significant challenges. Sixteen of the furnace
contractors (84%) said that they do not often face substantial difficulties installing the needed
venting and drainage to support the installation of a condensing furnace (rated based on a scale
from 1 (never) to 5 (very often). Nine contractors (47%) said that sometimes customers inquire
about a condensing furnace, but then install standard efficiency because of the costs of venting
and drainage. However, only two of these contractors stated that this happens frequently (rated
based on similar scale as described previously).
 Contractors who install tankless water heaters believe cost can be a barrier for customers. Eight
of the nine tankless water heater rebate contractors (89%) said they had not installed any systems
that were below .91 UEF in the last year (n = 9). Five tankless water heater contractors estimated
that a 0.91 UEF system was two to three times more expensive than a standard efficiency water
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heater. Two contractors explained that there were many variables involved and the additional
cost varied. Four of the contractors said they had customers inquire about a .91 UEF tankless
system, but then choose not to install it because of the costs of venting and drainage; however,
three of these contractors said this happened infrequently. Two of the four contractors said a
customer would be likely to purchase a storage tank system in this scenario and two said they
would be likely to purchase a .82 UEF tankless system.
Five contractors (56%) said that if IGC offered a rebate for 0.82 UEF systems it would not increase
how many applications they submit to the program. Three contractors (33%) believed it would
increase the number of applications they submit, and one (11%) did not know how the change
would affect their number of applications.
 Opportunities to expand participation. Eight of the 23 contractors (35%) said that they have
completed energy efficiency projects that would qualify for the program but did not apply for a
rebate. Table 7 summarizes the reasons contractors did not apply for a rebate for program-
qualifying equipment.
Table 7 Reasons Contractors Did Not Submit Rebate Application
Response n
Time and resources required to apply 3
Lack of program knowledge or understanding 2
Forgetfulness 1
Only completing the application upon customer request 1
Customers not wanting inspectors in home 1
 Contractors see the program design as straightforward and operations as running smoothly.
Thirteen contractors (57%) said they did not find anything challenging about the program.
Regarding the application process, all but one interviewee said they helped their customers
complete the required program paperwork, though the extent to which they assisted with the
paperwork varied. The following figure displays the extent to which the Contractors assisted with
customers’ applications (see Figure 3) Most contractors said they offer to complete the whole
application process for the customer and only required the customer to sign it.
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Figure 3 Contractor Assistance with Application Process

 Contractors are largely satisfied with the program design and participation process, with some
offering suggestions for improvement. Most contractors that were interviewed indicated they are
satisfied with the participation process and program design. Nine contractors did not have any
suggestions to improve the program and did not find challenges with program participation. Other
contractors noted challenges or ways in which the program might improve (see below).
 Some contractors felt that program participation was burdensome. Eight contractors
emphasized the challenge of allocating time and resources to the program and suggested
simplifying or easing the process if possible.
 Some contractors said there was room to improve communications and/or training. Six
of the contractors suggested that there was room for IGC to improve communication or
promotion through additional emails, flyers, newsletters for contractors and customers
as well as training for contractors explaining the program. Furthermore, eleven
contractors stated that promotional material provided by IGC would be helpful.
 Three contractors noted challenges with rebate form submission and suggested
allowing rebate form submittal online or via email. Three contractors said they felt the
process of printing and mailing the form was onerous and suggested digital methods to
submit the rebate form.1

1 This may indicate an opportunity to better inform contractors regarding application submission options as three
other contractors explained that they utilized an online form to submit their rebate applications and appreciated its
ease of use.
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 One contractor noted missing the opportunity to apply for rebates because of the limited
time frame and suggested expanding the time window to apply for the rebate.
 Two contractors reported being contacted about the status of customer rebates and
suggested that IGC enable them to track the status of rebate payment.
 Some contractors provided recommendations regarding participation requirements
and features. Contractors offered suggestions regarding the program’s design and
participation requirements. Contractors offered the following recommendations:
Table 8 Contractor Recommendations
Recommendation n
Higher incentive for eligible measures 2
Revisit boiler incentive2 2
Eliminate permit requirements for the program; instead
only require that contractors be licensed 1
1
compensate them for promoting the program and 1
3.3.4 Participating Builder Interviews
This section summarizes the findings of participating builder interviews conducted with Idaho builders.
The Evaluators conducted interviews with 22 participating builders to gain insights into the PY2017-2018
and PY2019 program years. The builders were contacted both through email and phone at a maximum of
four contact attempts. The purpose of these interviews was to understand the builder home
characteristics and efficiency levels, the level of awareness their customers had about the Energy
Efficiency Program and the Whole Home measure, and builder satisfaction and feedback.
As shown in the figure below, most of the builders (68%) contacted had businesses in Meridian and Boise
in Idaho. The other participating builders (32%) were dispersed between Eagle, Idaho Falls, and Ammon.

2 One contractor stated that when they are installing high efficiency boilers, the only way they qualify is if they are
combination systems (heating and water heating). That contractor suggested that high efficiency boilers should
qualify regardless of whether they are water and home heating systems.
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Figure 4 Builders Who Participated in an Interview by Territory

Evaluators followed up with phone calls to builders who had not responded by email. Interviews were
completed for eight builders who responded to the request for an interview. Out of the eight builders
who responded, six (75%) were in Meridian, Idaho. Many of these respondents built production homes
rather than custom built homes. Overall, all builders interviewed were satisfied with Intermountain Gas
Company’s Energy Efficiency program (Whole Home measure). According to the builders interviewed, the
two main factors that went into deciding to build homes to program-building standards included program
financial incentive and ability to differentiate themselves in the market.
The following details the key findings of the in-depth interviews.
 Most interviewed builders were leaders at their company. Thirty-eight percent of respondents
held the title of President of their company. The other respondents had management roles
including director of operations, chief operating officer, managing partner, asset manager, and
purchasing manager. Results are summarized in Figure 5 below.
36%
32%
18%
9%
5%
0%
5%
10%
15%
20%
25%
30%
35%
40%
Meridian Boise Eagle Idaho Falls Ammon
(n=22)
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Figure 5 Leadership Position

 Most interviewed builders (75%) were the primary decision maker in terms of participating in
the Energy Efficiency program (Whole Home measure). One respondent stated that they were
not the primary decision maker, but that they were one of three decision makers regarding their
involvement in the Energy Efficiency program. The other respondent stated that the President of
the company made the decision to participate in the program.
 All responding builders participated in the Energy Efficiency program (Whole Home Rebate) for
at least one year. The builders all mentioned that they have participated in the program since
either PY2017-2018 or PY2019.
 Custom homes were not common among builders. Builders provided feedback about how often
they build production homes (homes with pre-existing floor plans) versus custom homes. Most
builders reported that they typically built production homes. Three builders stated that they do
not have any custom homes. However, two builders stated that they mostly received rebates for
custom built homes (one builder at 75% and the other at 100%, respectively). Two other
responses included 20% custom built and 30% custom built.
 Builders who attended Energy Efficiency Program (Whole Home measure) marketing events
found the information presented to be helpful. Fifty percent of builders interviewed attended
events where an IGC program representative provided information about the program. This
included information at a Contractors Association meeting and discussions held by Building
Energy. Three out of four builders that attended these meetings stated that the information was
helpful to them and that they would attend these events in the future.
 All participating builders interviewed stated that program financial incentive and ability to
differentiate themselves in the market were the factors that went into deciding to build their
homes to program-building standards in PY2018 and PY2019. Fifty percent of respondents stated
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that there was a customer demand for energy efficient homes, and 38% stated that additional
program assistance was a factor for them to participate in the program. Thirty-eight percent of
respondents also stated there were other factors that went into deciding to build homes that
happened to qualify for the program, which included the building codes that are changing.
 All participating builders built homes with a HERS score of less than 75. Some of the strategies
builders currently implement to achieve a lower HERS scores are installing air infiltration,
installing 95% AFUE furnaces, meeting all the ENERGY STAR® requirements, and following the
building codes. High efficiency HVAC systems, such as the 95% AFUE furnaces, were mentioned
as one of the more expensive ways to get a lower HERS score. The builders also listed a variety of
ways that are the least costly to get a lower HERS score. Implementing non-HVAC measures such
as air sealing, duct sealing, and insulation were noted as some of the least costly ways to ensure
that a home met the program required HERS score of 75 or lower.
 Builders generally do not actively promote the Whole Home Rebate to homebuyers. Sixty-three
percent of builders stated that they do not provide marketing messages or materials to their
customers about the Whole Home measure. One reason for not promoting the rebate program
to homeowners was that the Whole Homes measure is advertised to builders and not
homeowners, so that is not the focus group. This marketing material comes in the form of
information on the builder’s website, flyers, or advertisements about the lifestyle of these energy
efficient homes.
 Home buyers are generally unaware of the Whole Home measure according to builders. Builders
provided feedback on the proportion of the customers who were aware of the Whole Home
measure before they began working with them to build or buy a home. Five out of eight builders
either did not know if their customers were aware of the program or stated that their customers
were unaware about the Whole Home measure. Three out of eight builders stated that their
customers knew about the Whole Home measure. Results are summarized in Table 9 using the
parameters of “Don’t Know,” “Yes,” and “None.”
Table 9 Home Buyer Awareness
Respondents
 There are not many challenges when marketing a program-qualifying home. Seven out of eight
builders expressed that there are not challenges when marketing a program qualifying home. In
fact, two of these seven builders expressed that people are more environmentally conscious and
keen on energy efficient homes. The one builder that expressed concern stated that as a builder
they have been hesitant to discuss utility bills with the buyer and would rather have a third party
take that liability.
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 Customers viewed lower energy bills and increased comfort as benefits of a program qualifying
home. All interviewed builders reported that their customers found various benefits when
purchasing a program qualifying home with the Whole Home measure. Benefits included lower
utility bills, increased comfort in the home (due to measures such as air sealing), and a better
environmental impact.
 Most builders believe homebuyer demand and expectations for energy efficient homes has
increased in recent years. Seven out of the eight builders interviewed (88%) believed that the
demand has changed. The seven builders believe concern for the environment and expectations
for energy efficiency standards in homes has changed. The one builder that stated demand has
not changed stated that most of their customers are focused on price and lower energy bills.
 Non-HVAC measures were noted as the least costly way to help participating builders build
homes with a HERS score of less than 75. According to the builders, the least costly ways to
ensure a home has a HERS score lower than 75 include implementing insulation, air sealing, and
having a conditioned crawl space. Other strategies that builders discussed included implementing
95% AFUE furnaces, meeting all the ENERGY STAR® requirements, and following the building
codes. However, these other strategies were not listed as lower cost, and the 95% AFUE furnaces
are seen as one of the more costly ways to achieve the HERS score requirement.
 Builders recommended that IGC increase marketing for and provide cost savings data for
homes. Suggestions included providing builders with additional flyers, increasing advertising (e.g.,
radio commercials), and cobranding opportunities (e.g., providing a logo for the builders to use
on their website that would associate the energy efficient homes with the Energy Efficiency
program). One builder recommended that IGC should do studies using empirical data that show
the cost savings from energy efficient homes.
 Overall, builders are satisfied with their HERS rater that they worked with in this program. Using
a scale from 1 (very dissatisfied) to 5 (very satisfied), 100% of the builders stated that they were
very satisfied with the HERS rater they worked with in the program. Interviewed builders reported
that 50% of the HERS raters apply for the rebate on behalf of the builder.
 All builders interviewed were satisfied with IGC’s Whole Home Rebate. All builders who were
interviewed stated that they were satisfied with the program and they would continue to
participate in the Energy Efficiency program.
3.3.5 Nonparticipant Builder Interviews
The Evaluators conducted interviews with six builders that have built homes in IGC’s service territory but
are not active participants in the Energy Efficiency Program. The Evaluators compiled a list of
nonparticipating builders and interviewed six nonparticipating builders after contacting 63 unique
builders with 79 phone calls and 54 emails.
The Evaluators spoke with one owner, one co-owner, and four managing partners or members. All six
interviewees said that they were the primary decisionmaker in determining the level of energy efficiency
in their companies’ homebuilding. Two of the nonparticipant builders said they primarily build homes
that use an existing floorplan, while the remaining four builders said they principally build custom homes.
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All six builders estimated that most of their homes (85% or more) were plumbed with natural gas. The
builders were based in different parts of the state (see Table 10).

Table 10 Nonparticipant Builder Locations
Respondents

The nonparticipating builders were generally unaware of the IGC Energy Efficiency Program and the Whole
Home Rebate though they were interested in learning more. Interviews indicated that there is strong
demand for new homes and some builders feel there is also strong demand for energy efficient homes. In
general, the nonparticipating builders indicated that they were satisfied with IGC as their gas service
provider.
The following details key findings of the in-depth interviews.
 Most nonparticipating builders were not aware of IGC’s Energy Efficiency program, specifically
the Whole Home measure. Three builders stated they were unfamiliar with the program. Two
builders noted that their companies participated in IGC’s Appliance Rebate measure but were not
familiar with the Whole Home program. One of the interviewees said they were familiar with the
IGC Whole Home program and said that they were considering participation in IGC’s Whole Home
Rebate and that they are in the process of becoming an ENERGY STAR® new home builder.
 Interest in learning more about the program and future participation. Four builders (67%) stated
that more information about program requirements would help them participate in the program
in the future. Two builders noted that for their company to participate in the program the benefits
of the program must outweigh the costs, that the building requirements must not be too difficult
to achieve, and that the program paperwork and process must not be too onerous. One builder
stated that they build homes infrequently but if they did construct another new home, they would
consider participation in the program, depending on its requirements. The remaining builder said
they had immediate plans to participate and were in the process of researching the program.
 Energy efficient practices and features are common among new homes from nonparticipating
builders. Four interviewees said that all their homes have more energy efficient features than are
required by Idaho building code. The two exceptions reported that about 90% of their homes have
more energy efficient features than are required by the Idaho building code. The nonparticipating
builders interviewed said they chose to build above-code and energy efficient homes because of
an internal company directive/mission, homebuyers with capital and desire for “add-ons” or top
of the line/best choices, or local code guidelines being above the Idaho state building code (e.g.
in Hailey and Blaine County).
 Diverging views regarding homebuyer demand for energy efficiency. The nonparticipating
builders shared a range of perceptions regarding homebuyer actual awareness or interest in
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energy efficiency features. Five of the builders said that homebuyers are willing to pay more for
energy efficient homes. Similarly, 83% of builders shared that they felt homebuyer demand and
expectations for energy efficient homes have increased over the past few years. Despite this,
three builders said most of their homebuyers do not ask about the energy efficiency of their new
homes. Three builders also noted that including energy efficient features in their new homes did
not help their homes sell more quickly and cited various reasons:
 Localities have above-code energy requirements. Builders noted that some municipalities
and counties have standards or required features that are above the national or state
building code (e.g. in Hailey and Blaine County);
 Realtors are unaware of energy efficient features or decide not to mention energy
efficiency in their sales strategy;
 Homebuyers placing higher value on square footage, cosmetics, and “curb appeal” rather
than energy efficiency.
 Strong demand for new homes in Idaho with little or no marketing required. Four builders stated
that there is currently significant demand for new homes and that marketing was not required or
was not a challenge. None of the builders noticed any challenges in marketing their homes. Two
builders noted that they are not involved in marketing homes. One builder stated a realtor
handles marketing. The other noted that homebuyers come to them and that they do not market
the energy efficiency aspects of their homes.
 Most builders have experience with HERS raters and were generally satisfied with their
performance. All builders had previously worked with HERS raters. One builder said that none of
his companies’ homes had a HERS rating but he had worked with another subcontractor’s HERS
rater. Three builders said that all their houses were HERS rated and one said that about 80% of
their homes were rated. The sixth builder noted that in total, less than 1% of their homes had
been HERS rated and were not aware of a HERS rater working in his company’s area. Five said
they had been satisfied with the performance of the HERS rater they had worked with; the other
builder noted that it had been several years since they had worked with a HERS rater and could
not recall how satisfied they were with their experience. Three builders noted that the HERS raters
they had worked with mentioned the IGC Whole Home measure.
 General satisfaction and only one minor issue with IGC service. During the Evaluator’s interviews
with nonparticipating builders, five of the six did not have any additional comments or feedback
regarding building homes in IGC’s service territory or IGC in general. However, one builder noted
that they had difficulty working with IGC to schedule running gas lines to home they were building.
The builder stated that on occasion it took five to six months. He shared that IGC’s local
salesperson in Twin Falls might have been overworked and that he had found a workaround by
contacting IGC’s subcontractor directly to run line to his new homes.
 Reasons for not participating in the Whole Home Rebate included lack of awareness and
understanding of cost and benefits. In general, there were three reasons that nonparticipating
builders were not participating in the IGC Whole Homes Rebate.
 Most nonparticipating builders did not know the details of the program and indicated
they were interested in learning more (n=5)
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 Some of the nonparticipating builders were concerned about the cost and amount of
effort the program might take and the lack of payoff they would receive (n=3)
 Some builders felt that they are already building above-code houses or houses that
are energy efficient and were not sure what the benefit of the program would be for
their business (n=3)
3.3.6 Whole Home Measure Participant Survey
The Evaluators conducted a survey with IGC customers who purchased a new home that received a rebate
in PY2017-2018 or PY2019. Out of 110 survey participants, 80 people completed the survey.
In general, participants indicated they were satisfied with the program. Although respondents were not
fully aware of all the home’s energy efficiency features, they were satisfied with the energy efficiency
measures installed in the home. Some of the factors that greatly influenced the participant’s decision to
buying the home include house price, being ENERGY STAR® certified, and having a HERS rater give the
home a score. Regarding attitudes about energy efficiency, most respondents believe that energy
efficiency saves money.
The following section summarizes key information about program awareness, experience, and satisfaction
as well as participant demographics.
Program Awareness
Participants worked with different stakeholders when deciding on buying their homes. Fifty percent
stated they worked with their own real estate agent, while 24% worked with the builder’s in-house realtor.
Figure 6 provides a summary of all other stakeholders. Some participants also mentioned they worked in
real estate or worked with more than one of the stakeholders below during this process.
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Figure 6 List of Home Buying Stakeholder Support

Seventy-nine percent were aware that their home was ENERGY STAR® certified, while 16% said they did
not know. Five percent stated they could not recall if they had been aware of their home’s certification at
the time. Of those who knew, 69% indicated they learned this information from the builder and 18%
learned from a real estate agent (see Figure 7 for more details).
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Figure 7 Primary Source of Information Regarding Home Being ENERGY STAR® Certified

Builders and real estate agents were the main source of information for many of the survey respondents
learned about the home’s ENERGY STAR® rating (63%), how the home was promoted by IGC’s Energy
Efficiency Program (47%), and of the home’s HERS score (38%) given by a certified HERS rater (42%). See
Table 11 for more details.
Table 11 Information about the Home Provided by Builders or Real Estate Agents
Description ENERGY STAR®
Rating (n = 79) Promoted by
IGC (n = 77)
by HERS Rater
(n = 77)
Score
(n = 77)
Knew it before I contacted the builder or real
estate agent about buying the home 11% 8% 4% 4%
63% 47% 38% 38%
take this survey 16% 30% 36% 36%
The factors to buy the home varied among participants. Although many indicated that energy efficiency
was either important or extremely important to them (86%), most stated the home’s price to be of greater
importance (94%). Participants also indicated that knowing their home had an ENERGY STAR® rating and
had been inspected by a HERS rater influenced their decision to purchase the home (85% and 70%
respectively, based on a 5-point scale). Between the number of respondents who indicated that the
ENERGY STAR® rating and the HERS score were influential, there is a 17 percentage-point difference, with
the home’s HERS score being less influential (68%). See Figure 8 for more details.
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Figure 8 Factors that Influenced Decision on Purchasing the Home

Other non-energy-efficient factors that greatly influenced the sale of the home include the location and
the fact that it was newly built. Below are some of the verbatim comments shared by respondents.
 “Great house and location. Fit our [family’s] needs.”
 “We bought the house based on the location and home features.”
 “We liked the location, specific neighborhood, and the layout of the house. The fact that it was
new and more energy-efficient, so lower bills, helped a lot.”
 “We knew it had some [energy-efficient] features and we were happy about that. I don't think we
knew all of it though. We liked the neighborhood and the design of the house.”
Though energy efficiency appeared to be an important factor when buying the home, some respondents
recalled not being fully aware of the home’s energy efficiency features. Thirty-eight percent of
respondents stated the home’s energy efficient characteristics had been clearly explained by the person
who helped them purchase the home, while 25% indicated it had not been clear (see Figure 9).
Participants also reported they were unaware of the differences between homes promoted by IGC (57%)
versus the other homes on the market at the time of their purchase.
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Figure 9 Participants Understanding of Home’s Energy Efficiency Features

The Evaluators also inquired about the heating and cooling systems installed in the energy-efficient
homes. Most respondents stated they had not made any changes to the heating (93%) or cooling (96%)
systems since they moved into their new homes. Of the respondents who did make changes, they shared
the following:
 “[We] [m]oved the A/C unit to the other side of the home. It was obstructing the walkway. For
example, we had to take the garbage cans and [lawnmower] through the garage instead of the
outside gate. [We are] considering circulated hot water to the kitchen.”
 “[Added a] programmable remote[ly] accessible thermostat.”
 “We added a garage heater and also heated master bath floors.”
Ten percent of respondents also indicated they changed the appliances included with the home. Clothes
washers (n = 3), clothes dryers (n = 2), and refrigerators (n = 2) were the appliances that homebuyers
changed the most. Others indicated they had to buy new appliances because they were not part of the
home purchase.
Energy Efficiency Knowledge and Attitudes
Table 12 summarizes how well informed the respondents perceive themselves regarding energy efficiency
practices and improvements for their homes, with most indicating they are very well informed or
somewhat informed.
Table 12 Perception of Energy Efficiency Comprehension
Rating
5- Very well informed 18%
4 36%
3 29%
2 14%
1- Not very well informed 4%

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Respondents also rated their level of agreement with statements regarding their attitudes towards energy
efficiency initiatives and practices. Most participants stated that energy efficiency saves money (95%,
based on a 5-point scale), while 85% indicated that it was possible to be energy efficient without sacrificing
comfort. Ninety-one percent are very concerned with the amount of energy they use at home (see Figure
10).
Figure 10 Attitudes towards Energy Efficiency

Participants also agreed that they knew of steps to take to reduce their household energy use (62%, based
on a 5-point scale), and 38% intended to reduce household energy use in the next 12 months. On the
contrary, 52% disagreed with the statement that it was not possible to make energy efficiency changes
on their budget, or they were too busy about making energy-related improvements (73%). Figure 11
summarizes their responses.
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Figure 11 Willingness to take Energy Efficiency Actions in the Home

Program Satisfaction
Overall, participants indicated they were satisfied with the program. Respondents were either satisfied or
very satisfied with the energy efficiency measures in their homes (92%) and the ENERGY STAR® certified
home promoted by IGC (84%). Ninety percent of respondents indicated they were either satisfied or very
satisfied with IGC as their gas service provider (see Figure 12). They noted that IGC was a trustworthy
source for information about saving energy (84%, based on a 5-point Likert scale).
Figure 12 Satisfaction with IGC as Participant’s Gas Service Provider

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Participant Demographics
Most of the participants stated that their source for electricity came from Idaho Power (91%), and they
owned their home (99%) (see table). They also stated they live in with four other people or less (91%) and
earn an annual household income of $50,000 or more (67%). The participant who completed the survey
was 50 years or older (51%) and had completed a bachelor’s degree or higher (73%). See Table 13 for
more details.
Table 13 Home Demographics
Question Response Percent
Source of Electricity
(n = 80)
(n = 80)

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Table 14 Participant Demographics
Question Response Percent
Household Income
(n = 79)
Less than $10,000 0%
$10,000 to less than $20,000 0%
3%
3%
1%
8%
9%
4%
5%
5%
Highest Level of Education
Completed
(n = 80)
6%
Age (n = 80)
Household size (n = 80)

The Evaluators compared survey demographics Idaho census data from 2018 to examine the distribution
among age, educational attainment, and income. The figures below provide a comparison of survey
respondents age and education compared to Idaho residents (see Figure 13 and Figure 14). According to
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U.S. Census data, the Idaho median income is $53,089.3 Eighty-one percent of survey respondents said
their income was greater than $60,000, while 19% said their income was less than $60,0000 (see Table
14).
Figure 13 Age Distribution of Survey Respondents Compared to Idaho Census Data

* Excluding “prefer not to answer” and “do not know” responses.

3 U.S. Department of Commerce. (2018). QuickFacts: Idaho. Retrieved May 26, 2020, from
https://www.census.gov/quickfacts/ID
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Figure 14 Educational Attainment4 of Survey Respondents and Idaho Residents

3.3.7 Appliance Rebate Measure Participant Survey
The Evaluators conducted a survey with IGC customers who received a rebate in PY2017-2018 or PY2019.
Out of 110 survey participants, 80 people completed the survey. A random sample of 572 IGC Appliance
Rebate measure participants (82 Tankless WH, 13 Fireplace, 8 Water Heater, 10 Radiant Combo, 459
Furnace) were selected to participate in the survey. A total of 138 IGC Appliance Rebate measure program
participants completed the survey. Table 15 displays the number of responses by appliance type.
Survey results indicate that customers are satisfied with the Appliance Rebate measure as well as their
rebated appliances. The Appliance Rebate measure survey also indicated that contractors play an
important role in the successful implementation of the program. Respondents noted that their appliances’
energy efficiency and warranty/reliability were the most important factors in their purchase decision.
Table 15 Survey Responses by Application Type
Appliance n
95% Furnace 80
Tankless Water Heater 40
Water Heater 6
Radiant Combo 6
Fireplace 5
Total 137

4 U.S. Census Bureau; American Community Survey (2018)
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Program Awareness and Decision-making
Nearly half of the respondents said they first learned of the program through their contractor. The next
most common source of program awareness was a retailer. Figure 15 displays how survey respondents
learned about IGC’s appliance rebate.
Figure 15 Source of Awareness of IGC's Appliance Rebate

*Respondents could provide more than one source of awareness.
Regarding the timing of when survey respondents learned about the rebate, about one-third (32%) said
they learned about the rebate after they decided to purchase their appliance. Table 16 displays when
respondents learned about IGC’s appliance rebate.
Table 16 When Respondents Learned About the Rebate
Response Respondents
After deciding to purchase the appliance 32%
At the time you made the purchase decision 26%
Before starting the process of purchasing the appliance 23%
After researching the product but before deciding to purchase 14%
Do not know 6%
Nearly half of survey respondents (47%) noted that a contractor/retailer recommendation was a reason
they purchased the model or type of appliance they chose. Figure 16 displays the reasons survey
respondents chose the specific model or type of appliance. Eleven respondents (5%) provided “other”
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reasons (eight tankless water heater, two furnace, and one fireplace respondent). Tankless water heater
respondents’ “other” responses included the new water heater providing on demand water, an IGC
recommendation, internet search, better maintenance, and no need for an additional gas line. The
fireplace participant wrote in that the fireplace they chose fit well. The two furnace write-in responses
referred to their need to replace their furnace and the model being the best, most efficient model.
Figure 16 Reason for Purchasing Appliance Model or Type5

*Respondents could provide more than one reason.
Rebate Process and Participants’ Experience
About half of respondents (47%) said they obtained their rebate application from the contractor they
worked with. Forty-three percent of respondents said they obtained their rebate application from IGC.
Other ways that respondents noted obtaining an application included in a retail store (4%), from another
website (1%), and from the gas line installation crew (1%). Four percent of respondents could not recall
how they obtained their application.
Most respondents said their application was accepted as submitted (81%) or could not recall if IGC had
followed up with them for clarifications or further information (7%). Twelve percent of respondents said
their application required follow-up from IGC. These respondents either noted needing to revise the
information they had provided in their application by including photos, city inspector certification,
invoices, signatures, or other information that they did not provide initially or did not recall the specific

5 n = 137.
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circumstances of IGC following up. One respondent said that IGC followed up with them and informed
them that their water heater was not eligible for a rebate.
Ninety percent of survey respondents said they received their rebate check in a prompt and timely
manner; 7% of respondents could not recall if they received it in a timely manner and 3% said they did
not receive their check in a prompt or timely manner.
All survey respondents reported that the appliance they received a rebate for was still installed and
working. The Evaluators also inquired about whether their previous system was functional. Table 17
displays if the appliance replaced were functional. Regarding fireplace rebate survey respondents, four
respondents noted that they previously had a wood-burning fireplace and did not have gas plumbed to it
and the fifth said they previously did not have a fireplace and had relied on electric heating for their home.
Table 17 Appliance Status at Time of Replacement
Appliance Type Functional Non-functional Could not recall
Tankless Water Heater (n = 40) 58% 42% 0%
Water Heater (n = 6) 67% 33% 0%
Radiant Combo (n = 5) 60% 40% 0%
Furnace (n = 75) 72% 27% 1%
Total (n = 122) 67% 32% 1%
Survey respondents provided feedback about whether the installation of the new equipment was part of
a larger project; most of respondents said that their appliance rebate went toward a stand-alone
replacement or installation project (see Table 18).
Table 18 Appliance Rebate Project Type
Appliance Type
Part of a larger
replacement of
my heating
system
Part of a newly
constructed
home
Part of a
remodeling
project
Stand-alone
replacement Other Do not
know
Tankless Water Heater (n = 40) 18% 0% 10% 68% 3% 3%
Water Heater (n = 6) 17% 0% 0% 83% 0% 0%
Radiant Combo (n = 6) 33% 17% 17% 33% 0% 0%
Total (n = 137) 26% 1% 9% 59% 3% 1%
The systems that were replaced were on average 18 years old. Table 19 displays the average age of the
equipment that survey respondents replaced.
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Table 19 Equipment Age at Time of Replacement
Appliance Equipment Age
(years)
Tankless Water Heater (n = 30) 13
Water Heater (n = 5) 14
Radiant Combo (n = 4) 17
Average (n = 101) 18
Contractor Experience
The vast majority (93%) of respondents noted that they hired a contractor to install their rebated
appliance. Seven percent of respondents said they installed it (6%) or a friend or family member installed
it (1%). The remaining respondent (1%) could not recall who installed their rebated appliance.
The six percent of respondents (8 respondents) who installed the appliance themselves noted that they
had learned how to install the appliance through prior occupational knowledge (3 respondents) or
through being self-taught (5 respondents).
Forty-six percent of respondents (n = 109) said they had hired the contractor they worked with previously.
Twenty respondents who hired a contractor were not asked this question because they selected “Other
(please specify)” and wrote in the name of their contractor when asked who installed their appliance. The
remaining respondents said they selected their contractor through online research (23%), a retailer
recommendation (16%), or a friend or family member recommendation (16%).
Most customers (71%) spoke to one or two contractors before choosing one to install their appliance.
Twenty-nine percent of customers spoke to three or four contractors before choosing one to install their
appliance.
Respondents were largely satisfied with their contractor experience (see Figure 17).
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Figure 17 Agreement with Statements regarding Contractor

Most survey respondents noted that their contractor emphasized energy efficiency and the new
equipment’s warranty and reliability. Similarly, most customers also related that the equipment’s energy
efficiency and good warranty/reliability mattered most to them. Figure 18 displays the features that
matter most to survey respondents and the features that respondents recall their contractor emphasizing.
“Other” responses included having hot water on demand, the size of the new equipment, not using a pilot
light/having electric ignition, and the comfort their new equipment provides.
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Figure 18 Equipment Features Contractors Emphasized and Mattered Most to Customers

*Percentage may exceed more than 100%. Respondents could choose more than one response.
Program Satisfaction
Most appliance rebate program participants were satisfied with their rebated appliance as well as their
overall participation experience. Figure 19 and Figure 20 display respondents’ satisfaction with their
rebated appliance and various aspects of their program participation, as well as their overall satisfaction
with IGC as their gas service provider. In addition to being satisfied with their overall program experience,
most respondents also said that their participation had also increased their satisfaction with IGC. Seventy-
eight percent of survey respondents rated how their experience with the Appliance Rebate measure
affected their satisfaction a 4 or 5 on a scale from 1 (greatly decreased satisfaction) to 5 (greatly increased
satisfaction).
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Figure 19 Satisfaction with Rebated Appliance

Seventeen percent of survey respondents noted that they had contacted IGC with questions about their
rebate application. The majority of these respondents reported that their questions were answered
thoroughly and in a satisfactory amount of time (see Figure 20).
Ten survey respondents (7%) provided written comments regarding a specific aspect of their
dissatisfaction with the Appliance Rebate Program. Two respondents mentioned dissatisfaction with
conversations with IGC staff; another respondent was unsure of the status of their rebate and was
dissatisfied with program communication. Five respondents stated that the rebate was not sufficient
compared to the cost of the equipment or were dissatisfied with the rebate qualifications. One
respondent said they were dissatisfied with their equipment’s installation and contractor’s work. Another
respondent was dissatisfied with their tankless water heater’s performance.
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Figure 20 Appliance Rebate Program Satisfaction

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Energy Efficiency Attitudes
Two-thirds of survey respondents said that they were informed about energy efficiency practices and
energy efficient products/improvement options for their household (rated themselves based on a 5-point
Likert scale).
The Evaluators inquired with respondents regarding their agreement with various statements about
energy efficiency (see Figure 21). Most respondents agreed with the statements that energy efficiency
saves money and that it is possible to be energy efficient without sacrificing comfort. A smaller portion of
respondents agreed with the statements that they knew the steps they could take to reduce energy use
in their household and that they intend to reduce energy use in the next twelve months.
Figure 21 Respondent Agreement with Statements Regarding Energy Efficiency

Eighty-seven percent of survey respondents said that IGC was a trustworthy source of information about
saving energy in their home (based on a 5-point scale).
Demographics and Background
The Evaluators asked questions about participants’ demographics and background.
Ninety-three percent of Appliance Rebate Program survey respondents (n = 135) said their home electric
service was provided by Idaho Power Company. Other respondents said their electric service was provided
by Rocky Mountain Power (3%) or Idaho Falls Power (3%). One respondent (1%) did not know their electric
service provider.
About half of respondents’ homes were built before 1990 (see Table 20).
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Table 20 Year Home was Built
Response Respondents
Before 1960 17%
1960 to 1969 8%
1970 to 1979 16%
1980 to 1989 10%
1990 to 1999 22%
2000 to 2009 22%
2010 to 2019 3%
Do not know/Prefer not to say 2%
The majority of survey participants said their home’s square footage was between 1,000 and 2,999 square
feet (see Table 21).
Table 21 Square Footage of Homes
Response Respondents
Less than 1,000 square feet 4%
1000-1999 square feet 47%
2000-2999 square feet 33%
3000-3999 square feet 10%
4000 square feet or greater 4%
Prefer not to say 1%
Most respondents said they used natural gas to heat their home (96%) and water (80%). Slightly over half
of respondents said their home relied on electricity for cooking (56%). Nearly all respondents (n = 136)
reported owning their home (99%). Similarly, 96% of respondents said they lived in a single-family house
that was detached from any other house; 3% of respondents said they lived in a house that was attached
to another home and 1% of respondents said they lived in a mobile home.
Table 22 displays Appliance Rebate survey respondents’ income, education, age, and household size.
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Table 22 Appliance Rebate Respondent Home Characteristics & Demographics
The Evaluators compared survey demographics to Idaho census data to examine the distribution among
age, educational attainment, and income. “Prefer not to answer” and “Do not know” responses were
excluded for analytical purposes. Figure 23 and Figure 23 provide a comparison of survey respondents age
and education compared to Idaho residents. According to U.S. Census data, the Idaho median income is
$53,089. Forty percent of survey respondents indicated their income was under $60,000 per year and the
other 60% of respondents said their income was $60,000 or greater.
Question Response Percent
Household Income
(n = 136)
Less than $10,000 1%
$10,000 to less than $20,000 0%
$20,000 to less than $30,000 5%
$30,000 to less than $40,000 7%
$40,000 to less than $50,000 10%
$50,000 to less than $60,000 10%
$60,000 to less than $70,000 8%
$70,000 to less than $80,000 8%
$80,000 to less than $90,000 3%

Highest Level of Education Completed
(n = 137)
Age
(n = 137)
Household Size
(n = 137)
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Figure 22 Age Distribution of Survey Respondents Compared to Idaho Census Data

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Figure 23 Educational Attainment of Survey Respondents and Idaho Residents

3.3.8 Nonparticipant Survey
The Evaluators conducted a survey with IGC customers who have not previously participated in either the
Appliance Rebate or Whole Home programs. A list of nonparticipating customers was provided by IGC.
The customers were sent emails soliciting their participation in an online survey. A total of 696 IGC
customers were contacted by email between April and May 2020. A total of 160 customers completed the
nonparticipant survey.
Most survey participants (88%) had not received any rebates in the last five years from their electricity
service provider, while 5% indicated they had received a rebate from their electricity service provider and
another 7% could not recall. Most nonparticipant survey respondents (91%) were not aware of rebates
for energy efficient equipment and home improvements offered by gas or electric utility providers. Less
than half (43%) of survey respondents reported they were knowledgeable about how to save energy in
their homes. Survey respondents reported the energy-saving activities they were most likely to follow
through with to reduce their utility bills by cleaning or changing furnace filters once a month during
heating season, adding weather stripping or caulking around doors and windows, and conducting regular
maintenance of central heating or cooling system. The following details the key findings of the
nonparticipant surveys.

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Energy Efficiency Actions
Survey respondents provided feedback about household purchases and equipment that had been
installed in the past three years. The majority (72%) had not done any of the home improvements listed
in the survey (see Table 23). Among those who indicated they had installed the equipment, 57% indicated
they installed an ENERGY STAR® furnace, while none of the other equipment installed was certified or
high efficiency.
Table 23 Home Improvements in the Past Three Years
Response n Percent of
Respondents
95% AFUE Natural Gas Furnace 7 4%
Combination Radiant Heat System 0 0%
80% AFUE Fireplace 3 2%
70% FE Fireplace 1 1%
0.67 Water Heater 8 5%
0.91 Tankless Water Heater 1 1%
I have not done any of these 113 72%
Other 1 1%
Could not recall 29 18%
Most survey respondents (91%) were not aware of rebates for energy efficient equipment and home
improvements offered by gas or electric utility providers, with 9% indicating they were aware. Among
those who were aware, six respondents indicated Idaho Power as a utility who offers rebates and two
recalled rebates offered by Intermountain Gas Company. The most common response for the type of
rebate survey respondents recalled was heating and cooling equipment. A utility bill was the most
common way respondents learned about the rebate (64%), followed by information that came in the mail
(21%), email (21%), utility website (21%), utility program staff (21%), word-of-mouth (14%), community
event (14%), social media (7%), and Intermountain Gas YouTube channel (7%) (n = 14).
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Figure 24 Types of Rebate Survey Respondents Recalled

Using a 5-point scale, customers provided feedback on how knowledgeable they are on ways to save
energy in their homes. Forty-three percent of survey respondents reported they were extremely or
somewhat knowledgeable on ways to save energy in their homes (see Figure 25).
Figure 25 Customers’ Self-Reported Knowledge of Ways to Save Energy

Using a 5-point scale, survey respondents provided feedback on how much they have done to lower their
monthly energy bills. Twenty-four percent of customers indicated they have done almost everything they
can, while 13% indicated they have not done much (see Figure 26).
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Figure 26 Customer Engagement in Lowering Monthly Energy Bills

Survey respondents had varying degrees of interest on home improvements. Thirty-two percent of
respondents were extremely or somewhat interested in improving their health, safety, and comfort by
using appliances powered by natural gas. Thirty-five percent of respondents were extremely or somewhat
interested in increasing their homes’ energy efficiency by using appliances that are powered by natural
gas.
Figure 27 Customer Interest in Home Improvements

Table 24 summarizes survey respondents’ feedback on how they would find out ways to reduce their
monthly energy bills. The most common responses included internet search (66%), followed by friends or
family (56%), and utility website (42%).
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Table 24 Ways Customers Learned How to Reduce Their Monthly Energy Bills
Response n Percent of Respondents
Internet search 104 66%
Friends, family, or colleagues 89 56%
Utility website 66 42%
ENERGY STAR® website 47 30%
Social media 37 23%
Information from a retail store 33 21%
Magazines/newspapers/television/radio 28 18%
From the utility customer service line 24 15%
Government information 21 13%
Home energy audit 21 13%
Other 3 2%
Did not know 22 14%
Energy Efficient Improvement Plans
Survey respondents provided feedback on whether they planned to make any energy efficient
improvements in the next year and the likelihood of doing various installations and low or no cost
activities. Most survey respondents (50%) indicated they did not have plans to make any energy efficiency
improvements in the next year, followed by 34% who stated they were unsure and 16% who reported
they did have plans. Figure 28 summarize customers’ responses to the likelihood of installing high
efficiency equipment or other energy-saving measures to reduce their utility bills.
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Figure 28 Likelihood Customers Would Install High Efficiency Equipment or Other Measures

Survey respondents reported the energy-saving activities they were most likely to follow through with to
reduce their utility bills by cleaning or changing furnace filters once a month during heating season, adding
weather stripping or caulking around doors and windows, and conducting regular maintenance of central
heating or cooling system. Installing a high efficiency heat pump or central heating system appeared to
the equipment that would be least likely for customers to install.
Closing the drapes or cover windows to reduce heat gain or loss, adjusting thermostat settings, and
installing efficient lighting were the low or no cost energy-efficient activities that survey respondents
indicated they were most likely to do in their home to save energy. Using low-flow showerheads or faucet
aerators and turning down water heater temperatures appeared to be the activities respondents were
least likely to do.
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Figure 29 Likelihood Customers Would do Low- or No-Cost Activities to Reduce Monthly Utility Bill

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Figure 30 Respondent Agreement with Statements Regarding Energy Efficiency

Customer Perceptions of Intermountain Gas Company
The majority (70%) of survey respondents indicated they were very satisfied with Intermountain Gas
Company as their natural gas service provider, followed by 20% who were somewhat satisfied. Many
respondents find IGC to be very or somewhat trustworthy as a source of information about saving energy
in their home (see Table 25).
Table 25 How Trustworthy is Intermountain Gas Company as a Source of Energy-Saving Information
Response n Percent of
Respondents
Very trustworthy 94 59%
4 28 18%
3 17 11%
2 1 1%
Not at all trustworthy 0 0%
Did not know 19 12%

More than half of respondents were very or somewhat interested in receiving additional information on
energy saving tips and rebate programs offered by IGC (see Table 26).
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Table 26 Interest in Receiving Energy Saving Tips and Rebates Offered by Intermountain Gas Company
Response n Percent of Respondents
1- Very interested 48 31%
4 41 26%
3 25 16%
2 21 14%
5- Not at all interested 15 10%
Did not know 5 3%
Home Characteristics and Participant Demographics
Most survey respondents used natural gas in their homes (96%), lived in a single-family house (88%), and
owned their home (81%) (see Table 27).
Table 27 Home Characteristics
Response n Percent of Respondents
Single-family house detached from any other house 137 88%
8 5%
Apartment in a building with 4 or more units 5 3%
Manufactured of mobile home 6 4%
Own 118 81%
Rent 26 18%
Own and rent to someone else 1 1%

Twenty-three percent of survey respondents indicated their home was built between 2010 and 2019,
followed by 22% who reported their home was built between 2000 and 2009 (see Table 28). Based on the
responses from this survey, it appears that most customers live in newer homes.
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Table 28 Year Home was Built
Response n Percent of Respondents
Before 1960 23 16%
1960 to 1969 11 8%
1970 to 1979 9 6%
1980 to 1989 4 3%
1990 to 1999 22 15%
2000 to 2009 32 22%
2010 to 2019 33 23%
Could not recall 10 7%
The majority of survey participants reported their home’s square footage to be between 1,000 and 2,999
square feet (see Table 29). Eighty-six percent (86%) of survey respondents indicated that natural gas is
their home’s main fuel, followed by 12% who reported electricity and 2% who did not know.
Table 29 Square Footage of Homes
Response n Percent of Respondents
Less than 1,000 square feet 7 5%
1,000 – 1,999 square feet 58 41%
2,000 – 2,999 square feet 46 32%
3,000 – 3,999 square feet 17 12%
4,000 square feet or greater 5 4%
Could not recall 9 6%
Most survey respondents (87%) utilize a central forced air furnace for their main heating equipment,
followed by 1% who use a heat pump and 4% who indicated something else. Those who reported other
types of heating equipment included gas fired boiler for radiant floor heating, gas stoves or fireplaces, gas
water heater to heat exchanger, and cadet heaters. A plurality of survey respondents (28%) indicated their
heating systems were 5 to 9 years old, followed by 14% who reported their systems to be 10 to 14 years
old and 13% who reported less than 2 years old. A summary is provided in Table 30.
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Table 30 Age of Heating System
Response n Percent of Respondents
Less than 2 years old 19 13%
2 to 4 years 16 11%
5 to 9 years 41 28%
10 to 14 years 20 14%
15 to 19 years 11 8%
20 or more years old 20 14%
Could not recall 18 12%

Seventy-five percent of survey respondents indicated their water heater’s main fuel source was natural
gas, followed by 16% who indicated electricity and 8% who could not recall. Twenty-six percent of survey
respondents indicated their water heater system was 5 to 9 years old, followed by 21% who stated 10 to
14 years old and 20% who reported 2 to 4 years old (see Table 31).
Table 31 Age of Water Heating System
Response n Percent of Respondents
Less than 2 years old 20 14%
2 to 4 years 29 20%
5 to 9 years 38 26%
10 to 14 years 31 21%
15 to 19 years 12 8%
20 or more years old 9 6%
Could not recall 14 10%

More than half (54%) of survey respondents indicated the main fuel source for cooking in their home was
electricity, followed by 45% who reported natural gas. Fifty-two percent indicated their home has an
existing fireplace with a gas line connection. Among those who indicated they have a fireplace (n = 54),
63% reported their fireplace has a pilot light (constant or intermittent).
Survey respondents mainly receive electric services from Idaho Power (87%), followed by 10% who stated
Rocky Mountain Power and Fall River Rural Electric Cooperative (1%). Almost half (49%) of respondents
indicated that two or less people reside in their household year-round and 47% reported making $80,000
or less per year (see Table 32).
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Table 32 Survey Participants’ Demographics
Question Response Respondents
Household Income
(n = 142)
$10,000 or less 4%
$10,000 to less than $20,000 4%
$30,000 to less than $40,000 6%

Highest Level of
Education Completed
(n = 139)
Age (n = 145)
Household Size
(n = 147)

Evaluators compared survey demographics to Idaho census data to examine the distribution among age,
educational attainment, and income. “Prefer not to answer” and “Do not know” responses were excluded.
The figures below provide a comparison of survey respondents age and education compared to Idaho
residents (see Figure 31 and Figure 32). According to U.S. Census data, the Idaho median income is
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$53,089. Less than half (46%) of survey respondents indicated their income was under $60,000 per year
and 54% reported an income greater than $60,000 (see Table 32).
Figure 31 Age Distribution of Survey Respondents Compared to Idaho Census Data

Figure 32 Educational Attainment of Survey Respondents and Idaho Residents

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3.3.9 Survey Respondent Attitude and Satisfaction Comparison
The Evaluators asked all survey respondents questions regarding their energy efficiency attitudes as well
as their overall satisfaction with IGC and the level of trustworthiness they associate with IGC.
Figure 33 shows the respondents’ level of agreement with various statements regarding energy efficiency,
with average ratings displayed in grey circles. There were significant differences between groups for most
of the questions. The two exceptions that did not display significant differences were the following
phrases: "energy efficiency saves money" and "I intend to reduce my household energy use in the next 12
months".6 The most notable differences in attitudes included:
 A higher portion of Appliance Rebate measure and Whole Home measure survey respondents
disagreed with the statement that they are not very concerned about the amount of energy used
in their home (81% of Appliance Rebate and Whole Home participants compared to 61% of
nonparticipants; based on a 5-point scale).
 About three-quarters of the Appliance Rebate measure and Whole Home measure survey
participants said they were not too busy to worry about making energy efficiency related
improvements to their home, compared to 64% of nonparticipant survey respondents.
 Over 80% of the Appliance Rebate measure and Whole Home rebate measure survey respondents
agreed that it was possible to be energy efficient without sacrificing comfort. This compares to
64% of nonparticipant survey respondents (based on a 5-point scale).
 About half of nonparticipant survey takers said they knew of the steps they could take to reduce
their households' energy use whereas 72% of Appliance Rebate measure respondents and 61% of
Whole Home rebate measure respondents said they knew what steps they could take to reduce
their households' energy use.

6 Differences noted here were significant at the 95% confidence interval with an alpha less than 0.05. The Evaluators
conducted two tailed z tests for two proportions (comparing the nonparticipant group against the whole home and
appliance rebate participants). The test’s purpose was to see if there is a significant difference between the different
surveys’ responses. Alpha is the significance level, which is the probability of rejecting the null hypothesis when it is
true (the null hypothesis is that there is no difference between the proportions). Here a significance level of 0.05
indicates a 5% risk of concluding that a difference exists when there is no actual difference
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Figure 33 Respondent Agreement with Statements Regarding Energy Efficiency 7

7 Don’t Know responses are not displayed in this figure. The percentage “don’t know” from top to bottom of the Figure 33, were: “Energy efficiency saves money”
(AR 1%, NP 2%, WH 1%), “I am not very concerned…” (AR 0%, NP 1%, WH 1%), “I am too busy…” (AR 0%, NP 1%, WH 3%), “I intend to reduce…” (AR 4%, NP 9%,
WH 3%), “I know of steps…” (AR, 1%, NP 2%, WH 5%), “It is not possible” (AR 3%, NP 6%, WH 6%), “It is possible…” (AR 1%, NP 2%, WH 0%)
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The Evaluators also compared survey respondents’ overall level of satisfaction with IGC and how
trustworthy the respondents’ rated IGC as a source of information for saving energy. Ninety percent of
appliance rebate measure survey respondents said they were satisfied with IGC compared to 90% of
whole home measure and nonparticipant survey respondents. Eighty-seven percent of appliance rebate
measure survey respondents and 84% of whole home measure survey respondents said they trusted IGC
as a source of information about saving energy compared to 77% of nonparticipant survey respondents
(based on a 5-point scale).
3.3.10 Tracking Database Review
The Evaluators reviewed the quality of program tracking data to assess whether the data allowed for
complete evaluation. The following sections detail the results of tracking database review for both the
Appliance Rebates and the Whole Home Rebates.
Appliance Rebate Tracking Database
The tracking system in the database requires some modifications to meet energy calculation standards.
These bullets below show a summary of how well the program tracking systems meets the requirements
to calculate energy savings via engineering equations.
 Participating Customer and Household Information – includes all information required including
customer contact information, customer identifier (account number), location of building, heating
and cooling type, date of install completion, and date of check issued.
 Installing Contractor Information – includes all information required including company name
and contact information, installer name, and location of company. There were, however, some
issues with consistency of contractor company name.
 Measure Specific Information – includes type and quantity of measures installed. The tracking
data was found to contain some of the information required to calculate energy savings. Tracking
includes appliance brand, appliance model number, and appliance serial number as well as sizing
and efficiency. However, project data should be modified to also require reason for installing
(replace on burnout, early retirement, new construction), and it is suggested to include collecting
prior efficiency of equipment as well.
The tracking data included a separate row for each measure installed. However, not every household had
a unique incentive identifier. The Evaluators recommend requiring this as an input in the tracking database
in order to streamline rebate lookup activities.
The tracking data provided values of efficient equipment input sizes and efficiency. The Evaluators found
some equipment input sizes collected to be equipment output sizes. The Evaluators recommend
performing quality control on the collection of equipment input and equipment output sizing during data
collection. The database does not require reason for equipment replacement (replace-on-burnout, early
retirement, or new construction), which is required for use in engineering calculations to estimate energy
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savings. The Evaluators recommend updating data collection to collect reason for equipment replacement
as well as efficiency of prior equipment, as it can be used in energy savings calculations as well.
The tracking database included home type (single family, apartment, town home), but inputs were not
standardized and included many variations. The tracking database included square footage of home and
year built, although there were instances where this information was not collected or seem to not be
verified. A number of line items included home vintage that were unlikely or impossible, such as years
greater than 2020. The Evaluators recommend standardizing and employing quality control on this data
collection process.
The tracking database does not currently collect AHRI certification numbers for its appliance measures.
The Evaluators recommend requesting this information in the appliance rebate form to assist with quality
control of verified appliance efficiency.
Included in the tracking database is an IGC comments section in which the status of the rebate is noted.
The Evaluators recommend standardizing the inputs in this column to assist with proper categorization
and tracking of rebate status. Similar issues are found in the rebates that have been rejected. IGC
thoroughly notes the reason for rebate rejection. However, an additional, standardized category or
rejection reasons would aid in the analysis of barriers to rebate completion.
The following list encompasses the Evaluator’s recommendations on improving data collection in the
tracking database:
 Track AHRI certification number for all rebates;
 Assign unique rebate identifier for all rebates;
 Track appliance input size, reason for replacement (replace-on-burnout, early retirement, new
construction), prior equipment input size, prior equipment efficiency, prior equipment age for
all equipment;
 Track storage volume and draw pattern for water heaters;
 Perform quality control on year built and square footage of home; and
 Standardize categorization of rebate status as well as rejection reasoning.

Whole Home Rebate Tracking Database
The tracking system in the database conforms well to required data collection standards. These bullets
below show a summary of how well the program tracking systems meets energy efficiency calculation
standards.
 Participating Customer and Household Information – includes all information required including
customer contact information, customer identifier (account number), location of building, heating
and cooling type, date of install completion, and date of check issued.
 Builder Contractor Information – includes all information required including company name and
contact information, installer name, location of company. There were some issues with
consistency of contractor company name.
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 Rater Information – includes all information required including company name and contact
information, rater name, RESNET rater ID, ENERGY STAR registry ID, and location of company.
 Installing Contractor Information – includes all information required including company name
and contact information, installer name, and location of company. There were some issues with
consistency of contractor company name.
 Household Rating Information – includes household verified HERS score as well as home
certification date.
 Appliance Specific Information – includes information on furnace and water heater appliances
installed in the home.
The evaluation began with a review of program tracking data. The tracking data included a separate row
for each measure installed. Every household had a unique incentive identifier. The tracking database
included home type (single family, apartment, town home), but inputs were not standardized and
included many variations. The tracking database also included square footage of home and year built.
The tracking data provided values of efficient equipment input sizes and efficiency. Also included in the
tracking database is an IGC comments section in which the status of the rebate is noted. The Evaluators
recommend standardizing the inputs in this column to assist with proper categorization and tracking of
rebate status. Similar issues are found in the rebates that have been rejected. IGC thoroughly notes the
reason for rebate rejection. However, an additional, standardized category or rejection reasons would aid
in the analysis of barriers to rebate completion.
The following list encompasses the Evaluators’ recommendations on improving data collection in the
tracking database:
 Standardize categorization of rebate status as well as rejection reasoning.
3.3.11 Rebate Rejection Analysis
The Evaluators examined rejected rebate applications for the Appliance Rebate and Whole Home
measures. In PY2017-2018 and PY2019, there were a total of one and seven rejected applications,
respectively (see Table 33).
Table 33 Rejection Rates for PY2017-2018 and PY2019
Program
PY2017-2018
Total Number
of Rejections
PY2019 Total
Number of
Rejections
Percent of
Rejections
Among all
Rebates
Total Number
of Rebate
Applications
(including
Percent of
Rejections
Among all
Rebates
Total Number
of Rebate
Applications
(including
Whole Home 1 7 0.2% 620 0.6% 1086
Less than 1% of the rebate applications submitted for the Whole Home measure were rejected and the
majority (75%) were rejected because the rebated had previously been paid out (see Table 34).
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Table 34 Reason for Rejection
Channel Description
Whole Home
13%
13%
75%
Among the Application Rebate measure, the measure most often rejected was the 80% AFUE fireplace at
100% rejection rate due to lack of required information on the rebate application or the equipment did
not meet minimum efficiency. Furnaces had the lowest reject rate at 7% and 6% for PY2017-2018 and
PY2019, respectively (see Table 35). The reason applications were rejected most often included not
meeting the minimum efficiency requirements, unsigned signature pages, lacking information on final
invoice or application, missing permit numbers, and/or the eligibility for application submission had
passed.
Table 35 IGC’s Energy Efficiency Program Offerings
Channel
PY2017-2018
Total Number
of Rejections
PY2019 Total
Number of
Rejections
PY2017-2018
Percent of
Rejections
Among all
Rebates
Total Number
of Rebate
Applications
(including
PY2019
Percent of
Rejections
Among all
Rebates
Total Number
of Rebate
Applications
(including
95% AFUE Natural Gas Furnace 106 125 7% 1440 6% 2191
90% Radiant Combo 2 2 40% 5 15% 13
80% AFUE Fireplace 15 7 100% 15 100% 7
70% FE Fireplace 63 28 83% 76 67% 42
0.67 Water Heater 77 108 90% 86 93% 116
0.91 Tankless Water Heater 15 17 15% 103 10% 176
Total 278 287 16% 1725 11% 2545

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4 Conclusions and Recommendations
The Evaluators provides the following conclusions and recommendations to improve the program and the
evaluation of the program in future years.
4.1 Conclusions
4.1.1 Cross-Cutting Findings
 IGC launched the Energy Efficiency program in PY2017-2018 with limited design or operational
changes in PY2019. IGC received approval to launch the Appliance Rebate and Whole Home
measures starting in PY2017-2018. The initial goal for the Energy Efficiency program was to
increase customer awareness and participation. When IGC staff started implementing the
program, they were focused on determining their logistical partners, leveraging existing
relationships, and refining the rebate process.
 IGC’s Energy Efficiency program performed well during first two program years. According to
IGC staff, both measures (Appliance Rebate and Whole Home) performed well in the first two
years of implementation and were on an “upward trend.” The Appliance Rebate program saw a
60% increase in the number of appliances rebated and the Whole Home program saw a 70%
increase in the number of homes between PY2017-2018 and PY2019. Staff also believed there
was increased customer awareness and improving relations with key program stakeholders (HERS
raters and HVAC contractors). The Appliance Rebate program launched online application forms
and a portal for HVAC contractors.
 Lessons learned between PY2017-2018 and PY2019 included how to effectively engage various
stakeholders. IGC staff indicated they learned various lessons about program implementation and
operation since they launched. Staff stated they learned how to effectively engage with the
community and to target events where program staff can reach the appropriate audience.
Program staff indicated that recruitment of builders and contractors can be most effective either
by phone or in-person, when possible. They also learned that attending events where builders are
in attendance can be one of the most effective ways to discuss the program with them, specifically
golf tournaments held by builder associations. Another lesson learned is to ensure that
participating contractors are licensed with the state to perform install. Staff have kept a list of
contractors’ licenses with their expiration date to compare against installation. This ensures that
installations are done in accordance with state permitting requirements.
 Program changes anticipated in PY2020 and beyond. IGC program staff indicated they do
anticipate some changes to the Energy Efficiency program for the remainder of 2020 and possibly
for the upcoming program year. A cost potential assessment was conducted, and staff anticipated
changes to their offerings based on the results of that study. Staff anticipated a commercial
offering and wanted to add smart thermostats to their list of eligible measures. Program staff also
discussed whether the HERS rating of 75 was appropriate for the Whole Home measure. Staff was
concerned increasing the stringency of the HERS score as a requirement for participating homes
and theorized it may be a barrier for builders who are not accustomed to building homes beyond
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code. Staff believed a tiered incentive system might be a future addition to the Whole Home
measure. Staff would also like to increase customer education efforts about energy efficiency.
 Broad range of marketing efforts and community outreach were used. IGC staff utilized a broad
approach to promote the availability of the Appliance Rebates and the Whole Homes measures.
Staff indicated they focused on three areas for outreach and promotion which included customers
and communities, home builders, and HVAC contractors. Staff indicated that HVAC contractors
are key to promoting the program to customers because they are seen as trusted experts.
Marketing activities included bill inserts, digital advertising, radio campaigns, and attending
community events. To promote the Whole Home program, IGC staff participated in the Parade of
Homes sponsored by the Builder Contractors’ Association. Promotional activities with builders
included attending association meetings, expositions, and other events. IGC does not provide
marketing collateral or cobranding opportunities for contractors or builders.
4.1.2 Whole Home Measure
 Participating and nonparticipating builders both reported an increase in customer demand for
energy efficient homes. The majority (88% of participating and 83% of nonparticipating builders)
believed that homebuyer demand and expectations for energy efficient homes have increased
over the past few years. Both participating and nonparticipating builders indicated the strong
demand for new homes in Idaho requires little to no marketing to sell.
 Builder participation in the Whole Home measure increased due to recruitment efforts of staff.
There was an increase in the number of participating builders from 18 to 24 between PY2017-
2018 and PY2019. IGC program staff stated they are typically recruited at BCA meeting as they
offer an opportunity to educate builders about the program. Staff also stated that community
outreach and home buyers about the benefits of ENERGY STAR® can help drive more builders to
build energy efficient homes. According to IGC, the feedback from builders has been positive and
that they are grateful to receive the rebates within 1 to 2 weeks. All the participating builders who
were interviewed indicated they have experience with the Whole Home measure starting in either
2018 or 2019
 Non-HVAC measures were seen as the least costly way to help participating builders build
homes with a HERS score of less than 75. According to the builders, the least costly ways to
ensure a home has a HERS score lower than 75 include implementing insulation, air sealing, and
having a conditioned crawl space. Other strategies that builders discussed included implementing
95% AFUE furnaces, meeting all the ENERGY STAR® requirements, and following the building
codes. However, these other strategies were not listed as lower cost, and the 95% AFUE furnaces
is seen as one of the more costly ways to achieve the HERS score requirement.
 Differentiation in the market and the program financial incentive were two factors important
to builders’ participation. All participating builders stated that program financial incentive and
ability to differentiate themselves in the market were the factors that went into deciding to build
their homes to program-building standards in 2019.
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 All Whole Home participating builders were satisfied with their HERS rater that they worked
with. In fact, from the interviews, it was found that 50% of the HERS raters are the parties involved
with the rebate application process.
 In general, HERS raters find IGC’s Energy Efficiency program to be one of the most effective in
the region. All three raters have been participating in IGC’s Energy Efficiency program since 2018.
Raters have also heard positive feedback from builders about the Energy Efficiency program.
Raters indicated that program requirements seem reasonable and attainable. According to one
HERS rater, IGC’s payment turn-around is efficient and dependable.
 HERS raters indicated the rebate incentive was the best marketing tool for reaching most
builders. According to the HERS raters, the upfront additional costs for builders continues to be a
barrier for participation. By marketing the incentive, builders were more likely try and become
eligible for the rebate(s).
 Raters use direct and indirect messaging to recruit builders to the program. Word-of-mouth was
their best direct messaging strategy, while having builders recommend the program to other
builders as their indirect messaging. All raters expect the number of constructions of new homes
for 2020 to remain about the same from 2019. Due to the economic downturn, raters may not
see the increase in construction as they had anticipated for 2020. All HERS raters have worked as
a rater in Idaho for more than two years.
 Program requirements are broadly understood by participants and would benefit from further
explanation. HERS raters indicated that IGC could improve their outreach to builders, realtors,
and homeowners about buying energy-efficient homes. Participating in events, using more
marketing collateral, or sponsoring educational events may help increase program participation,
according to HERS raters.
 Nonparticipating builders were generally aware of energy efficiency practices but not the
Whole Home measure. Among the nonparticipating builders who were interviewed, they
indicated they have a general understanding of energy efficiency practices and incorporate them
in their new homes. Most nonparticipating builders were not aware of the IGC Whole Home
measure but voiced interest in learning more and potentially participating. The builders noted
experience with energy efficiency and HERS raters. Some voiced disinterest in participating in
certification programs because of the time and resources required and the low perceived payoff.
 The decision about buying a new home varied among participants. Although many indicated that
energy efficiency was important to them (86%), most stated the home’s price to be of greater
importance (94%). Other non-energy-efficient factors that greatly influenced the home sale
include the fact that it was new construction and location.
 Participants worked with different stakeholders when deciding on buying their homes. Fifty
percent of Whole Home survey respondents stated they worked with their own real estate agent,
while 24% worked with the builder’s in-house realtor. Some participants also mentioned they
either worked in real estate or had worked with more than one of the stakeholders during this
process.
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 Most survey respondents were aware that their homes were ENERGY STAR® certified but not
necessarily part of the Whole Home measure. Seventy-nine percent of Whole Home survey
respondents were aware that their home was ENERGY STAR® certified, while 16% said they did
not know. Of those who knew, 69% indicated they learned this information from the builder and
18% learned from a real estate agent. Participants also indicated that knowing their home had an
ENERGY STAR® rating and had been inspected by a HERS rater influenced their decision on
whether to buy the home. However, according to the builders interviewed, home buyers were
generally unaware of the Whole Home measure. Five out of eight builders (62.5%) stated that
they either were unsure about the number of customers that knew about the Whole Home
measure or that none of the customers knew about the measure.
 Survey respondents could not recall all the home’s energy efficiency features. Thirty-eight
percent of respondents stated the home’s energy efficient characteristics had been clearly
explained by the person who helped them purchase the home, while 25% indicated it had not
been clear. Participants also reported they were unaware of the differences between homes
promoted by IGC (57%) versus the other homes on the market at the time of their purchase.
 Overall, participants indicated they were satisfied with the program. Respondents were either
satisfied or very satisfied with the energy efficiency measures in their homes (92%) and the
ENERGY STAR® certified home promoted by IGC (84%). Ninety percent of respondents indicated
they were either satisfied or very satisfied with IGC as their gas service provider.
 Eight of 1,698 Whole Home Rebates were rejected across both program years. The Evaluators
found that less than 1% of the Whole Home Rebate applications were rejected. The majority (75%)
of rejections were because the rebate had previously been paid out for the address in the
application.
4.1.3 Appliance Rebates
 Contractors play a crucial role in the Appliance Rebate measure. Most survey respondents
learned about the program from a contractor and selected the rebated equipment because of a
contractor recommendation. Nearly half received their rebate form from their contractor.
Contractors not required to be officially registered with IGC. IGC does not have an approved
contractor network but does require that contractors who participate in the program to be
licensed with the state.
 IGC provides training and educational opportunities for contractors. IGC program staff generally
communicates with contractors via email and phone. There was one training provided by IGC staff
where there was information about the HVAC portal and online application. If contractors were
not able to attend, IGC staff sent emails with videos of the training along with online resources.
IGC also offered a $300 reimbursement for contractors to participate in an ENERGY STAR®
certification course. IGC staff indicated they have received positive feedback from contractors
(e.g., they like the “straight forwardness of the program” and the responsiveness of staff).
 All contractors had experience with other energy efficiency programs and most indicated they
were largely satisfied with the design and participation process. Among the contractors
interviewed, most reported general satisfaction with the Appliance Rebate measure. Contractors’
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experience included working with Idaho Power, Rocky Mountain Power, Idaho Department of
Environmental Quality, and Bonneville Power. Interviewed contractors had largely positive things
to say about IGC’s Energy Efficiency program in comparison to their experience with other
programs, citing the responsiveness of program staff and the quick turnaround time for rebates.
 Contractors are an important source of program awareness for customers. Most contractors
reported that only a small portion of their customers know about the program before they inform
them. Most contractors interviewed indicated they encourage the sale of high efficiency
equipment. Most of the contractors said their only method of encouraging the sale of high
efficiency equipment was discussions with customers.
 Customers experienced high satisfaction with IGC overall as well as the Appliance Rebate
measure. Most respondents were satisfied with their IGC rebated equipment’s installation and
performance. Satisfaction was also high in respect to other aspects of the program including
interactions with IGC staff, amount of time to receive the rebate, and the overall quality of the
contractor’s work. Most respondents said their application was accepted as submitted and said
they received their rebate check in a prompt and timely manner.
 The energy efficiency and warranty/reliability of the equipment mattered most to customers.
Contractors emphasized these features as well. Though nearly half of contractors also emphasized
the rebate amount, a smaller portion of customers noted that the rebate amount mattered most
to them.
 Of the rebate applications, 16% and 11% were rejected in PY2017-2018 and PY2019,
respectively. The Evaluators found the highest rejection rates for the 80% AFUE Fireplace, the
95% AFUE Natural Gas Furnace, and the 0.67 Water Heater measures. The reason applications
were rejected most often included not meeting the minimum efficiency requirements, unsigned
signature pages, lacking information on final invoice or application, missing permit numbers,
and/or the eligibility for application submission had passed.
4.2 Recommendations
4.2.1 Cross-Cutting Recommendations
 Codify quality assurance and control processes in a standard operating procedures (SOP)
manual. The Evaluators recommend that IGC program staff develop and operationalize all quality
assurance and quality control (QA/QC) procedures. By doing so, this will help future staff
understand the procedures for ensuring rebates are processed correctly and identifying gaps.
 Increase communication opportunities with contractors, builders, and HERS raters. Some
interviewed stakeholders were not aware of the online application forms and other components
to the Energy Efficiency program. The Evaluators recommend that IGC program staff to host
listening sessions with each respective stakeholder group to gain insight into the challenges that
they face with the Energy Efficiency program and market conditions.
 Explore opportunities to incorporate new measures (i.e., smart thermostats) in IGC’s Energy
Efficiency program offerings. Some tankless water heater contractors believe cost can be a
barrier for customers and estimated a 0.91 UEF system was two to three times more expensive
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than a standard efficiency water heater. The Evaluators recommend exploring additional
opportunities for IGC to add new measures to the Appliance Rebate and Whole Home current
offerings. Specifically, the Evaluators recommend examining adding 0.82 efficiency tankless water
heaters and smart thermostats to the list of available measures. IGC could work with builders to
add smart thermostats to new homes. Staff could also collaborate with contractors to use smart
thermostats as a marketing tool (e.g., limited-time offers for free smart thermostats coupled with
new furnace installation).
 Consider establishing a builder and trade ally approved network. By creating an IGC-approved
network of builders and contractors this could help ensure the appliances are being installed at a
sufficient standard of quality and that the homes built exceed program requirements. The
approved network of builders and contractors could be added to the website, which would
provide legitimacy and marketing for those who are participating in the network, as well as
provide assistance to customers on resources for their future home upgrades.
 Consider standardizing the tracking database categorization of rebate status and rebate
rejection reasoning. The tracking database collected and maintained information on the status of
rebate approval as well as reasoning for rebate rejection. However, the inputs for these fields
were often detailed and high-level insights were difficult to parse. The Evaluators recommend
setting in place a standard categorization for both the rebate status as well as rebate rejection
reason in order to assist in tracking and prioritization of current rebates as well as assist in analysis
of barriers to rebate completion.
4.2.2 Whole Home Measure
 Work with builders to achieve HERS requirements at a lower cost. Builders provided
recommendations on how to achieve a lower HERS scores in a cost-effective manner. The
Evaluators recommend working closely with builders who are successfully achieving the HERS
requirements to identify additional opportunities to reduce overall costs (e.g., implementing non-
HVAC measures such as air sealing, duct sealing, and insulation).
 Provide builders with additional marketing materials to promote the Whole Home measure.
Builders suggested providing marketing materials such as flyers or a logo to use on their website
that will help promote the Whole Home measure to home buyers. Marketing materials can be
utilized to promote awareness of the Whole Home measure and to educate homebuyers about
energy efficiency. The Evaluators recommend exploring all opportunities to generate marketing
collateral that can be used by builders and IGC program staff at community events. Program staff
could also consider co-branding marketing collateral with participating contractors who would be
interested in additional materials.
 Engage builders with company-specific reports and cost-saving estimates that builders can use
with homebuyers. Builders indicated they were reluctant to provide cost-saving estimates to
homebuyers, citing liability, but believed it would be useful information to help sell and promote
program-qualifying homes. The Evaluators recommend working with builders to develop generic
cost-saving estimates that can be used by builders when promoting the benefits of purchasing an
energy efficient home. These cost-saving estimates could also provide an educational opportunity
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to engage homebuyers with energy saving tips. The Evaluators also recommend creating material
that will help engage participating builders, such as quarterly or biannual reports that are builder
specific or quarterly newsletters with Whole Home measure statistics and upcoming events that
IGC will be in attendance.
 Continue participating in community events to recruit additional builders and generate
awareness about the Whole Home measure. Many participating builders first learned of the
Energy Efficiency program through IGC-attended events where builders and HERS raters were
provided with information about the Whole Home measure. The Evaluators recommend
researching additional outreach opportunities to reach nonparticipating builders with
promotional activities (e.g., offer a free HERS rating to builders who have not received a rebate in
the past two years).
 Develop materials that increase knowledge of the benefits of owning an energy efficient home
among IGC customers. The homeowners who do value the idea of energy efficiency are
consumers who are actively researching the latest technology or are interested in the incentives.
Homeowners who do not actively seek this type of information would benefit from knowing about
the incentives from a knowledgeable realtor or other stakeholders. The Evaluators also
recommend developing educational materials that can be used at events where homebuyers
would be in attendance (e.g., home and garden shows). Consider how program participation could
improve by engaging in outreach geared at increasing homeowners’ knowledge of energy
efficiency.
 Provide additional education or training opportunities for HERS raters, builders, and real estate
agents. According to the raters, builders have a significant learning curve to overcome to fully
understand the benefits of energy efficiency. Builders reported they were already building above-
code homes there were energy efficient and were not sure the benefits of participating. The
Evaluators recommend developing trainings that can be either in person or online that builders
and HERS raters can attend to increase their knowledge of the Energy Efficiency program,
specifically the Whole Home measure. IGC staff could also explore creating a training or
presentation for realtors. By offering continuing education credits through the Idaho Board of
Realtors, real estate agents could educate homeowners about the differences between an
ENERGY STAR® certified home and a HERS ratings.
4.2.3 Appliance Rebates
 Ensure that contractors are aware of the different participation options. A portion of contractors
said that participating in the program was time consuming and burdensome. By increasing
awareness of the program’s online form submission option IGC could improve contractor program
experience and expedite customer rebate receipt. Ensuring familiarity with participation
processes may ease the burden of participation and encourage more frequent participation. The
Evaluators recommend conducting direct outreach to current and past program participants (e.g.,
invite contractors to an online webinar or schedule individual one-on-one sessions to explain the
participation process). IGC program staff should investigate and remedy barriers to participation.
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 Consider enabling contractors or customers to track the status of their rebate. Allowing the
tracking of rebate status could potentially reduce the number of customer inquiries to IGC.
 Consider creating and distributing IGC Appliance Rebate promotional materials to contractors
and/or customers directly. The contractors suggested that IGC collateral could help them to
promote the program and a large portion of customers noted that they had learned about the
program from contractors. The Evaluators recommend sending contractors program material
directly that could assist with their outreach and promotion of the Appliance Rebate measure.
 Develop new educational materials and continue community outreach. IGC program staff
indicated there is not a set education budget but they have created educational materials to
increase awareness of the Appliance Rebate measures and general understanding of energy
efficiency. The Evaluators recommend looking for partnership opportunities to create online
instructional videos and other educational materials that can be viewed by customers. The
Evaluators also recommend continuing community outreach efforts with a focus on increasing
customer knowledge and general understanding of energy efficiency.
 If furnaces have difficulty meeting cost-effectiveness requirements, consider the addition of an
Electronically Commutated Motor (ECM) for the furnace blower as a program requirement.
ECMs result in significant kWh savings when installed alongside a new furnace, and this additional
requirement could be used to enhance the cost-effectiveness of a retrofit furnace.
 Consider collecting application data on the condition of the replaced furnace. The Evaluators
found that 72% of survey respondents indicated that their preexisting furnace was functional. This
could be used to increase energy savings estimates via early retirement (in which savings are
estimated based on preexisting equipment efficiency rather than current minimum code).
 Incorporate Avoided Replacement Cost (ARC) into tankless water heater cost-effectiveness
calculations. Tankless water heaters have a 20-year measure life, while storage tank water
heaters have an 11-year measure life. The net present value (NPV) of the purchase of a second
water heater starting in year 12 should be deducted from incremental cost estimates used for this
measure, improving measure cost-effectiveness.
 Consider tracking additional fields in rebate application process to assist in future energy
savings estimates. The tracking database sufficiently collects customer information, household
information, and contractor information. However, additional quality assurance steps while
collecting some variables can be improved upon, such as year built, square footage, appliance
model brand, appliance model number, appliance serial number, and equipment size and
efficiency. In addition, the Evaluators recommend adding the following information as
requirements for rebate application to assist in quality control and future energy savings
calculations: equipment AHRI certification number, reason for replacement (replace-on-burnout,
early retirement, new construction), prior equipment input size, prior equipment efficiency, and
prior equipment age. The above fields are either required or optional values as inputs to
engineering equations to estimate annual energy savings for these measures.

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5 Appendix
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5.1 Survey Instruments
5.1.1 Whole Home Measure Participant Survey
INTRODUCTION
1. According to our records, you purchased the home located at [ADDRESS]. Is this
correct?
1. Yes
2. No [TERMINATE SURVEY]
98. Don’t know [TERMINATE SURVEY]
[DISPLAY IF Q1 = 1]
2. To buy your home, did you work directly with the builder, the builder’s real estate
agent, a personal real estate agent, a different real estate agent, or someone else?
1. Home builder
2. Builder’s real estate agent
3. Personal Real Estate Agent
4. Different real estate agent
96. Someone else (Please specify)
98. Don’t know
PROGRAM AWARENESS AND DECISION MAKING
3. Did you know the home you purchased was an ENERGY STAR® certified home
promoted by IGC’s energy efficiency program?
1. Yes
2. No
98. Don’t know
[DISPLAY Q4 IF Q3=1]
4. How did you first learn that your home is ENERGY STAR® certified? [RANDOMIZE 1-11]
1. Home builder
2. Real estate agent
3. From IGC’s website
4. Friend, family member, or colleague
5. Communication with IGC staff
6. IGC newsletter
7. Social media post (e.g., Facebook, Twitter, Flickr)
8. Through an internet search (e.g., Google search)
9. Through an internet advertisement
10. A radio or television advertisement
11. A print advertisement
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12. Previous experience with IGC programs
13. Bill inserts
14. Intermountain Gas YouTube Channel
15. Community event
16. Media coverage (segment on TV, radio, vlog, blog, article, etc.)
96. Other (Please specify)
98. Don’t know
[DISPLAY Q5 IF Q0 = 12]
5. What previous experience do you have with IGC programs?
[OPEN-ENDED]
6. IGC would like to understand what buyers knew about the ENERGY STAR® certified
new homes. For each of the following items, please select the response that best
describes when you learned about it. [INSERT MATRIX WITH FIVE RESPONSE
OPTIONS FOR EACH ITEM: 1 = KNEW IT BEFORE I CONTACTED THE BUILDER OR
REAL ESTATE AGENT ABOUT BUYING THE HOME; 2 = I LEARNED ABOUT IT FROM
THE BUILDER OR REAL ESTATE AGENT BEFORE I BOUGHT THE HOME; 3 = I
LEARNED ABOUT IT AFTER BUYING THE HOME; 4 = I DID NOT KNOW ABOUT IT
BEFORE BEING INVITED TO TAKE THIS SURVEY, WITH 98 = I DON’T KNOW WHEN I
LEARNED ABOUT IT]
1. The house was promoted by IGC's energy efficiency program
2. The house has an ENERGY STAR® rating
3. The house was inspected by an independent certified Home Energy Rating
System (HERS)
4. The house has a HERS score of [HERS_SCORE]
[DISPLAY IF Q6(4) = 1 or 2]
7. Did knowing that your home had a HERS score of [HERS_SCORE] influence your
decision to purchase?
1. Yes
2. No
98. Don’t know
8. Please tell me briefly your decision-making process for purchasing your home
located at [ADDRESS].
1. [OPEN-ENDED]
9. Using the scale below, please rate the importance of the following factors in your
decision to buy this home. [RANDOMIZE 1 - 11] [INSERT 1-5 SCALE, 1 = NOT AT ALL IMPORTANT AND 5 = EXTREMELY IMPORTANT, WITH 98 = DON’T KNOW]
1. Energy efficiency
2. House price
3. [DISPLAY IF Q6(1) = 1 OR 2] It was promoted through IGC's energy efficiency
program
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4. [DISPLAY IF Q6(2) = 1 OR 2] It has an ENERGY STAR® rating
5. [DISPLAY IF Q6(3) = 1 OR 2] It was inspected by an independent certified
HERS rater
6. [DISPLAY IF Q6(3) = 1 OR 2] The home’s HERS score
10. Are there any other important features or reasons you chose your new home? If
yes, could you please share them here and rate their importance on the same scale.
[OPEN-ENDED]
11. Are you aware of any differences between homes promoted by IGC’s energy
efficiency program and other homes?
7. Yes
8. No
98. Don’t know
[DISPLAY Q12 IF Q11 = 1]
12. What were the differences that were explained to you?
[OPEN-ENDED]
13. Using the scale below, how clearly were the energy efficient characteristics of the
home explained to you by the person you worked with when purchasing your
home? [INSERT 1-5 SCALE 1 = NOT AT ALL CLEAR, 5 = VERY CLEAR, 98 = DON’T
KNOW]
MEASURE PERSISTENCE
Now we would like to ask about any changes you may have made to your home since you bought it.
14. Have you made any changes to the heating system equipment that was originally
installed in your new home?
1. Yes
2. No
98. Don’t know
[DISPLAY IF Q0 = 1]
15. What changes did you make to your heating system?
1. [OPEN-ENDED]
16. Have you made any changes to the cooling system that was originally installed in
your new home?
1. Yes
2. No
98. Don’t know
[DISPLAY IF Q16 = 1]
17. What changes did you make to your cooling system?
1. [OPEN-ENDED]
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18. Have you changed out any large appliances such as furnaces, water heaters,
refrigerators, clothes washers, and dishwashers that came with the home?
1. Yes
2. No
98. Don’t know
[DISPLAY IF Q18 = 1]
19. What appliances did you change out?
1. [OPEN-ENDED]

ENERGY EFFICIENCY KNOWLEDGE, ATTITUDES, AND INTENTIONS
20. Using the scale below, how informed would you say you are about energy efficiency
practices and energy efficient products or improvement options for your
household?
[INSERT 1-5 SCALE 1 = NOT WELL INFORMED, 5 = VERY WELL INFORMED, WITH 98 =
DON’T KNOW]
21. Using the scale below, how trustworthy is Intermountain Gas Company as a source
of information about saving energy in your home?
[INSERT 1-5 SCALE 1 = NOT VERY TRUSTWORTHY, 5 = VERY TRUSTWORTHY, WITH
98 = DON’T KNOW]
22. Using the scale below, please rate your level of agreement or disagreement with
these statements.
[INSERT 1-5 SCALE 1 = STRONGLY DISAGREE, 5 = STRONGLY AGREE, WITH 98 = DON’T
KNOW] [RANDOMIZE 1-7]
1. Energy efficiency saves money.
2. I am not very concerned about the amount of energy used in my home
3. I am too busy to worry about making energy-related improvements in my
home
4. It is possible to save energy without sacrificing comfort by being energy
efficient.
5. I know of steps I could take to reduce my household energy use.
6. I intend to reduce my household energy use in the next 12 months.
7. It is not possible to make energy efficiency changes on my budget.
Using the scale below, how satisfied or dissatisfied are you with the following? [INSERT 1-5
SCALE 1 = VERY DISSATISFIED, 5 = VERY SATISFIED, WITH 98 = DON’T KNOW]
1. IGC’s marketing efforts to promote its discounts on energy efficient
products.
2. IGC overall as your gas service provider.
3. The ENERGY STAR® certified home promoted by IGC’s energy efficiency
program that you purchased in [YEAR].
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4. Energy efficient measures of the home purchased.
[DISPLAY Q23 IF Q0(3) <3]
23. Why are you not satisfied?
[OPEN-ENDED]
24. Are there any additional comments you would like to share with us about your
experience in the program?
[OPEN-ENDED]
DEMOGRAPHICS
This last set of questions will help Intermountain Gas Company develop more effective programs that
may best serve the needs of the community. Your answers will remain anonymous, and you also have
the option of not answering these questions.
25. Who provides your home with electric service?
Idaho Power Company
Rocky Mountain Power
Fall River Rural Electric Cooperative
Idaho County Light and Power
Other (Please specify)
Don’t know
Prefer not to state
26. Do you own or rent the home in which you live?
Own
Rent
Don’t know
Prefer not to state
27. Including yourself, how many people currently live in your home?
1
2
3
4
5
6
7
8
9 or more
Do not know
Prefer not to answer
28. Which of the following brackets contains your age?
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19-24
25-34
35-49
50-64
65 or over
Don’t know
Prefer not to state
29. What is your approximate total household income?
Less than $10,000
$10,000 to less than $20,000
$20,000 to less than $30,000
$30,000 to less than $40,000
$40,000 to less than $50,000
$50,000 to less than $60,000
$60,000 to less than $70,000
$70,000 to less than $80,000
$80,000 to less than $90,000
$90,000 to less than $100,000
$100,000 to less than $150,000
$150,000 to less than $200,000
$200,000 or more
Don’t know
Prefer not to state
30. What’s the highest level of education you’ve completed?
Up to 8th grate
Some high school
High school or GED equivalent
Some college
Associate’s degree
Bachelor’s college degree
Master’s degree
Professional degree (MD, JD, DDO, DDS)
Doctorate (Ph.D., D.Sc.)
Don’t know
Prefer not to state

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5.1.2 Appliance Rebate Measure Participant Survey
INTRODUCTION
1. According to our records you received a rebate through IGC’s [PROGRAM] program
for installing [MEASURE] at [LOCATION] in [YEAR]. Is that correct?
Yes
No [TERMINATE SURVEY]
Don’t know [TERMINATE SURVEY]
To begin, we have a few questions about how you learned about the rebate for the [MEASURE].
2. How did you first learn about IGC’s rebates for the [MEASURE]? [RANDOMIZE 1-15]
Friend, family member, or colleague
Contractor
Retailer
Bill insert or utility mailer
Email from IGC
IGC Newsletter
From IGC’s website
Social media post (e.g., Facebook, Twitter)
Intermountain Gas Company YouTube channel
Through an internet search (e.g., Google search)
Through an internet advertisement
A radio or television advertisement
A print advertisement
Media coverage (segment on TV, radio, podcast, vlog, article, etc.)
Community event
Other (please specify)
Don’t know
PROGRAM AWARENESS
3. When did you first learn about IGC’s Energy Efficiency Rebate Program? Was it…
Before starting the process of purchasing the [MEASURE].
At the time you made the purchase decision.
After researching the product but before deciding to purchase.
After deciding to purchase [MEASURE].
Don’t know
4. Why did you purchase the model or type of [MEASURE_SHORT]? [MULTISELECT]
[RANDOMIZE]
It was all that was available/only choice.
The contractor/retailer recommended it.
It was an emergency replacement.
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It had an ENERGY STAR® label.
It was a planned replacement.
It’s good for the environment.
It was the right size, color.
There was a rebate for it.
It costs less to operate it.
It had features I wanted.
It was a good price.
Other (Please specify)
Don’t know
5. Where did you obtain the rebate application?
From the IGC website
From another website
In a retail store
From a contractor
Other (Please specify)
Don’t know
6. Was your application accepted as-submitted, or did IGC follow up with you for
clarifications or further information?
Accepted as-submitted
Required follow-up
Don’t know
[DISPLAY Q7 IF Q6 = 2]
7. What type of follow-up did your application require?
[OPEN-ENDED]
8. Did you receive your rebate check in a prompt and timely manner?
Yes
No
Don’t know
9. Do you have any comments as to how the application process could be made easier
for customers like you?
[OPEN-ENDED]
MEASURES
[ASK Q10 THROUGH Q19 IF MEASURE_SHORT = 95% Furnace]
10. Was this new installation part of a larger project?
Yes, it was part of a larger replacement of my heating system
Yes, it was part of a newly constructed home.
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Yes, it was part of a remodeling project.
No, it was a stand-alone replacement.
Other (Please specify)
Don’t know
11. What type of fuel source does your home’s water heater use?
Natural Gas
Electricity
Propane
Other (Please specify)
Don’t Know
12. What kind of water heater does your home have?
Tank water heater
Tankless water heater
Heat pump water heater
Solar powered water heater
Don’t know
13. How old is your home’s water heater?
Less than 2 years old
2 to 4 years
5 to 9 years
10 to 14 years
15 to 19 years
20 or more years old
Don’t know
14. What type of thermostat does your home have?
Manual thermostat
Programmable thermostat
Smart thermostat
None
Don’t know
15. Is the new [MEASURE_SHORT] that you received a rebate for currently installed and
working?
Yes
No
Don’t know
[DISPLAY Q16 IF Q15 = 2]
16. Why is it not installed or working?
[OPEN-ENDED]
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102
[DISPLAY Q17 IF Q10 = 1, 3, OR 4]
17. Was the equipment that you replaced still functioning at the time you replaced it?
Yes
No
Don’t know
[DISPLAY Q17 IF Q10 = 1, 3, OR 4]
18. How old was the equipment that you replaced at the time you replaced it?
___ # Years
Don’t know
19. Using the scale below, how satisfied are you with the performance of your new
[MEASURE_SHORT]? [INSERT 1-5 SCALE. 1 = VERY DISSATISFIED AND 5 = VERY
SATISFIED, WITH 98 = DON’T KNOW]
Very Dissatisfied

Very Satisfied
Don’t know
[ASK Q20 THROUGH Q25 IF MEASURE_SHORT = RADIANT COMBO
20. Was this new installation part of a larger project?
Yes, it was part of a larger replacement of my heating system.
Yes, it was part of a newly constructed home.
Yes, it was part of a home remodeling project.
No, it was a stand-alone replacement.
Other (Please specify)
Don’t know
21. Is the new [MEASURE_SHORT] that you received a rebate for currently installed and
working?
Yes
No
Don’t know
[DISPLAY Q22 IF Q21 = 2]
22. Why is it not installed or working?
[OPEN-ENDED]
[DISPLAY Q23 IF Q20 = 1, 3, OR 4]
23. Was the equipment that you replaced still functioning at the time you replaced it?
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Yes
No
Don’t know
[DISPLAY Q24 IF Q20 = 1, 3, OR 4]
24. How old was the equipment that you replaced at the time you replaced it?
___ # Years
Don’t know
25. Using the scale below, how satisfied are you with the performance of your new
[MEASURE_SHORT]? [INSERT 1-5 SCALE. 1 = VERY DISSATISFIED AND 5 = VERY
SATISFIED, WITH 98 = DON’T KNOW]
Very Dissatisfied

Very Satisfied
Don’t know
[ASK Q26 THROUGH Q31 IF MEASURE_SHORT = FIREPLACE]
26. Was this new installation part of a larger project?
Yes, it was part of a larger replacement of my heating system.
Yes, it was part of a newly constructed home.
Yes, it was part of a home remodeling project.
No, it was a stand-alone replacement.
Other (Please specify)
Don’t know
27. Is the new [MEASURE_SHORT] that you received a rebate for currently installed and
working?
Yes
No
Don’t know
[DISPLAY Q28 IF Q27 = 2]
28. Why is it not installed?
[OPEN-ENDED]
29. Did you have natural gas plumbed to this fireplace before the installation of your
[MEASURE_SHORT]?
Yes
No [SKIP Q30]
Don’t know
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30. What type of system did you have in place before installing your
[MEASURE_SHORT]?
Wood burning with a gas burner to help ignite the fire
Gas logs, with a pilot light that was “always on”
Gas logs, with an ignition that you would control (via a push-button, remote,
or other control type)
Don’t know
31. Using the scale below, how satisfied are you with the performance of your new
[MEASURE_SHORT]? [INSERT 1-5 SCALE. 1 = VERY DISSATISFIED AND 5 = VERY
SATISFIED, WITH 98 = DON’T KNOW]
Very Dissatisfied

Very Satisfied
Don’t know
[ASK Q32 THROUGH Q37 IF MEASURE_SHORT = WATER HEATER]
32. Was this new installation part of a larger project?
Yes, it was part of a larger replacement of my water heating system.
Yes, it was part of a newly constructed home.
Yes, it was part of a home remodeling project.
No, it was a stand-alone replacement.
Other (Please specify)
Don’t know
33. Is the new [MEASURE_SHORT] that you received a rebate for currently installed and
working?
Yes
No
Don’t know
[DISPLAY Q34 IF Q33 = 2]
34. Why is it not installed?
[OPEN-ENDED]
[DISPLAY Q35 IF Q32 = 1, 3, OR 4]
35. Was the equipment that you replaced still functioning at the time you replaced it?
Yes
No
Don’t know
[[DISPLAY Q36 IF Q32 = 1, 3, OR 4]
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36. How old was the equipment that you replaced at the time you replaced it?
___ # Years
Don’t know
37. Using the scale below, how satisfied are you with the performance of your new
[MEASURE_SHORT]? [INSERT 1-5 SCALE. 1 = VERY DISSATISFIED AND 5 = VERY
SATISFIED, WITH 98 = DON’T KNOW]
Very Dissatisfied

Very Satisfied
Don’t know
[ASK Q38 THROUGH Q43 IF MEASURE_SHORT = TANKLESS WATER HEATER]
38. Was this new installation part of a larger project?
Yes, it was part of a larger replacement of my water heating system.
Yes, it was part of a newly constructed home.
Yes, it was part of a home remodeling project.
No, it was a stand-alone replacement.
Other (Please specify)
Don’t know
39. Is the new [MEASURE_SHORT] that you received a rebate for currently installed and
working?
Yes
No
Don’t know
[DISPLAY Q40 IF Q39 = 2]
40. Why is it not installed?
[OPEN-ENDED]
[DISPLAY Q41 IF Q38 = 1, 3, OR 4]
41. Was the equipment that you replaced still functioning at the time you replaced it?
Yes
No
Don’t know
[DISPLAY Q42 IF Q38 = 1, 3, OR 4]
42. How old was the equipment that you replaced at the time you replaced it?
___ # Years
Don’t know
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43. Using the scale below, how satisfied are you with the performance of your new
[MEASURE_SHORT]? [INSERT 1-5 SCALE. 1 = VERY DISSATISFIED AND 5 = VERY
SATISFIED, WITH 98 = DON’T KNOW]
Very Dissatisfied

Very Satisfied
Don’t know
MEASURE INSTALLATION
44. Who installed the [MEASURE_SHORT]?
I installed it.
A friend or family member installed it.
I hired a contractor to install it.
Other (Please specify)
Don’t know
[DISPLAY Q45 IF Q44 =1]
45. How did you learn to install the [MEASURE_SHORT]?
Prior occupational knowledge
Went to a workshop
Self-taught (You-tube videos, home courses, etc.)
Other (Please specify)
[DISPLAY Q46 and Q47 IF Q44 =3]
46. How did you select the contractor who installed the [MEASURE_SHORT]?
I have hired this contractor for other work in the past.
The retailer recommended the contractor.
I researched online.
Friend or family member recommended the contractor.
47. In total, how many contractors did you speak with before choosing one to install
your [MEASURE_SHORT]?
[OPEN-ENDED]
[DISPLAY Q48 IF Q44 =3]
48. Using the scale below, please indicate how much you disagree or agree with the
following statements regarding your experience with the contractor that installed
the new [MEASURE_SHORT]:
1 –
Strongly
Disagree 2 3 4
5 –
Strongly
Agree
DON’T
KNOW
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a. The contractor was courteous
and professional
1 2 3 4 5 98
b. The work was scheduled in a
reasonable amount of time
1 2 3 4 5 98
c. The work was completed in a
reasonable amount of time
1 2 3 4 5 98
d. The contractor was
knowledgeable about the
[MEASURE_SHORT]
1 2 3 4 5 98
e. The contractor answered all
my questions thoroughly.
1 2 3 4 5 98
[DISPLAY Q49 IF Q48 < 3]
49. Please elaborate regarding your dissatisfaction.
[OPEN-ENDED]
[DISPLAY Q50 IF Q48d > 3]
50. What features of your new [MEASURE_SHORT] did your contractor emphasize to
you? [MULT-SELECT] [RANDOMIZE]
Energy Efficiency
Low price
Rebate eligibility
Good warranty/reliability
Quiet operation
Emphasis on the brand and its reputation
Size of the equipment
Other (Please specify)
Don’t know
51. What feature of your new [MEASURE_SHORT] matter the most to you? [MULT-
SELECT] [RANDOMIZE]
Energy Efficiency
Low price
Rebate eligibility
Good warranty/reliability
Quiet operation
Emphasis on the brand and its reputation
Size of the equipment
Other (Please specify)
Don’t know

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ENERGY EFFICIENCY KNOWLEDGE, ATTITUDES, INTENTIONS
52. Using the scale below, how informed would you say you are about energy efficiency
practices and energy efficient products/improvement options for your household?
[INSERT 1-5 SCALE 1 = NOT WELL INFORMED, 5 = VERY WELL INFORMED, WITH 98
= DON’T KNOW]
53. Using the scale below, how trustworthy is IGC as a source of information about
saving energy in your home? [INSERT 1-5 SCALE 1 = NOT VERY TRUSTWORTHY, 5 =
VERY TRUSTWORTHY, WITH 98 = DON’T KNOW]
54. Using the scale below, please rate your level of agreement or disagreement with
these statements. [INSERT 1-5 SCALE 1 = STRONGLY DISAGREE, 5 = STONGLY
AGREE, WITH 98 = DON’T KNOW] [RANDOMIZE 1-7]
Energy efficiency saves money.
I am not very concerned about the amount of energy used in my home
I am too busy to worry about making energy-related improvements in my
home.
It is possible to be energy efficient without sacrificing comfort
I know of steps I could take to reduce my household energy use.
I intend to reduce my household energy use in the next 12 months.
It is not possible to make energy efficiency changes on my budget.
CUSTOMER SATISFACTION
55. Did someone in your household contact a program staff member from IGC with
questions about your rebate application (this question does not pertain to your
interaction with the contractor)?
Yes
No
Don’t know
56. Using the scale below, please rate how dissatisfied or satisfied you are with each of
the following …
1 – Very
dissatisfied 2 3 4
5 – Very
satisfied DON’T KNOW
a. [DISPLAY IF Q55 = 1] how long it
took program staff to address your
questions or concerns
1 2 3 4 5 98
b. [DISPLAY IF Q55 = 1] how
thoroughly they addressed your
question or concerns
1 2 3 4 5 98
c. the performance of the equipment
you installed overall
1 2 3 4 5 98
d. the energy savings on your utility
bill resulting from installing the
new [MEASURE_SHORT]
1 2 3 4 5 98
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e. the rebate amount 1 2 3 4 5 98
f. the process for applying for the
rebate
1 2 3 4 5 98
g. [DISPLAY IF 44 = 3] the overall
quality of the work performed by
your contractor
1 2 3 4 5 98
h. the time it took to receive the
rebate
1 2 3 4 5 98
i. the experience with the program
overall
1 2 3 4 5 98
j. Intermountain Gas Co. as your gas
service provider
1 2 3 4 5 98
[DISPLAY Q57 IF ANY IN Q56 <3]
57. You indicated some dissatisfaction. Why were you dissatisfied?
[OPEN-ENDED]
58. Using the scale below, how would you say your participation in the Appliance
Rebate program has affected your satisfaction with Intermountain Gas Company?
[INSERT 1-5 SCALE, 1 = GREATLY DECREASED YOUR SATISFACITON EITH IGC AND 5
= GREATLY INCREASED YOUR SATISFACITON EITH IGC, WITH 98 = DON’T KNOW]
Greatly decreased your satisfaction with IGC

Greatly increased your satisfaction with IGC
Don’t know
59. Are there any additional comments you would like to share with us about your
experience in the program?
[OPEN-ENDED]
HOME DEMOGRAPHICS
This last set of questions will help Intermountain Gas Company develop more effective programs that
may best serve the needs of the community. Your answers will remain anonymous, and you also have
the option of not answering these questions.
60. Who provides your home with electric service?
Idaho Power Company
Rocky Mountain Power
Fall River Rural Electric Cooperative
Idaho County Light and Power
Other (Please specify)
Don’t know
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Prefer not to state
61. Do you own or rent the home in which you live?
Own
Rent
Don’t know
Prefer not to state
62. Which best describes this building?
Single-family house detached from any other house
Single-family house attached to one or more other houses (e.g., duplex,
condominium, townhouse)
Mobile or manufactured home
Apartment with 2 or 3 units
Apartment with 4 or more units
Other (Please specify)
Do not know
Prefer not to answer
63. About when was this building first built?
Before 1960
1960 to1969
1970 to 1979
1980 to 1989
1990 to 1999
2000 to 2009
2010 to 2019
Do not know
Prefer not to answer
64. Please provide the estimated square footage of your house.
Less than 1,000 square feet
1,000 to 1,999 square feet
2,000 to 2,999 square feet
3,000 to 3,999 square feet
4,000 square feet or greater
Do not know
Prefer not to answer
65. What is the main fuel used for heating your home?
Natural Gas
Electricity
Propane
Other (Please specify)
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Do not know
Prefer not to answer
66. What fuel does your main water heater use?
Natural Gas
Electricity
Propane
Other (Please specify)
Do not know
Prefer not to answer
67. What is the main fuel used for cooking in your home?
Natural Gas
Electricity
Propane
Other (Please specify)
Do not know
Prefer not to answer
68. Including yourself, how many people currently live in your home?
1
2
3
4
5
6
7
8
9 or more
Do not know
Prefer not to answer
69. Which of the following brackets contains your age?
19-24
25-34
35-49
50-64
65 or over
Don’t know
Prefer not to state
70. What is your approximate total household income?
Less than $10,000
$10,000 to less than $20,000
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$20,000 to less than $30,000
$30,000 to less than $40,000
$40,000 to less than $50,000
$50,000 to less than $60,000
$60,000 to less than $70,000
$70,000 to less than $80,000
$80,000 to less than $90,000
$90,000 to less than $100,000
$100,000 to less than $150,000
$150,000 to less than $200,000
$200,000 or more
Don’t know
Prefer not to state
71. What’s the highest level of education you’ve completed?
Up to 8th grate
Some high school
High school or GED equivalent
Some college
Associate’s degree
Bachelor’s college degree
Master’s degree
Professional degree (MD, JD, DDO, DDS)
Doctorate (Ph.D., D.Sc.)
Don’t know
Prefer not to state
5.1.3 Nonparticipant Survey
1. According to our records, Intermountain Gas Company provides the natural gas
service at your residence located at [ADDRESS]. Is that correct?
Yes
No [TERMINATE SURVEY]
The location is not a residence [TERMINATE SURVEY]
Don’t know [TERMINATE SURVEY]
[DISPLAY Q2 IF Q5.1.3 = 1]
2. In the last five years, have you received a rebate from Intermountain Gas Company
or your electricity service provider for installing energy efficient equipment or
making energy efficiency improvements for the residence located at [ADDRESS]?
No, I have not received any rebates
Yes, from Intermountain Gas Company [TERMINATE]
Yes, from my electricity service provider
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Yes, from both [TERMINATE]
Don’t know
[DISPLAY Q3 IF Q2 = 2 OR 4]
3. You mentioned you received a rebate from IGC. What energy efficient equipment
did you receive a rebate for?
Tank water heater
Fireplace
Furnace
Tankless water heater
Combination radiant heat system
Efficient whole home new construction
Other (specify)
Don’t know
[DISPLAY Q4 IF Q2 = 3 OR 4]
4. You mentioned you received a rebate from your electric service provider. Can you
provide further detail?
[OPEN-ENDED]
HOME CHARACTERISTICS
5. Do you use natural gas in your home located at [ADDRESS]?
Yes
No
Don’t know
6. Which of the following best describes your home?
Single-family house detached from any other house
Single family house attached to one or more other houses (duplex, row
house, condominium, or townhome)
Apartment in a building with 2 to 3 units
Apartment in a building with 4 or more units
Manufactured or mobile home
Other (Please specify)
Don’t know
7. Do you own, rent, or own and rent to someone else the property located at
[LOCATION]?
Own
Rent
Own and rent to someone else
Don’t know
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8. About when was the home first built?
Before 1960
1960 to1969
1970 to 1979
1980 to 1989
1990 to 1999
2000 to 2009
2010 to 2019
Don’t know
9. Please provide the estimated square footage of your house.
Less than 1,000 square feet
1,000 to 1,999 square feet
2,000 to 2,999 square feet
3,000 to 3,999 square feet
4,000 square feet or greater
Don’t know
10. What is the main fuel used for heating your home?
Natural Gas
Electricity
Propane
Other (Please specify)
Don’t know
11. What is the main type of heating equipment used to provide heat for your home?
Heat pump
Central forced air furnace
Room or space heater
Something else (Please specify)
Don’t know
12. How old is your heating system equipment? Your best estimate is fine.
Less than 2 years old
2 to 4 years
5 to 9 years
10 to 14 years
15 to 19 years
20 or more years old
Don’t know
13. What fuel does your main water heater use?
Natural Gas
Electricity
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Propane
Other (Please specify)
Don’t know
14. How old is your main water heater? Your best estimate is fine.
Less than 2 years old
2 to 4 years
5 to 9 years
10 to 14 years
15 to 19 years
20 or more years old
Don’t know
15. What is the main fuel used for cooking in your home?
Natural Gas
Electricity
Propane
Other (Please specify)
Don’t know
16. Does your home have an existing fireplace with a gas line connection?
Yes
No
Don’t know
[DISPLAY Q17 IF Q16 = 1]
17. Does your home’s fireplace have a constant pilot light or an intermittent pilot light?
Constant pilot light
Intermittent pilot light
Don’t know
ENERGY SAVING ACTIONS
18. In the past three years, have you or anyone else in your household purchased,
installed or implemented any of the following equipment or home improvements at
this residence? [RANDOMIZE]
95% AFUE Natural Gas Furnace
Combination Radiant Heat System
80% AFUE Fireplace
70% FE Fireplace
0.67 Water Heater
0.91 Tankless Water Heater
I have not done any of these
Other (Please specify)
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Don’t know
19. Was the following item(s) that you mentioned ENERGY STAR® certified or high
efficiency? [SCALE: 1 = Yes, 2 = No, 98 = DON’T KNOW]
[DISPLAY IF Q18 = 1] 95% AFUE Natural Gas Furnace
[DISPLAY IF Q18 = 2] Combination Radiant Heat System
[DISPLAY IF Q18 = 3] 80% AFUE Fireplace
[DISPLAY IF Q18 = 4] 70% FE Fireplace
[DISPLAY IF Q18 = 5] 0.67 Water Heater
[DISPLAY IF Q18 = 6] 0.91 Tankless Water Heater
Other (specify)
Don’t know
PROGRAM AWARENESS
20. Are you aware of any rebates for energy efficient equipment and home
improvements or other services offered by a gas or electric utility?
Yes
No
Don’t know
[DISPLAY IF Q20= 1]
21. Which utilities provide the rebates that you recall hearing about?
[OPEN-ENDED]
[DISPLAY IF Q20= 1]
22. What types of rebates do you recall hearing about? [MULTI-SELECT] [RANDOMIZE]
Heating and cooling equipment
Appliances such as refrigerators, clothes washers
Home weatherization improvements such as air or duct sealing
Discounts for efficient lighting
Low flow faucet aerators or showerheads
Other (Please specify)
Don’t know
[DISPLAY IF Q20= 1]
23. How did you learn of these rebates or services? [MULTI-SELECT] [RANDOMIZE]
Information that came in the mail
Email
Newspaper or magazine article
Contractor
Word of mouth from friends, relatives, or others
TV ad
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Radio ad
Social media (Facebook, Twitter)
Utility bill message
Utility website
Intermountain Gas YouTube channel
Other website
Utility program staff
Community event
Other (Please specify)
Don’t know
ENERGY EFFICIENCY KNOWLEDGE, ATTITUDES, AND INTENTIONS
24. Using the scale below, how knowledgeable are you about ways to save energy in
your home? [SCALE: 1 (Not at all knowledgeable) – 5 (Very knowledgeable, 98 =
DON’T KNOW]
25. Using the scale below, how much have you done to lower your monthly energy bill
in your home? [SCALE: 1 (You have not done much) – 5 (You have done almost
everything you can), 98 = DON’T KNOW]
26. Using the scale below, how interested are you in making improvements to your
home that would... [SCALE: 1 (Not at all interested) – 5 (Extremely interested), 98 =
DON’T KNOW]
Increase its energy efficiency by using appliances powered by natural gas?
Improve your comfort by using appliances powered by natural gas?
Improve your health and safety by using appliances powered by natural gas?
27. How would you find out ways to reduce your monthly energy bills? [MULTI-SELECT]
From the utility customer service line
Utility website
Internet search
Social media
Government information
ENERGY STAR website
Friends, family, or colleagues
Information from a retail store
Magazines/newspapers/television/radio
Home energy audit
Other (Please specify)
Don’t know
28. Using the scale below, how likely is it that you would do any of the following in your
home to reduce your utility bill? [SCALE: 1 (Very unlikely) – 5 (Very likely), 98 =
DON’T KNOW]
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Add insulation in ceilings, walls, floors
Add weather stripping or caulking around doors and windows
Install energy efficient, double-pane windows
Insulate water heater tank and pipes
Install a programmable set-back thermostat or smart (Wi-Fi) thermostat
Sealing and insulation of ducts
Install a high efficiency central heating (furnace, boiler)
Clean or change your furnace filters once a month during heating season
Install glass doors on fireplace and close dampers when the fireplace is not
in use
Install a high efficiency heat pump or air conditioner
Conduct regular maintenance of central heating or cooling system
Use low-flow showerhead or faucet aerators
Adjust thermostat settings
Buy energy efficient appliances
Close the drapes or cover windows to reduce heat gain or loss
Turn down water heater temperature
Install efficient lighting
Unplug appliances or electronics when not use
Install a smart power strip
Use cold water when doing laundry
29. Are you planning to make any energy efficient updates in your home in the next
year?
Yes
No
Don’t know
[DISPLAY Q30 IF Q29 = 1]
30. Please describe what energy efficient improvements you plan to make to your home
in the upcoming year.
[OPEN-ENDED]
31. Using the scale below, how trustworthy is Intermountain Gas Company as a source
of information about saving energy in your home?
1- Not at all trustworthy
2
3
4
5- Very trustworthy
Don’t know
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32. Using the scale below, how interested are you in receiving additional information on
energy saving tips and rebate programs offered by Intermountain Gas
Company? Would you say…
1- Not at all interested
2
3
4
Very interested
Don’t know
[DISPLAY Q33 IF Q32>3]
33. What would be the best way for Intermountain Gas Company to provide
information on rebates for energy saving equipment and improvements?
Email
Utility Website
Bill inserts
Utility newsletter
Telephone call
Text messages
None, I prefer to not receive information
Other (Please specify)
Don’t know
34. Please rate your level of agreement or disagreement with these statements using
the scale below. There are no right or wrong answers, we just want your opinion.
[SCALE: 1 (Strongly disagree) – 5 (Strongly agree), 98 = DON’T KNOW]
[RANDOMIZE ORDER]
Energy efficiency saves money.
I know of steps I could take to reduce my household energy use.
It is possible to be energy efficient without sacrificing comfort.
I am not very concerned about the amount of energy used in my home.
I am too busy to worry about making energy-related improvements in my
home.
It is not possible to make energy efficiency changes on my budget.
I intend to reduce my household energy use in the next 12 months.
SATISFACTION
35. Using a scale below, how satisfied are you with Intermountain Gas Company as your
natural gas service provider? [SCALE: 1 (Very dissatisfied) – 5 (Very satisfied), 98 =
DON’T KNOW]
[DISPLAY IF Q35<3]
36. Why are you dissatisfied?
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[OPEN-ENDED]
DEMOGRAPHICS
This last set of questions will help Intermountain Gas Company develop more effective programs that
may best serve the needs of the community. Your answers will remain anonymous, and you also have
the option of not answering these questions.
37. Who provides your home with electric service?
Idaho Power Company
Rocky Mountain Power
Fall River Rural Electric Cooperative
Idaho County Light and Power
Other (Please specify)
Don’t know
Prefer not to state
38. Including yourself, how many people currently live in your home?
1
2
3
4
5
6
7
8
9 or more
Don’t know
Prefer not to answer
39. Which of the following brackets contains your age?
19-24
25-34
35-49
50-64
65 or over
Don’t know
Prefer not to state
40. What is your approximate total household income?
Less than $10,000
$10,000 to less than $20,000
$20,000 to less than $30,000
$30,000 to less than $40,000
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$40,000 to less than $50,000
$50,000 to less than $60,000
$60,000 to less than $70,000
$70,000 to less than $80,000
$80,000 to less than $90,000
$90,000 to less than $100,000
$100,000 to less than $150,000
$150,000 to less than $200,000
$200,000 or more
Don’t know
Prefer not to state
41. What’s the highest level of education you’ve completed?
Up to 8th grate
Some high school
High school or GED equivalent
Some college
Associate’s degree
Bachelor’s college degree
Master’s degree
Professional degree (MD, JD, DDO, DDS)
Doctorate (Ph.D., D.Sc.)
Don’t know
Prefer not to state

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5.2 Interview Guides
5.2.1 IGC Program Staff Interview Guide
ROLES AND RESPONSIBILITIES
1. Can you describe your role and responsibilities as it relates to IGC’s Whole Home
Rebate and Appliance Rebate Program?

2. How long have you been at your current role with IGC?

3. Have there been any changes to your responsibilities this program year?

PROGRAM DESIGN AND GOALS
4. Intermountain Gas Company first launched its EE programs in 2018. What was the
outcome of the 2018 program year for both high efficiency appliances and
residential new construction? How did these outcomes influence decisions made in
implementing the 2019 Whole Home Rebate and Appliance Rebate Programs?

5. What were the participation and savings goals of IGC’s Whole Home Rebate and
Appliance Rebate Program for PY2019? Have these goals changed since PY2018?
How have they changed?

6. How did IGC’s Whole Home Rebate and Appliance Rebate Program perform in
PY2019 relative to its goals? Can you speak to each component of the program
separately?
Whole Home Rebate Program
Appliance Rebate Program

7. What are some lessons learned after two years of implementing the Whole Home
Rebate and Appliance Rebate programs?

8. Were there any changes to the incentive amounts from PY2018 to PY2019 for the
Whole Home Rebate and Appliance Rebate Programs?

9. Are there any planned or anticipated changes to the Whole Home Rebate and
Appliance Rebate Programs for the remainder of the year or the upcoming program
year?

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10. Were minimum efficiency requirements for measures other than furnaces (such as
fireplaces and water heaters) lowered from PY2018 to PY2019 due to lower than
expected savings results in PY2018?
COMMUNICATION
11. What is the general method of communication between internal IGC program staff?
How many people are involved in internal meetings/calls, and how often do they
take place? What topics are covered?

12. Do you believe there could be improvements to communication with regards to the
Whole Home Rebate and Appliance Rebate Programs? Have there been changes
made from PY2018 to PY2019 that have improved communication efforts?
DATA MANAGEMENT
13. Please explain your program data management. What kind of database system or
software is used for program tracking?

14. Were there any substantial changes to your data management system from 2018 to
2019?

15. Are there any improvements you would to see to the current data management
system?

16. Are there any formal quality assurance and quality control procedures for either
program? Can you describe them?

17. Do you believe your data is kept current enough to effectively monitor the
programs?

18. Is there any system for gathering feedback from participants such as survey efforts
or customer interaction?
MARKETING AND OUTREACH
19. What were the main marketing and outreach activities in PY2019? Did any of those
vary for each component of the Whole Home Rebate and Appliance Rebate
Programs?

20. Were there increased marketing efforts for the Whole Home Rebate and Appliance
Rebate Programs compared to other programs in the portfolio? Did this change
from PY2018 to PY2019?

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21. Have there been any significant changes to the marketing and outreach approach in
PY2019?

22. Are there any planned or anticipated changes to marketing and outreach for the
remainder of the year or the upcoming program year?

23. Is marketing targeted to certain customers or territories? Is there any effort done to
target underserved or hard to reach customers?

24. What has been the most successful way of marketing the Whole Home Rebate and
Appliance Rebate Programs in PY2019?

25. How are marketing efforts different for customers, contractors, home builders, and
home energy raters? Which have proven to be successful and which are less
successful?

26. Are there plans to update the website? Do you use social media as part of your
marketing approach? Do you monitor the impact of various marketing activities?

27. Please explain the special partnership project that took place with Boise Valley
Habitat for Humanity, and future plans for similar projects.
BUILDERS
28. In PY2018, IGC was able to increase the amount of ENERGY STAR builders from 5 to
18. Has IGC been successful in adding more ENERGY STAR builders to the program in
PY2019?

29. How does IGC recruit builders? Are there special trainings or informational sessions
conducted by IGC staff?

30. How does a contractor get ENERGY STAR certified? Does IGC provide any assistance
with certification (for example, notifying about training sessions)?

31. Have HERS scores increased from PY2018 to PY2019?

32. What feedback have you gotten from purchasers and builders on the general
performance and satisfaction of the Whole Home Rebate Program?
HERS RATERS
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33. What is the general method of communication between IGC and HERS raters?

34. How are HERS raters recruited by IGC to participate in the Whole Homes program?

35. Does IGC conduct any trainings or informational sessions with or for HERS Raters? (if
yes) How many were trained a year by IGC staff? Are there contractor incentives?

36. What kind of feedback, if any, have you received from HERS raters that have
participated in the Whole Home Rebate Program?
CONTRACTORS
37. What is the general method of communication between IGC and contractors?

38. Are contractors required to be officially registered by IGC to participate in the
program?
39. Does IGC conduct any trainings or informational sessions with contractors? (if yes)
How many contractors are trained a year by IGC staff? Are there contractor
incentives?

40. What kind of feedback, if any, have you received from contractors that have
participated in the Energy Efficiency Rebate Program?
CONCLUSION
41. What do you think most needs to be changed or improved to the IGC Whole Home
Rebate and Appliance Rebate Programs?

42. What do you believe the IGC program staff need to do in the upcoming program
year to increase awareness of the program?

43. What specific concerns do you have about the IGC Whole Home Rebate and
Appliance Rebate Programs?

44. What would you like to learn from our evaluation report?

45. Those are all the questions I have for you. Do you have anything else you want to
mention to me in regards to the program?

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5.2.2 HERS Raters Interview Guide
INTRODUCTION AND BUSINESS SCOPE
I’d like to start with some general information about you and the company you work for.
1. How long have you been working as a HERS rater in Idaho?
2. Besides rating homes, what other services does your company offer to builders or
their contractors, if any? (E.g., HVAC installation/commissioning, Duct
sealing/testing, Lighting, Permitting, Inspections for other building programs,
General construction consulting, Green/EE construction consulting)
3. When did you first begin participating in the program?
4. Approximately how many total new homes did your company work with in Idaho in
2018 and 2019 as a rater?
And how many of these homes received a rebate from Intermountain Gas
Whole Homes program?
And about how many different Idaho home builders did you work with in
2018 and 2019?
5. Do you think your new homes business in Idaho will increase, decrease, or remain
about the same in 2020? Why?

PROGRAM REQUIREMENTS
Now I’d like to ask you about the program requirements.
6. Are any program requirements unclear to you?
If YES: Which ones?
7. Do you have any recommended changes to any of the program’s requirements? (If
needed: These changes could pertain to the equipment requirements, HERS ratings,
or rebate amounts, for instance.)
8. What are your biggest challenges as a home rater?
9. Do the builders and their subcontractors understand program requirements?
What don’t they understand?
Do they need additional training?
YES: Who should provide this? Why?
What are builders’ most common challenges that may limit program
participation?
10. How satisfied are you with the program’s technical support?
PROBE: What kind of support does IGC provide? (Confirm if this help is from
IGC or someone else)
How important is this support for your participation in the program?
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11. Are you aware of other “green” or energy efficiency related programs for new
homes in Idaho?
If YES: Do you also work with builders on these types of homes?
If YES: How do you think homebuyers perceive homes built to IGC’s
requirements compared to other green homebuilding programs?
What other rebate programs do builders take advantage of?
MARKETING
Now I’d like to ask you about program marketing.
12. Do you actively promote the program to builders?
13. Have you recruited any new builders to the program?
14. How do builders typically learn about this program?
15. Do you work with builder or real estate sales staff to help them promote the energy
efficiency of these program homes? Other groups?
IF YES: What do you do? (Probe to see if info on website, calling builders,
presentations, etc.)
Which benefits of energy efficient homes do you promote?
Could you provide me with the name of the realtor you use?
16. Do you think builders/realtors understand the advantages of IGC program homes?
17. Do you think builders/realtors are adequately promoting the advantages of these
homes?
18. Do you believe builders are changing their practices due to experience participating
in the program?
19. Which program features seem to be most beneficial/valuable to the homebuyers?
What about to builders? How about the least beneficial/valuable?
20. Are there certain energy efficient measures builders avoid due to the cost regardless
of the program’s rebates? Are there measures that are under-installed by builders
because the rebate amount is not sufficient to make these measures cost effective?
21. Have you received more or less inquiries about certifying energy efficient homes in
the past year? Why do you think that is?
22. What do you think IGC should do to effectively market the benefits of their program
homes
23. Do program-qualified homes provide a sales advantage in the current housing
market?

PROGRAM INTERACTIONS
Now I have a few questions about your interactions with other program actors and program tools.
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24. Who do you get most of your program information from (e.g., IGC staff or website, a
State or National Energy organization, program implementation staff, etc.)? By
program information, I mean updates on new home requirements, rebate levels,
trainings being offered, etc.
Who do you mostly work with at IGC?
25. What is the most critical support the program could provide to Raters in the near
future? (Probe to see if technical/field support, consumer marketing, subcontractor
training, other preferred.) Why do you say that?
26. Tell me about your collaboration/relationship with your builders in certifying homes.
What is the process?
What is going well?
What improvements could be made?
Are there any builders who have consistent issues with homes failing the
certification process? What are these issues and how should these be
addressed?
27. How efficiently is the home certification process performing? Could this be
improved in any way? Are there different stages of the certification process that
work better than others?
28. How do you work with the program providers who certify the homes and conduct
quality assurance inspections?
What is the process?
What is going well?
What improvements could be made?

OVERALL PROGRAM
I just have a few final questions about the program.
29. Given everything we’ve discussed, what has been the biggest challenge for you in
being a Rater for IGC’s Whole Homes Rebate Program? For builders?
(If needed) Are the incremental costs of building more energy efficient
homes a challenge for your builders, even after IGC’s rebates are
considered?
Are appraisals of your builder’s homes an issue?
30. What do you think are the biggest challenges for constructing and/or selling energy
efficient homes going forward? Do you have any suggestions for overcoming these
challenges?
31. On a scale of 1 to 5, where 1 is very dissatisfied and 5 is extremely satisfied, how
would you rate your satisfaction with IGC’s Whole Homes Rebate Program?
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32. What feedback have you received from customers, builders, and other raters
(positive and negative)? Do they have any suggestions for improving the program?
[Probe for measure specific feedback]
33. Those are all the questions I have for you. Do you have anything else you want to
mention to me in regards to the program?

5.2.3 Builders Interview Guide
BUILDER BACKGROUND GENERAL
I’d like to start with some general information about you and the company you work for.
1. Can you please confirm your name and the name of your company?
2. What is your title?
[DISPLAY Q3 IF PARTICIPATING?=Y]
3. What is your specific role for participating in the IGC Whole Homes program?
4. Are you the primary decision maker at your company in determining how energy
efficient your homes are?
NON-PARTICIPANT
Now I’d like to ask you some questions about your awareness of the program.
[DISPLAY Q5-Q11 IF PARTICIPATING?=N]
5. Are you familiar with the Whole Homes program?
[DISPLAY Q6 IF Q5=Y]
6. What was the reason for not participating in the program?
[DISPLAY Q7 IF Q5=Y]
7. Was this a company decision or a customer decision? (Probe: is it due to
certification, increased cost, increased building time, HERS rater availability,
customer interest, etc.)
8. Has your company participated in other utility sponsored energy efficiency
programs in the past?
9. Are you familiar with the code and HERS standards for which homes can qualify for
the Whole Homes program?
[DISPLAY Q10 IF Q9=Y]
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10. Does your company build any homes that meet the efficiency standards of the
Whole Home program?
11. What from IGC would help you participate in the Whole Homes program?

PROGRAM PARTICIPATION EXPERIENCE WITH PARTICIPANTS
Now I’d like to ask you some questions about your experience participating in the program.
[DISPLAY Q12-Q31 IF PARTICIPATING?=Y]
12. Were you also the primary decision maker in terms of participating in the Whole
Homes program or was that someone else?
13. How did you learn about the program?
14. When did your company first begin participating in the program?
15. Have you attended any events where a program representative provided
information about the program? (This might include home builder association
meetings, lunch & learns.)
[DISPLAY Q16 IF Q15=N]
16. Please elaborate.
[DISPLAY Q17-Q19 IF Q15=Y]
17. Please elaborate. What types of events? What information did you receive?
18. Was the information helpful?
19. Would you attend more events in the future?
20. How many of the homes you have built received a rebate from the Whole Homes
programs in 2018 or 2019?
21. How many homes did not receive a rebate?
[DISPLAY Q22 IF Q21>0]
22. Why didn’t those homes receive incentives? (Probe: did they know some homes
would not quality, or did they just choose not to have a HERS rating or submit an
application?)
[DISPLAY Q23 IF Q21>0]
23. Are the homes that did not participate in the program built differently than program
standards?

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[DISPLAY Q24 IF Q23=Y]
24. How do they differ from homes that do participate?
[DISPLAY Q25 IF Q23=Y]
25. Can you summarize why these homes did not participate in the program?
[DISPLAY Q26 IF Q23=Y]
26. How many of these homes were within IGC’s natural gas service territory?
27. Looking at your past participation in the program, your homes have had an average
HERS score of [AVG_HERS_SCORE]. On average, how much more does it cost to
build a home to this level than to build an otherwise comparable home at 100
HERS? An answer in dollars, or in percentage terms is fine.
28. What area accounts for most of the additional cost? (Probe: materials, HVAC
systems, insulation, labor costs, HERS rater costs)
29. What have you found to be the least costly way to ensure a home has a HERS score
lower than 75?
30. What do you do to reach your HERS score of [AVG_HERS_SCORE] in your homes?
31. Compared to a home with a HERS score of [AVG_HERS_SCORE], what would you
need to do differently to reach a HERS score of [HERS_GOAL]? [CALCULATE:
HERS_GOAL: AVG_HERS_SCORE – 10, ROUND TO NEAREST 5.]
32. What factors go into deciding to build homes to the program building standards in
2019?
Ability to differentiate yourself in the market?
Program financial incentive?
Customer demand for energy efficient homes?
Other program assistance?
Other factor (please write in)
Don’t Know

PARTICIPATING CUSTOMER PROGRAM AWARENESS AND MARKETING
I’d like to ask you a couple of questions about how you as the builder and the eventual homebuyer engage
with the program.
[DISPLAY Q33-Q43 IF PARTICIPATING?=Y]
33. Looking at the [BUILDER COUNT] homes you’ve received rebates for, what percent
of them were custom built, versus using a pre-existing floorplan? For homes that
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had minor modifications to suit a build site, count those as a “pre-existing
floorplan”.
34. What percent of your homes in the program had a buyer arranged before
construction was complete?

35. Do the energy efficient features of your program-qualified homes help them sell
more quickly?
36. To the best of your ability, could you estimate what proportion of the customers
knew about the program before they began working with you to build or buy a
home?
37. Does your company provide marketing messages or materials to your customers
about IGC’s Whole Homes programs?
[DISPLAY Q38 IF Q37=Y]
38. What kinds of marketing material or messages? Please elaborate.
39. What are your biggest challenges when marketing a program-qualifying home?
40. How can the marketing process by IGC be improved if at all?
41. How satisfied are you with the performance of the HERS raters you have worked
with in this program?
42. Are you applying what you have learned through participating in this program to
other homes that may not participate in the program?
[DISPLAY Q43 IF Q42=Y]
43. In what ways is your practice changing due to experience in the program?
44. In what ways is your practice changing due to market changes?

NONPARTICIPATING CUSTOMER PROGRAM AWARENESS AND MARKETING
[DISPLAY Q45-Q59 IF PARTICIPATING?=N]
45. What percent of your homes are custom built, versus using a pre-existing floorplan?
For homes that had minor modifications to suit a build site, count those as a “pre-
existing floorplan”.
46. What proportion of your homes have more energy efficient features than a home
built to Idaho building codes?
47. Do those homes sell more quickly?
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48. What percent of your homes do buyers arrange to buy before construction was
complete?

49. Do the energy efficient features of your homes help them sell more quickly?
50. To the best of your ability, could you estimate what proportion of your customers
that ask about energy efficient features of your built homes?
51. Does your company provide marketing messages or materials to your customers
about the energy efficiency features of your homes?
[DISPLAY Q52 IF Q51=Y]
52. What kinds of marketing material or messages? Please elaborate.
53. What are your biggest challenges when marketing one of your homes?
54. Have you worked with HERS raters?
[DISPLAY Q55 IF Q54=Y]
55. How satisfied are you with the performance of the HERS raters you have worked
with?
[DISPLAY Q56 IF Q54=Y]
56. What proportion of your homes has been rated by a HERS rater?
[DISPLAY Q57 IF Q54=Y]
57. Did any of them mention the program?
[DISPLAY Q58 IF Q57=Y]
58. What did they say about the program?
59. In what ways is your practice changing due to market changes?

CUSTOMER PROGRAM AWARENESS AND MARKETING
I’d like to ask you a couple of questions about how you as the builder and the eventual homebuyer engage
with the program.
60. Do you think homebuyers are willing to pay more for energy efficient homes?
61. What aspects of homes do your customers find most beneficial? (Probe: increased
comfort, lower energy use, lower utility bills, environmental impact, higher home
value)
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62. Do you believe homebuyer demand and expectations for energy efficient homes
have changed over the past few years?
[DISPLAY Q63 IF Q62=Y]
63. How? Please elaborate.
64. What percent of your homes are plumbed with natural gas?

PROGRAM FEEDBACK GENERAL
I just have a few final questions about the program…
[DISPLAY Q65-Q74 IF PARTICIPATING?=Y]
65. To what extent did you interact with IGC program staff?
66. How were your interactions with the program staff?
67. Were you satisfied with the overall interactions? Please use a scale from 1 (very
dissatisfied) to 5 (very satisfied).
68. Do you have any suggestions for streamlining or improving the participation
process?
69. In general, how long does it take for you to receive feedback or approval of a project
application?
70. When construction is complete, after you’ve submitted paperwork about how long
does it take you to receive your incentive check?
71. Did you have any concerns with the timing or amount of incentive?
72. Overall, are you satisfied with the Whole Homes program?
73. Do you have any recommendations for changes to the program?
74. Are there any gaps or unaddressed issues the program should consider?
75. Do you plan to participate in the IGC’s Whole Homes program in the future? And if
not, why not?
[DISPLAY Q76 IF PARTICIPATING?=Y]
76. Those are all the questions I have for you. Do you have anything else you want to
mention to me in regards to the program?
[DISPLAY Q77 IF PARTICIPATING?=N]
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77. Those are all the questions I have for you. Do you have anything else you want to
mention to me in regards to your work building homes in IGC's service territory?

5.2.4 Contractors Interview Guide
INTRODUCTION AND BUSINESS SCOPE
I’d like to start with some general information about you and the company you work for.
1. What is your role at your company?

2. Could you please describe your organization?
What services do you offer?
How many staff do you have?
3. Has your company worked with energy efficiency programs before?
Yes
No
Don’t Know
[DISPLAY Q4 IF Q3=1]
4. What is your past experience with energy efficiency programs?
5. What percent of your home energy efficiency improvement projects are at
residential single -family homes? What percentage are at multifamily homes?
6. Do you do any commercial projects?
PROGRAM AWARENESS & INVOLVEMENT
Now I’d like to ask you program awareness.
7. How did you first hear about IGC’s Energy Efficiency rebate program?
8. How long have you been active in the program?
9. Why did you decide to participate in the program?
10. Do you feel that the program communications are adequate and help you relay
information to your customers?
Yes
No
11. Do you have suggestions on how they can be improved?
12. Did you or employees receive any program training in the 2018 or 2019 program
years? These include any webinars by the utility or in person trainings.
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Yes
No

[DISPLAY Q13 IF Q12=1]
13. Could you please describe the training?
[DISPLAY Q14 IF Q12=1]
14. Were they helpful?
15. Do you have any suggestions for how the program could be improved in terms of
program design or operations?

CUSTOMER INTERACTION
I’d like to ask you about customer interaction.
16. What percent of your customers already know about the program before being told
about it by you?
17. What do you think are the main benefits your customers receive by participating in
the Energy Efficiency program?

18. In customer interactions, did you actively market the IGC Energy Efficiency program
to your customers during the 2018 and 2019 Program years?
19. Could you please explain a bit more or elaborate on how you actively marketed
IGC’s Energy Efficiency program to your customers?

20. Do you provide assistance with completing the rebate applications for customers?
Yes
No
21. What kind of assistance do you provide with completing the rebate applications?
22. Do you have any suggestions for improving the application process?
MARKETING
Now I’d like to ask you about program marketing.
23. Have you done some high efficiency projects that would qualify for the program; but
did not apply for the incentive?
Yes
No
[DISPLAY Q24 IF Q2341=1]
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24. Of the energy efficiency projects or equipment installations you do that would
qualify for the program; approximately what percentage do not apply for the
incentive?

[DISPLAY Q43 IF Q41=1]
25. Why not?
26. Does your organization encourage the sale of high efficiency equipment?
Yes
No
[DISPLAY Q45 IF Q4441=1]
27. What are some examples of equipment that is encouraged?
[DISPLAY Q28 IF Q4441=1]
28. In what ways does your organization encourage the sale of high efficiency
equipment?
[DISPLAY Q29 IF Q2841=1]
29. Are there challenges when trying to sell this equipment to customers?
30. What are their main concerns?
31. Has IGC provided you with marketing collateral to use with customers?
Yes
No
[DISPLAY Q32 IF Q3141=2]
32. Do you think those would be helpful?
[DISPLAY Q33 IF Q3141=1]
33. What type of collateral? Is it helpful?
34. What has been a successful strategy/approach in encouraging customers to choose
high efficiency equipment over standard efficiency equipment?

FURNACE QUESTIONS
Now I’d like to ask you about your experience installing furnaces.
35. I’d like to ask you about the condition of furnaces you’ve replaced. Let’s say there’s
three categories for the replaced furnaces: Fully functional, functioning but near-
failure, and non-functioning. What percent of your projects fall into each of the
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three categories? [REPEAT CATEGORIES AS NEEDED. ENSURE SUM TO 100%.
REQUEST CLARIFICATION IF SUM DIFFERS]
36. In what percent of your projects are you simultaneously replacing a furnace and air
conditioner?
37. [IF > 0%]: In these projects where you are replacing both systems, how often is the
need for replacement driven by a failed furnace versus a failed air conditioner?
38. What barriers do homes face in installing a condensing furnace?
39. How often do you face substantial difficulties in installing the needed venting and
drainage to support the installation of a condensing furnace?
40. Please elaborate.
41. Have you had any customers inquire about a condensing furnace, but then install
standard efficiency because of the costs of venting and drainage?
Yes
No
[DISPLAY Q4226 IF Q4141=1]
42. How many times has this happened?
WATER HEATER QUESTIONS
43. The program requires a minimum of .90 UEF to qualify for rebates. Have you
installed any tankless systems that are below .90 UEF in the last year? This would
include systems that range from .82 to .89 UEF.
Yes
No
44. How much more does a .90 UEF tankless system cost to install than a .90 UEF
system?
45. Have you had any customers inquire about a .90 UEF tankless system, but then
choose not to install it because of the costs of venting and drainage?
Yes
No
[DISPLAY Q4626 IF Q4541=1]
46. About how many times has this happened?
[DISPLAY Q47 IF Q45=1]
47. When this happens what is the customer more likely to purchase- a storage tank
system, or a .82 UEF tankless system?
Storage Tank System
.82 EUF Tankless System
Other (Write In)
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48. If a rebate were offered for .82 UEF systems, would it increase how many
applications you submit to the program?
Yes
No
Don’t Know
[DISPLAY Q49 IF Q48=1]
49. What percent of these customers would have otherwise installed a storage tank?
[DISPLAY Q50 IF Q48=1]
50. What percent of these customers would have installed a .82 UEF tankless system
anyway?
OVERALL PROGRAM
I just have a few final questions about the program.
51. What, if anything, do you find challenging about participating in this program?
52. What suggestions do you have to improve the IGC Energy Efficiency rebate
program?
53. What feedback have you received from customers (positive and negative)? Do they
have any suggestions for improving the program? [Probe for measure specific
feedback]
54. Those are all the questions I have for you. Do you have anything else you want to
mention to me in regards to the program?

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5.3 Tabulations
5.3.1 Whole Home Measure Participant Survey Results
According to our records, you purchased
the home located at [ADDRESS]. Is this
correct?
Response (n = 80) Percent of
Respondents
Yes 80 100%
No 0 0%
Don't Know 0 0%

To buy your home, did you work directly
with the builder, the builder’s real estate
agent, a personal real estate agent, a
different real estate agent, or someone
else?
Response (n = 78) Percent of
Respondents
Home builder 16 21%
Builder’s real estate agent 19 24%
Personal Real Estate Agent 39 50%
Different real estate agent 1 1%
Someone else (Please specify) 3 4%
Don’t know 0 0%

Did you know the home you purchased
was an ENERGY STAR® certified home
promoted by IGC’s energy efficiency
program?
Response (n = 80) Percent of
Respondents
Yes 63 79%
No 13 16%
Don't Know 4 5%

How did you first learn that your home is
ENERGY STAR® certified?
Response (n = 62) Percent of
Respondents
Home builder 43 69%
Real estate agent 11 18%
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From IGC’s website 0 0%
Friend, family member, or colleague 1 2%
Communication with IGC staff 0 0%
IGC newsletter 0 0%
Flickr) 0 0%
search) 2 3%
Through an internet advertisement 1 2%
A radio or television advertisement 0 0%
A print advertisement 2 3%
Previous experience with IGC programs 0 0%
Bill inserts 1 2%
Intermountain Gas YouTube Channel 0 0%
Community event 0 0%
Media coverage (segment on TV, radio, vlog,
blog, article, etc.) 0 0%
Other (Please specify) 1 2%
Don’t know 0 0%

IGC would like to understand what buyers
knew about the ENERGY STAR® certified
new homes. For each of the following
items, please select the response that best
describes when you learned about it.
Response n Percent of
Respondents
The house was promoted by IGC's energy
efficiency program
(n = 77)
Knew It Before I Contacted the Builder or Real
Estate Agent About Buying the Home 6 8%
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I Learned About It from The Builder or Real
Estate Agent Before I Bought the Home 36 47%
I Learned About It After Buying the Home 6 8%
I Did Not Know About It Before Being Invited to
Take This Survey 23 30%
I Don’t Know When I Learned About It 6 8%
The house has an ENERGY STAR® rating
(n = 79)
Knew It Before I Contacted the Builder or Real
Estate Agent About Buying the Home 9 11%
I Learned About It from The Builder or Real
Estate Agent Before I Bought the Home 50 63%
I Learned About It After Buying the Home 6 8%
I Did Not Know About It Before Being Invited to
Take This Survey 13 16%
I Don’t Know When I Learned About It 1 1%
The house was inspected by an
independent certified Home Energy Rating
System (HERS)
(n = 77)
Knew It Before I Contacted the Builder or Real
Estate Agent About Buying the Home 4 5%
I Learned About It from The Builder or Real
Estate Agent Before I Bought the Home 32 42%
I Learned About It After Buying the Home 10 13%
I Did Not Know About It Before Being Invited to
Take This Survey 18 23%
I Don’t Know When I Learned About It 13 17%
The house has a HERS score of
[HERS_SCORE]
(n = 77)
Knew It Before I Contacted the Builder or Real
Estate Agent About Buying the Home 3 4%
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I Learned About It from The Builder or Real
Estate Agent Before I Bought the Home 29 38%
I Learned About It After Buying the Home 9 12%
I Did Not Know About It Before Being Invited to
Take This Survey 28 36%
I Don’t Know When I Learned About It 8 10%

Did knowing that your home had a HERS
score of [HERS_SCORE] influence your
decision to purchase?
Response (n = 31) Percent of
Respondents
Yes 16 52%
No 14 45%
Don't Know 1 3%

Using the scale below, please rate the
your decision to buy this home.
Response n Percent of
Respondents
(n = 80)
Energy efficiency
1 - Not at all important 1 1%
2 2 3%
3 8 10%
4 31 39%
5 - Extremely important 38 48%
Don't Know 0 0%
(n = 80)
House price
1 - Not at all important 0 0%
2 0 0%
3 5 6%
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4 21 26%
5 - Extremely important 54 68%
Don't Know 0 0%
(n = 42)
It was promoted through IGC's energy
efficiency program
1 - Not at all important 3 9%
2 6 17%
3 13 37%
4 8 23%
5 - Extremely important 5 14%
Don't Know 0 0%
(n = 42)
It has an ENERGY STAR® rating
1 - Not at all important 0 0%
2 0 0%
3 6 14%
4 19 45%
5 - Extremely important 17 40%
Don't Know 0 0%
(n = 42)
It was inspected by an independent
certified HERS rater
1 - Not at all important 3 8%
2 1 3%
3 7 19%
4 14 39%
5 - Extremely important 11 31%
Don't Know 0 0%
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(n = 42)
The home’s HERS score
1 - Not at all important 2 5%
2 4 11%
3 6 16%
4 18 49%
5 - Extremely important 7 19%
Don't Know 0 0%

Are you aware of any differences
between homes promoted by IGC’s
energy efficiency program and other
homes?
Response (n = 80) Percent of
Respondents
Yes 15 19%
No 45 57%
Don’t know 20 25%

Using the scale below, how clearly were
the energy efficient characteristics of the
home explained to you by the person
you worked with when purchasing your
home?
Response (n = 79) Percent of
Respondents
1- Not at all clear 8 10%
2 12 15%
3 26 33%
4 14 18%
5- Very clear 16 20%
Don’t know 3 4%

Have you made any changes to the
heating system equipment that was
originally installed in your new home?
Response (n = 80) Percent of
Respondents
Yes 6 8%
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No 74 93%
Don't Know 0 0%

Have you made any changes to the
cooling system that was originally
installed in your new home?
Response (n = 80) Percent of
Respondents
Yes 3 4%
No 77 96%
Don't Know 0 0%

Have you changed out any large
appliances such as furnaces, water
heaters, refrigerators, clothes washers,
and dishwashers that came with the
home?
Response (n = 80) Percent of
Respondents
Yes 8 10%
No 72 90%
Don’t know 0 0%

Using the scale below, how informed
would you say you are about energy
efficiency practices and energy efficient
products or improvement options for
your household?
Response (n = 80) Percent of
Respondents
1- Not well informed 3 4%
2 11 14%
3 23 29%
4 29 36%
5- Very well informed 14 18%
Don’t know 0 0%

Using the scale below, how trustworthy
is Intermountain Gas Company as a
Response (n = 80) Percent of
Respondents
1- Not very trustworthy 0 0%
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source of information about saving
energy in your home? 2 2 3%
3 5 6%
4 22 28%
5- Very trustworthy 45 56%
Don’t know 6 8%

Using the scale below, please rate your
level of agreement or disagreement with
these statements.
Response n Percent of
Respondents
(n = 78)
Energy efficiency saves money
1 0 0%
2 0 0%
3 3 4%
4 11 14%
5 63 81%
Don't Know 1 1%
(n = 78)
I am not very concerned about the
amount of energy used in my home
1 44 55%
2 21 26%
3 3 4%
4 9 11%
5 2 3%
Don't Know 1 1%
(n = 80)
1 38 48%
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I am too busy to worry about making
energy-related improvements in my
home
2 22 28%
3 9 11%
4 8 10%
5 1 1%
Don't Know 2 3%
(n = 80)
It is possible to be energy efficient
without sacrificing comfort
1 3 4%
2 1 1%
3 8 10%
4 28 35%
5 40 50%
Don't Know 0 0%
(n = 80)
I know of steps I could take to reduce my
household energy use
1 2 3%
2 7 9%
3 18 23%
4 27 34%
5 22 28%
Don't Know 4 5%
(n = 80)
I intend to reduce my household energy
use in the next 12 months
1 9 11%
2 14 18%
3 25 31%
4 20 25%
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5 10 13%
Don't Know 2 3%
(n = 80)
It is not possible to make energy
efficiency changes on my budget
1 21 26%
2 21 26%
3 22 28%
4 8 10%
5 3 4%
Don't Know 5 6%

Using the scale below, please rate how
dissatisfied or satisfied you are with each
of the following …
Response n Percent of
Respondents
(n = 80)
IGC’s marketing efforts to promote its
discounts on energy efficient products.
1 - Very dissatisfied 2 3%
2 5 6%
3 22 28%
4 14 18%
5 - Very satisfied 16 20%
Don't Know 21 26%
(n = 80)
IGC overall as your gas service provider.
1 - Very dissatisfied 1 1%
2 0 0%
3 5 6%
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4 19 24%
5 - Very satisfied 53 66%
Don't Know 2 3%
(n = 79)
The ENERGY STAR® certified home
promoted by IGC’s energy efficiency
program that you purchased in [YEAR].
1 - Very dissatisfied 2 3%
2 0 0%
3 7 9%
4 21 27%
5 - Very satisfied 45 57%
Don't Know 4 5%
(n = 80)
Energy efficient measures of the home
purchased.
1 - Very dissatisfied 1 1%
2 0 0%
3 3 4%
4 17 21%
5 - Very satisfied 57 71%
Don't Know 2 3%

Who provides your home with electric
service?
Response (n = 80) Percent of
Respondents
Idaho Power Company 73 91%
Rocky Mountain Power 5 6%
Fall River Rural Electric Cooperative 0 0%
Idaho County Light and Power 0 0%
Other (Please specify) 0 0%
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Don’t know 2 3%
Prefer not to state 0 0%

Do you own or rent your home?
Response (n = 80) Percent of
Respondents
Own 79 99%
Rent 1 1%
Don’t know 0 0%
Prefer not to state 0 0%

Including yourself, how many people
currently live in your home year-round?
Response (n = 80) Percent of
Respondents
1 10 13%
2 35 44%
3 6 8%
4 7 9%
5 15 19%
6 3 4%
7 3 4%
8 0 0%
9 or more 0 0%
Don't know 0 0%
Refused 1 1%

Which of the following brackets contains
your age? Response (n = 80) Percent of
Respondents
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19-24 0 0%
25-34 14 18%
35-49 21 26%
50-64 16 20%
65 or over 25 31%
Don’t know 0 0%
Prefer not to state 4 5%

What is your approximate household
income?
Response (n = 79) Percent of
Respondents
Less than $10,000 0 0%
$10,000 to less than $20,000 0 0%
$20,000 to less than $30,000 2 3%
$30,000 to less than $40,000 2 3%
$40,000 to less than $50,000 1 1%
$50,000 to less than $60,000 6 8%
$60,000 to less than $70,000 7 9%
$70,000 to less than $80,000 3 4%
$80,000 to less than $90,000 4 5%
$90,000 to less than $100,000 4 5%
$100,000 to less than $150,000 12 15%
$150,000 to less than $200,000 7 9%
$200,000 or more 10 13%
Don’t know 0 0%
Prefer not to state 21 27%

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What’s the highest level of education
you’ve completed?
Response (n = 80) Percent of
Respondents
Up to 8th grate 0 0%
Some high school 0 0%
High school or GED equivalent 1 1%
Some college 9 11%
Associate’s degree 7 9%
Bachelor’s college degree 27 34%
Master’s degree 25 31%
Professional degree (MD, JD, DDO, DDS) 5 6%
Doctorate (Ph.D., D.Sc.) 1 1%
Don’t know 0 0%
Prefer not to state 5 6%

5.3.2 Appliance Rebate Measure Participant Survey Results

According to our records you received a
rebate through IGC’s [PROGRAM]
program for installing [MEASURE] at
[LOCATION] in [YEAR]. Is that correct?
Response (n = 137) Percent of
Respondents
Yes 137 100%
No 0 0%
Don't Know 0 0%

How did you first learn about IGC’s
rebates for [MEASURE]s?
Response (n = 136) Percent of
Respondents
Friend, family member, or colleague 2 1%
Contractor 64 47%
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Retailer 27 20%
Bill insert or utility mailer 9 7%
Email from IGC 0 0%
IGC Newsletter 2 1%
From IGC’s website 13 10%
Social media post (e.g., Facebook, Twitter) 1 1%
Intermountain Gas Company YouTube channel 0 0%
search) 5 4%
Through an internet advertisement 0 0%
A radio or television advertisement 1 1%
A print advertisement 2 1%
Media coverage (segment on TV, radio,
podcast, vlog, article, etc.) 1 1%
Community event 0 0%
Other (please specify) 5 4%
Don’t know 4 3%

When did you first learn about IGC’s
Energy Efficiency Rebate Program? Was
it…
Response (n = 137) Percent of
Respondents
Before starting the process of purchasing the
[MEASURE] 31 23%
At the time you made the purchase decision. 36 26%
deciding to purchase. 19 14%
After deciding to purchase [MEASURE] 43 31%
Don’t know 8 6%

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Why did you purchase the model or type
of [MEASURE]?
Response (n = 137) Percent of
Respondents
It was all that was available/only choice. 2 1%
The contractor/retailer recommended it. 64 47%
It was an emergency replacement. 21 15%
It had an ENERGY STAR® label. 32 23%
It was a planned replacement. 20 15%
It’s good for the environment. 30 22%
It was the right size, color. 15 11%
There was a rebate for it. 21 15%
It costs less to operate it. 54 39%
It had features I wanted. 45 33%
It was a good price. 20 15%
Other (Please specify) 6 4%
Don’t know 1 1%

Where did you obtain the rebate
application?
Response (n = 137) Percent of
Respondents
From the IGC website 59 43%
From another website 1 1%
In a retail store 5 4%
From a contractor 64 47%
Other (Please specify) 2 1%
Don’t know 6 4%

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Was your application accepted as-
submitted, or did IGC follow up with you
for clarifications or further information?
Response (n = 137) Percent of
Respondents
Accepted as submitted 111 81%
Required follow up 17 12%
Don’t know 9 7%

Did you receive your rebate check in a
prompt and timely manner?
Response (n = 137) Percent of
Respondents
Yes 123 90%
No 4 3%
Don’t know 10 7%

Was this 95% furnace installation part of
a larger project?
Response (n = 80) Percent of
Respondents
Yes, it was part of a larger replacement of my
heating system. 26 33%
Yes, it was part of a newly constructed home. 1 1%
Yes, it was part of a remodeling project. 7 9%
No, it was a stand-alone replacement. 43 54%
AC and furnace 2 3%
Furnace and Water Heater 1 1%
Don’t know 0 0%

What type of fuel source does your
home's water heater use?
Response (n = 80) Percent of
Respondents
Natural Gas 58 73%
Electricity 21 26%
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Propane 0 0%
Other (Please specify) 0 0%
Don’t know 1 1%

What kind of water heater does your
home have?
Response (n = 80) Percent of
Respondents
Tank water heater 72 90%
Tankless water heater 4 5%
Heat Pump water heater 1 1%
Solar Powered water heater 0 0%
Condensing water heater 0 0%
Don’t know 3 4%

How old is your home’s water heater?
Response (n = 80) Percent of
Respondents
Less than 2 years old 25 31%
2 to 4 years 13 16%
5 to 9 years 16 20%
10 to 14 years 11 14%
15 to 19 years 7 9%
20 or more years old 5 6%
Don’t know 3 4%

What type of thermostat does your
home have?
Response (n = 80) Percent of
Respondents
Manual thermostat 4 5%
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Programmable thermostat 39 49%
Smart thermostat 36 45%
None 0 0%
Don’t know 1 1%

Is the new 95% Furnace that you
received a rebate for currently installed
and working?
Response (n = 80) Percent of
Respondents
Yes 80 100%
No 0 0%
Don’t know 0 0%

Was the equipment that you replaced
still functioning at the time you replaced
it?
Response (n = 75) Percent of
Respondents
Yes 54 72%
No 20 27%
Don’t know 1 1%

Using the scale below, how satisfied are
you with the performance of your new
[95% Furnace]?
Response (n = 80) Percent of
Respondents
1 - Very Dissatisfied 6 8%
2 1 1%
3 0 0%
4 11 14%
5 - Very Satisfied 62 78%
Don't Know 0 0%

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Was this new radiant combo installation
part of a larger project?
Response (n = 6) Percent of
Respondents
Yes, it was part of a larger replacement of my
heating system.
2 33%
Yes, it was part of a newly constructed home. 1 17%
Yes, it was part of a remodeling project. 1 17%
No, it was a stand-alone replacement. 2 33%
Other (Please specify) 0 0%
Don’t know 0 0%

Is the new radiant combo that you
received a rebate for currently installed
and working?
Response (n = 6) Percent of
Respondents
Yes 6 100%
No 0 0%
Don’t know 0 0%

Was the equipment that you replaced
still functioning at the time you replaced
it?
Response (n = 5) Percent of
Respondents
Yes 3 60%
No 2 40%
Don’t know 0 0%

Using the scale below, how satisfied are
you with the performance of your new
radiant combo?
Response (n = 6) Percent of
Respondents
1 - Very Dissatisfied 0 0%
2 0 0%
3 0 0%
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4 1 17%
5 - Very Satisfied 5 83%
Don't Know 0 0%

Was this new fireplace installation part
of a larger project?
Response (n = 5) Percent of
Respondents
Yes, it was part of a larger replacement of my
heating system. 0 0%
Yes, it was part of a newly constructed home. 0 0%
Yes, it was part of a remodeling project. 1 20%
No, it was a stand-alone replacement. 4 80%
Other (Please specify) 0 0%
Don’t know 0 0%

Is the new Fireplace that you received a
rebate for currently installed and
working?
Response (n = 5) Percent of
Respondents
Yes 3 60%
No 2 40%
Don’t know 0 0%

Did you have natural gas plumbed to
this fireplace before the installation of
your fireplace?
Response (n = 5) Percent of
Respondents
Yes 0 0%
No 5 100%
Don’t know 0 0%

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Using the scale below, how satisfied are
you with the performance of your new
fireplace?
Response (n = 5) Percent of
Respondents
1 - Very Dissatisfied 0 0%
2 0 0%
3 0 0%
4 0 0%
5 - Very Satisfied 5 100%
Don't Know 0 0%

Was this new water heater installation
part of a larger project?
Response (n = 6) Percent of
Respondents
Yes, it was part of a larger replacement of my
heating system. 1 17%
Yes, it was part of a newly constructed home. 0 0%
Yes, it was part of a remodeling project. 0 0%
No, it was a stand-alone replacement. 5 83%
Other (Please specify) 0 0%
Don’t know 0 0%

Is the new water heater that you
received a rebate for currently installed
and working?
Response (n = 6) Percent of
Respondents
Yes 6 100%
No 0 0%
Don’t know 0 0%

Response (n = 6) Percent of
Respondents
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Was the water heater that you replaced
still functioning at the time you
replaced it?
Yes 4 67%
No 2 33%
Don’t know 0 0%

Using the scale below, how satisfied are
you with the performance of your new
water heater?
Response (n = 6) Percent of
Respondents
1 - Very Dissatisfied 1 17%
2 0 0%
3 0 0%
4 0 0%
5 - Very Satisfied 5 83%
Don't Know 0 0%

Was this new tankless water heater
installation part of a larger project?
Response (n = 40) Percent of
Respondents
Yes, it was part of a larger replacement of my
heating system. 7 18%
Yes, it was part of a newly constructed home. 0 0%
Yes, it was part of a remodeling project. 4 10%
No, it was a stand-alone replacement. 27 68%
Other (Please specify) 1 3%
Don’t know 1 3%

Is the new tankless water heater that
you received a rebate for currently
installed and working?
Response (n = 40) Percent of
Respondents
Yes 40 100%
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No 0 0%
Don’t know 0 0%

Was the equipment that you replaced
still functioning at the time you
replaced it?
Response (n = 36) Percent of
Respondents
Yes 21 58%
No 15 42%
Don’t know 0 0%

Using the scale below, how satisfied
are you with the performance of your
new water heater?
Response (n = 40) Percent of
Respondents
1 - Very Dissatisfied 1 3%
2 2 5%
3 3 8%
4 9 23%
5 - Very Satisfied 25 63%
Don't Know 0 0%

Who installed the [MEASURE_SHORT]?
Response (n = 137) Percent of
Respondents
I installed it. 8 6%
A friend or family member installed it. 1 1%
I hired a contractor to install it. 127 93%
Other (Please specify) 0 0%
Don't Know 1 1%

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How did you learn to install the
[MEASURE_SHORT]?
Response (n = 8) Percent of
Respondents
Prior occupational knowledge 3 38%
Went to a workshop 0 0%
Self-taught (You-tube videos, home courses, etc.) 5 63%
Other (Please specify) 0 0%

How did you select the contractor who
installed the [MEASURE_SHORT]?
Response (n = 109) Percent of
Respondents
I have hired this contractor for other work in the
past. 50 46%
The retailer recommended the contractor. 17 16%
I researched online. 25 23%
Friend or family member recommended the
contractor. 17 16%

Using the scale below, please indicate
how much you disagree or agree with
the following statements regarding your
experience with the contractor that
installed the new [MEASURE_SHORT]:
Response n Percent of
Respondents
(n = 109)
The contractor was courteous and
professional

1 - Strongly Disagree 3 3%
2 2 2%
3 3 3%
4 12 11%
5 - Strongly Agree 89 82%
Don't Know 0 0%
(n = 109)
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The work was scheduled in a reasonable
amount of time

1 - Strongly Disagree 2 2%
2 2 2%
3 2 2%
4 10 9%
5 - Strongly Agree 93 85%
Don't Know 0 0%
(n = 109)
The work was completed in a
reasonable amount of time

1 - Strongly Disagree 1 1%
2 3 3%
3 3 3%
4 11 10%
5 - Strongly Agree 91 83%
Don't Know 0 0%
(n = 108)
The contractor was knowledgeable
about the [MEASURE_SHORT]

1 - Strongly Disagree 2 2%
2 2 2%
3 4 4%
4 16 15%
5 - Strongly Agree 84 78%
Don't Know 0 0%
(n = 109)
The contractor answered all my
questions thoroughly.

1 - Strongly Disagree 1 1%
2 3 3%
3 4 4%
4 16 15%
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5 - Strongly Agree 85 78%
Don't Know 0 0%

What features of your new
[MEASURE_SHORT] did your contractor
emphasize to you?
Response (n = 100) Percent of
Respondents
Energy Efficiency 79 79%
Low price 7 7%
Rebate eligibility 46 46%
Good warranty/reliability 61 61%
Quiet operation 46 46%
Emphasis on the brand and its reputation 45 45%
Size of the equipment 33 33%
Other (Please specify) 8 8%
Don’t know 6 6%

What feature of your
new [MEASURE_SHORT] matters most to
you?
Response (n = 137) Percent of
Respondents
Energy Efficiency 111 81%
Low price 24 18%
Rebate eligibility 47 34%
Good warranty/reliability 83 61%
Quiet operation 61 45%
Emphasis on the brand and its reputation 39 28%
Size of the equipment 28 20%
Other (Please specify) 17 12%
Don’t know 2 1%
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Using the scale below, how informed
would you say you are about energy
efficiency practices and energy efficient
products/improvement options for your
household?
Response (n = 137) Percent of
Respondents
1 1 1%
2 7 5%
3 35 26%
4 56 41%
5 36 26%
Don’t know 2 1%

Using the scale below, how trustworthy
is IGC as a source of information about
saving energy in your home?
Response (n = 135) Percent of
Respondents
1 0 0%
2 3 2%
3 6 4%
4 42 31%
5 76 56%
Don’t know 8 6%

Using the scale below, please rate your
level of agreement or disagreement with
these statements.
Response n Percent of
Respondents
(n = 135)
Energy efficiency saves money.
1 7 5%
2 1 1%
3 6 4%
4 28 21%
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5 92 68%
Don't Know 1 1%
(n = 137)
I am not very concerned about the
amount of energy used in my home.
1 71 52%
2 40 29%
3 13 9%
4 6 4%
5 7 5%
Don't Know 0 0%
(n = 136)
I am too busy to worry about making
energy-related improvements in my
home.
1 70 51%
2 31 23%
3 22 16%
4 10 7%
5 3 2%
Don't Know 0 0%
(n = 137)
It is possible to be energy efficient
without sacrificing comfort.
1 4 3%
2 4 3%
3 14 10%
4 33 24%
5 80 58%
Don't Know 2 1%
(n = 137)
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I know of steps I could take to reduce my
household energy use.
1 3 2%
2 11 8%
3 24 18%
4 51 37%
5 47 34%
Don't Know 1 1%
(n = 136)
I intend to reduce my household energy
use in the next 12 months.
1 3 2%
2 16 12%
3 62 46%
4 33 24%
5 16 12%
Don't Know 6 4%
(n = 136)
It is not possible to make energy
efficiency changes on my budget.
1 45 33%
2 34 25%
3 31 23%
4 18 13%
5 4 3%
Don't Know 4 3%

Did someone in your household contact a
program staff member from IGC with
questions about your rebate application
(this question does not pertain to your
interaction with the contractor)?
Response (n = 137) Percent of
Respondents
Yes 23 17%
No 102 74%
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Don't Know 12 9%

Using the scale below, please rate how
dissatisfied or satisfied you are with each
of the following …
Response n Percent of
Respondents
(n = 22)

How long it took program staff to
address your questions or concerns

1 - Very dissatisfied 0 0%
2 1 5%
3 4 18%
4 2 9%
5 - Very satisfied 15 68%
Don't Know 0 0%
(n = 22)

How thoroughly they addressed your
question or concerns

1 - Very dissatisfied 1 5%
2 2 9%
3 2 9%
4 2 9%
5 - Very satisfied 15 68%
Don't Know 0 0%
(n = 134)

The performance of the equipment you
installed overall

1 - Very dissatisfied 0 0%
2 3 2%
3 7 5%
4 20 15%
5 - Very satisfied 102 76%
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Don't Know 2 1%
(n = 134)

The energy savings on your utility bill
resulting from installing the new
[MEASURE_SHORT]

1 - Very dissatisfied 1 1%
2 2 1%
3 21 16%
4 25 19%
5 - Very satisfied 67 50%
Don't Know 18 13%
(n = 134)
The rebate amount
1 - Very dissatisfied 5 4%
2 3 2%
3 19 14%
4 32 24%
5 - Very satisfied 68 51%
Don't Know 7 5%
(n = 133)
The process for applying for the rebate
1 - Very dissatisfied 2 2%
2 1 1%
3 16 12%
4 30 23%
5 - Very satisfied 79 59%
Don't Know 5 4%
(n = 107)
1 - Very dissatisfied 1 1%
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The overall quality of the work
performed by your contractor
2 0 0%
3 6 6%
4 18 17%
5 - Very satisfied 81 76%
Don't Know 1 1%
(n = 135)
The time it took to receive the rebate
1 - Very dissatisfied 2 1%
2 1 1%
3 10 7%
4 29 21%
5 - Very satisfied 85 63%
Don't Know 8 6%
(n = 135)

The experience with the program overall

1 - Very dissatisfied 2 1%
2 2 1%
3 7 5%
4 32 24%
5 - Very satisfied 89 66%
Don't Know 3 2%
(n = 135)
Intermountain Gas Co. as your gas
service provider
1 - Very dissatisfied 0 0%
2 1 1%
3 6 4%
4 22 16%
5 - Very satisfied 106 79%
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Don't Know 0 0%

Using the scale below, how would you
say your participation in the Appliance
Rebate program has affected your
satisfaction with Intermountain Gas
Company?
Response (n = 137) Percent of
Respondents
1- Greatly decreased your satisfaction with IGC 2 1%
2 0 0%
3 24 18%
4 68 50%
5- Greatly increased your satisfaction with IGC 39 28%
Don't Know 4 3%

Who provides your home with electric
service?
Response (n = 135) Percent of
Respondents
Idaho Power Company 126 93%
Rocky Mountain Power 4 3%
Fall River Rural Electric Cooperative 0 0%
Idaho County Light and Power 0 0%
Idaho Falls Power 4 3%
Don’t know 1 1%

Do you own or rent your home?
Response (n = 136) Percent of
Respondents
Own 135 99.3%
Rent 0 0.0%
Don’t know 0 0.0%
Prefer not to state 1 0.7%

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Which best describes this building?
Response (n = 137) Percent of
Respondents
Single-family house detached from any other
house 132 96%
Single-family house attached to one or more
townhouse)
4 3%
Mobile or manufactured home 1 1%
Apartment with 2 or 3 units 0 0%
Apartment with 4 or more units 0 0%
Other (Please specify) 0 0%
Do not know 0 0%
Prefer not to answer 0 0%

When was your home built?
Response (n = 137) Percent of
Respondents
Before 1960 23 17%
1960 to1969 11 8%
1970 to 1979 22 16%
1980 to 1989 14 10%
1990 to 1999 30 22%
2000 to 2009 30 22%
2010 to 2019 4 3%
Do not know 1 1%
Prefer not to answer 2 1%

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About how many square feet is your
home? If you are unsure, an estimate is
OK.
Response (n = 136) Percent of
Respondents
Less than 1,000 square feet 5 4%
1000-1999 square feet 64 47%
2000-2999 square feet 45 33%
3000-3999 square feet 13 10%
4000 sq. ft or greater 6 4%
Don’t know 2 1%
Prefer not to answer 1 1%

What is the main fuel used for heating
your home?
Response (n = 137) Percent of
Respondents
Natural Gas 132 96%
Electricity 4 3%
Propane 0 0%
Other (Please specify) 0 0%
Do not know 1 1%
Prefer not to answer 0 0%

What fuel does your main water heater
use?
Response (n = 137) Percent of
Respondents
Natural Gas 109 80%
Electricity 24 18%
Propane 0 0%
Other (Please specify) 0 0%
Do not know 3 2%
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Prefer not to answer 1 1%

What is the main fuel used for cooking in
your home?
Response (n = 137) Percent of
Respondents
Natural Gas 59 43%
Electricity 77 56%
Propane 0 0%
Other (Please specify) 0 0%
Do not know 1 1%
Prefer not to answer 0 0%

Including yourself, how many people
currently live in your home year-round?
Response (n = 137) Percent of
Respondents
1 22 16%
2 68 50%
3 21 15%
4 14 10%
5 6 4%
6 3 2%
7 1 1%
8 0 0%
9 or more 0 0%
Don't know 0 0%
Refused 2 1%

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Which of the following brackets contains
your age?
Response (n = 137) Percent of
Respondents
19-24 1 1%
25-34 12 9%
35-49 25 18%
50-64 36 26%
65 or over 58 42%
Don’t know 0 0%
Prefer not to state 5 4%

What is your approximate household
income?
Response (n = 136) Percent of
Respondents
Less than $10,000 2 1%
$10,000 to less than $20,000 0 0%
$20,000 to less than $30,000 7 5%
$30,000 to less than $40,000 10 7%
$40,000 to less than $50,000 13 10%
$50,000 to less than $60,000 13 10%
$60,000 to less than $70,000 11 8%
$70,000 to less than $80,000 11 8%
$80,000 to less than $90,000 4 3%
$90,000 to less than $100,000 6 4%
$100,000 to less than $150,000 18 13%
$150,000 to less than $200,000 8 6%
$200,000 or more 9 7%
Don’t know 2 1%
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Prefer not to state 22 16%

What’s the highest level of education
you’ve completed?
Response (n = 137) Percent of
Respondents
Up to 8th grate 0 0%
Some high school 2 1%
High school or GED equivalent 9 7%
Some college 29 21%
Associate’s degree 8 6%
Bachelor’s college degree 47 34%
Master’s degree 25 18%
Professional degree (MD, JD, DDO, DDS) 5 4%
Doctorate (Ph.D., D.Sc.) 4 3%
Don’t know 1 1%
Prefer not to state 7 5%

5.3.3 Nonparticipant Survey Results

According to our records, Intermountain
Gas Company provides the natural gas
service at your residence located at
[ADDRESS]. Is that correct?
Response (n = 159) Percent of
Respondents
Yes 159 100%
No 0 0%
Don't Know 0 0%

In the last five years, have you received a
rebate from Intermountain Gas Company Response (n = 148) Percent of
Respondents
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or your electricity service provider for
installing energy efficient equipment or
making energy efficiency improvements
for the residence located at [ADDRESS]?
No, I have not received any rebates 130 88%
Yes, from Intermountain Gas Company
[TERMINATE] 0 0%
Yes, from my electricity service provider 7 5%
Yes, from both [TERMINATE] 0 0%
Don’t know 11 7%

You mentioned you received a rebate
from IGC. Which of the following?
Response (n = 0) Percent of
Respondents
Tank Water heater 0 #DIV/0!
Fireplace 0 #DIV/0!
Furnace 0 #DIV/0!
Tankless water heater 0 #DIV/0!
Combination radiant heat system 0 #DIV/0!
Efficient Whole Home new construction 0 #DIV/0!
Other (specify) 0 #DIV/0!
Don’t know 0 #DIV/0!

Do you use natural gas in your home
located at [ADDRESS]?
Response (n = 155) Percent of
Respondents
Yes 149 96%
No 2 1%
Don't Know 4 3%

Which of the following best describes
your home? Response (n = 156) Percent of
Respondents
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Single-family house detached from any other
house 137 88%
Single family house attached to one or more
other houses (duplex, row house,
condominium, or townhome)
8 5%
Apartment in a building with 2 to 3 units 0 0%
Apartment in a building with 4 or more units 5 3%
Manufactured or mobile home 6 4%
Other (Please specify) 0 0%
Don’t know 0 0%

Do you own, rent, or own and rent to
someone else the property located at
[LOCATION]?
Response (n = 145) Percent of
Respondents
Own 118 81%
Rent 26 18%
Own and rent to someone else 1 1%
Don’t know 0 0%

About when was the home first built?
Response (n = 144) Percent of
Respondents
Before 1960 23 16%
1960 to1969 11 8%
1970 to 1979 9 6%
1980 to 1989 4 3%
1990 to 1999 22 15%
2000 to 2009 32 22%
2010 to 2019 33 23%
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Do not know 10 7%

Please provide the estimated square
footage of your house.
Response (n = 142) Percent of
Respondents
Less than 1,000 square feet 7 5%
1000-1999 square feet 58 41%
2000-2999 square feet 46 32%
3000-3999 square feet 17 12%
4000 square feet or greater 5 4%
Don’t know 9 6%

What is the main fuel used for heating
your home?
Response (n = 158) Percent of
Respondents
Natural Gas 136 86%
Electricity 19 12%
Propane 0 0%
Other (Please specify) 0 0%
Do not know 3 2%
Prefer not to answer 0 0%

What is the main type of heating
equipment used to provide heat for
your home?
Response (n = 150) Percent of
Respondents
Heat pump 2 1%
Central forced air furnace 138 87%
Room or space heater 0 0%
Something else (Please specify) 7 4%
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Don’t know 3 2%

How old is your heating system
equipment? Your best estimate is fine.
Response (n = 145) Percent of
Respondents
Less than 2 years old 19 13%
2 to 4 years 16 11%
5 to 9 years 41 28%
10 to 14 years 20 14%
15 to 19 years 11 8%
20 or more years old 20 14%
Don’t know 18 12%

What fuel does your main water heater
use?
Response (n = 158) Percent of
Respondents
Natural Gas 119 75%
Electricity 25 16%
Propane 0 0%
Other (Please specify) 1 1%
Do not know 13 8%
Prefer not to answer 0 0%

How old is your main water heater?
Your best estimate is fine.
Response (n = 153) Percent of
Respondents
Less than 2 years old 20 14%
2 to 4 years 29 20%
5 to 9 years 38 26%
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10 to 14 years 31 21%
15 to 19 years 12 8%
20 or more years old 9 6%
Don’t know 14 10%

What is the main fuel used for cooking
in your home?
Response (n = 146) Percent of
Respondents
Natural Gas 66 45%
Electricity 79 54%
Propane 0 0%
Other (Please specify) 0 0%
Do not know 1 1%
Prefer not to answer 0 0%

Does your home have an existing
fireplace with a gas line connection?
Response (n = 146) Percent of
Respondents
Yes 76 52%
No 69 47%
Don’t know 1 1%

Does your home’s fireplace have a
constant pilot light or an intermittent
pilot light?
Response (n = 54) Percent of
Respondents
Yes 34 63%
No 15 28%
Don’t know 5 9%

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In the past three years, have you or
anyone else in your household
purchased, installed, or implemented
any of the following equipment or
home improvements at this
residence?
Response (n = 7) Percent of
Respondents
95% AFUE Natural Gas Furnace 7 4%
Combination Radiant Heat System 0 0%
80% AFUE Fireplace 3 2%
70% FE Fireplace 1 1%
0.67 Water Heater 8 5%
0.91 Tankless Water Heater 1 1%
I have not done any of these 113 72%
Other (Please specify) 1 1%
Don’t know 29 18%

Was the following item(s) that you
mentioned ENERGY STAR® certified
or high efficiency?
Response (n = 4) Percent of
Respondents
95% AFUE Natural Gas Furnace 4 57%
Combination Radiant Heat System 0 0%
80% AFUE Fireplace 0 0%
70% FE Fireplace 0 0%
0.67 Water Heater 0 0%
0.91 Tankless Water Heater 0 0%

Are you aware of any rebates for
energy efficient equipment and home
improvements or other services
offered by a gas or electric utility?
Response (n = 158) Percent of
Respondents
Yes 14 9%
No 117 74%
Don’t know 27 17%

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What types of rebates do you recall
hearing about?
Response (n = 9) Percent of
Respondents
Heating and cooling equipment 9 64%
Appliances such as refrigerators, clothes washers 7 50%
Home weatherization improvements such as air or
duct sealing 7 50%
Discounts for efficient lighting 2 14%
Low flow faucet aerators or showerheads 4 29%
Other (Please specify) 0 0%
Don’t know 2 14%

How did you learn of these rebates or
services
Response (n = 3) Percent of
Respondents
Information that came in the mail 3 21%
Email 3 21%
Newspaper or magazine article 0 0%
Contractor 0 0%
Word of mouth from friends, relatives, or others 2 14%
TV ad 0 0%
Radio ad 0 0%
Social media (Facebook, Twitter) 1 7%
Utility bill message 9 64%
Utility website 3 21%
Intermountain Gas YouTube channel 1 7%
Other website 0 0%
Utility program staff 3 21%
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Community event 2 14%
Other (Please specify) 0 0%
Don’t know 1 7%

Using the scale below, how
knowledgeable are you about ways
to save energy in your home?
Response (n = 158) Percent of
Respondents
1 - Not at all knowledgeable 5 3%
2 20 13%
3 62 39%
4 52 33%
5 - Very Knowledgeable 16 10%
Don’t know 3 2%

Using the scale below, how much
have you done to lower your monthly
energy bill in your home?
Response (n = 157) Percent of
Respondents
1 - You have not done much 21 13%
2 34 22%
3 50 32%
4 37 24%
5 - You have done almost everything you can 14 9%
Don’t know 1 1%

Using the scale below, how interested
are you in making improvements to
your home that would...
Response (n = 158) Percent of
Respondents

1 - Not at all interested 33 21%
2 23 15%
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Increase its energy efficiency by using
appliances powered by natural gas?

3 38 24%
4 31 20%
5 - Extremely interested 23 15%
Don't Know 10 6%

(n = 130)
Improve your comfort by using
appliances powered by natural gas?
1 - Not at all interested 32 25%
2 18 14%
3 30 23%
4 27 21%
5 - Extremely interested 14 11%
Don't Know 9 7%
(n = 130)
Improve your health and safety by
using appliances powered by natural
gas?
1 - Not at all interested 29 22%
2 21 16%
3 29 22%
4 26 20%
5 - Extremely interested 16 12%
Don't Know 9 7%

How would you find out ways to
reduce your monthly energy bills?
Response (n = 24) Percent of
Respondents
From the utility customer service line 24 15%
Utility website 66 42%
Internet search 104 66%
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Social media 37 23%
Government information 21 13%
ENERGY STAR website 47 30%
Friends, family, or colleagues 89 56%
Information from a retail store 33 21%
Magazines/newspapers/television/radio 28 18%
Home energy audit 21 13%
Other (Please specify) 3 2%
Don’t know 22 14%

Using the scale below, how likely is it
that you would do any of the
following in your home to reduce
your utility bill?
Response (n = 159) Percent of
Respondents

Add insulation in ceilings, walls,
floors

1 - Very unlikely 49 31%
2 17 11%
3 30 19%
4 20 13%
5 - Very likely 28 18%
Don't Know 15 9%
(n = 148)
Add weather stripping or caulking
around doors and windows
1 - Very unlikely 20 14%
2 11 7%
3 22 15%
4 26 18%
5 - Very likely 62 42%
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Don't Know 7 5%
(n = 146)
Install energy efficient, double-pane
windows
1 - Very unlikely 52 36%
2 19 13%
3 21 14%
4 17 12%
5 - Very likely 24 16%
Don't Know 13 9%
(n = 147)
Insulate water heater tank and pipes
1 - Very unlikely 31 21%
2 18 12%
3 36 24%
4 22 15%
5 - Very likely 30 20%
Don't Know 10 7%

(n = 146)
Install a programmable set-back
thermostat or smart (Wi-Fi)
thermostat
1 - Very unlikely 33 23%
2 18 12%
3 23 16%
4 18 12%
5 - Very likely 43 29%
Don't Know 11 8%
(n = 146)
Sealing and insulation of ducts 1 - Very unlikely 45 31%
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2 14 10%
3 35 24%
4 21 14%
5 - Very likely 21 14%
Don't Know 10 7%

(n = 146)
Install a high efficiency central
heating (furnace, boiler)
1 - Very unlikely 57 39%
2 20 14%
3 26 18%
4 10 7%
5 - Very likely 19 13%
Don't Know 14 10%

(n = 147)
Clean or change your furnace filters
once a month during heating season
1 - Very unlikely 15 10%
2 7 5%
3 21 14%
4 21 14%
5 - Very likely 77 52%
Don't Know 6 4%
(n = 146)
Install glass doors on fireplace and
close dampers when the fireplace is
not in use
1 - Very unlikely 51 35%
2 7 5%
3 21 14%
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4 12 8%
5 - Very likely 33 23%
Don't Know 22 15%

(n = 159)
Install a high efficiency heat pump or
air conditioner
1 - Very unlikely 59 37%
2 25 16%
3 27 17%
4 20 13%
5 - Very likely 18 11%
Don't Know 10 6%
(n = 150)
Conduct regular maintenance of
central heating or cooling system
1 - Very unlikely 13 9%
2 11 7%
3 31 21%
4 33 22%
5 - Very likely 55 37%
Don't Know 7 5%
(n = 150)
Use low-flow showerhead or faucet
aerators
1 - Very unlikely 38 25%
2 24 16%
3 26 17%
4 16 11%
5 - Very likely 36 24%
Don't Know 10 7%
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(n = 150)
Adjust thermostat settings
1 - Very unlikely 15 10%
2 4 3%
3 16 11%
4 30 20%
5 - Very likely 83 55%
Don't Know 2 1%
(n = 150)
Buy energy efficient appliances
1 - Very unlikely 25 17%
2 7 5%
3 29 19%
4 31 21%
5 - Very likely 52 35%
Don't Know 6 4%

(n = 150)
Close the drapes or cover windows to
reduce heat gain or loss
1 - Very unlikely 14 9%
2 6 4%
3 21 14%
4 23 15%
5 - Very likely 84 56%
Don't Know 2 1%

(n = 149)
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Turn down water heater temperature
1 - Very unlikely 27 18%
2 17 11%
3 38 26%
4 25 17%
5 - Very likely 38 26%
Don't Know 4 3%
(n = 148)
Install efficient lighting
1 - Very unlikely 17 11%
2 6 4%
3 26 18%
4 22 15%
5 - Very likely 71 48%
Don't Know 6 4%

(n = 149)
Unplug appliances or electronics
when not use
1 - Very unlikely 22 15%
2 19 13%
3 26 17%
4 28 19%
5 - Very likely 50 34%
Don't Know 4 3%

(n = 148)
Install a smart power strip
1 - Very unlikely 23 16%
2 11 7%
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3 33 22%
4 22 15%
5 - Very likely 46 31%
Don't Know 13 9%
(n = 149)
Use cold water when doing laundry
1 - Very unlikely 20 13%
2 11 7%
3 25 17%
4 29 19%
5 - Very likely 59 40%
Don't Know 5 3%

Are you planning to make any
energy efficient updates in your
home in the next year?
Response (n = 158) Percent of
Respondents
Yes 26 16%
No 79 50%
Don’t know 53 34%

Using the scale below, how
trustworthy is Intermountain Gas
Company as a source of
information about saving energy in
your home?
Response (n = 159) Percent of
Respondents
1- Not at all trustworthy 0 0%
2 1 1%
3 17 11%
4 28 18%
5- Very trustworthy 94 59%
Don’t know 19 12%
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Using the scale below, how
interested are you in receiving
additional information on energy
saving tips and rebate programs
offered by Intermountain Gas
Company? Would you say…
Response (n = 155) Percent of
Respondents
1- Not at all interested 15 10%
2 21 14%
3 25 16%
4 41 26%
5- Very interested 48 31%
Don’t know 5 3%

Who provides your home with
electric service?
Response (n = 79) Percent of
Respondents
Email 46 29%
Utility Website 9 6%
Bill insert 15 10%
Utility newsletter 7 4%
Telephone call 1 1%
Text messages 0 0%
None, I prefer to not receive information 1 1%
Other (Please specify) 0 0%
Don’t know 0 0%

Using the scale below, please rate
your level of agreement or
disagreement with these
statements.
Response (n = 155) Percent of
Respondents

1 2 1%
2 0 0%
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 324 of 329
IGC Energy Efficiency Program Evaluation
196
Energy efficiency saves money
3 15 10%
4 30 19%
5 105 68%
Don't Know 3 2%
(n = 145)

I am not very concerned about the
amount of energy used in my home

1 60 41%
2 28 19%
3 31 21%
4 13 9%
5 12 8%
Don't Know 1 1%
(n = 145)

I am too busy to worry about
making energy-related
improvements in my home

1 53 37%
2 40 28%
3 37 26%
4 9 6%
5 4 3%
Don't Know 2 1%
(n = 145)

It is possible to be energy efficient
without sacrificing comfort

1 3 2%
2 7 5%
3 38 27%
4 44 31%
5 48 34%
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 325 of 329
IGC Energy Efficiency Program Evaluation
197
Don't Know 3 2%
(n = 145)

I know of steps I could take to
reduce my household energy use

1 6 4%
2 15 10%
3 46 32%
4 42 29%
5 33 23%
Don't Know 3 2%
(n = 143)

I intend to reduce my household
energy use in the next 12 months

1 5 3%
2 24 17%
3 50 35%
4 34 24%
5 17 12%
Don't Know 13 9%
(n = 144)

It is not possible to make energy
efficiency changes on my budget

1 27 19%
2 37 26%
3 39 27%
4 14 10%
5 18 13%
Don't Know 9 6%

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 326 of 329
IGC Energy Efficiency Program Evaluation
198
Using a scale below, how satisfied
are you with Intermountain Gas
Company as your natural gas
service provider?
Response (n = 159) Percent of
Respondents
1 - Very dissatisfied 0 0%
2 1 1%
3 11 7%
4 32 20%
5- Very satisfied 111 70%
Don't Know 4 3%

Who provides your home with
electric service?
Response (n = 159) Percent of
Respondents
Idaho Power Company 137 87%
Rocky Mountain Power 15 10%
Fall River Rural Electric Cooperative 1 1%
Idaho County Light and Power 0 0%
Other (Please specify) 3 2%
Don’t know 1 1%

Including yourself, how many
people currently live in your home
year-round?
Response (n = 147) Percent of
Respondents
1 22 15%
2 50 34%
3 27 18%
4 19 13%
5 14 10%
6 9 6%
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 327 of 329
IGC Energy Efficiency Program Evaluation
199
7 2 1%
8 2 1%
9 or more 0 0%
Don't know 0 0%
Refused 2 1%

Which of the following brackets
contains your age?
Response (n = 145) Percent of
Respondents
19-24 5 3%
25-34 19 13%
35-49 40 28%
50-64 51 35%
65 or over 28 19%
Don’t know 0 0%
Prefer not to state 2 1%

What is your approximate
household income?
Response (n = 142) Percent of
Respondents
Less than $10,000 6 4%
$10,000 to less than $20,000 6 4%
$20,000 to less than $30,000 12 8%
$30,000 to less than $40,000 9 6%
$40,000 to less than $50,000 4 3%
$50,000 to less than $60,000 15 11%
$60,000 to less than $70,000 6 4%
$70,000 to less than $80,000 9 6%
Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 328 of 329
IGC Energy Efficiency Program Evaluation
200
$80,000 to less than $90,000 7 5%
$90,000 to less than $100,000 3 2%
$100,000 to less than $150,000 19 13%
$150,000 to less than $200,000 10 7%
$200,000 or more 6 4%
Don’t know 0 0%
Prefer not to state 30 21%

What’s the highest level of
education you’ve completed?
Response (n = 139) Percent of
Respondents
Up to 8th grade 1 1%
Some high school 1 1%
High school or GED equivalent 11 8%
Some college 34 24%
Associate’s degree 12 9%
Bachelor’s college degree 45 32%
Master’s degree 18 13%
Professional degree (MD, JD, DDO, DDS) 2 1%
Doctorate (Ph.D., D.Sc.) 5 4%
Don’t know 0 0%
Prefer not to state 10 7%

Exhibit No. 5 Case No. INT-G-20-06 Intermountain Gas Company Page 329 of 329

EXHIBIT NO. 6
CASE NO. INT-G-20-06
INTERMOUNTAIN GAS COMPANY

2019 Energy Efficiency Cost-Effectiveness
(8 pages)

2019 Energy Efficiency Cost-Effectiveness
Exhibt No. 6 Case No. INT-G-20-06 Intermountain Gas Company Page 1 of 8
Introduction 
Intermountain’s Energy Efficiency Program (EE Program) offers individual customers a way to lower their 
usage and monthly energy bills. It additionally benefits all customers by ensuring resources are used 
efficiently which delays the need for expensive system upgrades and additional supply contracts, 
thereby keeping costs low for everyone. Cost‐effectiveness testing is vital to ensuring the Company’s EE 
Program is in fact a least‐cost resource, and is integral to the design, implementation and success of the 
EE Program. 
Intermountain initially launched the EE Program as a modest residential rebate offering. The original 
program allowed Intermountain to gauge customer interest in installing high‐efficiency natural gas 
equipment and served as a starting point to refine and further develop cost‐effective, relevant program 
offerings.   
Following the launch of the EE Program, Intermountain commissioned an independent 3rd party to 
conduct a comprehensive Conservation Potential Assessment (CPA) to quantify energy efficiency 
resources available within the Company’s service territory, to support both short‐term energy efficiency 
planning and long‐term resource planning activities, and to provide the most up‐to‐date market data for 
both the residential and commercial sectors. This study serves as the basis for modifications to current 
rebates that underperformed and was used to design new rebates and programs.  
To improve the two most popular rebates, Intermountain also commissioned an Evaluation, 
Measurement and Verification (EM&V) study. The EM&V study provided data on actual savings related 
to the EE Program as well as insights that could be used to further refine and improve both the furnace 
and whole home rebates. 
Cost‐Effectiveness and Methodology 
Intermountain’s objective is for all rebates is to have benefit/cost ratios greater than one for the Utility 
Cost Test (UCT).  The UCT measures cost‐effectiveness from the utility company’s perspective and takes 
into consideration avoided supply costs, program administration costs and incentives paid by the utility. 
Rebates undergo cost tests at several stages: preliminary design, implementation, and an annual review. 
For a different perspective, cost‐effectiveness of rebates is also evaluated based on the customer’s 
perspective using avoided supply costs, program administration costs and net participant costs, or the  
Total Resource Cost Test (TRC), but this is not the primary cost test used for decisions regarding the 
inclusion or exclusion of rebate offerings. In calculating the UCT and TRC, Intermountain relies on the 
Exhibt No. 6 Case No. INT-G-20-06 Intermountain Gas Company Page 2 of 8
calculations outlined in the California Standard Practice Manual and the National Action Plan for Energy 
Efficiency’s (NAPEE) Understanding Cost Effectiveness of Energy Efficiency Programs: Best Practices, 
Technical Methods, and Emerging Issues for Policy‐Makers. 
Rebate characteristics such as estimated useful life, deemed therm savings, and incremental cost used 
for cost‐effectiveness testing are provided by the CPA study for all rebates other than the furnace and 
whole home. Estimated therm savings for the furnace rebate and whole home are based on the EM&V 
impact evaluation. The rebate count used in the cost‐effectiveness calculation is the actual number of 
rebates paid for the program year.   
Cost‐effectiveness of EE Program rebates is reviewed annually. The results are reported in the annual 
report and reviewed with the Energy Efficiency Stakeholder Committee (EESC). Rebate performance, 
cost‐effectiveness, market insights and lessons learned, are taken into consideration when deciding 
whether to continue, revise or retire a rebate.   
Assumptions  
In calculating cost‐effectiveness for each rebate and for the program as a whole, the Company relied 
upon several assumptions as well as studies provided by independent third‐party sources.  The section 
below discusses the key inputs in calculating cost‐effectiveness under the UCT and the assumptions and 
sources used. 
Energy Savings 
Energy savings for each rebate are calculated by multiplying each rebate’s gross annual therm savings by 
the total number of rebates issued.  The energy savings are then valuated based on the Company’s 
Avoided Cost. The Avoided Cost is used both to economically evaluate the present value of the therms 
saved over the life span of a rebate and to track the performance of the program as a whole. A more in‐
depth discussion of the Avoided Cost calculation and its components can be found in Case No. INT‐G‐20‐
06, Exhibit No. 3. 
Rebate Costs 
Total rebate costs are calculated by multiplying the value of each rebate by the number of rebates 
issued for the year. 

Exhibt No. 6 Case No. INT-G-20-06 Intermountain Gas Company Page 3 of 8
Equipment & Installation Cost 
The incremental equipment and installation costs are inputs to the TRC cost test and were provided by 
the CPA.  These costs represent the incremental purchase and installation costs the participant will pay 
between a base case measure and a higher efficient alternative.  These costs are not offset by the 
amount of the rebate received by the participant. 
Program Delivery & Administration  
Program delivery and administration costs are allocated to each rebate based on the percentage of the 
total annual portfolio of therm savings that rebate represents.  Any cost incurred solely for a particular 
rebate will be directly assigned to that rebate. 
Real Discount Rate 
The real discount rate is used to account for the time‐value of money and accurately compare costs. The 
real discount rate is based on the Company’s tax‐affected weighted average cost of capital. The 
calculation of the real discount rate can be found in Case No. INT‐G‐20‐06, Exhibit No. 3, Page 17. 
Inflation Rate 
An inflation assumption is used in cost‐effectiveness testing to convert nominal, forward‐looking costs 
into real dollars. The company assumes an inflation rate of 2.0%, which is a standard value tied to the 
Consumer Price Index (CPI). 
Net‐to‐Gross  
Net‐to‐gross (NTG) is a ratio that adjusts the therm savings of rebates and/or programs, so they solely 
reflect energy efficiency gains that are the direct result of energy efficiency programs. The NTG deducts 
therm savings resulting from free‐ridership, or savings that would have occurred regardless of the 
program. It also increases therm savings to account for spillover, or savings that occurred but were not 
counted by the program, as well as therm savings resulting from market transformation. Unfortunately, 
estimates of net savings require making sweeping assumptions to model a theoretical scenario where 
the EE Program did not exist. Because of the difficulty in accurately calculating NTG percentages, the 
Company used an NTG of 100% for all rebate and program cost‐effectiveness analysis. Intermountain 
also performs a sensitivity analysis for each rebate that determines the minimum allowable NTG ratio 
where the rebate would remain (or become) cost effective under the Utility Cost Test. 
Exhibt No. 6 Case No. INT-G-20-06 Intermountain Gas Company Page 4 of 8
Results 
As stated previously, Intermountain uses the UCT as the primary measure for determining cost‐
effectiveness for each rebate offering and for the entire program.  In its original filing of Case No. INT‐G‐
20‐06, the 2019 Energy Efficiency Program was not cost‐effective, with a UCT of 0.87 (see the 2019 
Annual Report, Page 9).  After meeting with the EESC, the Company decided to amend its original filing 
of Case No. INT‐G‐20‐06 to incorporate the final results of its EM&V study.  This led to an update of the 
Whole Home and Furnace cost‐effectiveness tests based on the results of the EM&V study.   
As seen on Exhibit No. 6, Page 6, the UCT ratio for the 2019 Energy Efficiency Program increased to a 
cost‐effectiveness of 1.3 (from the previously reported 0.87) as a result of incorporating the final results 
of the EM&V study.  The UCT ratios for all rebates, as shown on Exhibit No. 6, Page 6, also changed from 
the ratios previously reported.  These changes in cost‐effectiveness are entirely related to the increase 
in annual therm savings (as determined in the EM&V study) for the Whole Home and Furnace rebates, 
and the resulting re‐allocation of overhead costs based on therm savings.  The updated cost‐
effectiveness results for the Whole Home and Furnace rebates are presented on Exhibit No. 6, Pages 7‐
8.         

Exhibt No. 6 Case No. INT-G-20-06 Intermountain Gas Company Page 5 of 8
INTERMOUNTAIN GAS COMPANY
Residential Energy Efficiency Program
Program Year 2019 Cost-Effectiveness
Benefits
Lifetime Energy Savings by Rebate Therm Savings $ Savings (PV)
Whole Home 7,391,150 1,979,645$
Combi Radiant Heat System 26,103 7,490
Furnace 5,536,880 1,617,207
70% Fireplace 2,800 818
Water Heater 3,952 1,315
Tankless Water Heater 258,375 69,203
Total Energy Savings 13,219,260 3,675,678$
Costs
Costs by Rebate # Rebates Rebate Costs Incremental Costs
Whole Home 1,079 1,294,800$ 2,284,243$
Combi Radiant Heat System 11 11,000 38,742
Furnace 2,066 723,100 2,700,262
70% Fireplace 14 1,400 1,498
Water Heater 8 400 3,120
Tankless Water Heater 159 23,850 286,200
Total Rebate & Incremental Costs 2,054,550$ 5,314,065$
Program Delivery & Administration Costs Cost
Total Program Delivery & Administration Costs 748,795$
Cost-Effectiveness
Program Cost-Effectiveness Benefit Cost Ratio
Utility Cost Test 3,675,678$ 2,803,345$ 1.3
Total Resource Cost Test 3,675,678 6,062,860 0.6
Utility Cost Test by Rebate
Whole Home 1,979,645$ 1,673,540$ 1.2
Combi Radiant Heat System 7,490 12,592 0.6
Furnace 1,617,207 1,077,754 1.5
70% Fireplace 818 1,579 0.5
Water Heater 1,315 789 1.7
Tankless Water Heater 69,203 37,090 1.9
Exhibt No. 6 Case No. INT-G-20-06 Intermountain Gas Company Page 6 of 8
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Exhibt No. 6 Case No. INT-G-20-06 Intermountain Gas Company Page 7 of 8
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Exhibt No. 6 Case No. INT-G-20-06 Intermountain Gas Company Page 8 of 8

EXHIBIT NO. 7
CASE NO. INT-G-20-06
INTERMOUNTAIN GAS COMPANY

Proposed Residential Energy Efficiency Program Revisions
(9 pages)

Intermountain Gas Company 
Proposed Residential EE Program Revisions 
Based on the recommendations from the EM&V study (see Exhibit No. 5) performed by ADM Associates, 
Inc. (ADM), input from the Energy Efficiency Stakeholder Committee (EESC) (see Exhibit No. 4), and 
other resources such as the 2019 Idaho Residential Energy Code Field Study (see Exhibit No. 8), the ADM 
“Residential Whole Home Modeling Results” memorandum (see Exhibit No. 8), the Idaho Code 
Collaborative, and upcoming energy code changes, the Company plans to update its Residential Energy 
Efficiency Program (EE Program) by retiring, adding to and revising its current set of rebates. Following is 
an outline of the proposed program in detail.  
No changes:  
Furnace ‐The only rebate offering that remains unchanged is the furnace rebate of $350 for a
natural gas furnace with a 95% AFUE minimum efficiency rating. The EM&V impact evaluation of
the furnace rebate found savings of 134 therms per rebate based on the Equivalent Full Load
Hours for Heating (EFLH) approach recommended by ADM (see Exhibit No. 5, page 84). The
EM&V study recommended that additional information be required on the furnace rebate
application to determine the efficiency of the equipment being replaced as well as the reason
for replacement, such as replace‐on‐burnout, early retirement, or new construction. The EM&V
study also identified HVAC sizing as an area for contractor training as over‐sizing, a common
practice in Climate Zone 5, negatively affects program savings (see Exhibit No. 5, page 32).
Additionally, the EESC suggested collecting the size of the equipment being replaced (see Exhibit
No. 4, page 10). Collecting these additional data points should help provide a more complete
picture from which to evaluate savings attributed to the furnace rebate in the future. The billing
analysis conducted by ADM provided measurable savings, “However, the observable energy
savings through billing analysis are much lower than expected equipment savings. Billing
analyses include any changes in household behavior, equipment, or occupancy, and therefore
may include factors other than the impact of improved equipment efficiency” (see Exhibit No. 5,
page 32). Because of the large variation in therm savings between the Billing Analysis approach
and the EFLH approach, the Company has decided to collect the additional information noted
above to help provide a more refined EM&V analysis in the future. In the meantime,
Intermountain will use the 2019 Conservation Potential Assessment (CPA) annual therm saving
estimate of 87 therms as a conservative therm savings estimate for this rebate. The furnace
Exhibit No. 7 Case No. INT-G-20-06 Intermountain Gas Company Page 1 of 9
rebate has shown consistent growth year‐over‐year, and the amount of furnace rebates year‐to‐
date for 2020 has already exceeded the 2,066 rebates issued in 2019. The cost‐effectiveness 
tests were based on an estimate of 2,500 rebates for 2021.   

Retirements:  
 70% Fireplace ‐The 70% FE Fireplace rebate had very low participation over the life of the 
offering with 13 and 14 units installed in 2018 and 2019, respectively.  While some customers 
may use a fireplace insert as a substitute heat source, fireplace inserts are designed to be a 
decorative feature and are therefore not normally rated for energy efficiency, or if rated, there 
is not a standard efficiency rating applied to all fireplace inserts. Based on the findings of the 
CPA, updated annual therm savings were reduced from 56 therms to 10 therms, and to be cost‐
effective only a minimal incentive could be offered which would make this an ineffective 
offering. When presented to the EESC in the October 27 meeting, the Committee had no 
objections to Intermountain’s proposal to retire this rebate (Exhibit No. 4, Page 13).  

Additions:  
The Company proposes the addition of three new rebates to its Residential EE Program. Unless 
specifically stated, all rebate inputs for cost‐effectiveness testing were based on the 2019 CPA.   
 Smart Thermostat – The smart thermostat has consistently been the most frequently requested 
appliance rebate by HVAC contractors, customers, and members of the EESC.  It was also 
included as a recommended program addition in the Company’s EM&V study (see Exhibit No. 5, 
Page 143). The proposed rebate is $100 for a wi‐fi enabled smart thermostat with estimated 
annual savings of 44 therms per appliance. Discussions with the EESC at the September 16 
committee meeting revealed the CPA estimated useful life (EUL) of 8 years for the appliance was 
far too low, and ADM recommended an EUL of 11 years (see Exhibit No. 4, Page 11).  The 
Company believes the smart thermostat will complement other space heating rebates, and 
therefore the Company used a participation estimate of 2,500 thermostat rebates in 2021 for 
cost‐effectiveness testing.  

 Boiler – Based on customer and HVAC contractor feedback, the addition of a residential boiler 
rebate will provide a high‐efficient space heating option for those customers that do not have a 
forced air furnace, and cannot utilize the combination space and water heat system rebate 
because water used for domestic purposes cannot also be used for space heat. The EESC 
Exhibit No. 7 Case No. INT-G-20-06 Intermountain Gas Company Page 2 of 9
supported adding this equipment rebate when it was presented at the October 27 meeting (see 
Exhibit No. 4, Page 13). The proposed rebate is for $800, requires a minimum efficiency of 95% 
AFUE, and has an estimated annual savings of 159 therms. As a first time offering, the Company 
estimated 25 rebate applications in 2021 for cost‐effectiveness testing.  

 Tankless Water Heater – Tier II – One of the recommendations of the EM&V study was to 
explore opportunities to add a tankless water heater at a lower price point than the current 
offering (see Exhibit No. 5, Page 143). An additional tankless water heater rebate will provide 
customers another high‐efficient water heating option at a slightly lower estimated incremental 
cost of $1,152 for an 0.87 UEF tankless water heater compared to the 0.91 UEF tankless water 
heater with an estimated incremental cost of $1,800 that is currently included in the Company’s 
program. The proposed rebate is for $300, requires a minimum efficiency of 0.87 UEF, and has 
an estimated annual savings of 58 therms. The Company estimated a participation level of 200 
rebates in 2021 for cost‐effectiveness testing.  

Revisions:  
Intermountain proposes to revise the following four rebates. 
 Storage Water Heater – Based on the 2019 CPA, annual therm savings for this rebate have 
increased from 22 therms to 38 therms while EUL has decreased from 16 years to 13 years.  The 
Company proposes to increase the 0.68 UEF (previously 0.67 EF) water heater rebate from $50 
to $115 and anticipates that this increase will encourage more participation from the 2019 level 
of 8 rebates. The Company estimated 50 rebates in 2021 for cost‐effectiveness testing. The 
efficiency rating will be updated from Energy Factor (EF) to Uniform Energy Factor (UEF) to be 
consistent with the Department of Energy’s industry standard for measuring energy efficiency 
in water heaters. This new standard required new testing procedures that resulted in consistent 
standards for measuring energy efficiency performance, a better reflection of real‐world results 
that impact energy efficiency ratings, apples‐to‐apples‐comparison of water heaters, and a 
simplified water heater selection process.  

 Tankless Water Heater‐Tier I – Based on increased estimated annual therm savings (from 58 to 
65 therms) and increased EUL (from 18 years to 25 years) identified in the CPA, the Company 
proposes to increase this rebate from $150 to $325. Due to offering an additional tankless 
water heating option, the number of rebates estimated for 2021 is lower than the number of 
Exhibit No. 7 Case No. INT-G-20-06 Intermountain Gas Company Page 3 of 9
rebates paid in 2019 or during 2020. The Company estimated 100 rebates will be paid in 2021 
for cost‐effectiveness testing. The efficiency rating will be updated from EF to UEF for the 
reasons outlined above.  

 Combination Boiler for Space and Water Heat ‐ This equipment rebate, with a requirement of 
“90% or greater efficiency condensing tankless combo system for space and water heat,” has 
had low participation, 3 and 11 rebates in 2018 and 2019, respectively, and has been one of the 
more misunderstood offerings, both from an equipment standpoint and application standpoint. 
Intermountain has received applications where HVAC contractors have installed two tankless 
appliances, negating the minimum requirement of one appliance serving both space and water 
heat. The Company has also received applications where a boiler was installed, negating the 
tankless requirement. Since a combination system can be served by either a tankless water 
heater, or a combination boiler, the Company proposes to clarify this rebate by requiring a 
combination boiler, designed for both space and water heat.  The proposed rebate is for $800, 
requires a minimum efficiency of 95% AFUE, and has an estimated annual therms savings of 155 
therms.  The Company estimated a participation level of 25 rebates in 2021 for cost‐
effectiveness testing. 

 Whole Home ‐ The new construction rebate, Whole Home, is the Company’s most revised 
rebate offering proposal. Restructuring of the rebate was based on EM&V recommendations to 
increase efficiency requirements to keep up with code improvements and to isolate the therm 
saving features in new construction. The Company also considered therm saving opportunities 
identified in the evaluation of current building practices in the 2019 Idaho Residential Energy 
Code Field Study (see Exhibit No. 8), the energy code requirements that will become effective 
January 2021, and feedback from the EESC.  In addition, Intermountain commissioned a follow‐
up study by ADM to identify potential therm savings of a variety of therm saving requirements 
(see Exhibit No. 8). One of the more significant changes to the rebate is the retirement of the 
Energy Star Certification. The EM&V study recommended removing the ENERGY STAR 
certification requirement as it “seems to be a barrier to builder participation” (Exhibit No. 5, p. 
12). Due to code improvements to be implemented in January 2021, therm savings for the 
proposed offering are reduced from the estimated 274 therm savings identified in the EM&V 
study (see Exhibit No. 5, Page 85). Subsequently the rebate amounts have also been reduced 
from $1,200 to $900 for the proposed Tier I rebate and $700 for the proposed Tier II rebate. 
Exhibit No. 7 Case No. INT-G-20-06 Intermountain Gas Company Page 4 of 9
Under the current program, new construction and appliance rebates are mutually exclusive. 
The Company proposes to allow Whole Home participants to layer on the smart thermostat 
and/or water heating rebates to capture additional therm savings. The Company proposes the 
following two‐tiered new construction offering with specific requirements:  

Whole Home Tier I ‐ $900 (Estimated annual therm savings of 161) 
 HERS rated 
 Air sealing at or below 3 ACH at 50 Pa 
 Ceiling insulation at or above R‐49 
 Ducts and air handler located inside conditioned space or duct leakage to outside of less than 
4 CFM25/100 ft2 CFA 
 Furnace efficiency at or above 97% AFUE 

Whole Home Tier II ‐ $700 (Estimated annual therm savings of 128) 
 HERS rated 
 Air sealing at or below 4 ACH at 50 Pa 
 Ducts and air handler located inside conditioned space or duct leakage to outside of less than 
4 CFM25/100 ft2 CFA 
 Furnace efficiency at or above 95% AFUE 

Both tiers of the rebate will require the home to be HERS scored, but no specific HERS threshold 
is required. Since the EM&V evaluation found a lower HERS score did not correlate with more 
therm savings and lower HERS scores could be achieved by implementing non‐energy saving 
measures, setting a specific HERS threshold requirement appeared to not be directly related to 
therm savings (see Exhibit No. 5, Page 74). While the HERS score threshold is not related to 
exact therm savings, requiring that the home be HERS scored is important. The HERS score is a 
way for builders to quantify and certify a home’s energy performance, and it is a simple, 
transparent way for consumers to easily compare homes based on energy efficiency 
performance, much like comparing cars based on a miles‐per‐gallon formula. Builders having 
homes HERS scored, one indication of energy efficient home building, is not yet a common 
building practice in Idaho. According to RESNET, in 2019 only 14% of all new home starts 
received a HERS score. Intermountain believes the requirement to have the home HERS scored 
Exhibit No. 7 Case No. INT-G-20-06 Intermountain Gas Company Page 5 of 9
will help to educate both customers and builders on energy efficient building. Requiring a HERS 
certificate will also provide an efficient and reliable process for the Company to verify 
compliance with the proposed program requirements that do affect therm savings. A HERS 
score can only be obtained by a certified home energy rater, who is subject to certification, 
quality control, and quality assurance by the governing body RESNET. In addition, the specific 
requirements added to the proposed rebate are all components of a HERS score and will not 
require additional tests be conducted or additional documentation be provided by the builder to 
enable Intermountain to verify that the rebate requirements have been met.  

The proposed specific requirements will be an above‐code stretch for builders since the 2018 
amended IECC Idaho code will require 5 ACH, R‐38 insulation, and 4 CFM25/100 ft2 CFA. 
Standard equipment efficiency for furnaces is still 80% AFUE.  While not a building code 
requirement, the 95% and 97% AFUE furnace requirements proposed by Intermountain are 
significant high‐efficient equipment upgrades from the standard. In the Residential Whole Home 
Modeling Results Memo by ADM, evaluators found “the individual component that contributes 
the largest magnitude of savings is the movement of ducts and air handler to conditioned 
space” (Exhibit 8, page 9). The EESC and the 2019 Idaho Residential Energy Code Field Study 
(Field Study) identified ACH and duct leakage as energy saving opportunities for Idaho 
residential building.  Due to some of the builder challenges inherent in moving HVAC systems to 
conditioned space, and current building practices of locating the furnace in the garage rather 
than in conditioned space, the Company and the EESC agreed it was important to provide 
builders with an option for reducing duct leakage to outside. When moving the entire HVAC 
systems into conditioned space is not an option, an alternative energy saving route is provided 
by setting an above code duct leakage target of “less than 4 CFM25/100 ft2 CFA for duct leakage 
to outside.”  It is important to specify duct leakage to outside (LTO) versus total duct leakage 
(TL). Idaho code currently only tests for total duct leakage. Minimizing total duct leakage allows 
for management of static pressures and controlling designed air flows, which are essentially 
comfort issues. While homeowner comfort is important, leakage to outside keeps 
unconditioned air outside and conditioned air inside, which is an energy saving and cost issue.  

The duct leakage requirement will not only incent energy and cost savings for homeowners, as 
much as 22 therms per home based on statewide annual measure‐level savings according to the 
Field Study (Exhibit No.8, p. 58), but also promote improved building practices in general.  In 
Exhibit No. 7 Case No. INT-G-20-06 Intermountain Gas Company Page 6 of 9
addition to the highest total energy and cost savings potential, the Field Study found “reductions 
in duct leakage represent a significant area for improvement and should be given increased 
attention in future training and enforcement” (Exhibit No 8, Page 54) as the majority of 
observations did not meet code requirement.  

Increased air sealing measured by Air Change per Hour (ACH) was the second largest 
incremental savings identified by ADM and contributes 10 to 30% of total household energy 
savings (Exhibit No. 8, Page 9). Tier I will require a more energy efficient air change target of 3 
ACH and Tier II will require 4 ACH. Both proposed ACH requirements are above the 2021 
building code requirement of 5 ACH.  

The next largest contributor of therm savings is ceiling insulation (see Exhibit No. 8, Page 9). 
Only the highest therm saving rebate offering, Tier I, will require the installation of R‐49 ceiling 
insulation. Currently, Idaho Code only requires R‐38 ceiling insulation and only in Climate Zone 
6.  ADM’s analysis found the R‐49 ceiling insulation upgrade, as part of an integrated energy 
efficient home design, contributed an estimated 10 therms to total savings. For those builders 
that do not want to invest in a ceiling insulation upgrade, the Tier II rebate will still provide a 
rebate opportunity for energy efficient home building.   

This two‐tiered approach will provide two rebate options for new construction of energy 
efficient homes. The Tier I whole home rebate has an estimated annual therm savings of 161 
therms, while Tier II has 128 as calculated by ADM (see Exhibit No. 8, page 10).  The EUL of 25 
years and $2,117 estimated incremental cost of energy efficient new construction provided by 
the CPA will be used in cost‐effectiveness calculations. High level calculations estimate that 
retiring the ENERGY STAR certification requirement and instead implementing the specific 
rebate requirements outlined here, are equitable incremental costs. For cost‐effectiveness 
testing, an estimate of 600 rebates each was used for Tier I and Tier II participation. It is 
anticipated that alleviating a market barrier to participation by retiring ENERGY STAR 
certification will result in an increase in participation. The estimate of 1,200 total homes in 2021, 
is conservatively greater than 2019 participation of 1,079 homes, but less than 2020 year‐to‐
date participation numbers.  

Exhibit No. 7 Case No. INT-G-20-06 Intermountain Gas Company Page 7 of 9
Conclusion 
The resulting proposed Residential EE Program, including cost‐effectiveness, can be seen on Exhibit No. 
7, Page 9.  As proposed in the Commercial EE Program filing, the Company estimates that its program 
delivery and administration costs will be approximately $848,000 to be split 80% to the residential 
program and 20% to the proposed commercial program (which is currently before the Commission as 
Case No. INT‐G‐20‐04).  The Company estimates that 2021 rebate costs will be approximately $2.2M as 
seen on Exhibit No. 7, Page 9, Column (h).  Intermountain measures the cost‐effectiveness of the 
proposed program using the Utility Cost Test (UCT).  As seen on Exhibit No. 7, Page 9, Column (i), all 
proposed rebates, as well as the proposed Residential EE Program in total, are cost‐effective with UCT 
ratios of 1 or greater.      
Exhibit No. 7 Case No. INT-G-20-06 Intermountain Gas Company Page 8 of 9
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Exhibit No. 7 Case No. INT-G-20-06 Intermountain Gas Company Page 9 of 9

EXHIBIT NO. 8
CASE NO. INT-G-20-06
INTERMOUNTAIN GAS COMPANY

Residential Energy Efficiency Program Design Support
(76 pages)

RESIDENTIAL WHOLE HOME MODELING RESULTS

(10 pages)
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 1 of 76
Memorandum: Residential Whole Home Modeling
Results

Prepared for:
Intermountain Gas Company

November 17, 2020

Prepared by:

ADM Associates, Inc.

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 2 of 76
A. Whole Home Analysis
This memorandum summarizes an exploratory analysis for Intermountain Gas Company (IGC) Whole
Home program. The Evaluators sought to estimate savings for the program with baseline building codes
updated to reflect those of which will be in effect in the upcoming program year. In addition, the
Evaluators explored a range of additional program requirements that could be enforced to increase per-
household savings.
This analysis was requested to explore program requirements for future program years in hopes of
increasing savings per household, as well as increasing program cost effectiveness. In this analysis, the
Evaluators compared simulated savings with several component requirements that are easy to test,
measure, and enforce.
The Evaluators provide a recommendation of a two-tiered rebate offering in which program household
requirements are incrementally increased to increase program savings. The Evaluators believe the
provided recommendations will increase program savings and cost effectiveness, leading to the success
of the program.
The Evaluators recommend the following combination of requirements as the first-tier rebate offering in
future program years:
◼ Air sealing at or below (3 ACH at 50 Pa)
◼ Ceiling insulation at or above (R49)
◼ Ducts and air handler located inside conditioned space
◼ Furnace efficiency at or above 97% AFUE
◼ Change rebate incentive to $1,000
An incentive of $1,000 would allow IGC to produce sufficient UCT benefits to pass the UCT based on IGC’s
commodity costs and WACC specified in their 2019-2023 Integrated Resource Plan (IRP). This rebate
offering will also allow suitable budget for home outreach and administration costs.
The Evaluators recommend the following combination of requirements as the second-tier rebate offering
in future program years:
◼ Air sealing at or below (4 ACH at 50 Pa)
◼ Ducts and air handler located inside conditioned space
◼ Furnace efficiency at or above 95% AFUE
◼ Change rebate incentive to $800
An incentive of $800 would allow IGC to produce sufficient UCT benefits to pass the UCT based on IGC’s
commodity costs and WACC specified in their 2019-2023 IRP. This rebate offering will also allow suitable
budget for home outreach and administration costs.

A.1. Program Summary
The Whole Home program had previously undergone an evaluation for the 2018 and 2019 program years.
A billing analysis was used to estimate household program savings. A simulation analysis was completed
to compare to the results of the billing analysis. The billing analysis resulted in significantly lower
household savings than the program had anticipated.
In addition, on January 1, 2020, the Idaho Division of Building Safety had accepted the 2018 International
Energy Conservation Code (IECC) with Idaho Amendments to replace the current 2012 IECC with Idaho
Amendments residential building code, effective January 1, 2021. Due to these results and changes, the
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 3 of 76
Evaluators explored simulated household savings against this new baseline to project household savings
in the next program year. Table A-1 presents the overall ex ante, ex post, and projected savings by analysis
type.
Table A-1 Ex Ante and Ex Post Program Home Savings by Analysis Type
Incentive per
Rebate
Ex Ante
Savings per
Rebate
(Therms)
Billing Analysis
Savings per
Rebate
(Therms)
2012 IECC
Simulated
Savings per
Rebate
(Therms)
2018 IECC
Simulated
Savings per
Rebate
(Therms)
First Tier
Requirements
Savings per
Rebate
(Therms)
Second Tier
Requirements
Savings per
Rebate
(Therms)
$1,200 204 58 264 85* 161* 128*
*A weighted average, based on distribution of number of floors in sample

A.2. Methodology
The Evaluators present the simulation method used for deriving projected savings from the Whole Homes
measure. This approach complies with the International Performance Measurement and Verification
Protocol (IPMVP) maintained by the Efficiency Valuation Organization (EVO) with sponsorship by the U.S.
Department of Energy (DOE) 1.
The Evaluators estimated savings in this exploratory analysis using simulation analyses. This method
compares the HERS Rater’s modeling files to a User Defined Reference Home (UDRH) specific to Idaho’s
residential building code requirements. The UDRH was updated from 2012 IECC residential building code
with Idaho amendments to 2018 IECC residential building code with Idaho amendments to reflect the
building code in the upcoming program year.
The results of the updated simulation analysis are used to inform recommendations to change program
incentive levels and program requirements. These recommendations are made to increase the likelihood
of future program years’ cost effectiveness.
A.2.1. User Defined Reference Home
This approach involves the comparison of participating homes with a User Defined Reference Home
(UDRH). The UDRH represents a home built to meet the state of Idaho’s current minimum energy
efficiency code requirements.
Idaho has accepted the 2018 International Energy Conservation Code (IECC) with amendments2 for newly
constructed residential homes on January 1, 2020. The 2018 IECC will be effective one year from the
accepted date, on January 1, 2021. The Evaluators used the residential 2018 IECC with Idaho-specific
amendments efficiency values when creating the UDRH in REM/Rate. This comparison provides an
accurate simulation of a newly constructed minimum efficient code residential home to compare against
efficient, program-participating homes for the upcoming program year.
The 2018 IECC with Idaho amendments is referenced from the Idaho Administrative Code, Division of
Building Safety, IDAPA 07.03.01 – Rules of Building Safety. The UDRH incorporates the required minimum
efficiency values for insulation, fenestration, air leakage, and lighting for Idaho. The minimum
requirements for each building components are shown in the table below. For air leakage, a building must
have less than five air changes per hour (ACH) when tested with a blower door at 50 Pascals. For lighting,
a building must have at least 75% percent efficient lights.

1 Core Concepts: International Measurement and Verification Protocol. EVO 100000 – 1:2016, October 2016.
2 https://www.energycodes.gov/adoption/states/idaho
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 4 of 76
Table A-2 presents the insulation and fenestration requirements by component in the 2018 IECC with
Idaho amendments.
Table A-2 Insulation and Fenestration Requirements by Component
Climate
Zone
Fenestration
U-Factor
Skylight
U-Factor
Ceiling
R-Value
Wood
Frame
Wall R-
Value
Mass
Wall R-
Value
Floor R-
Value
Basement
Wall R-
Value
Slab R-
Value &
Depth
Crawlspace
Wall R-
Value
5 0.32 0.55 38 20 or
13+5 13/17 30 15/19 10, 2ft 15/19
6 0.3 0.55 49 22 or
13+5 15/20 30 15/19 10, 4ft 15/19

A.2.2. Simulated Analysis Calculation
The Evaluators utilized the whole building energy modeling program REM/Rate for this analysis. REM/Rate
is software used by organizations to conduct HERS Ratings. REM/Rate files were reviewed for a sample of
four participating homes that represented average home characteristics. Energy savings was calculated
per-home with the following calculation:
Equation A-1 Simulation Savings
Tℎ𝑒𝑟𝑚 𝑆𝑎𝑣𝑖𝑛𝑔𝑠=𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑈𝐷𝑅𝐻−𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝐸𝐹𝐹𝐼𝐶𝐼𝐸𝑁𝑇
Where,
𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑈𝐷𝑅𝐻 = Simulated energy consumption values from REM/Rate for a household under the
UDRH efficient code standards
𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝐸𝐹𝐹𝐼𝐶𝐼𝐸𝑁𝑇 = Simulated energy consumption from REMRate for a household built
referencing the HERS certification values and additional tested household requirements

The Evaluators also tested differences in consumption between homes with altered design. This requires
a slightly different methodology than the simulated savings portrayed above. In order to quantify energy
savings between a home with duct and air handlers located outside of conditioned space and a home
with duct and air handlers located inside of conditioned space, the Evaluators employed the following
equation:
Equation A-2 Simulation Savings with Duct/Air Handler Location Change
Tℎ𝑒𝑟𝑚 𝑆𝑎𝑣𝑖𝑛𝑔𝑠=𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑈𝐷𝑅𝐻−𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝐸𝐹𝐹𝐼𝐶𝐼𝐸𝑁𝑇 𝑤𝑖𝑡ℎ 𝐴𝑙𝑡𝑒𝑟𝑒𝑑 𝐷𝑒𝑠𝑖𝑔𝑛
Where,
𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑈𝐷𝑅𝐻 = Simulated energy consumption values from REM/Rate for a household under the
UDRH efficient code standards. This UDRH is built with the ducts and air handlers located outside of
conditioned space.
𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝐸𝐹𝐹𝐼𝐶𝐼𝐸𝑁𝑇 𝑤𝑖𝑡ℎ 𝐴𝑙𝑡𝑒𝑟𝑒𝑑 𝐷𝑒𝑠𝑖𝑔𝑛 = Simulated energy consumption from REMRate for a
household built referencing the HERS certification values and duct and air handlers located inside
conditioned space with additional tested household requirements if applicable.
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 5 of 76
A.2.3. Selected Homes
The Evaluator received a sample of energy models from program HERS raters as well as application
materials via IGC. The Evaluators identified homes in the program that were representative of the
participating homes, based on home square footage, HERS score, number of bedrooms, and Therm
savings. The Evaluator selected (2) one story homes and (2) two story homes for this analysis. The table
below shows the four selected homes compared to the average home in the program.
Table A-3 Sample Home Comparison
House # of
Stories
Square
Footage
Number of
Bedrooms
HERS
Score
2012 IECC
Therm
Savings
House 1, One-Story 1 2,160 3 68 223
House 2, One-Story 1 2,207 3 63 255
House 1, Two-Story 2 2,959 3 60 353
House 2, Two-Story 2 2,988 5 61 296
Average One-story house 1 2,199 3 62 248
Average Two+-story house 2.1 2,804 4 62 318
Average house 1.3 2,388 3 62 262*
*A weighted average, based on distribution of number of floors in sample

The average household savings value is a weighted average, based on the distribution of the number of
floors in the sample. The Evaluators verified 72% of households in the sample are one-story households.
Therefore, the average household savings for one-story households is more indicative of the projected
program savings.

A.3. Results
The following section details the Evaluator’s impact evaluation findings in this exploratory analysis. Table
A-4 displays the average annual UDRH and rebated home Therm values for space heating, water heating,
lighting, and appliances for homes with one story and homes with two stories. These values represent the
results of the simulation model against the 2018 IECC UDRH without any additional component
requirements.
Table A-4 2018 IECC UDRH and Whole Home Modeled Annual Therm Usage
Number of
Stories
UDRH Program Homes
Space
Heating
(Therms)
Water
Heating
(Therms)
Lights &
Appliances
(Therms)
Total
(Therms)
Space
Heating
(Therms)
Water
Heating
(Therms)
Lights &
Appliances
(Therms)
Total
(Therms)
1 596 150 31 777 544 130 31 705
2 645 192 36 855 545 171 36 734
Average* 610 162 32 799 544 141 32 713
*A weighted average, based on distribution of number of floors in sample
Table A-5 displays the average of the per-home difference between UDRH and participating home annual
usage. Both program years display a zero savings value for lighting and appliances. The simulated
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 6 of 76
participating one-story homes saved an average of 72 Therms per home and the simulated participating
two-story homes saved an average of 121 Therms per home. The weighted average savings per home
across both categories is 86 Therms.
The occupancy and home characteristics between the UDRH modeled simulation and the program
modeled simulation remain constant, which means the program home, with all else constant, uses less
energy to heat the space of the home than the code-built efficient home, leading to savings for those
categories. The majority of the savings (98%) are displayed in space heating due to higher home sealing
and more efficient HVAC equipment.
Table A-5 Average Modeled Therm Savings per Home
Number of
Stories
Space
Heating
(Therms)
Water
Heating
(Therms)
Lights &
Appliances
(Therms)
Total
(Therms)
1 52 20 0 72
2 100 21 0 121
Average 65 20 0 86

A.3.1. Program Requirements Explored
The Evaluators updated the efficient home building specifications to explore the incremental impacts of
additional requirements the program may enforce. The goal of this analysis is to identify additional
program requirements that are projected to increase the average home’s energy savings. The Evaluators
selected building requirements that would be relatively easy to test, measure, and enforce in addition to
being relatively low-cost for builders.
The following component requirements were tested on simulated homes for total and incremental
impact individually and in combination with one another.
◼ Increase duct sealing requirement to 0.03 CFM25/cfa
◼ Increase duct sealing requirement to 0.02 CFM25/cfa
◼ Increase air sealing requirement to 4 ACH at 50 Pa
◼ Increase air sealing requirement to 3 ACH at 50 Pa
◼ Change location of ducts and air handler to inside conditioned space
◼ Increase furnace efficiency requirement to 95 AFUE
◼ Increase furnace efficiency requirement to 97 AFUE
◼ Require gas (.94 UEF min) tankless water heater
◼ Increase ceiling insulation requirement from R-38 to R-49

The table below summarizes the above components for the 68 households in which REM/Rate files were
delivered.
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 7 of 76
Table A-6 Summary Statistics for Sampled Home Components
Number of
Stories
Attic
Insulation
R-value

Furnace
efficiency
AFUE
Gas Water
Heater UEF

Air
Sealing
ACH
Duct
Leakage
CFM25/cfa
Minimum 38 92.1 0.60 1.7 0.0000
First Quartile 38 95 0.62 3.3 0.0162
Median 38 95.5 0.64 3.5 0.0237
Third Quartile 42 96 0.64 4.3 0.0283
Maximum 50 96 0.96 5.9 0.0457

The following table displays the average total and average incremental Therms savings for each
potential program requirement, by story of home.
Table A-7 Potential Program Requirement Savings
Potential requirement
Average Total
Savings (Therms)
Average Incremental
Savings (Therms)
One-
Story
Two-
Story
One-
Story
Two-
Story
New baseline (IECC 2018 with Idaho
Amendments) 72 121 - -
New baseline
Increased duct sealing (0.03 CFM25) 72 121 0 0
New baseline
Increased duct sealing (0.02 CFM25) 76 123 4 2
New baseline
Increased air sealing (4 ACH) 88 121 16 0
New baseline
Increased air sealing (3 ACH) 102 134 30 13
New baseline
Ducts and air handler in conditioned space 121* 147 35* 26
New baseline
Increased furnace efficiency (97% AFUE) 81 129 9 9
New baseline
Increased ceiling insulation (R-49) 83 131 11 10
New baseline
Increased air sealing (3 ACH)
Increased furnace efficiency (97% AFUE)
107 142 35 21
New baseline
Increased air sealing (3 ACH)
Increased duct sealing (0.03 CFM25)
106 135 34 15
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 8 of 76
Potential requirement
Average Total
Savings (Therms)
Average Incremental
Savings (Therms)
One-
Story
Two-
Story
One-
Story
Two-
Story
New baseline
Increased air sealing (3 ACH)
Increased duct sealing (0.03 CFM25)
Increased ceiling insulation (R-49)
116 146 44 25
New baseline
Increased air sealing (4 ACH)
Increased duct sealing (0.03 CFM25)
Increased furnace efficiency (95% AFUE)
88 121 16 0
New baseline
Increased air sealing (3 ACH)
Increased duct sealing (0.03 CFM25)
Increased ceiling insulation (R-49)
Increased furnace efficiency (97% AFUE)
110 141 38 21
New baseline
Increased air sealing (4 ACH)
Ducts and air handler in conditioned space
Increased furnace efficiency (95% AFUE)
121* 147 35* 26
New baseline
Increased air sealing (3 ACH)
Ducts and air handler in conditioned space
Increased furnace efficiency (97% AFUE)
155* 178 69* 57
*These values are modeled from one of the one-story sampled homes
The individual component that contributes the largest magnitude of savings is the movement of ducts and
air handler to conditioned space. This single restriction increased household savings by 35 and 26 Therms
per year for one-story and two-story homes, respectively. The Evaluators note that the movement of
location of the ducts and air handler provides much more savings for homes that have higher duct and air
sealing leakage. Conversely, the movement of location of the ducts and air handler would not provide
much more incremental benefits for homes that already display stringent air and duct sealing values.
The next component displaying large incremental savings is increased air sealing, with a restriction
increasing from 5 ACH as required by the building code, to 3 ACH. This single restriction increased
household saving by 30 Therms per year and 13 Therms per year for one-story homes and two-story
homes, respectively, and constitutes 10 to 30% of total household energy savings.
Reducing the air sealing restriction to 4 ACH instead of 3 ACH displays an additional savings of 16 Therms
per year for one-story homes and 0 Therms per year for two-story homes. This lower restriction in air
sealing was considered to reduce the cost burden on builders for this component while still increasing
efficiency relative to the building code, however, three out of the four sampled homes had already met
or exceeded the efficiency requirements of 4 ACH. Therefore, the incremental savings is low compared to
3 ACH.
The next largest contributor is ceiling insulation, increasing from R-38 to R-49. The largest contributor
after ceiling insulation is increased duct sealing. The Evaluators tested two levels of restriction for duct
sealing. The Evaluators found that all four sampled homes already meet or exceed the 0.03 CFM25 duct
sealing restriction and therefore display no incremental savings for this level of duct sealing restriction.
However, a duct sealing restriction of 0.02 CFM25 results in an additional 4 Therms and 2 Therms saved
per year for one- and two-story homes, respectively.
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 9 of 76
In addition, the Evaluators found that program-qualifying homes already largely have 95% AFUE furnaces.
However, increasing the furnace efficiency to 97 AFUE leads to a reasonable amount of incremental
savings (9 Therms per year for both one- and two-story homes).

A.3.2. Associated Rebate Incentive
In addition to the simulation analysis for projected savings, the Evaluators have calculated projected
Whole Home Rebate incentives that align with the first-tier and second-tier household program
requirements. IGC provided the Evaluators with the utility’s avoided cost values and discount rate from
its 2017 IRP filing. Avoided cost is the avoided natural gas commodity cost. Discount rate is the after-tax
weighted average cost of capital (WACC).
The Evaluators have calculated the following rebate incentive levels with the projected household savings
to meet program effectiveness.
◼ Rebate amount for first tier program requirements: $1,000
◼ Rebate amount for second tier program requirements: $800
Provided the avoided costs remain constant, and no additional benefits are identified, the above rebate
values are the highest amount the utility can reasonably provide to customers with the projected energy
savings for each restriction level.

A.3.3. Recommendations
This section outlines the Evaluator’s recommendations for future program requirements, based on the
simulation analysis above. The Evaluators identified the 95% furnace efficiency to be common in program-
participating homes. However, there is currently no restriction on furnace efficiency for the Whole Home
Rebate. Therefore, in order to reduce variation in savings, the Evaluators recommend including 95% or
97% furnace efficiency as a minimum requirement for the program.
The following sections details two options for future program requirements in addition to the 95% or 97%
furnace efficiency requirement. These options were identified as a combination of the highest cumulative
impact energy savings compared to the Idaho building code with consideration of builder feasibility and
cost-effectiveness in mind.
The following table displays the average household simulated savings with the 2012 IECC baseline, with
the 2018 IECC baseline, and with the first-tier and second-tier program requirements presented above.
Table A-8 Average Program Home Savings by Analysis Type
2012 IECC
Simulated
Savings per
Rebate
(Therms)
2018 IECC
Simulated
Savings per
Rebate
(Therms)
First-Tier
Requirements
Savings per
Rebate
(Therms)
Second-Tier
Requirements
Savings per
Rebate
(Therms)
264 86* 161* 128*
*A weighted average, based on distribution of number of floors in sample

A.3.3.1. First-Tier Program Restriction
The Evaluators have identified component requirements for a first-tier restriction to increase program
savings. Various combinations were tested and are displayed in Table A-7. The Evaluators recommend the
following combination of requirements as the first-tier rebate offering in future program years:
◼ Air sealing at or below (3 ACH at 50 Pa)
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 10 of 76
◼ Ceiling insulation at or above (R49)
◼ Ducts and air handler located inside conditioned space
◼ Furnace efficiency at or above 97% AFUE
◼ Change rebate incentive to $1,000
Requiring these four metrics, the Evaluators estimate that 155 Therms will be saved in a one-story home
and 178 Therms will be savings in a two-story home, detailed in the final row in Table A-7. The average
home in the program, weighted by number of stories, is projected to save 161 Therms with these
requirements.
An incentive of $1,000 would allow IGC to produce sufficient UCT benefits to pass the UCT based on IGC’s
commodity costs and WACC specified in their 2019-2023 IRP. This rebate offering will also allow suitable
budget for home outreach and administration costs.

A.3.3.2. Second-Tier Program Restriction
The Evaluators have also defined an alternative to the above program requirements, defined as the
second-tier program restriction. This alternative removes the restriction for R-49 ceiling insulation, as this
component requires high builder costs and may discourage builders from applying for a rebate.
The Evaluators recommend the following combination of requirements as the second-tier rebate offering
in future program years:
◼ Air sealing at or below (4 ACH at 50 Pa)
◼ Ducts and air handler located inside conditioned space
◼ Furnace efficiency at or above 95% AFUE
◼ Change rebate incentive to $800
Requiring these three metrics, the Evaluators estimate that 121 Therms will be saved in a one-story home
and 147 Therms will be savings in a two-story home, detailed in the final row in Table A-7. The average
home in the program, weighted by number of stories, is projected to save 128 Therms with these
requirements.
The Evaluators note that three of the four sampled homes already meet or exceed the 4 ACH air sealing
requirement. However, as shown in Table A-6, some program homes do not meet the 4 ACH air sealing
requirement and annual energy savings may increase from this additional requirement. The Evaluators
recommend this set of restrictions to facilitate additional savings from households applying to the
program with a lower efficiency than the average program household and requiring a lower cost to
builders than the first tier recommendation in the section above.
An incentive of $800 would allow IGC to produce sufficient UCT benefits to pass the UCT based on IGC’s
commodity costs and WACC specified in their 2019-2023 IRP. This rebate offering will also allow suitable
budget for home outreach and administration costs.

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 11 of 76

IDAHO RESIDENTIAL ENERGY CODE FIELD STUDY

(64 pages)
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 12 of 76
PNNL-28380

Prepared for the U.S. Department of Energy
DE-AC05-76RL01830
Idaho Residential Energy
Code Field Study
February 2019
R Bartlett
M Halverson
Y Xie

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 13 of 76

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 14 of 76
PNNL-28380

Idaho Residential Energy Code
Field Study

R Bartlett
M Halverson
Y Xie

February 2019

Prepared for
the U.S. Department of Energy
under Contract DE-AC05-76RL01830

Pacific Northwest National Laboratory
Richland, Washington 99352
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 15 of 76
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 16 of 76

iii
Executive Summary
A research project in the state of Idaho identified opportunities to reduce homeowner energy costs in
residential single-family new construction by increasing compliance with the current state energy code.
The study was initiated in January 2018; data collection began in March 2018 and continued through June
2018. During this period, research teams visited 127 homes during various stages of construction,
resulting in a collection of data based on observations made directly in the field. Analysis of the data has
led to a better understanding of the energy features present in homes and indicates nearly $500,000 in
potential annual savings to Idaho homeowners that could result from increased compliance with the Idaho
Energy Conservation Code.
Methodology
The project team was led by David Freelove, Idaho Energy Code Circuit Rider, with support from
Cadmus, the Idaho Association of Building Officials, and the Northwest Energy Efficiency Alliance. The
team applied a methodology that was previously developed and tested by the U.S. Department of Energy
(DOE). The methodology identified the energy code-required building components that have the largest
direct impact on energy consumption. These key items are a focal point of the study, and provide the data
used in the analysis and savings estimates. The project team implemented an Idaho-specific sampling
plan representative of new construction within the state. This sampling plan was developed by Pacific
Northwest National Laboratory (PNNL).
Following data collection, PNNL conducted three stages of analysis on the resulting data set (Figure
ES.1). The first stage identified compliance trends within the state based on the field observations for
each key item. The second stage modeled energy consumption of the homes observed in the field relative
to what would be expected if sampled homes just met minimum code requirements. The third stage then
calculated the potential energy savings and consumer cost savings associated with increased code
compliance. Together, these findings provide valuable insight on challenges facing energy code
implementation and enforcement, and are intended to inform future energy code education, training and
outreach activities.

Figure ES.1. Stages of Analysis Applied in the Study
Results
The key items with the greatest potential for savings in Idaho are presented in Table ES.1. The estimates
presented in the table represent the savings associated with each measure and are extrapolated based on
projected new construction. These items may be considered during consideration of compliance-
improvement programs within the state, including energy code education, training and outreach
initiatives.
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 17 of 76

iv
Table ES.1. Estimated Annual Statewide Savings Potential in Idaho
(MMBtu) ($)
27,966 307,201
17,088 167,182
1,383 5,436

Figure ES.2. Modeled distribution of regulated EUI (kBtu/ft2/year) in Idaho
In terms of overall energy consumption, the analysis shows that homes within the state use less energy
than would be expected relative to homes built to the current minimum state code requirements (Figure
ES.2). Analysis of the collected field data indicates average regulated energy use intensity (EUI) of 34.62
kBtu/ft2-yr statewide compared to 40.51 kBtu/ft2-yr for homes exactly meeting minimum prescriptive
energy code requirements. This suggests that on average the typical home in the state is about 15% better
than code.
Note that in an EUI analysis, items found to be better than code offset savings from items found to be
worse than code. These below-code items represent a savings opportunity regardless of the above-code
items. In this study, a significant portion of homes were found to not meet code in several key areas
impacting energy use, durability, and comfort. Thus, there is still a significant energy savings opportunity
(estimated at $500,000 annually) from energy code compliance enhancement activities in Idaho.

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 18 of 76

v
Acknowledgments
The following members comprised the Idaho project team:
x David Freelove, Idaho Energy Code Circuit Rider
x Jerica Stacey and Jolyn Green, Cadmus
The Idaho project team wishes to acknowledge the Northwest Energy Efficiency Alliance (NEEA) for
generously providing the funding for this project, as well as the Idaho Association of Building Officials
for its support.
The authors would also like to thank the following reviewers who provided insightful reviews of the
report:
x Bing Liu, Steve Phoutrides, Dulane Moran and Christina Steinhoff, NEEA
x Sharon Grant and Ken Baker, Idaho Energy Code Collaborative group
x John Chatburn and Katie Pegan, Idaho Office of Energy and Mineral Resources
Northwest Energy Efficiency Alliance
NEEA is a non-profit organization working to effect market transformation through the acceleration and
adoption of energy-efficient products, services and practices. NEEA is an alliance of more than 140
Northwest utilities and energy efficiency organizations working on behalf of more than 13 million energy
consumers. For more information, visit neea.org.
Idaho Association of Building Officials
The Idaho Association of Building Officials (IDABO) was first recognized as a State organization in
1958. IDABO’s mission is to promote public health and welfare by facilitating safe and accessible
buildings and by educating citizens and elected officials on Idaho codes and standards. See more
information on IDABO at http://www.idabo.org/.
Cadmus
The Cadmus Group LLC was founded in 1983 in Watertown, MA. They provide services in the areas of
energy, climate, water, public health, international development, transportation, and safety, security, and
resiliency. See more information on Cadmus at https://www.cadmusgroup.com/.

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 19 of 76
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 20 of 76

vii
Acronyms and Abbreviations
AC air conditioning
ACCA Air Conditioning Contractors of America
ACH air changes per hour
AFUE annual fuel utilization efficiency
AIA American Institute of Architects
Btu British thermal unit
cfm cubic feet per minute
CZ climate zone
DOE U.S. Department of Energy
EDC electric distribution company
EERE Office of Energy Efficiency and Renewable Energy
EUI energy use intensity
FOA funding opportunity announcement
HERS home energy rating system
HSPF heating season performance factor
ICC International Code Council
ID Idaho
IDABO Idaho Association of Building Officials
IECC International Energy Conservation Code
kBtu thousand British thermal units
MMBtu million British thermal units
NA not applicable
NEEA Northwest Energy Efficiency Alliance
PNNL Pacific Northwest National Laboratory
RESNET Residential Energy Services Network
RFI request for information
SHGC solar heat gain coefficient
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 21 of 76
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 22 of 76

ix
Contents
Executive Summary ..................................................................................................................................... iii
Acknowledgments ......................................................................................................................................... v
Acronyms and Abbreviations ..................................................................................................................... vii
1.0 Introduction ....................................................................................................................................... 1.1
1.1 Background ............................................................................................................................... 1.1
1.2 Project Team ............................................................................................................................. 1.1
1.3 Stakeholder Interests ................................................................................................................. 1.2
2.0 Methodology ...................................................................................................................................... 2.1
2.1 Overview ................................................................................................................................... 2.1
2.2 State Study ................................................................................................................................ 2.2
2.2.1 Sampling ........................................................................................................................ 2.2
2.2.2 Data Collection ............................................................................................................... 2.2
2.3 Data Analysis ............................................................................................................................ 2.3
2.3.1 Statistical Analysis ......................................................................................................... 2.3
2.3.2 Energy Analysis ............................................................................................................. 2.4
2.3.3 Savings Analysis ............................................................................................................ 2.5
2.4 Limitations ................................................................................................................................ 2.6
2.4.1 Applicability of Results .................................................................................................. 2.6
2.4.2 Determination of Compliance ........................................................................................ 2.6
2.4.3 Sampling Substitutions ................................................................................................... 2.6
2.4.4 Site Access ..................................................................................................................... 2.6
2.4.5 Analysis Methods ........................................................................................................... 2.7
2.4.6 Presence of Tradeoffs ..................................................................................................... 2.7
3.0 State Results ...................................................................................................................................... 3.1
3.1 Field Observations..................................................................................................................... 3.1
3.1.1 Key Items ....................................................................................................................... 3.1
3.1.2 Additional Data Items .................................................................................................. 3.18
3.2 Energy Intensity ...................................................................................................................... 3.20
3.3 Savings Potential ..................................................................................................................... 3.21
4.0 Conclusions ....................................................................................................................................... 4.1
5.0 References ......................................................................................................................................... 5.1
Appendix A – State Sampling Plan ........................................................................................................... A.1
Appendix B – Additional Data...................................................................................................................B.1

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 23 of 76

x
Figures
Figure 2.1. Sample Graph .......................................................................................................................... 2.4
Figure 3.1. Envelope Tightness (ACH50) ................................................................................................. 3.2
Figure 3.2. Window SHGC ........................................................................................................................ 3.3
Figure 3.3. Window U-Factor .................................................................................................................... 3.4
Figure 3.4. Wall R-Values ......................................................................................................................... 3.5
Figure 3.5. Wall Assembly Performance, including Wall Insulation Installation Quality ........................ 3.6
Figure 3.6. Ceiling R-Value ....................................................................................................................... 3.7
Figure 3.7. High-efficacy Lighting Percentage .......................................................................................... 3.8
Figure 3.8. Basement Wall Cavity R-Values ............................................................................................. 3.9
Figure 3.9. Basement Wall Assembly Performance, including Wall Insulation Installation Quality ...... 3.10
Figure 3.10. Floor R-Values .................................................................................................................... 3.11
Figure 3.11. Floor Assembly Performance, including Insulation Installation Quality ............................ 3.12
Figure 3.12.Unvented Crawlspace Wall Continuous Insulation R-Value ............................................... 3.14
Figure 3.13. Unvented Crawlspace Wall Assembly Performance, including Insulation Installation
Quality ............................................................................................................................................. 3.15
Figure 3.14. Raw Duct Tightness (CFM25/100ft2 CFA) ........................................................................ 3.16
Figure 3.15. Adjusted Duct Tightness (CFM25/100ft2 CFA) ................................................................. 3.17
Figure 3.16. Statewide EUI Analysis for Idaho ....................................................................................... 3.21

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 24 of 76

xi
Tables
Table 3.1. Envelope Tightness (ACH50) ................................................................................................... 3.2
Table 3.2. Window SHGC ......................................................................................................................... 3.3
Table 3.3. Window U-Factor ..................................................................................................................... 3.4
Table 3.4. Wall R-Value ............................................................................................................................ 3.5
Table 3.5. Wall U-Factor, including Wall Insulation Installation Quality ................................................. 3.6
Table 3.6. Ceiling R-Value ........................................................................................................................ 3.7
Table 3.7. High-efficacy Lighting Percentage ........................................................................................... 3.8
Table 3.8. Basement Wall Cavity R-Values ............................................................................................ 3.10
Table 3.9. Basement Walls U-Factor ....................................................................................................... 3.11
Table 3.10. Floor R-Value ....................................................................................................................... 3.12
Table 3.11. Floor U-Factor ...................................................................................................................... 3.13
Table 3.12. Unvented Crawlspace Wall Continuous R-Value ................................................................. 3.14
Table 3.13. Unvented Crawlspace Wall U-Factor ................................................................................... 3.15
Table 3.14. Raw Duct Tightness (CFM25/100ft2 CFA) .......................................................................... 3.16
Table 3.15. Adjusted Duct Tightness (CFM25/100ft2 CFA) .................................................................. 3.17
Table 3.16. Insulation Installation Quality ............................................................................................... 3.18
Table 3.17. Statewide Annual Measure-Level Savings for Idaho ........................................................... 3.22
Table 3.18. Statewide Annual Measure-Level Savings by Foundation Type for Idaho .......................... 3.22
Table 3.19. Five-years, Ten-years, and Thirty-years Cumulative Annual Statewide Savings for Idaho . 3.23
Table 4.1. Annual Statewide Savings Potential in Idaho ........................................................................... 4.1

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Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 26 of 76

1.1
1.0 Introduction
A research project in the state of Idaho investigated the energy code-related aspects of unoccupied, newly
constructed, single family homes across the state. The study followed a DOE-developed and tested
methodology, which allowed the project team to build an empirical collection of data based on
observations made directly in the field. The data was then analyzed to identify compliance trends, their
impact on statewide energy consumption, and calculate savings that could be achieved through increased
code compliance.
The Idaho field study was initiated in January 2018; data collection began in March 2018 and continued
through June 2018. During this period, research teams visited 127 homes across the state during various
stages of construction. At the time of the study, the state had the Idaho Energy Conservation Code
effective from January 1, 2015. The study methodology, data analysis and resulting findings are
presented throughout this report.
1.1 Background
This project was built upon the U.S. Department of Energy (DOE)’s field study, “Strategies to Increase
Residential Energy Code Compliance Rates and Measure Results”.1 The purpose of this study is to gather
field data on energy code measures, as installed and observed in actual homes and through the subsequent
analysis to identify trends and issues, which eventually can inform energy code training and other
compliance- improvement programs.
Energy codes for residential buildings have advanced significantly in recent years, with today’s model
codes approximately 30% more efficient than codes adopted by the majority of U.S. states. 2,3 Hence, the
importance of ensuring code-intended energy savings, so that consumers reap the benefits of improved
codes—something which will happen only through high levels of compliance. More information on
overall DOE interest in compliance is available on the DOE Building Energy Codes Program website.4
1.2 Project Team
David Freelove, the Idaho Energy Code Circuit Rider, led the Idaho project team and collected the field
data; Cadmus and the Idaho Association of Building Officials (IDABO) provided support to Mr. Freelove
throughout the project. The Pacific Northwest National Laboratory (PNNL) defined the methodology,
conducted data analysis, and provided technical assistance to the project team. Funding for the project
was provided by the Northwest Energy Efficiency Alliance (NEEA), with technical analysis provided by
PNNL funded by DOE. More information on the organizations comprising the project team is included in
the Acknowledgements section of this report.

1 Available at https://www.energycodes.gov/compliance/residential-energy-code-field-study
2 National Energy and Cost Savings for New Single- and Multifamily Homes: A Comparison of the 2006, 2009, and
2012 Editions of the IECC, available at http://www.energycodes.gov/development
3 Available at http://www.energycodes.gov/adoption/states
4 Available at https://www.energycodes.gov/compliance
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1.2
1.3 Stakeholder Interests
The project started with the formation of a stakeholder group comprised of interested and affected parties
within the state. Following an initial kickoff meeting, the project team maintained active communication
with the stakeholders throughout the course of the project. Stakeholders were sought from the following
groups:
x Building officials
x Homebuilders
x Energy efficiency advocates
x Utilities
x Other important entities identified by the project team
Members of these and other groups are critical to the success of the project, as their buy-in to the results is
necessary for future activities. Such stakeholders hold important information (e.g., building officials have
the lists of homes under construction and are therefore key to the sampling process), control access to
homes needed for site visits, and are targets for training. The Idaho team most frequently communicated
with building officials and homebuilders, including the members of local IDABO chapters; local building
associations; and state and local building officials. Utilities were also identified as a crucial stakeholder
and were updated regularly on the progress of the study.
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2.1
2.0 Methodology
2.1 Overview
The Idaho field study was based on a methodology developed by DOE to identify savings opportunities
associated with increased energy code compliance. This methodology involves gathering field data on
energy code measures, as installed and observed in actual homes. In the subsequent analysis, trends and
issues are identified, which can inform energy code training and other compliance- improvement
programs.
Highlights of the methodology:
x Focuses on individual code requirements within new single-family homes
x Based on a single site visit to reduce burden and minimize bias
x Prioritizes key items with the greatest impact on energy consumption
x Designed to produce statistically significant results
x Data confidentiality built into the experiment—no occupied homes were visited, and no personal
data shared
x Results based on an energy metric and reported at the state level
PNNL identified the code-requirements (and associated energy efficiency measures) with the greatest
direct impact on residential energy consumption. 1 These key items drive sampling, data analysis, and
eventual savings projections:
1. Envelope tightness (ACH at 50 Pascals)
2. Windows (U-factor & SHGC)
3. Wall insulation (assembly U-factor)
4. Ceiling insulation (R-value)
5. Lighting (% high-efficacy)
6. Foundation insulation (R-value)2
7. Duct tightness (expressed in cfm per 100 ft2 of conditioned floor area at 25 Pascals)
PNNL evaluated the variability associated with each key item and concluded that a minimum of 63
observations would be needed for each one to produce statistically significant results at the state level.
Both the key items themselves and the required number of observations were prescribed in the DOE
methodology.
The following sections describe how the methodology was implemented as part of the Idaho study,
including sampling, data collection, and resulting data analysis. More information on the full DOE

1 Based on the mandatory and prescriptive requirements of the International Energy Conservation Code (IECC).
2 Floor insulation, basement wall insulation, crawlspace wall insulation, and slab insulation were combined into a
single category of foundation insulation.
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2.2
protocol and PNNL analysis is published separately from this report (DOE 2018) and is available on the
DOE Building Energy Codes Program website.3
2.2 State Study
The prescribed methodology was customized for Idaho to reflect circumstances unique to the state, such
as state-level code requirements and regional construction practices. Customization also ensured that the
results of the study would have credibility with stakeholders.
2.2.1 Sampling
PNNL developed a statewide sampling plan statistically representative of recent construction activity
within the state. The samples were apportioned to jurisdictions across the state in proportion to their
average level of construction compared to the overall construction activity statewide. This approach is a
proportional random sample, which PNNL based on the average of the three most recent years of Census
Bureau permit data.4 The plan specified the number of key item observations required in each selected
jurisdiction (totaling 63 of each key item across the entire project coverage area).
2.2.2 Data Collection
Following confirmation of the sample plan, the project team began contacting local building departments
to identify homes currently in the permitting process. Code officials responded by providing lists of
homes at various stages of construction within their jurisdiction. These lists were then sorted using a
random number generator and utilized by the team’s field personnel to contact builders to gain site access.
As prescribed by the methodology, each home was visited only once to avoid any bias associated with
multiple site visits. Only installed items directly observed by the field teams during site visits were
recorded. If access was denied for a particular home on the list, field personnel moved onto the next
home on the list.
2.2.2.1 Data Collection Form
The field teams relied on a data collection form customized to the mandatory and prescriptive
requirements of the state energy code, the Idaho Energy Conservation Code.5 The final data collection
form is available in spreadsheet format on the DOE Building Energy Codes Program website.6 The form
included all energy code requirements (i.e., not just the eight key items), as well as additional items
required under the prescribed methodology. For example, the field teams were required to conduct a
blower door test and duct leakage test on every home where such tests could be conducted, using
RESNET7 protocols.
The information beyond the key items was used during various phases of the analysis, or to supplement
the overall study findings. For example, insulation installation quality impacts the energy-efficiency of
insulation was used to modify that key item during the energy modeling and savings calculation.

3 Available at https://www.energycodes.gov/compliance/residential-energy-code-field-study.
4 Available at http://censtats.census.gov/ (select the “Building Permits” data).
5 Available at https://dbs.idaho.gov/rules/2018/2018_Building_Statutes_Rules_pkt.pdf.
6 Available at https://www.energycodes.gov/compliance/residential-energy-code-field-study and based on the forms
typically used by the REScheck compliance software.
7 See http://www.resnet.us/standards/RESNET_Mortgage_Industry_National_HERS_Standards.pdf
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2.3
Equipment, including fuel type and efficiency rating, and basic home characteristics (e.g., foundation
type) helped validate the prototype models applied during energy simulation. Other questions, such as
whether the home participated in an above-code program, can assist in understanding whether other
influencing factors are at play beyond the code requirements.
The data collected were the energy values observed, rather than the compliance status. For insulation, for
example, the R-value was collected, for windows the U-factor. The alternative, such as was used in
DOE’s older work, simply stated whether an item did or did not comply. The current approach provides
an improved understanding of how compliance equates to energy consumption and gives more flexibility
during analysis since the field data can be compared to any energy code.
2.2.2.2 Data Management and Availability
Once the data collection effort was complete, the project team conducted a thorough quality assurance
review. This included an independent check of raw data compared to the information provided to PNNL
for analysis, and helped to ensure completeness, accuracy and consistency across the inputs. Prior to
submitting the data to PNNL, the team also removed all personally identifiable information, such as
project site locations and contact information. The final dataset is available in spreadsheet format on the
DOE Building Energy Codes Program website8.
2.3 Data Analysis
All data analysis in the study was performed by PNNL, and was applied through three basic stages:
1. Statistical Analysis: Examination of the data set and distribution of observations for individual
measures
2. Energy Analysis: Modeling of energy consumption for a simulated population of homes
3. Savings Analysis: Projection of savings associated with improved compliance
The first stage identified compliance trends within the state based on what was observed in the field for
each key item. The second stage modeled energy consumption (of the homes observed in the field)
relative to what would be expected if sampled homes just met minimum code requirements. The third
stage then calculated the potential energy savings and consumer cost savings associated with increased
code compliance. Together, these findings provide valuable insight on challenges facing energy code
implementation and enforcement, and are intended to inform future energy code education, training and
outreach activities.
The following sections provide an overview of the analysis methods applied to the field study data, with
the resulting state-level findings presented in Section 3.0, State Results.
2.3.1 Statistical Analysis
Standard statistical analysis was performed with distributions of each key item plotted by climate zone.
This approach enables a better understanding of the range of data and provides insight on what energy-
efficiency measures are most commonly installed in the field. It also allows for a comparison of installed
values to the applicable code requirement, and for identification of any problem areas where potential for

8 Available at https://www.energycodes.gov/compliance/residential-energy-code-field-study.
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2.4
improvement exists. The graph below represents a sample key item distribution and is further explained
in the following paragraph.

Figure 2.1. Sample Graph
Each graph is set up in a similar fashion, identifying the state, climate zone, and specific item being
analyzed. The total sample size (n) is displayed in the top left or right corner of the graph, along with the
distribution average. The metric associated with the item is measured along the horizontal axis (e.g.,
window U-factor is measured in Btu/ft2-hr-F), and a count of the number of observations is measured
along the vertical axis. A vertical line is imposed on the graph representing the applicable code
requirement (e.g., the prescriptive requirement in climate zone 4 is 0.35)—values to the right-hand side of
this line are better than code. Values to the left-hand side represent areas for improvement.
For walls and foundations, two graphs are included – one for R-value observations and another for U-
factor observations. The R-value graphs show whether or not homes are being constructed with the
required amount of insulation for the climate zone. The U-factor graphs indicate whether or not the
combination of installed R-value and insulation installation quality meets the U-factor requirements in the
climate zone. The combination of these two graphs can be used to determine if there is an issue with the
amount of insulation, insulation installation quality, or both.
2.3.2 Energy Analysis
The next phase of the analysis leveraged the statistical analysis results to model average statewide energy
consumption. A consequence of the field study methodology allowing only one site visit per home to
count of field
observations at
a particular
observed values of
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2.5
minimize bias is that a full set of data cannot be gathered on any single home, as not all energy-efficiency
measures are in place or visible at any given point during the home construction process. This lack of
complete data for individual homes creates an analytical challenge, because energy modeling and
simulation protocols require a complete set of inputs to generate reliable results. To address this
challenge, a series of “pseudo homes” were created, comprised of over 1,500 models encompassing most
of the possible combinations of key item values found in the observed field data. In aggregate, the
models provide a statistical representation of the state’s population of newly constructed homes. This
approach is known in statistics as a Monte Carlo analysis.
Energy simulation was then conducted using the EnergyPlus™ software.9 Each of the 1,500 models was
run multiple times, to represent each combination of heating systems and foundation types commonly
found in the state. This resulted in upwards of 30,000 simulation runs for each climate zone within the
state. An EUI was calculated for each simulation run and these results were then weighted by the
frequency with which the heating system/foundation type combinations were observed in the field data.
Average EUI was calculated based on regulated end uses (heating, cooling, lighting and domestic hot
water) for two sets of homes—one as-built set based on the data collected in the field, and a second code-
minimum set (i.e., exactly meeting minimum code requirements). Comparing these values shows whether
the population of newly constructed homes in the state is using more or less energy than would be
expected based on minimum code requirements.
Further specifics of the energy analysis are available in the methodology report (DOE 2018).
2.3.3 Savings Analysis
To begin the third phase, each of the key items was examined individually to determine which had a
significant number of observed values that did not meet the associated code requirement10. For these
items, additional models were created to assess the savings potential, comparing what was observed in the
field to a scenario of full compliance (i.e., where all worse-than-code observations for a particular item
exactly met the corresponding code requirement)11. This was done by individually upgrading each worse-
than-code observation to the corresponding prescriptive code requirement, resulting in a second set of
models (full compliance) that could be compared to the first (as-built). All other components were
maintained at the corresponding prescriptive code value, allowing for the savings potential associated
with a key item to be evaluated in isolation.
All variations of observed heating systems and foundation types were included, and annual electric, gas
and total EUIs were extracted for each building. To calculate savings, the differences in energy use
calculated for each case were weighted by the corresponding frequency of each observation to arrive at an
average energy savings potential for each climate zone. Potential energy savings for each climate zone
were further weighted using construction starts in that zone to obtain the average statewide energy
savings potential. State-specific construction volumes and fuel prices were used to calculate the
maximum energy savings potential for the state in terms of energy (MMBtu) and energy cost ($).
Note that this approach results in the maximum theoretical savings potential for each measure as it does
not take “interaction effects” into account such as the increased amount of heating needed in the winter

9 See https://energyplus.net/
10 “Significant” was defined as 15% or more of the observed values not meeting the associated code requirement.
Only the items above this threshold were analyzed.
11 Better-than-code items were not included in this analysis because the intent was to identify the maximum savings
potential for each measure. The preceding energy analysis included both better-than-code and worse-than-code
results, allowing them to offset each other.
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2.6
when energy efficient lights are installed. A building’s energy consumption is a dynamic and interactive
process that includes all the building components present within a given home. In a typical real building,
the savings potential might be higher or lower; however, additional investigation indicated that the
relative impact of such interactions is very small, and could safely be ignored without changing the basic
conclusions of the analysis.
2.4 Limitations
The following sections address limitations of the project, some of which are inherent to the methodology,
itself, and other issues as identified in the field.
2.4.1 Applicability of Results
An inherent limitation of the study design is that the results (key item distributions, EUI, and measure-
level savings) can be considered statistically significant only at the state level. Other results, such as
analysis based on climate zone level, or reporting of non-key items (such as gas furnace efficiency), are
included but should not be considered statistically representative.
2.4.2 Determination of Compliance
The field study protocol is based upon a single site visit, which makes it impossible to know whether a
particular home complies with the energy code as not enough information can be gathered in a single visit
to know whether all code requirements have been met. For example, homes observed during the earlier
stages of construction often lack key features (e.g., walls with insulation), and in the later stages many of
these items may be covered and therefore unobservable. To gather all the data required in the sampling
plan, field teams therefore needed to visit homes in various stages of construction. The analytical
implications of this are described above in Section 2.3.2
2.4.3 Sampling Substitutions
As is often the case with field-based research, substitutions to the state sampling plan were sometimes
needed to fulfill the complete data set. If the required number of observations in a jurisdiction could not
be met because of a lack of access to homes or an insufficient number of homes (as can be the case in
rural areas), substitute jurisdictions were selected by the project team. In all cases, the alternative
selection was comparable to the original in terms of characteristics such as the level of construction
activity and general demographics. More information on the sampling plan and any state-specific
substitutions are discussed in Appendix B.
2.4.4 Site Access
Site access was purely voluntary and data was collected only in homes where access was granted, which
can be characterized as a self-selection bias. While every effort was made to limit this bias (i.e., sampling
randomization, outreach to builders, reducing the burden of site visits, etc.), it is inherent due to the
voluntary nature of the study. The impacts of this bias on the overall results are not known.
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2.7
2.4.5 Analysis Methods
All energy analysis was conducted using prototype models; no individually visited homes were modeled,
as the self-imposed, one-visit-per-home limitation meant that not all necessary modeling inputs could be
collected from a single home. Thus, the impact of certain field-observable factors such as size, height,
orientation, window area, floor-to-ceiling height, equipment sizing, and equipment efficiency were not
included in the analysis. In addition, duct leakage was modeled separately from the other key items due
to limitations in the EnergyPlusTM software used for analysis. It should also be noted that the resulting
energy consumption and savings projections are based on modeled data, and not on utility bills or actual
home energy usage.
2.4.6 Presence of Tradeoffs
Field teams were able to gather only a minimal amount of data regarding which code compliance paths
were being pursued for homes included in the study; all analyses therefore assumed that the prescriptive
path was used. The project team agreed that this was a reasonable approach. The overall data set was
reviewed in an attempt to determine if common tradeoffs were present, but the ability to do this was
severely limited by the single site-visit principle which did not yield complete data sets for a given home.
To the extent it could be determined, it did not appear that there was a systematic presence of tradeoffs.
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3.1
3.0 State Results
3.1 Field Observations
The key items form the basis of the study and are therefore the focus of this section. Idaho is comprised
of multiple climate zones; zone 5 (CZ 5) and zone 6 (CZ 6). Both climate zones are represented in the
sampling, data collection, and resulting analysis and statewide savings calculations. A discussion of other
findings is also covered in this section, including a description of how certain observations, such as
insulation installation quality, are used to modify key items. (See Section 2.3.1 for a sample graph and
explanation of how they should be interpreted.)
3.1.1 Key Items
The field study and underlying methodology are driven by key items that have a significant direct impact
on residential energy efficiency. The graphs presented in this section represent the key item results for
the state based on the measures observed in the field. Note that these key items are also the basis of the
results presented in the subsequent energy and savings phases of analysis.
The following key items were found applicable within the state:
1. Envelope tightness (ACH at 50 Pascals)
2. Window SHGC
3. Window U-factor
4. Exterior wall insulation (assembly U-factor)
5. Ceiling insulation (R-value)
6. Lighting (% high-efficacy)
7. Foundations – basement walls and floors (assembly U-factor)
8. Duct tightness (expressed in cfm per 100 ft2 of conditioned floor area at 25 Pascals)
The three main foundation types observed in Idaho were floors over vented crawlspaces (82
observations), heated basements (22 observations), and unvented crawlspaces. In addition there were four
slab observations, but due to that small number, a graphic is not provided for slabs. Note that these
counts are for the number of homes observed to have these foundation types. The graphs below report the
observed foundation insulation values and the number of observations will be less than or equal to the
number of homes that have a particular foundation type.
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3.2
3.1.1.1 Envelope Tightness

Figure 3.1. Envelope Tightness (ACH50)
Table 3.1. Envelope Tightness (ACH50)
Number 53 10 63
Range 1.4 to 6.4 2.3 to 3.8 1.4 to 6.4
Average 4.2 2.9 4.0
Requirement 7 7 7
Compliance Rate 53 of 53 (100%) 10 of 10 (100%) 63 of 63 (100%)
x Interpretations:
– Overall, the distribution exhibits significantly lower air leakage than expected based on the
current code requirement.
– All the observations met or exceeded the prescriptive code requirement.
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3.3
3.1.1.2 Window SHGC

Figure 3.2. Window SHGC
Table 3.2. Window SHGC
Number 63 14 77
Range 0.20 to 0.41 0.27 to 0.33 0.20 to 0.41
Average 0.30 0.30 0.306
Requirement NA NA NA
Compliance Rate NA NA NA
x Interpretations:
– SHGC values were very consistent, and nearly meet the prescriptive requirement for Climate
Zones 1-3, even though there are no SHGC requirements in Climate Zones 5 and 6.
– The vast majority of the observations were in the 0.26 to 0.34 SHGC range.
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3.4
3.1.1.3 Window U-Factor

Figure 3.3. Window U-Factor
Table 3.3. Window U-Factor
Number 63 14 77
Range 0.24 to 0.35 0.23 to 0.34 0.23 to 0.35
Average 0.31 0.29 0.31
Requirement 0.35 0.35 0.35
Compliance Rate 63 of 63 (100%) 14 of 14 (100%) 77 of 77 (100%)
x Interpretations:
– There is an extremely high rate of compliance for fenestration products.
– This represents one of the most significant findings of the field study, with all of the observations
at or above the code requirement.
– Window U-factor requirements appear to have been implemented with a high rate of success.
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3.5
3.1.1.4 Wall Insulation
Two graphs are shown for each climate zone for walls, cavity and continuous insulation (R-value) and
binned wall assembly (U-factor). The R-value graphs show both the cavity and continuous insulation R-
values observed, sorted in order of increasing cavity insulation R-value. The binned U-factor graphs
indicate the U-factor of the wall assembly, including both cavity and continuous insulation layers,
framing, and considering insulation installation quality, as observed in the field. The U-factors are binned
to reduce the number of bars in the chart as individual U-factor observations may be only slightly
different.

Figure 3.4. Wall R-Values
Table 3.4. Wall R-Value
Number 53 11 64
Range R-19 to R-22 R-21 to R-22 R-19 to R-22
Average R-20.6 R-21.6 R-20.8
Requirement R-21 R-21 R-21
Compliance Rate 42 of 53 (21%) 11 of 11 (100%) 53 of 64 (83%)
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3.6

Figure 3.5. Wall Assembly Performance, including Wall Insulation Installation Quality
Table 3.5. Wall U-Factor, including Wall Insulation Installation Quality
Number 53 11 64
Range 0.067 to 0.050 0.064 to 0.050 0.067 to 0.050
Average 0.062 0.054 0.061
Assembly U-Factor
(expected) 0.057 0.057 0.057
Rate
x Interpretations:
– Looking at the R-values, most of the observations in CZ5 met or exceeded the prescriptive code
requirement, and all in CZ6 did, indicating that the only issue with the amount of insulation are
the homes with R-19 insulation in CZ5.
– In more than half of the above-grade wall observations, the insulation installation quality was
rated as Grade II, indicating an issue that should be addressed.
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3.7
3.1.1.5 Ceilings

Figure 3.6. Ceiling R-Value
Table 3.6. Ceiling R-Value
Number 53 10 63
Range R-38 to R-49 R-49 to R-50 R-38 to R-50
Average R-40.2 R-49.8 R-41.7
Requirement R-38 R-49 Varies
Compliance Rate 53 of 53 (100%) 10 of 10 (100%) 63 of 63 (100%)
x Interpretations:
– The vast majority of observations met the code requirement exactly.
– All of the roof cavity insulation installation quality observations were Grade I, indicating that
roofs are well insulated in Idaho.
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3.8

Figure 3.7. High-efficacy Lighting Percentage
Table 3.7. High-efficacy Lighting Percentage
Number 53 10 63
Range 4 to 100 70 to 96 4 to 100
Average 89 83 88
Requirement 50 50 50
Compliance Rate 52 of 53 (98%) 10 of 10 (100%) 62 of 63 (98%)
x Interpretations:
– Nearly all of the field observations met the requirement.
3.1.1.6 Foundation Assemblies
There were three predominant foundation types observed in Idaho, heated basements, floors over vented
crawlspaces and unvented crawlspaces. Two graphs are shown for each climate zone for foundations,
insulation (R-value) and binned assembly (U-factor). The R-value graphs show the insulation R-values
observed. The binned U-factor graphs indicate the U-factor of the assembly, including both cavity and
continuous insulation layers, framing, and considering insulation installation quality, as observed in the
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3.9
field. The U-factors are binned to reduce the number of bars in the chart as individual U-factor
observations may be only slightly different.
While initially combined into a single key item (i.e., foundation assemblies), the variety of observed
foundation types are disaggregated in this section, as described above. This approach helps to portray the
combinations of cavity and continuous insulation employed across each foundation type and climate zone,
which is anticipated to be of value for energy code training programs. From a savings perspective, results
are calculated for both the aggregated perspective and for individual foundation types (presented later in
Section 3.3), however; only the aggregated observations should be considered statistically representative
at the statewide level.
Basement Wall Insulation (Conditioned Basements)
For basement wall R-values, the R-value plot shows only the cavity insulation observations. There are an
additional 5 homes that have only continuous insulation and these homes are not shown on Figure 3.8.
These 5 homes are shown in the U-factor plot (Figure 3.9).

Figure 3.8. Basement Wall Cavity R-Values

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3.10
Table 3.8. Basement Wall Cavity R-Values
Number 2 9 11
Range R-11 to R-13 R-13 to R-19 R-11 to R-19
Average R-12 R-15 R-14.5
Assembly U-Factor
(expected) R-13 R-19
Rate

Figure 3.9. Basement Wall Assembly Performance, including Wall Insulation Installation Quality

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3.11
Table 3.9. Basement Walls U-Factor
Number 4 12 16
Range 0.069 to 0.031 0.081 to 0.038 0.081 to 0.031
Average 0.049 0.065 0.061
Assembly U-Factor
(expected) 0.059 0.051
Rate
x Interpretations:
– Comparison of the U-factor and R-value graphs for CZ5 indicates that insulation installation
quality may be an issue for basement walls with cavity insulation in CZ5. However, the two
homes with basement walls with continuous insulation did meet code. The sample size for CZ5
is very small.
– Comparison of the U-factor and R-value charts for CZ6 indicates that the main reason for the
poor performance on the U-factor chart is the amount of insulation. In CZ6, the presence of three
homes with continuous basement insulation again provided all of the homes that meet the code
requirement. This implies that for the two homes that meet the cavity insulation R-value, the
insulation installation quality for those homes raised their U-factor.
Insulation in Floors over Unconditioned Spaces

Figure 3.10. Floor R-Values
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3.12
Table 3.10. Floor R-Value
Number 58 0 58
Range 30 to 38 NA 30 to 38
Average 30.8 NA 30.8
Assembly U-Factor
(expected) 30 30 30
Rate

Figure 3.11. Floor Assembly Performance, including Insulation Installation Quality

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3.13
Table 3.11. Floor U-Factor
Number 58 0 58
Range 0.040 to 0.028 NA 0.040 to 0.028
Average 0.035 NA 0.035
Assembly U-Factor
(expected) 0.033 0.033 0.033
Rate
x Interpretations:
– Comparison of the U-factor and R-value charts for CZ5 indicates that insulation installation
quality is an issue for floors in CZ5. The R-values all meet or exceed the code requirement, but
only half of the U-factors meet or exceed the code requirement.
Insulation in Walls of Unvented Crawlspaces
For this assembly, the majority of observations involved continuous insulation, so the R-value plot shown
is for vented crawlspace wall continuous R-value. There are an additional three observations of
crawlspace walls with cavity insulation, all of which meet prescriptive R-value requirements. These
observations are included in the vented crawlspace wall U-factor plot below.

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 49 of 76

3.14

Figure 3.12.Unvented Crawlspace Wall Continuous Insulation R-Value
Table 3.12. Unvented Crawlspace Wall Continuous R-Value
Climate Zone CZ5 CZ6 Statewide
Number
Range
Average
Assembly U-Factor
(expected) R-10 R-10 R-10
Rate
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3.15

Figure 3.13. Unvented Crawlspace Wall Assembly Performance, including Insulation Installation Quality
Table 3.13. Unvented Crawlspace Wall U-Factor
Number 17 1 18
Range 0.065 to 0.032 0.065 0.065 to 0.032
Average 0.040 0.065 0..041
Assembly U-Factor
(expected) 0.068 0.068 0.068
Rate

x Interpretations:
– All but one unvented crawlspace wall observation met the code requirement.
3.1.1.7 Duct Tightness
For ducts, this report presents both raw duct leakage and adjusted duct leakage. Raw duct leakage is
simply the values of duct leakage observed in the field. Adjusted duct leakage looks at the location of the
ducts and adjusts the leakage values for any ducts which are entirely in conditioned space by setting the
leakage of those ducts to zero (0). The adjustment reflects the fact that duct leakage tests are not required
if the ducts are entirely in conditioned space.
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 51 of 76

3.16

Figure 3.14. Raw Duct Tightness (CFM25/100ft2 CFA)
Table 3.14. Raw Duct Tightness (CFM25/100ft2 CFA)
Number 53 11 64
Range 3.2 to 64.0 6.9 to 63.6 3.24 to 64.0
Average 8.9 35.9 13.6
Requirement 4 4 4
Compliance Rate 3 of 53 (6%) 0 of 11 (0%) 3 of 64 (5%)
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3.17

Figure 3.15. Adjusted Duct Tightness (CFM25/100ft2 CFA)
Table 3.15. Adjusted Duct Tightness (CFM25/100ft2 CFA)
Number 53 11 64
Range 0.0 to 64.0 0.0 to 63.61 3.24 to 64.0
Average 7.6 1.72 6.6
Requirement 4 4 4
Compliance Rate 17 of 53 (32%) 10 of 11 (91%) 26 of 64 (37%)
x Interpretations:
– The average total duct leakage is 8.5 CFM 25/100 ft2 for the 50 systems with ducts in
unconditioned space, and 31.8 CFM 25/100 ft2 for the 14 systems located entirely in conditioned
space.
– The majority of raw observations do not meet the Idaho code requirement for duct leakage.
– The majority of adjusted observations do not meet the Idaho code requirement for duct leakage.
However, nearly all adjusted duct leakage values in CZ6 meet the requirement, indicating that
many homes in CZ6 are installing ducts entirely in conditioned space.
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 53 of 76

3.18
– Reductions in duct leakage represent a significant area for improvement and should be given
increased attention in future training and enforcement.
3.1.1.8 Impact of Insulation Installation Quality
While insulation installation quality is not an explicit energy code requirement, at the start of DOE’s FOA
projects, it was noted as a particular concern among project teams and stakeholders, as it plays an
important role in the energy performance of envelope assemblies. Insulation installation quality was
therefore collected by the project team whenever possible and applied as a modifier in the analyses for
applicable key items (i.e., ceiling insulation, wall insulation, and foundation insulation). The team
followed the RESNET1 assessment protocol which has three grades, Grade I being the best quality
installation and Grade III being the worst.
Table 3.16 shows the insulation installation quality levels for framed envelope assemblies, as observed in
the state. A slight majority of the observations (1247 of 243) were classified as Grade I, with remainder
Grade II, indicating that there is some improvement needed in insulation installation quality. Roof
insulation installation quality was all Grade I, but other assemblies show the majority of observations to
be Grade II.
Table 3.16. Insulation Installation Quality
Grade I Grade II Grade III
63 0 0
27 31 0
25 39 0
2 9 0
0 3 0
7 35 2
3.1.2 Additional Data Items
The project team collected data on all code requirements within the state as well as other areas to inform
the energy simulation and analysis for the project (e.g., home size, installed equipment systems, etc.).
While these items were not the focal point of the study, and many are not considered statistically
representative, they do provide some insight surrounding the energy code and residential construction
within the state.
The following represents a summary of this data and outlines some of the more significant findings, in
many cases including the observation or compliance rate associated with the specified item. A larger
selection of the additional data items collected as part of the state field study is contained in Appendix B.
3.1.2.1 Average Home
x Size: 2,486 ft2 and 1.35 stories

1 See http://www.resnet.us/standards/RESNET_Mortgage_Industry_National_HERS_Standards.pdf
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3.19
3.1.2.2 Compliance
x Almost all homes (98%) were permitted under the Idaho Energy Conservation Code. Two percent
selected the 2015 edition of the International Energy Conservation Code to meet the local jurisdiction
requirements. (n=127)
x Nine homes were noted as participating in an above-code program.
3.1.2.3 Envelope
x Profile:
– Walls: All were wood-framed walls with 2x6” studs
– Foundations: Mix of vented crawlspaces (65%), basements (17%), unvented crawlspaces (15%),
and slab on grade (3%).
x Successes (percentage of observations that complied):
– Insulation labeled (100%)
– IC-rated light fixtures sealed (100%)
– Utility penetrations sealed (98%)
x Areas for Improvement:
– Attic access openings complied (54%)
– Knee walls sealed (55%)
– Envelope areas behind bathroom tubs & showers sealed (39%)
– Rim joists sealed (27%)
– Dropped ceilings sealed (49%)
3.1.2.4 Duct & Piping Systems
x Profile:
– Ducts were generally located within conditioned space (percentage of duct system):
○ Supply: 41% (35 homes with 37 duct systems entirely within conditioned space)
○ Return: 31% (25 homes with 27 duct systems entirely within conditioned space)
– About 28% of duct systems located supply ducts entirely within conditioned space
– About 21% of duct systems located return ducts entirely within conditioned space
– About 19% of duct systems had the entire system within conditioned space.
– Pipe Insulation (R-value): 2.7
x Successes:
– Air handlers sealed (88%)

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3.20
3.1.2.5 HVAC Equipment
x Profile:
– Heating: Mostly gas furnaces with an average efficiency of 90 AFUE.
– Cooling: Mostly central AC with an average efficiency of 13.1 SEER
– Water Heating: Mix of gas (92%) and electric (8%) storage (98%) with an average capacity of 50
gallons and average efficiency rating of EF 0.65.
x Successes:
– User manuals for mechanical systems provided (100%)
3.2 Energy Intensity
The statewide energy analysis results are shown in the figure below, which compares the weighted
average energy consumption of the observed data set to the weighted average consumption based on the
state energy code. The observed data set (as gathered in the field) was compared against the same set of
homes meeting prescriptive code requirements. In terms of overall energy consumption, the average
home in Idaho appears to use less energy than would be expected relative to a home built to the current
minimum state code requirements.
Analysis of the collected field data indicates an average regulated EUI (dashed line in Figure 3.16) of
approximately 34.62 kBtu/ft2-yr compared to 40.51 kBtu/ft2-yr for homes exactly meeting minimum
prescriptive energy code requirements (black line in Figure 3.16). This suggests the EUI for a “typical”
home in the state is about 15% better than code.
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3.21

Figure 3.16. Statewide EUI Analysis for Idaho
3.3 Savings Potential
Those key items with the greatest potential,1 shown below followed by the percent that did not meet code,
were analyzed further to calculate the associated savings potential, including energy and cost savings.
x Duct Leakage (63% of adjusted observations),
x Exterior Wall Insulation (61%),
x Foundations
– Basement Wall Insulation (69%), and
– Floor Insulation (50%).
For analytical details refer to Section 2.3.3 (Savings Analysis) or the DOE methodology document
(2018).
Estimated savings resulting from the analysis are shown below in order of highest to lowest total energy
and cost savings (Table 3.17). As can be seen, there are significant savings opportunities, with the
greatest total savings potential associated with these measures. In addition,

1 Defined here as those with more than 15% of observations not meeting the prescriptive code requirement. For
insulated assemblies, the U-factor observations are used.
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3.22
Table 3.19 shows the total savings reductions that will accumulate over 5, 10, and 30 years of
construction.
Table 3.17. Statewide Annual Measure-Level Savings for Idaho
5B 107 21 2,430 8,760 21,281 237,040
6B 107 26 2,959 2,259 6,684 70,161
5B 42 13 1,477 8,760 12,939 128,174
6B 41 17 1,837 2,259 4,149 39,008
5B -13 3 240 NA 814 2,474
6B -30 12 1,112 NA 570 2,962

* Negative values mean that savings or reductions decrease if the measure is brought up to code.
**See Table 3.18 for annual measure-level savings results by foundation type.
Table 3.18. Statewide Annual Measure-Level Savings by Foundation Type for Idaho
Measure
Climate
Zone
Electricity
Savings
(kWh/
home)
Natural
Gas
Savings
(therms/
home)
Total
Savings
(kBtu/
home)
Number
of homes
Total
Energy
Savings
(MMBtu)
Total
Energy
Cost
Savings ($)
Heated
Basement
Wall* State Total -9 3 291 1,837 534 2,703
Floor*
5B -7 1 121 5,293 641 1,815
6B -6 2 153 1,365 209 918
*For basement wall insulation and floor over unvented insulation, note that while total energy savings are positive, electricity
savings are negative. This is the result of increased insulation leading to lower natural gas usage in the winter, but higher
electricity usage in the summer.
** For foundation measures, the total number of homes is multiplied by the foundation share for each foundation type and is
therefore smaller than the total number of homes shown for other measures.

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 58 of 76

3.23
Table 3.19. Five-years, Ten-years, and Thirty-years Cumulative Annual Statewide Savings for Idaho
Measure Total Energy Savings (MMBtu) Total Energy Cost Savings ($)
419,483
Exterior
Wall
Insulation
256,313
Foundation
Insulation 20,748 81,541
TOTAL 696,544 2,553,996 21,592,877 $7,197,290 $26,390,063 $223,115,988

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 59 of 76
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 60 of 76

4.1
4.0 Conclusions
The Idaho field study provides an enhanced understanding of statewide code implementation and suggests
that potential savings are available through increased compliance. From a statewide perspective, the
average home in Idaho uses about 15% less energy than a home exactly meeting the state energy code.
However, savings potential remains through increased compliance with targeted measures. Potential
statewide annual energy savings are 46,436 MMBtu, which equates to $479,819 in cost savings. Over a
30-year period, these impacts grow to 21.6 million MMBtu and $223 million.
Several key measures directly contribute to these savings, and should be targeted through future
education, training and outreach activities. The savings associated with each are shown in Table 4.1
below.
Table 4.1. Annual Statewide Savings Potential in Idaho
(MMBtu) ($)
Duct Leakage 27,966 307,201
Exterior Wall Insulation 17,088 167,182
Foundation Insulation 1,383 5,436
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 61 of 76
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 62 of 76

5.1
5.0 References
Census Bureau. 2017. Censtats Building Permits Database. http://censtats.census.gov/
DOE. 2012. National Energy and Cost Savings for New Single- and Multifamily Homes: A Comparison
of the 2006, 2009, and 2012 Editions of the IECC, available at http://www.energycodes.gov/development
DOE Building Energy Codes Program’s residential field study website is available at
https://www.energycodes.gov/compliance/residential-energy-code-field-study (accessed August 8, 2016).
DOE/PNNL 2018. “Residential Building Energy Code Field Study: Data Collection & Analysis
Methodology” Available at https://www.energy.gov/sites/prod/files/2018/06/f52/bto-Res-Field-Study-
Methodology-060618-2.pdf
EnergyPlus. https://energyplus.net/.
Residential Energy Services Network. 2013. “Mortgage Industry National Home Energy Rating System
Standards.” Residential Energy Services Network, Oceanside, CA. www.resnet.us.

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 63 of 76
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 64 of 76

Appendix A
–
State Sampling Plan

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 65 of 76
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 66 of 76

A.1
Appendix A

State Sampling Plan
A.1 State Sampling Plan
Table A.1. State Sampling Plan
Kootenai County Unincorporated Area, Kootenai 4 4
Coeur d’Alene, Kootenai 3 3
Post Falls, Kootenai 4 4
Hayden, Kootenai 2 2
Rathdrum, Kootenai 1 1
Latah County Unincorporated Area, Latah 1 1
Ada County Unincorporated Area, Ada 5 5
Meridian, Ada 7 7
Eagle, Ada 5 5
Boise, Ada 3 3
Kuna, Ada 2 2
Star, Ada 1 1
Canyon County Unincorporated Area, Canyon 2 2
Nampa, Canyon 4 4
Caldwell, Canyon 2 2
Middleton, Canyon 1 1
Mountain Home, Elmore 1 1
Valley County Unincorporated Area, Valley 1 1
Bonneville County Unincorporated Area, Bonneville 1 1
Idaho Falls, Bonneville 2 2
Ammon, Bonneville 1 1
Jefferson County Unincorporated Area, Jefferson 1 1
Pocatello, Bannock 1 1
Chubbuck, Bannock 2 2
Rexburg, Madison 4 4
Fremont County Unincorporated Area, Fremont 1 1
Twin Falls, Twin Falls 1 1

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 67 of 76

A.2
A.2 Substitutions
No substitutions to the state sampling plan were required.
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 68 of 76

Appendix B
–
Additional Data

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 69 of 76
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 70 of 76

B.1
Appendix B

Additional Data
B.1 Additional Data Collected by Field Teams
The project team made observations on several energy efficiency measures beyond the key items alone.
The majority of these additional items are based on code requirements within the state, while others were
collected to inform the energy simulation and analysis for the project (e.g., installed equipment, whether
the home participated in an above-code program, etc.). While these items were not the focal point of the
study, and many are not considered statistically representative, they do provide some additional insight
surrounding the energy code and residential construction within the state.
The following is a sampling of the additional data items collected as part of the Idaho field study. Each
item is presented, along with a brief description and statistical summary based on the associated field
observations. The full data set is available on the DOE Building Energy Codes Program website.1
B.1.1 General
The following represents the general characteristics of the homes observed in the study:
B.1.1.1 Average Home
x Size (n=127): 2486 ft2
x Number of Stories (n=127): 1.35
Table B.1. Conditioned Floor Area (ft2)
< 1000 1000 to 1999 2000 to 2999 3000 to 3999 4000+
1% 35% 42% 17% 5%
Table B.2. Number of Stories
1 2 3 4+
65% 35% 0% 0%
B.1.1.2 Wall Profile
x Framing Type (n=127):
– All were framed construction (100%)
x Framing Material (n=127):
– Wood (100%)

1 Available at https://www.energycodes.gov/compliance/residential-energy-code-field-study
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B.2
– Steel (0%)
x Framing Depth (n=127):
– 6” (100%)
B.1.1.3 Foundation Profile
x Foundation Type (n=127):
– Heated Basement (17%)
– Slab on Grade (3%)
– Unvented Crawlspace (15%)
– Vented Crawlspace (65%)
B.1.2 Compliance
The following summarizes information related to compliance, including the energy code associated with
individual homes, whether the home was participating in an above-code program, and which particular
programs were reported. The percentages provided in the sections below represent percentages of total
observations or the percentage of observations that complied.
B.1.2.1 Energy Code Used (n=127):
Table B.3, Energy Code Used
Idaho Energy Conservation 2015 IECC
Percentage
x Was the home participating in an above-code program (n=102)?
– Yes (9%)
– No (91%)
B.1.3 Envelope
The following list of questions focus on average characteristics of the thermal envelope:
B.1.3.1 Insulation Labels
x Was insulation labeled (n=12)?
– Yes (100%)
– No (0%)
B.1.3.2 Ceilings
x Did the attic hatch/door exhibit the correct insulation value (n=8)?
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B.3
– Yes (100%)
– No (0%)
B.1.3.3 Air Sealing1
x Thermal envelope sealed (n=64) (97%)
x Openings around windows and doors sealed (n=64) (100%)
x Utility penetrations sealed (n=95) (98%)
x Dropped ceilings sealed (n=39) (49%)
x Knee walls sealed (n=53) (55%)
x Garage walls and ceilings sealed (n=68) (100%)
x Envelope behind tubs and showers sealed (n=64) (39%)
x Common walls sealed (n=0) (0%)
x Attic access openings sealed (n=35) (54%)
x Rim joists sealed (n=30) (27%)
x Other sources of infiltration sealed (n=63) (97%)
x IC-rated light fixtures sealed (n=127) (100%)
B.1.4 Duct & Piping Systems
The following represents an average profile of observed air ducting and water piping systems, followed
by a list of additional questions related to such systems:
B.1.4.1 System Profile
x Duct Location in Conditioned Space (percentage):
– Supply (n=131): 28% (37 homes with systems located entirely within conditioned space)
– Return (n=131): 21% (27 homes with systems located entirely within conditioned space)
x Duct Insulation (R-value):
– Supply (n=61): 7.93
– Return (n=15): 6.8
x Air ducts sealed (n=114) (88%)
x Air handlers sealed (n=123) (98%)
x Filter boxes sealed (n=114) (94%)

1 Note that results in this section are from checklist items that are addressed via visual inspection. When comparing
these visual results with the actual tested results, it is clear that there can be significant differences in the two
methods.
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B.4
B.1.5 HVAC Equipment
The following represents an average profile of observed HVAC equipment, followed by:
B.1.5.1 Heating
x Fuel Source (n=127):
– Gas (96%)
– Electricity (4%)
x System Type (n=122):
– Furnace (97%)
– Heat Pump (3%)
x System Capacity (n=124):
– Furnace: 69,639 Btu
– Heat Pump: 38,800 Btu
x System Efficiency (n=124):
– Furnace: 90 AFUE (many furnaces listed as 80 AFUE)
– Heat Pump: 8.3 HSPF
B.1.5.2 Cooling
x System Type (n=110):
– Central AC (96%)
– Heat Pump (4%)
x System Capacity (n=110):
– 33,350 (Btu/hr)
x System Efficiency (n=64):
– 13.1 SEER (observations ranged from 13 to 14.5 SEER)
B.1.5.3 Water Heating
x Fuel Source (n=127):
– Gas (92%)
– Electric (8%)
x System Type (n=126):
– Storage (98%)
– Tankless (2%)
x System Capacity (n=63):
– 52 gallons (observations ranged from 50 to 100 gallons)
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 74 of 76

B.5
Table B.4. Water Heating System Storage Capacity Distribution
< 50 gal 50-59 gal 60-69 gal 70-79 gal 80-89 gal 90+ gal
0% 98% 0% 0% 0% 2%
x System Efficiency (n=63):
– EF 0.65 (range from EF 0.62 to EF 0.92)
B.1.5.4 Ventilation
x System Type (n=127):
– Exhaust Only (55%)
– AHU Integrated (45%)
x Exhaust Fan Type (n=70):
– Dedicated Exhaust (4%)
– Bathroom Fan (96%)
B.1.5.5 Other
x Mechanical manuals provided (n=54) (100%)

Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 75 of 76
Exhibit No. 8 Case No. INT-G-20-06 Intermountain Gas Company Page 76 of 76

EXHIBIT NO. 9
CASE NO. INT-G-20-06
INTERMOUNTAIN GAS COMPANY

Proposed Tariff – Legislative and Clean
(6 pages)

PROPOSED TARIFF
(Legislative Format)
(2 pages)
Exhibit No. 9 Case No. INT-G-20-06 Intermountain Gas Company Page 1 of 6
I.P.U.C. Gas Tariff
Rate SchedulesOriginal Sheet No. 16 (Page 1 of 2)
Name
of Utilit Intermountain Gas Company
Issued by:Intermountain Gas Company
By: Michael P. McGrath Title: Director – Regulatory Affairs
Effective: October 1, 2017
Rate Schedule EE
RESIDENTIAL ENERGY EFFICIENCY REBATE PROGRAM
AVAILABILITY:
The Intermountain Gas Company Energy Efficiency Rebate Program (EE Program) is available
throughout Intermountain’s service territory to eligible residential account holders served under the
Company’s Rate Schedule RS.
PROGRAM DESCRIPTION:
The purpose of the program is to encourage upgrades to, or use of, high efficiency natural gas
equipment. This will be achieved through the use of rebates, offered towards the purchase and
installation of qualified energy-efficient natural gas equipment and construction of Energy Star certified
homes.
ELIGIBILITY:
To qualify for incentives, customers must meet the end-use qualifications identified in the
Measures/Incentive Table below.
To qualify for space heating rebates, a dwelling must use natural gas as the sole heat source upon
installation of rebate-qualified equipment.
To qualify for water heating rebates, a dwelling must utilize natural gas for water heating upon installation
of rebate-qualified equipment.
Rebates for furnaces and water heating equipment for new construction may not be combined with the
Energy Star whole home package rebates.
IDAHO PUBLIC UTILITIES COMMISSION
Approved Effective
Sept. 27, 2017 Oct. 1, 2017
Per O.N. 33879, 33887 & 33888
Diane M. Hanian Secretary
energy efficient new
Rebates ll equipment must use natural gas as the fuel source. The customer
must also comply with the current program Terms and Conditions.
Space Heating rebates
New Construction Water Heating and Thermostat rebates may be combined with New
Construction rebates.
Lori A. Blattner
March 1, 2021
First Revised
-RS
rebates
Exhibit No. 9 Case No. INT-G-20-06 Intermountain Gas Company Page 2 of 6
IDAHO PUBLIC UTILITIES COMMISSION
Approved Effective
Feb. 4, 2020 March 1, 2020
Diane M. Hanian Secretary
Conditions
program
March 1, 2021
REBATES:
-RS
Second
Exhibit No. 9 Case No. INT-G-20-06 Intermountain Gas Company Page 3 of 6
PROPOSED TARIFF
(Clean Format)
(2 pages)
Exhibit No. 9 Case No. INT-G-20-06 Intermountain Gas Company Page 4 of 6
I.P.U.C. Gas Tariff
Rate Schedules
First Revised Sheet No. 16 (Page 1 of 2)
Name
of Utility

Intermountain Gas Company

Issued by: Intermountain Gas Company
By: Lori A. Blattner Title: Director – Regulatory Affairs
Effective: March 1, 2021

Rate Schedule EE-RS
RESIDENTIAL ENERGY EFFICIENCY REBATE PROGRAM

AVAILABILITY:

The Intermountain Gas Company Energy Efficiency Rebate Program (EE Program) is available
throughout Intermountain’s service territory to eligible residential account holders served under the
Company’s Rate Schedule RS.
PROGRAM DESCRIPTION:
The purpose of the program is to encourage upgrades to, or use of, high-efficiency natural gas
equipment. This will be achieved through the use of rebates offered towards the purchase and installation
of qualified energy efficient natural gas equipment and construction of energy efficient new homes.
ELIGIBILITY:
To qualify for rebates, customers must meet the end-use qualifications identified in the Rebates Table
below. All equipment must use natural gas as the fuel source. The customer must also comply with the
current program Terms and Conditions.
To qualify for Space Heating rebates, a dwelling must use natural gas as the sole heat source upon
installation of rebate-qualified equipment.
To qualify for Water Heating rebates, a dwelling must utilize natural gas for water heating upon
installation of rebate-qualified equipment.
Space Heating rebates may not be combined with New Construction rebates. Water Heating and
Thermostat rebates may be combined with New Construction rebates.

Exhibit No. 9 Case No. INT-G-20-06 Intermountain Gas Company Page 5 of 6
I.P.U.C. Gas Tariff
Rate Schedules
Second Revised Sheet No. 16 (Page 2 of 2)
Name
of Utility

Intermountain Gas Company

Issued by: Intermountain Gas Company
By: Lori A. Blattner Title: Director – Regulatory Affairs
Effective: March 1, 2021
Rate Schedule EE-RS
RESIDENTIAL ENERGY EFFICIENCY REBATE PROGRAM
(Continued)
REBATES:
Rebate Rebate Type Minimum Efficiency Rating Rebate
Amount
Whole Home Tier I New
Construction
Tier I Requirements:
• HERS rated
• Air sealing at or below 3 ACH at 50 Pa
• Ceiling insulation at or above R-49
• Ducts and air handler located inside
conditioned space or duct leakage to
outside of less than 4 CFM25/100 ft2 CFA
• Furnace efficiency at or above 97% AFUE
$900
Whole Home Tier II New
Construction
Tier II Requirements:
• HERS rated
• Air sealing at or below 4 ACH at 50 Pa
• Ducts and air handler located inside
conditioned space or duct leakage to
outside of less than 4 CFM25/100 ft2 CFA
• Furnace efficiency at or above 95% AFUE
$700
Combination Boiler for
Space and Water Heat
Space Heating 95% AFUE $800
Furnace

Space Heating 95% AFUE $350
Boiler

Space Heating 95% AFUE $800
Storage Water Heater

Water Heating .68 UEF $115
Tankless Water
Heater Tier I
Water Heating .91 UEF $325
Tankless Water
Heater Tier II
Water Heating .87 UEF $300
Smart Thermostat

Thermostat Wi-Fi Enabled $100

GENERAL PROVISIONS:
All installations of equipment must comply with all codes and permit requirements applicable in the state
of Idaho and must be properly inspected, if required, by appropriate agencies. Customers must submit
required documentation of purchase and installation to the Company under the Terms and Conditions of
the current rebate program. The Company reserves the right to verify installation prior to the payment of
any rebates.
Exhibit No. 9 Case No. INT-G-20-06 Intermountain Gas Company Page 6 of 6