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Do High Voltage Electric Transmission Lines Affect
Property Value'?
Stanley W. Hamilton and Gregory M. Schwann
ABSTRACT. This paper empirically analyzes the
t a high voltage (de ned as S 0
greater) ele nsmission ines on the prices of
nearby single detached houses. The study demon-
strates the importance of using the correct function
specification and correcting for (commonly found)
heteroscedasticity. We find the electric transmission
lines do have an effect on property value, but such
effects are restricted to a narrow band and are
primarily due to the visual externalities of the trans-
mission towers. (JEL 024, R14)
I. INTRODUCTION
The purpose of this study is to add to the
literature on the impact of high voltage (de-
fined as 69,000 volts or greater) electric
transmission lines on nearby property val-
ues. Existing research suggests that proxim-
ity to such lines has a small negative impact
on property values, but this impact ,is re-
stricted to properties adjacent to or within
200 meters of the line (Kroll and Priestley
1991). This study adds to the existing litera-
ture in two important ways. First, we are
able to use a much richer database than any
previous study. Our sample contains a
greater range of neighborhoods, more var-
ied properties, properties in quite different
price ranges, and transmission lines of dif-
fering sizes. Second, we are more rigorous
in our analysis than previous studies.
Research suggests that some people be-
lieve proximity to high voltage transmission
lines poses potential health and safety haz-
ards (Priestley and Evans 1990), althoug~
the evidence of such. health hazard is
inconclusive~ Additionally, many believe that
proximity to high voltage electric transmis-
sion lines reduces property values, either
because potential buyers are concerned
about the health and safety risks or because
of the unsightliness of the lines themselves.
We focus on the potential impact of trans-
mission lines on property value.
The existing literature on the impact of
transmission lines on property vaiues fails
into three general categories. First are the
appraisal or valuation based studies, gener-
ally utilizing small samples of similar prop-
erty values. Blanton (1980) and Earley and
Earley (1988) are examples of this approach.
The second type of research' are surveyor
attitudinal studies which focus on the per-
ceived effects of transmission lines on prop-
erty values. Priestley and Evans (1990) is the
most thorough attitudinal study to date.
Other similar studies, using single markets,
include Kinnard et a!. (1984), Rhodeside
and HaIWeIl (1988), Market Trends (1988),
and Economics Consultants Northwest
(1990). These studies are generally not so-
phisticated and the survey respondents have
a tendency to overestimate the negative im-
pacts of the transmission lines (Kroll and
Priestley 1991). The third, more rigorous,
set of studies use regression models to esti-
mate the impact of the transmission lines on
property values. 19nelzi and Priestley (1989,
1991) provide the most comprehensive anal-
ysis to date. Earlier studies using regression
models include Carriere Chung, and Lam
(1976), Kinnard et al. (1984)t Colwell and
Foley (1979), and Colwell (1990).
While the details vary, the results are
generally consistent: overhead transmission
lines can, in some instancest reduce the
value of nearby properties (Kroll and Priest-
ley 1991). These impacts, where they exist.
The authors are, respectively, associate professor of
urban land economics and 1994 Lusk Center Summer
Research Fellow, Faculty of Commerce and Business
Administration at the University of British Columbia,
and assistant professor of real estate, Lusk Center for
Real Estate Development at the University of South-
ern CaHfomia. This study is funded in part by the Real
Estate Foundation British Columbia and the Lusk Cen-
ter for Real Estate Development, University of South-
ern California. All errors or omissions are solely the
responsibility of the authors. This paper extends earlier
exploratory research by Hamilton and Carruthc rs
(1993). The authors wish to express their appreciation
to B. C. Hydro for permission to use data from this
earlier work.
Land f:.'conomks . November 1995 . 71 (4): 43ft
Copyriflht ~ 2001 . All Riflhts Reseved.
71(4)Hamilton and Schwann: Property Value 437
are generally less than 5 percent of the
property value. The effects are confined to
the immediate area of the transmission lines
and dissipate quickly with distance. Neither
the height of the transmission structures nor
the voltage of the lines are found to have
significant impact on property values. In all
studies, other neighborhood factors domi-
nate the explanation of variations in prop-
erty values.
Most studies involve properties which
were sold after the transmission lines were
in place, as does this study. In this case, the
estimated impact of a transmission line on
property values should be interpreted as a
long-run equilibrium effect. When a new
transmission line is constructed, or an old
line extended in an existing subdivision, the
measured effect will aJso have a dynamic
component. Studies of transmission line ex-
tensions report that the impacts are initially
significant, but quickly diminish over time
(Kroll and Priestley 1991).
In some studies a (small) positive impact
is found. This is generally associated with a
right-or-way (R-of-W) which is accessible for
recreational use, or which is attractively
landscaped, or provides added privacy to
adjacent properties (Rhodeside and HalWell
1988). However, the value of greenspace
should not be overrated. Peiser and Schwann
(1993) report that pure greenspace exerts a
very small effect on property values.
II. DATA
The data used for this study includes all
arms-length sales of single detached
dwellings in four separate neighborhoods in
the metropolitan Vancouver areal over the
period 1985-91. The four neighborhoods are
in proximi ty to existing transmission lines
and the time frame corresponds to a rela-
tively stable period in the market place. The
rights-or-ways in the four areas include two
areas with a 140m corridor with two 50OkV
and one 230kV lines on steel towers; one
area with two transmission lines on steel
towers; and one area with a 60kV line on
wood poles. Each property in our sample
was located on a map and the distance to
the center of the transmission line right-of-
way was recorded (DIS). Properties which
were adjacent to the right-of-way were noted
with a dummy variable (ADJACENT), and
if a property was partially within the right-
of-way, this was noted as a dummy variable
(WITHIN). All properties within a 200m
band2 of the transmission line were in-
spected to determine the number of towers
(TOWERS) which were visible from the
property and to determine if the transmis-
sion lines were visible (VISIBLE).
Where necessary, the impact of vendor
financing was removed from those sales
prices involving vendor-supplied financing.
Accurate and current property characteris-
tics were then obtained for all properties
sold within the time period of our sample.The property characteristics used in the
analysis include the types of variables com-
monly found in the analysis of real property
prices. Dummy variables were used for. the
presence of a GARAGE, POOL SEWER,
CURB and CORNER lot. Continuous vari-
ables are used for the AGE of the dwelling,
number of fireplaces (FIREPL), basement
rooms (BASRMS), bedrooms (BEDRMS),
fun baths (FBATHS) and partial bathrooms
(PBATHS), number of other rooms
(OTHIUfS), and the width (WTDE) and
depth (DEEP) of the lot.
The final sample included 12 907 transac-
tions of single detached dwellings in the
four study areas (Table 1). Of this sample.
364 were within 200m of the transmission
line and of these, 426 were adjacent to or
partially within the right-of-way.
III. FUNCTIONAL SPECIFICATION
The question we address in this section is
whether the simple iinear or log-Ii near func-
I British Columbia uses a Torrens
system of landregistration in which all sales must be recorded in
central registry and the market value or sales price
reported. Analysis of the reported sales prices indicates
they are accurately reported. TIle sales data used in
this study were supplied by the provincial Assessment
Authority. As part of their annual real property tax
assessment function, they identified non-arms-Je,ngth
sales (e.g.. sales between family members at less than
full price).
2 Previous studies report that 200m is the outer
bound for the region affected by the transmission lines.
Copvri~ht
(g)
2001. All Rij:lht~ Reseved.
438 Land Economics
TABLE I
SAMPLE SIZE AND SAMPLING AREAS OF
PROPERTY TRANSACflONS
Total Within Adjacent
Study Subarea Sample 200m to R-of-
C10verdale 605 3lH
Newton East-west 086 235
Newton North-south 815 961 166
Walnut Grove 401 850 162
All Areas 12,907 364 426
TABLE 2
TESTS FOR FUNCTIONAL FORM
Dependent Variable
Vii In V
Test Statistic frob.Statistic Prob.
test 25.832 0000 22.546 (1.0000
Quadratic 43.651 0001 42.933 0001
Tenns = 0
Cross-product 13.489 0001 12.463 (WOO I
Terms = 0
tional forms used by other authors provide
an adequate approximation of the function
relating house prices to property character-
istics or whether more comprehensive func-
tional specifications are needed. All of our
test results are presented in Table
We begin by using MacKinnon test
test the null hypothesis that the hedonic
form is linear against the alternative hy-
pothesis that the hedonic form is log-linear.
This is a two-step test. In the first step, we
estimate the log-linear model and calculate
the predicted values from the regression. In
the second step, the linear model is esti-
mated, with the predicted values from. the
first regression included as an extra regres-
sor. The (-statistic for the predicted values
is the test statistic for the test. Our (-sta-
tistic for this test is 25.8 (Table 2, column 2);
dearly, we can reject the null hypothesis of
a linear functional form. The analogous test
of a log-linear functional form against a
linear functional form yields a (-statistic of
22.5 (Table 2, column 3). Again, we can
reject the null. Our results for this pair of
tests show that neither the linear nOJ log-
linear functional forms arc up to the task of
November' 995
approximating the functional form for the
hedonic relationship.
We next consider approximating the he-
donic function using a flexible functional
form. After exploring the data by fitting a
number of Box-Cox, Box-Tidwell, and spline
regressions (not reported), we chose the
translog functional form as our basic esti-
mating equation. OUf explorations showed
that the general curvature of the hedonic
function was best captured by a form which
was logarithmic in the independent varia-
bles3 and with price as the dependent vari-
able. We later address the functional form
of the dependent variable.
It is well known that all flexible func-
tional forms provide second-order differen-
tial approximations to an arbitrary function.
However, in our past works, we have found
that flexible forms suffer from mu lti-
collinearity. That is, the number of regres-
sors might be reduced without severely com-
promising the fit. To examine whether this
is true for our data, we estimate three
translog functions of increasing complexity.
Each of the three equations use logarithmic
independent variables. The first includes
only linear terms; the second includes linear
and quadratic terms; and the third includes
linear, quadratic, and cross-product terms.
We test whether each increase in complex-
ity is warranted using a standard Wald test.
We first test whether the coefficients on
the quadratic terms added to the linear
specification are jointly zero. The value of
the F-test of this hypothesis is 43.6 when
the dependent variable is iI' and 42.9 when
the dependent variable is In II (Table 2.
columns 2 and 3). Obviously, the nuB hy-
pothesis is rejected. Next, we add the cross-
products to each regression and test whether
the coefficients on the cross:-product terms
are jointly zero. The F-test statistics are
13.4 and 12.4, respectively, for the tWo c..k-
\ Flexible functional forms are usually chosen nc-
cause of desirable global curvature properties in d\.~.
mand or supply systems, or b~causC of the domain
the independent variables. Since theory provides no
guidance regarding the functional form for
hedunk
regressions. no such criteria operate here.
Copvri~ht (Q) 2001 . All RiQhts Reseved,
71(4)Hamilton and Schwann: Property Value 439
pendent variables. The null is rejected in
both cases. Thus, despite our concern about
becoming overly complex, the test results
indicate that the full translog specification is
warranted.
We now examine the appropriate trans-
formation for the dependent variable condi-
tional on a fWl translog specification of the
independent variables. We fit a simple Box-
Cox regression and estimate the power
transformation by maximum likelihood. The
estimated coefficient is .106 with at-statistic
of 8.4. Hence, the dependent variable is
dose to logarithmic in absolute terms, but
not close enough to be statistically indistin-
guishable from zero.
Unfortunately, residual diagnostics reveal
that the regressions have a significant de-
gree of heteroscedasticity. We test the ho-
moscedasticity of the two regressions using
the statistic proposed by Harvey (1974). The
test values are 14 192.5 and 1,609.8 for the
Box-Cox and log dependent variable regres-
sions, respectively, and the values are both2 distributed with 224 degrees of freedom.
The null hypothesis of homoscedasticity rejected.
Based on the preceeding tests, we con-
dude that the curvature of the function can
best be approximated using the following
functional form:
v1,9) (30 + i' + E E ~ijZil;=1 i=lj~l
+ E 'Yjdir + E 'Y,Qjl
;=1 t~l
where Vft9) (vg - 1)/6 is the Box-Cox
transformed dependent variable, Zit are
continuously measured dwelling unit char-
acteristics
;,
are discretely measured
dwelling unit characteristics . and Qit are
quarterly dummy variables for the date of
sale. We deal with the problem of ho-
moscedasticity by estimating the Box-Cox
model with endogenous multiplicative het-
eroscedasticity. The log of the variance
taken to be linear in the unit characteristics.' t like eS.lma.lOns are one ~y mron..mum e-
TABLE 3
CoRRECTED TEsTS FOR fuNCTIONAL FORM
Adjacent Mjd-Range Far
Properties Properties Properties
de x df x
Adding Quadratic 30.11 120.11 530.3 9
Terms
Adding Cross
Product Tenns
All Terms
154.52 207.5 57 663.5 38
185.4 68 328.68 1,194.3 47
TABLE 4
TEsrs FOR FUNCTIONAL HOMOGENEITY
130
234
Adjacent and Mid-Range
Adjacent, Mid-Range. and Far
329.5
766,
(1)
lihood. We then apply equation fll to the
entire sample and examine the residuals.
The residuals from equation (1) indicate
that the hedonic functional form for proper-
ties adjacent to the high voltage electric
transmission lines may be different from
that for properties further removed fTom
the lines. We examine this by dividing the
sample into three sets of properties: proper-
ties adjacent to a transmission line (Ad-
jacent), properties within 200m of a trans-
mission line, but not adjacent to a line
(Mid-Range), and properties more than
200m from a transmission line (Far), and
. test for the equality of the coefficients of
the hedonic regression across the subsam-
pies.
In Table 3, we report the likelihood ratio
tests of functional specification for our three
distance zones. The X2 statistics are all sig-
nificant at a p-value of 0.001 or less. Thus, a
full translog specification is strongly vali-
dated, even after the incorporation of a
Box-Cox dependent variable and correcting
for heteroscedasticity.
In Table 4, we present the tests for a
common functional relationship across the
three distance zones in our sample. These
tests are based on the full Box-Cox/trans-
log functional form. The null hypothesis that
the Adjacent and Mid-Range properties
CopyriRht (g) 2001 , All RiQhts Reseved.
TA
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442 Land Economics NOl'emba Jl)(')5
have a common functional form is rejected
(p-value .( 0.001), as is the test of the null
hypothesis that all three subareas have a
common functional form (p-value .( 0.001).
Because of these results, our assessmen
of the effects of high voltage electric trans-
mission lines on property value are based on
separate regressions for the three distance
zones. And, our estimates are based on the
heteroscedasticity corrected Box-Coxjtrans-
log model. The estimated regression results
are presented in Table 5.
IV. THE EFFECT OF HIGH
VOLTAGE ELECTRIC
TRANSMISSION LINES ON
PROPERTY VALUES
We are now in a position to address the
central question of this paper: Do high volt-
age electric transmission tines affect prop-
erty value? To answer this question, we.
perform three experiments based on the
estimated equations. These experiments de-
termine the increase (decrease) in property
value from removing the transmission line
effects. The results from these experiments
are presented in Table 6.
In the first experiment, we calculate the
change in property value for an average
dwelling unit from removing the existing
visual externality of the high voltage electric
transmission line towers. For properties ad-
jacent to the towers, we estimate that re-
moving the unsightliness of the towers in-
creases property value by 5.percent
($6 669). The I-statistic for the test of the
hypothesis of no change in value is 1.91 and
this effect is significant at the 6 pacenl
level. For the Mid-Range properties, we find
no significant change in property value from
removing the visual externality of the tOwer
in either model.
In our next experiment, we examine the
effect of proximity to the high voltage elec-
tric towers. For the Adjacent properties, we
calculate the effect of increasing the right-
of-way so that the average property is 100m
or 200m from the towers. Moving the houses
to the 100m point increases property value
by 5.8 percent ($6,740 for our average prop-
erty). This increase is highly statistically sig-
nificant, with I-values of 5.3. Recall that
previous studies have shown that 200m is
the boundary of the effects of towers on
property value.
We next calculate the effect of increasing
the distance of Mid-Range properties from
the transmission lines to 200m (an averag~
increase of approximately 30m). We assume
that this move reduces the visibility of the
towers. This increase in distance results in a
8 percent increase in property value. which
is statistically significant. Note that increa~-
jog average distance of a Mid-Range prop-
erty from a transmission line by 30m in-
creases its property value by $3,43S. which is
approximately half of the $6,740 increase in
property value from moving an Adjacent
property to 100m. Thus, our estimates for
Adjacent and Mid-Range properties are
consistent.
In our final experiment, we remove both
the visual effect of the towers and the prox-
imity effect. The result is more, than a sim-
ple addition of the individual effects he-
TABLE
THE EFFECTS OF HIGH VOLTAGE ELECTRIC TRANSMISSION LINES ON
PROPERTY VALUE
Mid-Range PropertiesAdjacent Properties
stat
669 I.l))
740
7.139
(!/;,
Tower Visibility
Distance from Tower ( 100m)
Distance from Tower (200m)
Joint Effect (1oom)
Joint Effect (200m)
h..
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Copyri~ht (ID 2001. All Ri~hts Reseved.
71(4)Hamilton and Schwann: Property Value 44~
cause of interaction effects in the translog
form and the significance test will be dif-
ferent because the effects are correlated.
We find that electric transmission line tow-
ers do have a significant impact on the val-
ues of properties located adjacent to the
towers. After removing both of the effects
of the lines, property values increase by 6.
percent ($7,339) for 100m. We find also that
transmission lines affect Mid-Range proper-
Hes, but the effect is small. The properties
increase in value by 1.1 percent, or $1,338
after both of the effects of the electric
transmisSion line are removed. All of these
impacts are statistically significant.
. Our estimates of the effects of the high
voltage electric transmission lines are com-
parable to those obtained in previous stud-
ies; they are not very large. A detailed ex-
amination of our regression results shows
that there is a strong interactive relation-
ship between the distance from a transmis-
sion line and lot width, and number of tow-
ers visible and lot width. Inspection of the
affected properties reveals that the build-
ers/ developers of these properties have, to
significant degree, compensated for the
transmission lines by reconfiguring the lots
- and reorienting the house to mitigate the
visual externalities.
V. CONCLUSION
High voltage electric transmission lines
do have an effect on property value. We
find that properties adjacent to a line lose
3 percent of their value due to proximity
and the visual impact. This is in the mid-
range of results reported by earlier studies.
As expected, properties more distant from
transmission lines are scarcely affected, los-
ing roughly 1 percent of their value.
Our study also demonstrates the impor-
tance of thorough econometric work in de-
termining the effect of transmission lines on
property value. We obtain three results in
this regard. First, the functional specifica.
tion is crucial. Cavalier use of linear or
log-linear specifications yields faulty results.
Second, the error term in hedonic equations
is heteroscedastic for all of the functional
specifications we tried. This is a common
finding. But, our work highlights how impor-
tant it is to correct for heteroscedasticity
when trying to uncover the impact of exter-
nalities on property value through statistical
testing. Finally, we find that the functional
form of the regression for properties close
to electric transmission lines is different for
that of properties far from the lines.
References
Blanton, Herman W. 1980. A Study of Tran.rmis-
sion Line Effects on Subdivi....ions in Hams
County, Texas. Unpublished report, Austin,
Texas.
Carriere, Jean, Joseph H. Chung, and Kim Anh
Lam. 1976. impact des Lignes de TranspoJ1
energie Electrique sur la Valeur Fonciere.
Laboratoire de recherche en sciences immo-
bilieres, Universite de Quebec a Montreal.
December.
Colwell, Peter F. 1990. "Power Lines and Land
Value.Journal of Real Estate Research
(1):117-27.
Colwell, Peter F., and Kenneth W. Foley. 1979.
Electric Transmission Lines and the Selling
Price of Residential Property,The Appraisal
Journal 47:490-99.
Earley, Edward M., and Michael H. Earley. 1988.
Real Estate Market Data Analysis." (For a
Proposed 230 KV Electrical Transmission
Line, Transylvania County, North. Carolina.)
Prepared for Duke Power Company. Golden,
Colorado.
Economics Consultants Northwest. 1990. GaTTi-
son-West High Voltage Transmission Line So.
cial Monitoring Study. Report Submitted to the
Facility Siting Bureau of the Energy Divisionof the Montana Department of Natural Re-
sources and Conservation and the Bonneville
Power Administration, Helena, Montana.
Hamilton, S. W.. and Cameron Carruthers. 1993.
The Effects of Transmission Lines on Property
Values in Residential Areas. University of
British Columbia.
Hamilton, S. W.. Dean Uyeno, and Andrew Biggs.
1993. "Density of Rcsidential Land Use and
the Impact of Airport Noise.Journal of
Transport Economics and Policy 27 (1 ):3-18-
Harvey, A. C. 1974. "Estimating the Parameters
in a Heteroscedastic Regression Model." Pa-
per presented at the European Meeting of theEconometric Society, Grenoble, September.
Ignelzi, Patrice, and Thomas Priestley. 1989.
Copyrij:lht (g) 2001. All Rij:lhts Reseved,
444 Land Economics November 1995
Methodology for Assessing Transmission Line
Impacts in ReSidential Communities. Prepared
for Edison Electric Institute Siting and Envi-
ronmental Planning Task Force, Washington,
Dc.
. 1991. A Statistical Analysis of Transmis-
sion Line Impacts on Residential Property Val-
ues in Six Neighborhoods. Prepared for South-
ern California Edison Environmental Affairs.
Kinnard, William N., Jr., M. B. Geckler, J. K.
Geckler, J. B. Kinnard, and P. S. Mitchell.
1984. An Analysis of the Impact of High Volt-
age Electric Transmission Lines on ResidentiaL
Property Values in Orange County, New York.
Storrs: Real Estate Counseling Group of Con-
. necticut.
Kroll, Cynthia A., and Thomas Priestley. 1991.
The Effects of Overhead Transmission Lines on
Property Values: A Review and Analysis of the
Literature. Prepared for the Siting and Envi-
ronmental Planning Task Force of the Edison
Electric Institute (draft), Washington~ Dc.
Market Trends, Inc. 1988. Arizona Utility Aesthet-
ics Summary Report, June.
Peiser, Richard, and Gregory Schwann. 1993.
The Private Value of Public Open Space
within Subdivisions," Journal of ArchitectUral
and Planning Research 10:91-104.
Priestley, Thomas, and Gary Evans. 1990. Percep-
tions of Transmission Lines in Residential
Neighborhoods: Results of a Case Study ill
Vallejo, California. Study prepared for the
Southern California Edison Company.
Rhodeside and Harwell: Inc. 1988. Perceptions of
Power Lines. Residents' AttitUdes. Report prc.
pared for Virginia Power Company, Rich-
mond, Virginia.
Copvri!:jht
(g)
2001, All Ri~hts Reseved.
Series in Spatial
Econometrics -
Draft Paper
11t!;II~"lflrllllllll'I""i"III.I_lilii~J!illl\i IIII;'j!1111;11 ,;!;; I;!;I II
Impact of Power Lines
on Freehold Residential
"""".- " "., ", --""."", ..,""",'....-"..---.. ....""'- .-,.-"""""-- ....'" --",.. '......'..-- -
;&;;ilrtj~l~itl;;;Y ,.
MurfuQlHaider &AnbiJreHaroun
Depm1ment of Civil
Email: murtazaCGJ,regionomics.com. Tel: [phone]
. ,
1111111111,111Il',IIIIII".111I1;I'.I.IIII(III,(I;1111!;II!I!II!IJI'II;IIIIII,'IIII(lllr'III
l8111
Table of Contents
A CKN'OWLEDGE:MENTS .............. .............. ...... ..................................................................... II
1.0 IN'TR ODU CTI ON........ ...... .................. .................... .............. ........................................ ............. 1
LITERA TIJRE REVIEW.......... ........ ......................
.................. ..... ...................
......................... 2
METHODOLOGY........ ....
.................... .................. ................ ................
................................... 9
ESCRIP'fIVE ANALYSES
.................... .................... .......... ....................
.............................. 11
SPATIAL AUTO-REGRESSNE SPECIFICATION.............................................................. 15
DETECTION OF SPATIAL AUTOCORRELATION ............................................................ 17
ECONOMETRIC MODELS TO QUANTIFY INFLUENCE OF POWER-LINES ON
RESIDENTIAL REAL ESTATE VALVES ........................................................................................ 18
CONCLUSIONS ................ ............
.......... ............ ........................ ........ .... ............
........ ...... ....... 22
REFERENCES................ ..............
...... ............. .......
.................................... ............................. 24
APPENDIX A.... ........
........ .......... ...... .............. ...................... ...... ............ ................ ...... ........ ...... ........ .....
LIST OF TABLES
...............,......,................,.................'" ,...........,...,...,.....,.....,...........................,....." ...,..
LIST OF FIGlJRES ....
"""" ,... ..............,..,........" .,........ ....................,....,...... ".... .... .,.,........ '.,......,. ... ,...,.,...,
APPENDIX B........ ................ ..........................
.............. ............ .................... ..........................................-
DERIVED LOCATION VARIABLES ..,...
............ .............,..., .... ...... .,.. .....,.. .............. ,....,.. ..........".. ....... ,...,'..,
DESCRIPTIVE RESULTS OF LOCATIONAL VARIABLES.,
........,..". ....., ...,.. ....,.,...'..... .............",.. ......" .... ,... ...
APPENDIX C...................... ........
...... ................ .............. .............. .......... .................. ...... ...... .... ...............
DET AILED RESULTS FROM DISAGGREGATE ANALYSIS OF MUNICIPALITY -WIDE VARIATION IN ATTITUDES
TOWARD POWER-LINES,....,.......,..".....,
................,............,..,..,.....,',.............,.....,.,.,.,.................
APPENDIX D ......
...... .............. ........ ........ .......... ................ ............ ................ ............ ...............................
DETAILED RESULTS FROM OLS AND SPATIAL MODELS ,..,..,..............,.,..,.,........,...,.,.
:.".,..........,..,..,....,.,
OLS Models Pages Dl-DiO.. ............ .......' ........ ..".... oo..........,.....
........".. .......,......
,.....oo.. ........
...,
Spatial Models Pages DiO-Di4 oo......oo..,........, ""OOOO ......
...............",....
..oo............ .oo...."""""""""'"
ACKNOWLEDGMENTS
This research is based on Murtaza Haider s Masters thesis research at the Department of Civil Engineering.
University of Toronto, The Masters research was funded by NSERC Collaborative Project anJ. Individual
Operating Grants. In addition, the author was also supported by an Ontario Graduate Scholarship.
ll1e author would like to thank the Toronto Real Estate Board for access to its MLS databasc. Asmus
Georgi, Research Associate at the Departme11t of Civil Engineering, University of Toronto, is recognized
for writing the code to estimate the lag variable.
This research project is part of a major research initiative Integrated TranspOf1ation Land Use
Transportation Environment Modelling (IL
\j
1 L Professor Eric 1. Miller, author s research supervisor, is
the lead investigator for ILlITE, This research project has been conducted lmder Prof. Eric Miller
supervIsIOn,
Influence of Power Lines on Freehold Property Vallies in the GT A .. Page-
----------"""----'----"- --------,....,._- ..---...--- ,. ",..
1.0 INTRODUCTION
High-voltage power-lines are an integral and indispensable part of urban, as \vell as ruraL
landscape around the world. Often running along major highways, transmission lines arc yisihle
from great distances as they are mounted over tall towers and pylons, The benefits of high-
voltage power-lines are manifold as they extend far beyond the communities intersected by the
transmission lines. However, the perceived environmental costs, both health-related hazards and
loss of property values, associated with these power-lines are often confined to the immediate
zone of influence of power-lines that extends only up to few hundred meters. Loss in value in
properties proximate to power lines is often attributed to the visual extcmalities and
environmental hazards associated with hig.h-~(oltage power-lines.
A health-related hazard, such as higher incidence of cancer in residents of adjacent properties
remains a controversial subject to date as researchers on either side of the divide are yet to
forward conclusive evidence in support of their claim. Not so long ago, the issue of loss in
property values was also marred with controversy. Numerous researchers published their works
arguing either presence or absence of a direct influence of power-lines on property values. Most
of this research has been either qualitative or based on summary statistics derived from surycys of
residents and real estate experts. It was only in the recent past that researchers adopted
econometric techniques in their study of actual market prices of properties proximate to po\Vcr-
lines.
Most econometric studies have suggested that proximity to power lines capitalise into 100ver
property values. This study contributes to the on-going discourse on the influence of high-
voltage power-lines on property values. Using a sample of approximately 27,400 freehold
residential properties sold in the Greater Toronto Area (GTA) during 1995, an attempt has been
made to quantify the loss in property -,;" aiu.."s that could be attributed to the proximity of these
residential properties to the high-voltage power-lines. This study makes use of GIS and spatial
econometrics to quantify the influence of power-lines on property values. The study finds strong
evidence of a negative influence of power-lines on property values, For example, properties in
close proximity of high-voltage power-lines were sold, at an average, 30/0 to 60/0 less than a
comparable unit that lied at a greater distance from the power-lines. It was observed that the
influence of high-voltage power-lines in the GTA extends at least up to 50G-meters from the
centre-line of transmission lines.
This paper is organised as follows. The introduction is fol1owe~ by a comprehensive literature
review. Both empirical and descriptive research was reviewed in this section. Literature review
Influence of Power Lines on Freehold Property Values in the GT A Page- 2
,-----_._---------_
__--_,n
'__--__------'-- ....'-
is followed by a brief discussion on methodology. Results from an exhaustive spatially
disaggregate analysis of property values in the GT and their propinquity to power-lines is
presented next. The issue of spatial autocoITelation latent in housing data and the discussion on
spatial autoregressive techniques forms the next sections, which in tum is followed by a
discussion on econometric models. This paper ends with a conclusion and suggestions for further
research, To maintain flow in the main body of this paper graphs and tables are not reported in
the main text. Instead they are produced in sections following references. Othcr detailed
summary statistics and models, which were not commented upon in the main text, are also
bundled together in the appendices.
LITERATURE REVIEW
The influence of high-voltage power-lines on property values is in fact a function of residents
perception of the net side effects or benefits of proximity to power-lines. Often it is believed that
proximity to power lines exerts a negative bias on property values due to the perceived or
assumed health hazards commonly attributed to high-voltage power-lines. Others associate a
downward bias in the price of contiguous properties due to "unsightliness of the lines However
there are exceptions to the commonly held belief of health hazards associated with propinquity to
power-lines, Often properties located adjacent to the power-lines exhibit structural attributes that
are both unique and, at the same time, tend to compensate (sometimes over-compensate) for
proximity to a noxious facility. It has been reported in the past that properties contiguous to the
power-lines often had larger lot sizes.
In addition, influence on property values is also a question of taste where certain individuals
might not be troubled by the proximity to a noxious facility. This could be true for situations
where structural attributes of properties abutting power-lines are similar to the rest of the sample
and at the same time the socio-economic characteristics of the neighbourhoods of the two sub-
samples are very similar. We also found some evidence to this affect where for certain
municipalities within the GT A, properties located in close vicinity of high-voltage power-lines, at
an average, returned higher values than the rest of the sample. An earlier research by Rhodeside
and Harwell (1988) also observed a positive impact on property values, The positive influence
could be attributed to added privacy, easement, and landscaping resulting from the hydro s right-
of-way. On the other hand, Peiser and Schwann (1993), cited in Hamilton and Sch\vann (1995),
observed that "pure green space" does not have a profound impact on property values.
Influence of Power Lines on Freehold Property Values in the GT ,-- -,---------_u
___,_._
I~~1 f!-.c~u
Kroll and Priestly (1991) conducted a comprehensive literature review on the influence of
power-lines on property values, Half of the research reviewed by them found httle or
influence of high-voltage power-lines on property values. However, the remaining studies
observed negative influence of high-voltage power-lines on properties located within 200 meters
of the power-lines. The research material reviewed by them reported a loss of 2(10 to 100/0 in
property values proximate to power-lines.
Hamilton and Schwann (1995) conducted one of the "recent" econometric studies on the
influence of high-voltage power-lines. They observed influence of high-voltage power lines on a
nan-ow band of properties around the transmission lines. They paid considerable attention to the
functional form and heteroscedasticity. Other studies cited in Hamilton and Sclnvann (1995)
observed an impact of 5% or less on the property values fIgnelzi and Priestly (1989 1991);
Kinnard et al, (1984); and Colwell and Foley (1979H.
Hamilton and Schwann (1995) considered 13 000 properties sold between 1985 and 1991 in 4
different neighbourhoods of Vancouver, British Columbia, in their study. A total of 2364
properties were located within the 200 m of the power-lines. Using McKinnon s two-step J- Test
they concluded that both Linear and Log-linear specifications were not feasible and hence
adopted Translog functional form (logarithmic independent variables) for their model. The
dependent variable, housing values, was Box-Cox transformed. The fmal model included 104
linear, quadratic, and cross-product terms. They considered power lines over 69 000 volts as
high-voltage lines.
Hamilton and Schwann (1995) observed that hedonic functional form of properties adjacent to
the line, within 200 meters of the power-lines, and rest of the sample might be different. Based
on the Likelihood Ratio Test they observed that the three sets of properties might not have the
same functional form. Hence they reported results for the three sub-samples: adjacent, mid rage
(within 200-m), and far.
Hamilton and Schwann (1995) observed that proximity to high-voltage power-lines take away
3% from property values. In addition, when the visual externality of transmission line towers
was removed, it added $6670 (5.7%) to adjacent property values. In addition moving the
properties to a distance of 100-m from the power-lines added $6740 to the property values. For
the mid range properties (within 200-m of the power-lines) they observed an increase of $3438
(2.8%) in the property values when the properties were moved to a distance of 200-m from the
power-lines.
Influence of Power Lines on Freehold Property Values in the GT A Pagc-
,--.,------------.---.-.."-. -,-,
Despite the attention paid to the con-ect functional form , models retuTIlcd numerous strange
results. For example, Log of fireplace, bedrooms and full bathrooms returned negative
coefficients, suggesting that all else being equal, additional of a bathroom or a bedroom ""ill
result in the decline in property value. Some other results shed light on how property values were
valued differently because distance from power lines might result in distinct structural attributes.
For example, the variables log of housing age returned negative coefficient for adjacent and mid
range properties, while it returned a positive coefficient for far properties.
Peter F. Colwell (1990) adopted a temporal study of the impact of power-lines and towers on
the proximate land. Colwell tried to observe if growth of trees in the right-of-way gradually
reduces the impact of visual externalities on property values. This study addresses the
fundamental issues of distinguished structuf.U attributes of proximate land, especially casement.
Colwell argues: "Developers tend to increase the area of lots that have an easement for a power
line, while perceived lot areas go beyond the true lot line along a corridor right-of-way." He
argued that lot area (true or perceived) have to be held constant to gauge the effect of
transmission lines,
An earlier study by Kinnard (1967), cited in Peter F. Colwell (1990), also made the case for
easement.They argued that lots contiguous to the corridor s right-of-way should reflect a
premium due to access to large green space, which is available for recreational use to households.
In a similar study Kinnard (1967) and Reese (1967) suggested that the impact of power-lines
would diminish over time.
Colwell used a data set of 200 properties, within 400 feet (122 meters) of the power-lines, sold
over a period of 11 years. The model specification is mentioned in the following equation:
SPi=, o Il x exp(L, Xij+(MOSJ+, 9 (lIDLNJ+, loCrvIOS IDLNJ
j=l j=6
, 11 (1IDTWR , 12 (MOS IDTWR
SPi
Xij
DLN=
MOSi =
DTWR =
the selling price of the ith property
the /h characteristic of the ithproperty or sale, e.g, liveable area, no. of washrooms etc.
Distance from property to power-lines
the month of sale of the ith property,
the distance from ith property to the nearest tower.
Variables MOSj / DLNj and MOSj / DTWRj in the equation are trying to gauge '"the impact of
time on the effects of the two proximity variables (DLN, DTWR).He presented three
variations of the main model. Briefly, the models in,dicated that selling price of a property
Influence of Power Lines on Freehold Property Values in the GT A
, '
t:' ..r
___
h--_____-...._
_..
'h'
'- -,---, .-,- .,--....
increased with the distance from the power-lines. For properties not impacted by power-lines. the
annual appreciation rate was almost 70/0. The coefficient for proximity to the to\\'or \vas not
significantly different from zero at 90% confidence level. However, coefficient for proximity to
power-lines was significantly different from 0 at 90% confidence leveL The coefficient for
variable MOSi / DLNi was significantly positive, suggesting that the int1uence of power lines
diminishes over time. In a separate model, Colwell estimated the inf1uence of easement
property values. The model revealed that easement had a negative impact on property values.
Delaney and Timmons (1992), using a survey of appraisers conducted in 1990 observed that
proximity to power lines is capitalised into lower property values for residential properties.
Market value of such properties were, on average, found to be 10.00/0 less than the comparable
sales not influenced by the proximity to pNVer lines.
They cited a study by Kinnard (1988) that reviewed over 75 studies from 1950 to 1988. Only
four studies employed models to estimate the influence of power-lines. Three out of the four
studies found no "discernible impact" with the exception of Colwell (1990).
Almost 94% of the respondents in Delaney and Timmons (1992) cited visual Unattractiveness
as the reason for the decline in property values followed by health concerns (590/0): disturbing
sound (43%); sound intrusions (29%); and safety (290/0). In order to offset the negative influence
of power-lines, developers often lowered price of such properties, offered larger lots or invested
in landscaping. One key rIDding was that appraisers with no experience \vith properties
proximate to high-voltage power-lines assumed greater decline in property values than those
appraisers who have worked with such properties.
In a study conducted in New Zealand, Bond (1995) studied the impacts of high-voltage
overhead transmission lines. The study tried to assess the perceptions of various sections of
societies toward power-lines, for which the cut-off was set at 110 kV running on 26-111eter high
steel pylons. The study focussed on hr1\\ residents of properties proximate to power-lines. real
estate agents and appraisers with experience in neighbourhoods ""ith power-lines, evaluate the
. impact of such facilities. The study was based on a survey conducted on the follo\ving three
groups:
Residents within 300 m of high-voltage power-lines
Real estate agents
Appraisers
Two different questionnaires were used in the study. First questionnaire studied the reaction
of residenis or properties proximate to power-lines, while the second questionnaire was sent to
Influence of Power Lines on Freehold Property Values .in the 2TA
--,---------_.._---_._---?-,~g:~=~
real estate agents and appraisers. Residents living within 300-meters of the po\-ver-lines were
further sub-divided into two groups. Properties within 50-meters of the po\ver lines \Vcre labelled
as 'Close , while those located at a minimum distance of no less than 50-meters and no larger
than 300-meters were categorised as 'Distant'.
The study revealed that almost 790/0 residents believed that the presence of povver lines had a
negative impact on property values. In addition, 87% residents reported hearing the "buzzing" or
crackling" sound, while 78.3 % were troubled by noise. Almost 62.50/0 respondents were
concerned about the health hazards imposed by power lines
, '
while 52.30/0 respondents were
concerned about lines being damaged during an earthquake, Almost 80(Yo of the respondents
revealed that the price paid for the property was not influenced (counter-intuitive!) by the
presence of power-lines.
Survey respondents comprising of appraisers returned similar results. Appraisers observed
that 92% of residents related power-lines with negative influence on values. Real estate agents
who operate in areas with power-lines, reported a 10% influence on property values due to
power-lines, The study concluded that the three major players in the residential real estate
industry, namely residents, real estate agents and appraisers, all valued power-lines negatively. It
was also mentioned that the perceived negative attitude towards power lines might not truly
reflect in the transaction price.
Callanan and Hargreaves (1994) conducted a parallel study that complimented Sandy Bond'
work cited earlier. OLS models reported in the study found a negative association bet\veen
propinquity to power lines and property values, A residential unit located at 100 meters from the
power-lines will lose $3551 to its proximity to the high-voltage power-lines. When a residential
unit was moved closer to the power-lines, the model predicted a greater loss in value. If the same
unit was located at lO-meters from the power-line, the predicted loss in value at $35 510 \vas ten
times the loss at 100 meters.
Callanan and Hargreaves (1994) and Bond (1995) based their research on the same study
area: Wellington, a suburb of Newlands in New Zealand, Using an econometric approach
Callanan and Hargreaves (1994) tried to quantify the influence of High Voltage Overhead
Transmission Lines on residential property values, The sample data consisted of 330 properties
within 300 meters of the high-voltage over head property lines, sold over a period of Ii years,
Zoning policies in New Zealand, which deal with proximity to power-lines, differ from those
in North America. Unlike in North America, residential properties could be located directly
Influence of Power Lines on Freehold Property Values in the GT A Pauc- 7
----,_._--------,. ---,-.."..----.---- ,--
beneath the power-lines in New Zealand. Within the sample data 4.5(10 properties vvcre located
directly under the power-lines, while another 10% were located within 50 meters of the power
lines, OLS models reported in the study used structural attributes of the residential properties.
aggregate locational variables, temporal indicators of sale, and reciprocal of distance from the
power lines as explanatory variables.
Locational elements other than the proximity to power-lines, such as distance from the Central
Business District (CBD), have long been utilised as an explanatory variable in hedonic models.
Similarly, effects of LR T, subways and highways on property values have also been quantified in
hedonic models, The impact of distance from CBD depends upon the geography and economy of
a city. In a study of house and land prices in Sydney, Australi~ it was found that house and land
prices fell dramatically with distance from the CBD (Abelson, 1997). The analysis was
conducted in two stages: a) between 19-; i tn 1968 , and b) between 1970 and 1989. For the two
periods, a negative exponential relationship between property values and distance to the CBD was
discovered. LOGCBD (log of distance from CBD) was found to be the most significant variable.
Locational variables, such as accessibility to rail or to the regional shopping centre, were not
significant variables in explaining house prices,
The assumption that cities are mono-centric may not hold for modem cities. This fact
evident from the layout of high-voltage power-lines as the lines criss-cross through the
urban/suburban landscape of the GT A. Modem cities have become, or are in the process
becoming, polycentric with increases in suburban office and retail centres. In a study of travel
behaviour, it was discovered that suburb-to-suburb trips have increas'ed in number, relative to
suburb to CBD trips due to the decentralisation of employment (Levine, 1995). Provision of
electric power is imperative for suburban job growih. Thus in the GTA, high-voltage power-lines
are more distinctly recognisable in the suburbs since their explicit visibility is a prelude to future
development opportunities.
In another study of housing values, Vfination in housing prices was explained using a distance
decay function, changes in population and housing stock, and changes in ethnic mix (Archer.
1996). A generalised version of the repeat-sales index was used to estimate housing price
appreciation. The data set consisted of 42 890 repeat sales in 79-Census Tracts (CT) groups in
metropolitan Miami. The properties were geo-coded to the respective CT. When the CT group
ill was excluded from the model specification, the model explained 76% of the variance in house
price appreciation. The addition of CT group ID explained an additional 30/0 of house price
Influence of Power Lines on Freehoid Property Values in the GTA PucTc-I::-
.---,-,---------.,-.-- - -,-., ,
appreciation, The log of distance variable returned a negative coefficient, indicating that the
house price appreciation rate declined with increase in distance from CBD.
Zeiss (1998, 1999) stated that highly controversial facilities do not consistently cause
significant impacts on residential property values, yet some less objectionable facilities do. Zeiss
looked at typical physical, psychological and trigger impacts of ten categories of noxious
facilities. He concluded that nuclear power plants, waste facilities, electrical power plants and
transmission lines cause inconsistent property value impacts. These facilities were characterised
by multiple and complex physical and socio-economic impacts and medium to high perceived
risks. On the other hand, airports, highways, all- pollution, visibility impacts and natural hazards
were found to consistently cause property value effects, create single observable physical impacts
arid are perceived as low risk. Zeiss combined electrical power plants and transmission lines in
one category in his studies. As a result of this combination, it is hard to isolate the impacts on
residential property values due to transmission lines only.
Verkasalo et ale (1997) studied the effects of magnetic fields from transmission lines on the
risk of depression. Two data sets were used in the analysis. The first consisted of 12 063 persons
who had answered the 2l-item Beck Depression inventory of self-rated depressive symptoms.
The other data set looked at the personal 20-year histories of exposure to overhead 11 Ok V -400k V
power lines, They reported that the adjusted mean Beck Depression Inventory scores did not
differ by exposure, provided that proximity to power lines is not associated with changes \vithin
the common range of depressive symptoms. However, they stated that the risk of severe
depression increased 4.7 times among subjects living within 100m of high-voltage transmission
lines.The aforementioned statement was based on small numbers and further validation is
required.
Furby et al. (1988) reviewed and critiqued the methods of detennining transmission line
impacts on land values and methods for compensating property owners for losses associated to
power lines.The authors also reviewed empirical studies that dealt with the effects of
transmission lines on property values. A key issue in detemlining the effect of transmission lines
on property values is the identification and evaluation of perceived losses. The authors stated that
carefully conducted studies could capture the influence of power-lines on properties. Based on
the studies that they reviewed the authors stated that there was a clear discrepancy behveen "vhat
lay people and experts think about the effects of transmission lines on property values,
Influence of Power Lines on Freehold Property Values in the GTA Page-
,---_._------'---'----,.----'- ---- ",.-- ,--",
Gregory & von Winterfeldt (1996) identified some studies that reported a significant
association between indirect measures of exposure and cancer. They also reported that nmncrous
other studies found no statistical evidence of such effects. The issue is further complicated by the
ubiquity of sources of electromagnetic fields. The authors list following reasons for loss in
property values due to power-lines:
A possible reduction in the visual attractiveness of the property
A possible increase in the level of residents' fear about potential health effects
A possible reduction in the pool of buyers, and thus an increase in the cost of selling.
A possible increase in the length of time required to sell the property,
For the pre-1979 studies, the range of decrease in property values was between O~Io-30%. For
the post-1979 studies the decline in value was between 50/0-10%. They claimed that there were
many situational factors that influence whether a property will decline in value or not due to
power lines.
METHODOLOGY
This research uses a subset of the data created for a larger study of housing values, hedonic
price indices, in the GTA (Haider, 1999). The earlier study employed a large data set of 285JJOO
freehold properties in the Greater Toronto Area (GTA), which transacted during 1987 and 1995,
Haider (1999) documents the detailed results from the spatio-temporal analyses of the complete
sample of 285 000 sales. A key objective of that study was to ascertain significant detenninants
of housing values, including the influence of locational elements, such as proximity to subway, or
a mall, on housing values. The presence of spatial autocorrelation in the propcrtv; values "vas also
evaluated.
Housing values (actual transaction prices) and other structural attributes of the properties \vere
obtained from the Toronto Real Estate Board (TREB). The TREB database captures almost 80010
of all residential transactions in the study area.
TREB data were geocoded, based on the street name and number, using a modified version of
the Geocoding algorithm available in Map Info (!J. A success rate of 88% was achieved for
geocoding. The remaining 120/0 properties could not be geocoded because of
incomplete/incorrect address information in the TREB data set. Initially, properties sold for less
than $10 000 were excluded from the analysis. Later, during model building, records with
incomplete or eIToneous infonnation were excluded from the database, leaving the number of
Influence of Power Lines on Freehold Prope,rtv Values in the GT A
----_._------------.)~~~~~.
~l!)
sales' for the year 1995 at approximately 27,400, Figure-AI presents the spatial distribution of
sample property values in the GT A.
A series of locational variables were created to gauge the effects of accessibility to utilities
such as subways, highways and shopping centres. , Details on locational variables arc reproduced
from Haider and Miller (1999) in Appendix B. This research focussed exclusively on the
influence of power lines on residential properties in the vicinity. The entire spatial analysis was
performed in a GIS. A GIS map of high-voltage power-lines "vas extracted from the Statistics
Canada Street Network Files obtained from Data Centre at the University of Toronto.
Concentric buffers were created around the power-lines at distances of IDO-m, 200-Ill , 300-m
400-m, 500-m, 750-m, IOOO-m, 1500-m, 2000-m, and 3 ()QO-m. Figure-A2 shows the layout of
power-lines and the concentric buffers of 1000 meters and 2000 meters draw-n along the power-
lines, CTs intersected by power-lines have been shaded in grey. The buffer maps were overlaid
on the geocoded property map to create binary variables that control for proximity to power-lines,
For example, B IOO is a binary variabk that carried a value of 1, if the property \vas located
within 100-m of the high-voltage transmission line and 0 othenvise. Similarly nine other binary
variables were created corresponding to the buffers mentioned above.
Research cited in this paper indicated the extent of influence of high-voltage power-lines was
observed up to only a few hundred meters from the power-lines, OUf research stands out from the
previous work for the reason that we explored influence of power-lines on property values up to a
distance of 3-km. This research also stands out for the fact that we employed a huge data set of
000 observations, which is quite larger than the data used in previous research. Our research
concludes that direct influence of power-lines does not extend explicitly beyond 500- meters from
the centre line of the power-lines.
This research also tried to answer the following questions:
a) Are the houses in close proximity to power-lines any different in their structural attributes
from housing units at greater distances from power-lines
Do residents in different neighbourhoods perceive power-lines differently?
c) Are socio-demographic characteristics of Census Tracts intersected by power-lines any
different from those of CTs with no high-voltage power-lines?
Answers to the above three questions are documented in descriptive analyses.
1 All dollar figures are in 1995 Canadian dollars,
Influence of Power Lines on Freehold Property Values in the GT ' Page-
------,-,--.-,----.-------..----...'"---.' '..'--,-"
The analysis of spatial dependency in housing values fonned the basis of spatial 11l0del of
property values being presented in this paper. We based our decision to apply spatial
autoregressive techniques after quantifying spatial autocorrelation in the data~ and not on the mere
assumption of its presence. We found the spatial lag variable as the most significant variable in
the spatial econometric models along with variables controlling for propinquity to power-lines.
When the spatial lag variable was excluded from model specification , the exlJlanatory power of
the model was compromised and some coefficients in the models retunled counter-intuitive
results.
DESCRIPTIVE ANALYSES
As mentioned earlier, a series of concentric buffers were created along the centre-line of the
high-voltage power-lines in the GTA. Based on those buffers we compared the average sale price
of properties within the buffers with rest of the sample (Table-AI). The average price of
properties located within 100-m of the buffers was $210 000 against $228 000 for rest of the
sample. Within our sample of 27400 pmpct-bes, 650 properties were located within 100-1ll of a
high-voltage power-line in the GTA. Similarly, mean price of properties within 200-meters of the
power-lines was $212 000 against $229 000 for rest of the sample. In Table-, the second
buffer of 200-meters also included the properties that were located at a distance of less than 100-
meters of the power-lines, Hence the average property values and nwuber of sales are cumulative
as each buffer adds more properties to the previous buffer and computes the Illean for all
properties that are located within the buffer.
Table-AI explicitly reveals the mcrease of property values as the distance from the powcr-
lines increases. However, the trend reverses for properties located within I-kill of the power-
lines. There is no evidence to believe that the influence of power-lines extends up to 1000-
meters. Hence statistics reported in Table-AI for larger buffers are offered for comparison only,
We conducted a one-tailed T -test t6 detennine if the properties outside the buffers at an
average fetch higher prices than the properties located within the buffer. The test statistics
reported in Table-AI mdicated that for a 99% confidence level, mean price of properties located
outside of buffers was higher than the mean. price of properties located within the buffers. The
above statement is true for mean prices estimated at all distance thresholds for this study.
We will now compare the mean sale prices discussed in the previous paragraphs with the
mean values of properties located exclusively within the buffers. Consider Figure-A3 where
mean prices are reported for properties located in the "doughnuts . For 200-m buffer, the mean
Influence of Power Lines on Freehold Property Values in the G:!,
~--_._-- ---_._------
F~~~~-
price ($213 200) is reported for those properties that are at a distance of more than 100-m and less
than 200-m from the power-lines. Compare this mean value with the mean price ($212J)OO)
reported in Table-AI for properties within 200-m of the power-lines. Similarly, for properties
that are located at a distance greater than 400-m and less than 500-m the mean value is $23 LOOO.
For a comparison, mean price of all properties at a distance of less than 500-m is $219_000
(Table-AI ).
Figure-A3 vividly explains the relationship between property values and propinquity to
power-lines. As the distance between power-lines and properties increases, the mean price of
property values also increases. The relationship holds up to a distance of 500-m from the power-
lines. The average price of properties that are located at a distance no less than 400-m and no
greater than 500-m is higher than the properties that are located at a distance of greater than 500-
m but less than 750-m. It is assumed that beyond 500-m, price of residential properties
affected more by factors other than their proximity to power-lines.
Figure-A4 presents the number of sales captured at various distances ITom the high-voltage
power-lines in the GTA. It could be deduced from Figure-A4 that for every additional IOO-m
distance from the power-lines, 1000 additional sales were recorded. A total of 1011 properties
were sold a distance greater than 300-m and less than 400-m from high-voltage power-lines.
Meanwhile a total of 3636 sales were recorded within 400-m of the pm,ver-lines.
The above discussion leads us to a very important issue: Do residents in different
neighbourhoods perceive power-lines differently? To answer this question we conducted the
above-mentioned analysis on a disaggregate level of municipalities (Table-A2). We will discuss
in detail results from municipality-wide analysis for properties located at a distance of less than
IOO-meters from the power-lines, Detailed results of disaggregate a.t'lalyses for properties located
at distances greater than IOO-m from the power-lines are documented in Appendix C. It should
be noted that these municipalities were amalgamated earlier into a mega-city now called GT
However, it is hypothesised that over the years these municipalities developed specilic traits and
attributes that attracted households with peculiar characteristics. For example, average price of
housing units in Etobicoke at $235 000 is significantly higher than the average price recorded in
Ajax at $170 000 (Table-A2),
A quick review of Table-A2 reveals that several municipalities, such as Aja.x, Mississauga
and Markham reported higher mean price for properties within 100-Ill of the high-voltage power-
lines than those properties that were at a distance greater than IOO-m. TIle number of sales for
properties located very close to the power-lines is very small for most municipalities.
Influence of Power Lines on Freehold Property Values in the GT
___--
_m- -----
--- ------.. --_,
a~~~_
Mississauga remains an exception with 11 7 sales reported within lOO-m of the power-lines.
Could it be true that for certain suburbs of the GTA, residents are not wary of high-voltage
power-lines? This hypothesis would lead to further assumptions. It could be true that residents
of those municipalities do not consider proximity to power-lines a health hazard or a visual
externality. We lack data to effectively answer the questions raised here. Our database can best
make assumptions about public attitudes and perceptions toward power-lines by looking at the
market price of a property and its locational amenities.
However, we can try to answer two other questions that we raised in the previous section:
a) Are the houses in close proximity to power-lines any different in their stmctural attributes
from housing units at greater distances from power-lines, and
b) Are socio-economic characteristics of Census Tracts intersected by povver-lines any
different from those of CTs with no high-voltage power-lines?
To answer the fIrst question, we compared structural attributes of housing units by
municipality within 100-meters of the power-lines with attributes of those units that were located
at a distance greater than 100-m (Table-3), For the municipality of Ajax, one can argue that
the housing units located closer to the power-lines are comparatively larger in size than rest of the
stock within Ajax. The average number of rooms bedrooms, washrooms, and parking places is
higher for properties that are within 100-m of a power-line. Similarly, 1000/0 of the properties
located closer to the power-lines are detached against 85% properties in rest of the sample in
Ajax. All properties within the IOO-m buffer hiKl a flfeplace against 650/0 in rest of the sanlplc.
Again, 73% properties located closer to the power-line were centrally air-conditioned against
600/0 of rest of the stock. F or the municipality of Mississauga, variables acting as a proxy for the
size of the housing unit indicate that properties within 100-meters of the power-lines are larger in
size than rest of the freehold stock within Mississauga. Almost 92010 properties within the lOO-m
buffer were detached, another 88% reported at least one fITeplace, and 72010 were located close to
a major regional highway. While for rest of the stock in Mississauga only 720/0 properties were
detached, 73% reported a flfeplace and only 30% properties were located close to a regional
highway.
From the above discussion and resHlls portrayed in Table-A3 one can argue that for those
municipalities where average price of properties proximate to power-lines is higher than the rest
of the sample, those adjacent units generally are of better quality and are of larger size. 'Ve
observed the sa..T..e trends in other buffers (please see Appendix C for results) where adjacent
Influence of Power Lines on Freehold Property Values in the GT
_,__-,-- ------ --_._
~~1I~.:.::~~.
properties with better structural attribute~ returned higher mean price than the rest of the sample
for certain municipalities.
It should be noted that certain traits and characteristics that might be sought after in one
municipality might not be as desirable in another municipality. Continuing with thc comparisons
of Ajax and Mississauga, one could see that Mississauga residents attach high priority to highway
accessibility. Since the high-voltage power-lines corridor nm along a regional highway in
Mississauga, greater accessibility to regional highway, to an extent, compensated for the assumed
losses associated with proximity to power-lines, On the other hand, higln-vay accessibility
premiums are enjoyed by a smaller sub sample of residents in Ajax, It can be argued that
highway accessibility is not a highly desirable trait in Ajax. Accessibility to a highway for
housing units in Ajax did not capitalise into higher property values (Table A-3).
It has often been argued that low-income households are forced into choosing localities with
undesirable features, such as propinquity to a nuclear power plant or a landfill, Assuming that
households associate power-lines with undesirable features, one can argue that CIs punctuated
with high-voltage power-lines should be- home to low-income households. To test this
hypothesis, 1991-CT data on socio-economic characteristics for CIs intersected by power-lines
was compared with the remaining tTs (Table-, Figure-A2). The comparison revealed that
CTs intersected by power-lines demonstrated equally good socio-economic characteristics, if not
better, than the remaining CTs in the GTA that were not intersected by power-lines. Average
household income for CTs intersected by power-lines was surprisingly higher than the average
household income for remaining CTs in the GT A. Similarly average number of census families
per CT, earning more than $70 000, was higher for CTs intersected by power-lines.
Average number of households spending more than 30% of their income on shelter was lesser
for CTs with power-lines, A comparison of structural attributes of housing units reported in
Census revealed that CIs intersected by power-lines had larger housing units than CIs that are
. not home to high-voltage power-lines. After comparing the socio-economic characteristics of the
CTs groups in Table-, it can be argued that the CIs intersected by power-lines are no fUn
down areas inhabited by low-income by households. In fact, for numerous indicators of social
quality, CTs with power-lines outperformed remaining CIs without high-voltage povver-lines.
From the above it can be deduced that the observed loss in property values could be attributed
to the propinquity to high-voltage transmission lines. The analysis revealed that properries and
neighbourhoods abutting power-lines in the GTA were of high quality and not dilapidated areas.
Influence of Power Lines on Freehold Property Values in the GTA
---.
Page-
--------- ,---,-
SPATIAL AUTO-REGRESSIVE SPECIFICATION
The need to adopt auto-regressive techniques in hedonic price equations is documented in
detail in Haider (1999). For brevity the discussion about the use of SAR techniques will not be
reproduced in this paper. There is a consensus in the housing literature that the hedonic price
method offers the best econometrics environment to model housing prices (Can and 1-1cgbolugbc
1997). The development of a hedonic model in this paper relies heavily on the model developed
by Can and Megbolugbe (1997). They have argued in the past that the most hedonic models were
insensitive to the geographic location of dwellings within the metropolitan area, thus overlooking
the inter-metropolitan variation in housing prices, Spatial spillover effects, they argued, in the
operation of local housing markets require one to focus on spatial dependence in specification of
housing price function. Spatial dependence varies with metro areas and over time.
Can and Megbolugbe (1997) adopted the "Comparable Sales" approach in specifying the
spatial lag variable. At the heart of this approach lies the assumption that the price history in the
immediate neighbourhood of a given property will have spillover effects on its market value. The
prices of the most recent sales of similar properties are considered in estimating the market value
of a property, controlling for differencc~ in their structural attributes and lleighbourhood
characteristics.
The Spatial hedonic model specification is portrayed in the follo-wing equation:
~t 2: j wij'j,t-+2: k f3k ik + 2: I Y1 Nilt ;it
m=l 6; j:t:i; Wij = Lj((l/dij)/ Lj l/dij
j= 1
,.. .
N; dij :::;; 2
The variations in the house prices are explained in tenn of the differences in their stnlctural
characteristics (S) for k= 1
, ...
, K and/or neighbourhood characteristics (N) for J
, ...
, L. f3:yare
the parameter vectors corresponding to S & N, while a is a constant. W~i is the weight that
specifies the extent of influence of price of prior sales Pj (that occurred between time t-ill and t)
on the transaction price of the concerned property, which we would refer to as the anchor
property. Meanwhile p is a measure of overall level of spatial dependence between tPi, Pj,t-m
paIrs.
This model incorporates both spatial and temporal functional interdependencies. The
influence of prior sales is hypothesised as an inverse function of distance, dij. The lesser the
distance between a prior sale and the anchor property, the more influence that prior sale will have
Influence of Power Lines on Freehold Property Values in the GTA Page-
--,----_._---
,_,m_- ---
over the transaction price of the anchor property. By introducing a spatially autoregressive tenll.
Wij x Pj,t-m, as an explanatory variahk ~ we have explicitly controlled for the functional
interdependence.
The spatial lag variable was used to quantify the influence of neighbouring properties on the
value of a specific property referred to as the "anchor property." The correct specification of the
spatial lag variable is imperative for the statistical validity of the model. If specified correctly,
the spatial lag variable, Wij x Pj,t-m, will control for spatial autocorrelation that exists in data.
hypothesised that the value of a property at time, t, is influenced by the most recent sales of
comparable properties in the vicinity of the anchor property. We also hypothesised that the
spatial spillover effects do not extend beyond a 2-km radius of the anchor property. In other
words, we assumed that housing values are not correlated if a distance of more than 2 kilometres
separates properties. We also hypothesised that property values are not correlated if the sales are
more than six months apart. These cut-off points are arbitrary.
Our study involves huge data sets with approximately 30 000 records in every estimated
model. The large sample size affords us the opportunity to apply OLS or Weighted Least Square
techniques instead of Maximum Likelihood Estimators, since OLS estimates are unbiased for
large sample sizes (Cliff and Ord, 1981).
Table-A5 presents the summary statistics of certain explanatory variables used in reduced
models. The average sales price for the sample was $227 600. The average number of rooms in
a house was 7, while the average number of bedrooms was 3.3. The average number of
washrooms was 2., with the average parking capacity at 1.2, 70% of the properties in our
sample were detache4 housing. 30% of housing units reported at least one fireplace, \vhile 10%
of the housing units reported more than one fireplace. Almost 50% of the houses in the sample
were centrally air-conditioned.
We used the semi-log specification to control for non-linearity in the data set. The follmving
equation describes the models discussed in this section.
Housing Values
= (p
*Lag Variable) * exp(a+~lSl + BzSz + ... + ~nSn + YI ... + Yn
S= Structural attributes (type & size of unit)
N= Variables controlling for pn:T~6mity of housing units to various landmarks, such as
power-lines, subways and highways.
Lag = Spatial Lag Variable
Influence of Power Lines on Freehold Property Values in the GT Paoe- i7
-- -
- __--_nu
' ____'
n ,.. ,_,nn
'Y, P are the parameter vectors corresponding to S, N, and Lag Van able respectively
Variance Inflation Factors (VIF) were estimated (results not shO\\I'n) to check multicollinearity
within the explanatory variables. Low \ alues for VIF were observed that suggested little or 110
multicollinearity in explanatory variables. Models were weighted by number of rooms to control
for an increase in variance of residuals with the increase in the value of dependent variable.
DETECTION OF SPATIAL AUTOCORRELATION
The impetus for advocating SAR techniques is premised on the assumption that spatial
autocorrelation exists in housing data. Moran s I was calculated for housing values to quantify
spatial autocorrelation. We specified a weight matrix, Wij, by relying on level of adjacency among
CTs. We preferred Moran s I to Geary s C, since Moran s coefficient, in case of a mis-specified
Geometric Weight Matrix, seems to retain power better than other spatial autocorrelation test
statistics (FlonD( and Rey, 1995).
Moran s I is defmed as following:
n~L W (Yi Y)(Yj
i=l j=l
(~(Yi
y)2 )(L: Lio'j W ij
i=l
Where Yi is the variable of interest and Wij is the spatial weight matrix.
The calculation of Moran s I is computation ally very intensive. In order to reduce the sample
size, we aggregated the property values to the CT level. This aggregation will result in a higher
value for Moran s I due to the aggregation bias. However, estimating Moran s I for large number
of observations was not possible with the existing computing power. Figure-A 1 also offers a good
indication of presence of spatial autocorrelation in the disaggregated data set.
, The weight matrix was specified using three techniques. For two contiguous CTs, level of
adjacency could be expressed as a function of the length of common border. Therefore. the
greater the length of the common border between the two CTs, the more contiguous they arc.
Another simpler approach is to use a binary variable as the weight matrix: the weight value is L if
the two CTs are contiguous and 0 otherwise. The third method tested for specifying the weight
matrix is similar to the fIrst technique where the length of the common border between
contiguous CTs derIDes adjacency. However, to explicitly incorpomte spatial structure of the
Influence of Power Lines on Freehold Property Values in the GT A Page-
----------.----.-------.-.--."------------.....-' ....-.
CTs, the common border length between the two CTs was weighted by the average perimeter of
the CTs. Table-A6 presents results for Morati's I calculations for freehold properties sold in the
GTA during 1994. AutocolTelation statistic is offered for the three measures of contiguity in
weight matrix: length of common border, adjdCency, and weighted common border length,
Results from these computations indicate the presence of spatial autocorreJation in average
housing values for the CT, Weighted common border length specification, which is assumed to be
sensitive to the spatial structure of the region, returned the highest value for Moran
Surprisingly, the common border length technique returned a higher value for spatial
autocolTelation than the simpler weight matrix. However one should realise that the binary
weight matrix is oblivious to the spatial structure of the region.
ECONOMETRIC MODELS TO QUANTIFY INFLUENCE OF POWER-LINES ON
RESIDENTIAL REAL ESTATE VALUES
As mentioned earlier, the data set and the modelling techniques applied in this study are the
same as were used in Haider (1999), Haider and Miller (1999), and Miller and Haider (1999),
The set of variables used in the models for this study was selected from a pool of hundreds of
explanatory variables. After a detailed systematic analysis, documented in Haider (1999), a
smaller set of variables was selected that best explained the variance in property values in the
GTA. Hence for this study, apart from variables that gauge proximity to power-lines, other
variables used are those that were short-listed in earlier research by Haider (1999), Haider and
Miller (1999), and Miller and Haider (1999). Therefore this study will not repeat the process of
identifying the most significant predictors of property values. Instead the models discussed in
this study would extend the scope of earlier research by quantifying the influence of high-voltage
power-lines on property values.
Two model specifications were developed for each buffer variable: Ordinary Least Squares
(OLS) and Spatial Auto-regressive (SAR). The fITst set of OLS models is discussed in the
following paragraphs. Table-A7 presents the details for the OLS models. OLS mode)s explained
52% variance in housing values. All parameter coefficients in the reduced models 'were
significant at 950/0 confidence level. The coefficient values were almost unifonn across the
models. It is evident from the OLS models that proximity to power lines has been valued
negatively in all models. Results from the ten models reveal that proximity to high-voltage power
lines is associated with a decline of $11 000 to $27 000 in property values. For properties
located within I-km of the power-lines: the loss in value was between 4 to 6,2% of the average
price of the sample stock. If a property is located within 100-m of a power-line, its value is
Influence of Power Lines on Freehold Property Values in the GT~
___----,--,----__..
~~~e-=-!.:?
$17 700 less than a similar property at a distance greater than 100 meters from the power-lines
all else being equal. These results are consistent with other research reviewed earlier in the
paper. Though we have estimated models for buffers up to 3 kilometres, there is little evidence
that influence of power-lines extends be'ood 500-meters.
Figure-A5 shows how the coefficients of buffer variables decline as the distance fTom the
power-lines increases. Figure-A5 reveals that as the distance from the power line increases the
loss in value attributed to the proximity to power-lines decreases up to a distance of 500-m.
However, the coefficient for buffer variables start to increase in absolute value for properties
located at a distance greater than 500-m and less than 3-, suggesting that property values
decline at a greater rate as the distance from the power lines increases beyond 500-ro. This
rIDding is counter-intuitive. We believe that buffer variables for more than 500-meters are
reacting to influences other than that of power-lines, which are causing the models to predict a
greater decline in property values with the increase in distance from power-lines beyond the 500-
m threshold.
We also believe that these counter-intuitive results are due to the current model specification.
As we employed different model-specifications (e.g. spatial auto-regressive methods), vve were
able to control for this anomaly. Another point of concern for these models is the behaviour of
variable D CBD that contains EuclideaR distances in kilometres between properties and
downtown Toronto (intersection of King and Bay streets). D CBD is in fact the price gradient
with an average value of approximately $1950. It implies that property values decline by $1950
per kilometre from CBD. It can be argued that the downtown effect does not ex.1end too far into
the suburban GT A. Hence models return negative property values for certain smaller properties
located at Euclidean distances greater than 45-km from CBD.
One way of getting around this problem is to estimate two set of models: one where D - CBD
is entered as an explanatory variable while the other model for far-off properties is estimated
without D - CBD as an explanatory variable. However, when a semi-log model specification was
applied, the models did not return negative property values. A semi-log version of the model
with B lOO as the buffer variable controlling for proximity to power lines is presented in Table-
A8. The minimum predicted value obtained from the model was equal to $63 800 (elI-O64). The
model offered a better fit and explained 64% variance in property values, This was a significant
improvement over prior OLS estimates with adjusted R-square at 52%.
A close look at estimated OLS models in Table-A7 reveal that washrooms playa critical role
in property valuation, with each additional washroom valued at $45 000, ceteris paribus.
Influence of Power Lines on Freehold Property Values in the GT A
----------------....- .,,_
~f-~.
~?~:'~..
Accessibility to the subway system in the GTA reflects a premium in property values. If a
property is located within 1.5-km of a subway line in the GTA, it is expected to fetch an
additional $38 000, all else being equal. Proximity to a major highway was associated with a loss
of $7 000. Presence of multiple fIreplaces in a housing unit is indicative of high quality: better
styled units probably with location in an up-beat neighbourhood. In order to control for the
quality of housing, we added a binary variable FIRE - ML T, which carried a value of 1 if the
housing unit portrayed multiple fIreplaces and 0 otherwise. FIRE MLT added $106 000 to the
property value.
Detailed results from the above-mentioned models are reported in Appendix D,
Results from the spatial model specifications, discussed earlier in the paper, are presented in
Table-A9. The spatial autoregressive models by far offered the best fit against all other model
specifications tried in this research. The spatial model explained 82% variance in the housing
values. The a priori expectations were met for all parameter coefficients. With the exception
Mall , a variable controlling for proximity to a large shopping centre, all other variables
returned significant coefficients at 95% confidence level. All else being equal, the addition of a
bedroom or a washroom will add to the value of a housing unit. Proximity to subway adds to the
value of a property, while the property values decline as the distance from Toronto CBD
mcreases.
The coefficient for buffer variable, B I00, was negative and significant, suggesting that a loss
in value is associated with properties that are proximate to power lines,Predicted property
values varied between $30 000 and $2 300 000. The residual statistics in Table-A9 confinn the
fact that spatial models were by far better fit than the non-spatial models. Other spatial models
with larger buffer values are reported in Appendix D (page D 1 0 and onwards).
A quick comparison of residual plots in Figures A6 and A 7 explicitly indicates that spatial
models are better fit than the OLS models. Figure A7 reyeals that residuals from the spatial
model are evenly placed around the mean value of 0 with no obvious trend. However, residual
plot for the OLS model has a downward slope, In addition, the residual plot for OLS model
reveals that as the predicted value increases, the spread around the mean for residuals also
increases simultaneously, suggesting the presence of heteroscedasticity. Several measures were
adopted to control for heteroscedasticity, including weighted model along with semi-log
specification. In addition, use of spatial autoregressive specifications controlled the spatial
con-elation in housing data.
Influence of Power Lines on Freehold Property Values in the GT Pagc- 2 u_-'-
---' .------- ,-------""-' ,...---'--- ,., ,..'
The residual histogram for spatial autoregressive model (Figure-A8) offers another evidence
of a close fit since residual values are focussed around the mean zero and the plot approximates
normal curve. Histograms were also plotted for the predicted values obtained from OLS and
spatial models (Figures 9 and 10). The SAR model approximates a Honnal curve better with
slight positive skewness. On the other hand, histogram for OLS model is marred with extreme
values and shows negative predicted values along with some very high property values. In
summary, spatial autoregressive models offered better fit than the OLS models. Also, SAR
models treated properties with unusual characteristics better than the OLS models.
Influence of Power Lines on Freehold Property Values in the GTA Page-
---------.--'--.--.-'---- ..'_.-
CONCLUSIONS
This research offers conclusive evidence to the claim that propinquity to high-voltage pO\:vcr-
lines capitalises into lower property values. Results from OL8 models estimated for freehold
properties within I-Ian of the power-lines suggest a loss of 4% to 6.20/0 in value, Loss in value
decreases with distance from power-lines. At an average proximity to high-voltage power lines
resulted in a decline of $11 000 to $27 000 in property values.
Two different model techniques (OL8 and 8AR) were used in this study.The residual
analysis revealed that spatial autoregressive specification offered a better fit and explained 81 %
variance in property values. In addition, spatial and s~mi-Iog specifications dealt with the
idiosyncrasies of records with ex.1reme characteristics better than the 0 L8 ill odels. We found the
spatial lag variable as the most significant variable in the spatial econometric models along with
variables controlling for propinquity to power-lines. We based our decision to apply spatial
autoregressive techniques after quantifying spatial autocoITelation in the data, and not on the mere
assumption of its presence.
This research also stands out for the fact that we employed a huge data set of 27AOO
observations, which was quite larger than the data used in previous researches. In addition, we
based our research on the actual transaction prices and not the assessed property values. Our
decision to use actual transaction prices and not the assessed values is based on the assumption
that only market prices can reflect the true perceptions of consumers of residential real estate of
high-voltage power-lines.
. We observed that as the distance between power-lines and properties increases, the mean price
of property values also increases. The relationship holds up to a distance of 500-In from the
power-lines. The average price of properties that are located at a distance no less than 400-m and
no greater than 500-m is higher than the properties that are located at a distance of greater than
500-m but less than 750-m. It is assumed that beyond 500-m, price of residential properties is
affected more by factors other than their proximity to power-lines, A visual inspection of the
layout of power-lines suggests that location of power-lines corridors is often colTelated with the
location of highways, subways or prime real estate in the GT A. Hence properties located at
greater distances from the power-lines are also missing on certain much-desired urban traits, such
as accessibility to the transportation network (Figure-2).
Influence of Power Lines on Freehold Property Values in the GTA
- '
t:J ~.
._.-'-'--------'-'- ----,_...'-,---
We also discovered that relationship between proximity to power-lines is not unifonn
throughout the GTA. We discovered certain localities within the GTA where propeI1ic:s abutting
the power-lines were of greater value than rest of the sample in the locality. The number of such
properties was vel)' small when compared with the total sample size. Based on our analysis \ve
conclude that for municipalities where average price of properties proximate to pm,ver-lines is
higher than the rest of the sample, the adjacent units are of better quality and are larger in size
than the rest.
We also tried to compare socio-economic characteristics of Census Tracts intersected by
power-lines with those of CTs with no high-voltage power-lines The compmison revealed that
CTs intersected by power-lines demonstrated equally good socio-economic characteristics, if not
better, than the remaining CTs in the GT A that were not intersected by power-'lil1es. It can be
argued that the CTs intersected by power-lines are not run-down neighbourhoods~ inhabited by
low-income by households. In fact, for numerous indicators of social quality, CTs with power-
lines outperformed remaining CTs.
The econometric modelling could have offered better results if a continuos variable. distance
from the power-lines, was used instead of a discrete variable. We tried to work around this
problem by using a series of discrete variables, B lOO, B 200
...
We believe that the results
from the proposed change would shed more light on the relation between influence on power-
lines on property values.
Similarly, the rate of influence of power-lines could also depend upon the voltage of power-
lines and the height of towers supporting these power lines. We made no attempt in our research
to determine if property values are sensitive to voltage levels or height of towers, in addition to
the distance from power-lines.
Some vel)' important variables were mlsslllg from our database, such as lot size and
information on easement. Therefore we could not test the effects of easement 011 property values,
Attempts were made to identify any differences between properties proximate to power-lines and
the rest of the sample. We used average number of rooms, bedrooms and similar structural
attributes as' a proxy for size. However, we believe that information on actual lot sizes and
easement could have helped us make more conclusive conclusions.
Influence of Power Lines on Freehold Property Values in the GT Pao-e-_m____
-' --, -.,
5: .--.. ...'
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APPEND IX A
List of Tables
Table-AI:
Table-A2:
T able- A3 :
Table-A4:
T able- A5:
Table-A6:
Table-A?:
Table-A8:
Table-A9:
Difference in Average Sales Price of Properties within and outside of Buffers.
Disaggregate Comparison of Properties within B 1 00 with Rest of the Sample.
Disaggregate Comparison of Property Attributes within B 1 00 with Rest 0 f the Sample,
Comparison of Socia-Demographic characteristics of CIs intersected by HV lines with
the remaining CIs in the GTA..
Summary statistics of Explanatory Variables Used in Reduced Models.
Moran s I Calculations fOl Freehold Properties Sold in 1994.
01S estimates for models using numerous distance thresholds to capture intluence of
power lines,
Semi-log version of the 01S model for Buffer B IOO,
Parameter estimates for SAR model using Buffer B 1 00,
List of Figures
Figure-AI: Spatial distribution of freehold property values in the GTA.
Figure-A2: Spatial layout of power-lines and buffers at I-kIn and 2-km around the power-lines.
Figure-A3: Variation in Freehold Property Values Sold in the GTA in 1995 Due to Proximity to
High-voltage power-lines.
No. of Sales Captured within Each Buffer.
Variation in Buffer Coefficients for 01S models.
Plot of residuals against predicted values for 01S model.
Plot of residuals against predicted values for SAR.
Residual histogram for spatial autoregressive model.
Histogram of predicted values for 01S model.
Histogram of predicted values for SAR model.
Figure-A4:
Figure-AS:
Figure-A6:
Figure-A?:
Figure-A8:
Figure-A9:
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TABLE-A5 Summary statistics of Explanatory Variables Used in Reduced Models
Variables Descnption Mean Std.M inim lU11 Maximum
Dev,
---- -",-------------'-----"'--'-"--'--
SLDPRlCE Actual Sale Price 227600 134100 0000 4250000
ROOMS No, of rooms 1.95
BEDS No. of Bedrooms
NO WASH No. of Wash-rooms 2.49 1.03
PARK CAP Parking Capacity 1.16
D - CBD Distance from CBD 21.5 13.80.
CF A VINC Avg, Income of Census Family in CT 68000 25000 26900 231700
LOG PRIC Ln of Sale Price 12.15.
LOG LAG Ln (Lag- var)12.0.32 14.
DETACH Binary: 1 if detached 0 otherwise 70%
THREE ST Binary: 1 , if three-storey , 0 otherwise
FIRE ML T Binary: if multiple fireplace, 0 otherwise 10%
FIRE NO Binary: 1 , if no fIreplace , 0 otherwise 30%
AIR CON Bin, 1 if Cent. Air Conditioned, 0 otherwise 50%
TABLE-A6 Moran s I Calculations for Freehold Properties Sold in 1994
Common Border
Length
Observations
Sum of Weights
Moran s I
Expected Value
Std Error
T Statistic
95% C.!. Upper
Lower
802
13113.815
112138.411
3361.005
626
001
033
18.493
693
Adjacency Weighted Common
Border Leng
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7720
84272
3860
655
001
022
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699
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