here - Sustainable Prosperity

Understanding the Amenity Impacts of Wind
Development on an International Border
∗
Martin D. Heintzelman, Richard J. Vyn, and Sarah Guth
January 13, 2016
DRAFT WORKING PAPER: DO NOT CITE OR QUOTE
Abstract
Wind energy developments are often controversial. Concerns are often
raised about negative impacts on local communities, including impacts
on property values. Some of these negative impacts may be oset by
compensatory payments made by wind developers. Communities often
also have a say in approving the development or setting parameters which
might dictate terms such as the density of towers or minimum setbacks
from property borders or homes. However, if the development is near a
border between municipalities, states, or even countries, it is often the case
that one or more jursidictions will not have an opportunity to set such
rules or demand compensation, but will, nonetheless, face some costs or
impacts from the development. We explore exactly this situation at the
border between Canada and the United States in the Thousand Islands
region where a wind farm is currently operating on the Canadian border
island of Wolfe Island. Using a parcel-level hedonic analysis of property
sales transactions, we nd that properties in NY with a view of and/or
in close proximity to the turbines signicantly depreciated in value after
construction while parcels in Canada saw no signicant change in their
value.
∗ Heintzelman (corresponding author) is Associate Professor and Fredric C. Menz Scholar
of Environmental Economics, School of Business, Clarkson University, P.O. Box 5790, Potsdam, NY 13699, [email protected]. Vyn is Associate Professor, University of Guelph,
Ridgetown Campus.
Sarah Guth is Research Assistant, Harvard University.
This research
was supported by the Fredric C. Menz endowment fund for Environmental Economics at
Clarkson University.
Background research was conducted while Guth was participating in
Clarkson University's Research Experience for Undergraduates (REU) program, supported
by NSF Grant No. EEC-1359256. Additional research assistance was provided by Brittany
Berry at the University of Guelph and Chuan Tang at Clarkson University. Some GIS analysis
was provided by Adam Bonnycastle, also of the University of Guelph.
We are indebted to
seminar respondents at the University of New Hampshire and the 2015 Biennial Meeting of the
Association for Canadian Studies in the United States for helpful comments and suggestions.
1
1
Introduction
Renewable energy sources are a steadily increasing portion of our global energy
mix. Such energy sources are a global public good - by substituting for more
pollution-intensive fossil-fuel sources they reduce global pollution of both criteria pollutants, such as NOx, SOx, Mercury, and others, as well as greenhouse
1
pollutants like CO2 .
The benets of these reductions are generally spread over
a large area and, in the case of greenhouse gases, over the entire planet. The
costs of these reductions, however, are more likely to fall on a much smaller geographic area. In some cases, in fact, renewable energy facilities can be thought
of simultaneously as global public goods and local public bads. As evidence of
this, siting new renewable energy facilities, particularly wind farms, is often controversial, with local governments and/or activists putting up sti resistance.
This can be true even in areas with populations which are generally supportive
of environmental issues, as in the case of the Cape Wind development o the
2
coast of Massachusetts .
Common local concerns about wind developments include visual and aural
disamenities, potential human health impacts, and impacts on wildlife.
The
visual disamenities cited by wind opponents stem from the introduction of large
man-made structures into the landscape. At the most basic level, such a change
to the landscape can be o-putting to residents of an area, who may have
bought their property under the assumption that the landscape would remain
unchanged.
More specically, opponents often cite the impacts of the large
(depending on the facility) array of blinking red lights which sit atop the wind
turbine hubs, as well as shadown icker which occurs when the turning blades
are between the sun and a home, resulting in rhythmically moving shadows.
The rst aect may be amplied when the view of the turbines is over water, as
this clears the visual eld of other features which would otherwise obstruct or
reduce such views. Noise impacts are also often cited by homeowners opposed to
wind development. Of particular concern is low frequency noise which dissipates
more slowly over distance (Bolin et al., 2011). Many people have complained
about serious health impacts in relation to this low frequency noise. There have
been some studies which nd evidence of health impacts, in particular sleep
deprivation and psychological stress, but larger summary studies have found
limited evidence linking the noise itself to these impacts (Council of Canadian
Academies, 2015).
Instead, as found by a recent Health Canada study, wind
turbine noise is more likely to cause increased levels of annoyance rather than
3
health impacts .
Regardless of scientic evidence, the perception of health
impacts remains, and these perceived amenity and health impacts are likely
to be reected in property values as bids for properties in close proximity to
1 The
exact emissions reductions from a wind facility depends very much on what other
energy sources are displaced, the focus of Kane et al. [2013].
2 The
Cape Wind project is a proposed oshore wind facility in Nantucket Sound. It has
met strict opposition from many local landowners and, most famously, the Kennedy family.
3 Health
able at:
Canada. 2014. Wind turbine noise and health study: Summary of results. Avail-
http://www.hc-sc.gc.ca/ewh-semt/noise-bruit/turbine-eoliennes/summary-resume-
eng.php, accessed December 11, 2015.
2
wind turbines will be reduced.
Acting counter to these negative impacts are some benets which often accrue to local landowners and communities. First, individual landowners who are
able to let their land out to wind energy companies stand to gain from streams
of rental payments from developers.
These contracts vary across both sites
and landowners in magnitude and the allocation of risk (some contracts include
xed payments without regard to electricity production whereas others include
payments which are a function of electricity output, transferring some risk to
landowners).
In addition, developers often compensate communities through
payments-in-lieu-of-taxes (PILOTs). These payments help to mitigate negative
impacts in the local community and, again, vary in size and structure across
sites. Payments to communities will also be reected in property values as these
payments could result in either lower local taxes or increased local services, or
a combination of both (Kahn, 2013). Payments to landowners are unlikely to
broadly impact property values, but, if transferable, would certainly increase the
value of parcels that include wind leases. Weber et al. [2013] note that energy
royalties, including wind turbine lease payments, are resulting in substantial
payments to farms across the United States
It is also possible that the development of wind energy will result in increased economic activity through direct employment impacts and indirectly as
payments made to individual landowners and the municipality ow through the
local economy. Brown et al. [2012] nd signicant and positive economic impacts of wind facilities on personal income and employment, while Munday et al.
[2011] nd less denitive results on economic impacts. Any positive economic
impacts would likely also be reected in property values.
Another issue in public acceptance of wind development is public involvement in the development/approval process. If a community feels that they have
not had sucient input to the approval process, they are more likely to oppose
and ght the development.
In addition, if the project moves forward despite
substantial opposition, this may lead to bad feelings and drive landowners out
of the area, possibly lowering property values in the process. On the other hand,
if the community is able to be actively involved in the process, they may have
a stronger hand in negotiating better compensation for their community in ad4
dition to aecting details about the design of the project to their advantage.
As an extreme form of this, if the development happens near a border between
communities, but is wholly contained within one community, the community
without the development is likely to be held out of the approval process, and
will also not receive compensation from the developer. These neighboring communities, in other words, will bear some of the cost of the project with little
prospect to receive any benets.
All of these issues may be further complicated by the presence of secondhome or vacation home owners. These owners are likely to have more elastic
preferences regarding changes in the amenities surrounding their property. Since
4 See
Baxter, Morzaria, and Hirsch [2013] , Groth and Vogt [2014] , Ladenburg et al. [2013],
Lindén et al. [2015], and Petrova [2013]for thorough discussions of the determinants of and
strategies for social acceptance of wind energy.
3
they are not tied as closely to a particular region because of concerns about
employment, they are more likely to sell their property, or less likely to buy a
new property due to an adverse change in amenities. In addition, to the extent
that additional local services, such as improved school systems, are provided
through the use of PILOTs, they are less likely to be able to take advantage of
these improvements. Finally, they may also not have as much of a voice in the
approval process, and may feel like an outsider as a result. All of these factors
make it possible that properties that are primarily used as second or vacation
homes, or communities with a large share of parcels in this use, will face even
larger changes in property values as a result of wind farm developments.
This paper explores the issues discussed above using a unique scenario in
which a large wind farm was constructed on the Canadian island of Wolfe Island
in the St. Lawrence River at the border between Canada and the United States.
Wolfe Island, which is the largest island in the Thousand Islands region, is
situated at the entrance of the St.
Lawrence River in Lake Ontario, directly
across the river from the community of Cape Vincent in the state of New York.
The Wolfe Island wind farm was developed by Canadian Hydro Developers,
which initially submitted a proposal for construction of this wind farm on the
western half of the island in July 2005 (Keating, 2006). The ocial plan and
zoning bylaw amendments necessary to allow this project to move forward were
passed by council in November 2006, and the project was ocially announced
on the Wolfe Island website in April 2007. Construction of the 86-turbine, 197.8
MW facility began in May 2008 and was completed in June 2009, at which time
the wind farm became operational (Ontario Power Authority).
We examine
for impacts of this wind farm on property values on both sides of the border
using data from both Canada and the United States on property sales in close
proximity to the wind turbines.
On the American side, we focus on Jeerson County, which sits at the northern edge of New York and borders both the St. Lawrence River and Lake Ontario. Throughout the past decade, wind energy has divided public opinion in
the county. The region has been the targeted site for several recent American
wind facility proposals, including in the Town of Cape Vincent and in the Town
of Hounseld, on Galloo Island in Lake Ontario, all of which have been highly
controversial.
Newspaper coverage and letters to the editor in the New York media regarding the Wolfe Island facility clearly expressed Jeerson County residents'
opposition to turbines due to negative aesthetic impacts. A Cape Vincent journalist feared that the turbines will take away [the] image and. . . beauty of [his]
township, deterring prospective seasonal residents who contribute so much in
taxes and expertise (Radley, 2009).
A Chaumont resident characterized the
wind farm as blight on landscape (Lynne, 2009). Finally, a seasonal resident
of Chippewa Bay described the waterfront view of facility nighttime lighting
as a jolt to the entire landscape and to [his] mind, . . . like a jab in the ribs
(Quarrier, 2009).
Similar sentiments have been expressed by residents of Wolfe Island, where
the construction of this wind farm has generated considerable controversy and
4
public opposition. Opponents of the wind turbines have expressed concerns regarding the industrialization of this rural community and how the turbines
forever change the landscape into something that doesn't t here (Fast et al.,
2015). As with Cape Vincent, there are a considerable number of seasonal residences on Wolfe Island, many of which are waterfront properties. According to
Fast et al (2015), summer cottages comprise about one-third of all residences
on the island. As such, visual amenities play a signicant role in the value of
these properties, and owners of these properties have expressed concerns regarding potential negative impacts on property values arising due to the visual
disamenities associated with wind turbines.
As one seasonal resident stated,
why would I want to live there [with the turbines]?
(Fast et al 2015).
In
one case, property owners brought an appeal to Ontario's Assessment Review
Board to have the assessed value of their waterfront property reduced due to the
devaluation caused by the wind turbines. This appeal was ultimately rejected
due to a lack of evidence of negative impacts. But this case highlights the underlying concerns that exist among residents of Wolfe Island regarding impacts
of wind turbines. However, not all residents of Wolfe Island were opposed to
this project. As evident from interviews conducted by Fast et al (2015), there
are a considerable number of area residents that were supportive of the project
and of wind energy in general.
It is interesting to note that similar concerns and issues were raised by residents on both sides of the border despite the fact that public meetings and open
houses were held for residents on the Canadian side throughout the application
and development process.
Public open houses for this project were rst held
in March of 2006, only a few months after the project was initially proposed.
In October 2006, a public meeting was held to consider a proposed zoning bylaw amendment applicable to all Wolfe Island lands optioned for a wind plant
zone (this amendment was passed by council the following month). In March
2007, public open houses were held that included maps indicating 86 turbine
locations. Overall, public consultation focused only on residents of Wolfe Island,
while residents of Cape Vincent had little to no involvement in this process. This
dierence in the level of involvement could potentially contribute to a dierence
in the nature of the resulting impacts on either side of the border.
This study area is ideal for examining many of the issues discussed above.
We have homes (in Canada) in very close proximity to the facility, but where residents had some knowledge of and, perhaps, some input into the project before
it was built. The Township of Frontenac Islands, which includes Wolfe Island,
5
also receives C$645,000 per year in payments from the developer, TransAlta .
In addition, we also have American homes with substantial views of the turbines, but from a distance, whose owners had no input to the project and whose
communities receive no compensation from the developer. Many of the American homes with views of the turbines, in particular, are also vacation or second
homes rather than primary residences.
We use a hedonic analysis to examine and compare how property values on
5 TransAlta
acquired Canadian Hydro Developers in 2009.
5
both sides of the border have been impacted by the Wolfe Island wind turbines.
In this analysis, a dierence-in-dierences approach is used to compare transaction prices before and after approval or construction of the project as well
as between homes which can and cannot view the turbines, or are at varying
distances to the turbines. We employ xed eects to mitigate potential omitted
variables bias as well as controls for property market trends and seasonality of
prices. We nd robust evidence of negative property value impacts on the NY
side after construction of the turbines for homes in close proximity to the turbines and/or with a view of the turbines. In contrast, we do not nd evidence
of signicant negative impacts on the Canadian side.
2
Literature Review
There is a substantial and growing literature that examines property value impacts of wind development, focusing primarily on Canada, the United States,
and Europe. This literature reveals mixed conclusions, with some studies nding
no signicant impacts on property values, while others nd signicant negative
impacts. Ben Hoen and his co-authors, in a series of studies looking at a large
number of wind facilities across the United States, has consistently found no
impact of wind turbines on property values (Hoen et al., 2011, 2015).
These
studies are impressive because of the large sample sizes which come from studying multiple sites around the country. However, by estimating an average impact
across all included sites, this may obscure signicant impacts at individual sites.
In fact,Heintzelman and Tuttle [2012] nd signicant negative impacts at two
sites in northern New York but no impacts at a third site, which suggests that
the impacts of turbines on property values can vary signicantly across dierent
sites. Hence, combining multiple sites in one study can avoid the issue of low
numbers of observations that has plagued prior wind turbine studies but does
not permit observing dierences in impacts from one site or region to another.
There are a number of other studies that also nd no signicant impacts of
wind turbines on property values. Most recently, Lang et al. [2014] use a large
dataset of transactions in Rhode Island to nd no signicant property value
impacts of small wind facilities with either only a single turbine or a few small
turbines. Laposa and Mueller [2010] nd no eects from the announcement of a
proposed wind farm in Colorado, but that facility was never built so it dicult
to interpret these results in the context of completed wind facilities.
Shultz
et al. [2015] examine the impacts of wind turbines on agricultural land values,
and nd no impact. A limitation of this study, however, is that it uses assessed
values rather than transaction values. As assessed values reect only the expert
opinion of the local assessor, it is hard to know how accurate this analysis would
be in assessing relatively new wind facilities. Sims and Dent [2007] and Sims
et al. [2008] nd very limited evidence of turbine impacts, but use very small
samples from sites with other confounding factors. Finally, Vyn and Mccullough
[2014] study a large wind farm in rural Ontario and nd no evidence of signicant
impacts on nearby property values. They do however point to the possibility
6
that impacts may occur in other contexts. This may particularly be the case in
the province of Ontario where wind energy development has become increasingly
controversial in recent years, and the concerns of impacts on health and property
values have become prominent issues in the public forum.
Heintzelman and Tuttle [2012] is the rst study that found signicant negative impacts from wind turbines. Importantly, however, they nd impacts in
only 2 of 3 counties in their study area, suggesting that local context matters a
great deal in determining the impact. Subsequently, a number of other studies
have found signicant negative impacts. Sunak and Madlener [2012] implement
a combination of spatial xed eects methods and Geographically Weighted Regression (GWR) and nd consistent evidence that proximity to wind turbines
negatively impacts property values in a region of Germany. The GWR nds substantial variation in these impacts over space. Likewise, Jensen et al. [2014] and
Gibbons [2015] both nd signicant negative impacts of wind turbines and, in
the case of Jensen et al. [2014], both visual and aural disamenities are separately
found to have negative impacts.
Taken together, this growing literature suggests that there may not be any
single, global answer to the impact of wind turbines on property values.
In-
stead, the literature strongly suggests that the specic context of each facility
will drive outcomes for that community. This paper takes the next step of trying to determine what some factors might be by examining for impacts in the
border region where part of our sample is closer to the facility, but also receives
compensatory payments from the developer, while the rest of our sample views
the turbines from a distance and does not receive any compensation.
3
Methodology
Hedonic analysis is a well-accepted form of revealed preference (as opposed to
stated preference) non-market valuation in the eld of environmental economics.
This approach is derived from Rosen [1974] and others.
6
Essentially, Rosen
[1974] lays out a model of buyers and sellers with preferences over the attributes
of a compound good, like housing.
In this case, he derives a hedonic price
function showing that, under certain assumptions, the market price of a house
will be a function of its attributes. One can then estimate this hedonic function
using data on a set of homes with varying prices and attributes, where the
estimated coecients represent the marginal willingness-to-pay for changes in
these individual attributes. The strength of this approach is that it allows for
the estimation of the value of marginal changes in attributes that are otherwise
not bought and sold on markets, such as environmental amenities.
The hedonic method is quite powerful because any amenity which is valued
by consumers and associated in some way with housing markets can, in theory,
be valued through this method under the right conditions. A limitation of this
approach, however, is that the hedonic method can only estimate what are
6 See
III et al. [2014] and Taylor [2003]for comprehensive treatments of the hedonic method
in environmental economics.
7
called use values of homeowners or renters, and not broader societal values.
For instance, the hedonic method could estimate the value of having a healthy
ecosystem to residents in a local area, but cannot estimate the value to societyat-large of a particular ecosystem.
As in any econometric exercise, there are a number of empirical issues that
are common in hedonic analyses.
functional form.
First, one must take care in choosing the
Traditionally, based on Cropper et al. [1988], the log-linear
or log-log forms are preferred. However, more recently, Kumino et al. [2010]
advocate for testing the appropriateness of this functional form using the BoxCox formulation (Box and Cox, 1964). Subsequent applications, including recent
studies on the impacts of wind turbines on property values (Heintzelman and
Tuttle [2012], Vyn and Mccullough [2014]), have found that the Box-Cox analysis
supports the log-linear form in this context. Similarly, our robustness testing
in this study indicates that the results based on a Box-Cox functional form are
consistent with those of the log-linear form. As a result, we primarily use the
log-linear form in our analysis.
Kumino et al. [2010] also advocate for the inclusion of spatial xed eects,
temporal controls, and quasi-experimental identication to control as best as
possible for omitted variables bias, which is endemic to applications of the hedonic method.
Omitted variables bias arises when one or more factors that
are correlated with both the dependent variable and one or more included explanatory variables are omitted from the specication. In this case, the analysis
will assign explanatory power which properly belongs with the omitted variable
to included variables, resulting in biased estimates of the eect of those variables. Given the large number of factors which are both unobservable to the
analyst and correlated with property values (local neighborhood attributes, for
instance), this has serious implications for hedonic analysis.
Similarly, given
that we are trying to identify the impact of a feature which changes over time
(the Wolfe Island turbines are built mid-sample period), if we fail to adequately
control for trends over time, we may similarly bias our estimates of the turbine
impacts.
Fixed eects approaches help overcome these issues. By implicitly including
a large number of spatial dummy variables, xed eects allows each area (township or census block, for instance) to have its own intercept term, accounting for
time invariant factors which aect property values in these areas. The smaller
the level of the xed eects, the less likely one is to have an omitted variables
problem (as more and more spatial factors will be subsumed in the xed eect).
However, xed eects also rely on within unit variation to identify the eects
of remaining variables. This often means that as the level of the xed eects
gets smaller, the analyst loses power to identify eects. Thus, it is important to
carefully balance these eects when interpreting results and choosing a level of
xed eects. In our case, we found that the use of xed eects at the township
level provided an appropriate balance, as our preliminary analysis indicated
that the ability to identify eects was diminished with the use of xed eects at
the census block level. In addition, dierences exist in the specication of the
American census blocks and the closest Canadian equivalent, the homogeneous
8
neighborhood which would undermine our ability to compare results across the
border.
We include year xed eects to account for sample wide trends as well as
month xed eects to account for seasonality in prices.
This is particularly
important given the turbulent real estate markets of the late 2000s.
7
Our quasi-
experimental identication stems from the fact that we have transactions taking
place both before and after the turbines were built and in areas that are at varying distances and with varying views of the turbines. We include clustered error
terms at the same level as our xed eects. This allows error terms to be correlated across transactions in the same local area (i.e., townships) while requiring
them to be independent across local areas. This generalization helps to control
for spatial autocorrelation and is a simplied form of spatial econometrics.
With all of this in mind, our specication follows Heintzelman and Tuttle
[2012] and takes the general form:
ln(Pijt ) = λt + αj + β1 windi + β2 windt + β3 windit + β4 xit + ηjt + ijt
where
λt
represents the time (year and month) xed eects,
area xed eects,
for parcel
windi
i, windt
expected to occur,
αj
(1)
represents local
represents the proximity to or view of wind turbines
represents the time period,
windit
t,
in which turbine impacts are
represents the interaction term between proximity
to or view of wind turbines for parcel
i
area and individual specic error terms.
t, xit is a vector of
ijt represent spatial
with time period
other parcel and structural characteristics, and
ηjt
and
The model represented in equation
(1) is estimated separately for real estate markets in Ontario and New York,
which permits determining whether the turbine impacts diered between the
two locations.
4
Data
We use data on 8,279 single-family residential property transactions on both
8
sides of the border in our analysis: 6,017 in Jeerson County, NY , and 2,262
across the border in Frontenac County, Ontario. 1 provides a map of the study
area and transaction locations.
Data on NY transactions between January
2004 and July 2013, inclusive, come from the New York State Oce of Real
Property Taxation Services (NYSORPTS). This data includes sale price, sale
date, and parcel identiying information. This transaction data is then merged
with parcel and home characteristics data from the asseessment process, also
from NYSORPTS. We bring in parcel shapele (GIS) data which we acquired
7 It
is important to note that real estate markets in Northern New York and in Ontario
were relatively insulated from this turmoil, and there is no dramatic crash in prices following
the nancial crisis in our sample. Nonetheless, it is important to control for these possible
impacts.
8 There
were an additional 11 transactions that had to be omitted from the dataset due to
incomplete information.
9
from the Jeerson County Assessor's Oce. With this spatial data we calculate a number of distance and spatial variables in ArcGIS. Data on Canadian
transactions comes from Ontario's Municipal Property Assessment Corporation
(MPAC). This detailed data includes all open-market sales of residential properties in Frontenac County between September 2004 and July 2013, inclusive.
An extensive set of property and structural variables is included in the MPAC
data, while additional distance and spatial variables are calculated using ArcGIS. There are no sales in this dataset of properties on which a turbine is
located.
The key variables that capture the visual and aural impacts of the wind turbines include both visibility and distance variables, and we estimate separate
models using each of these variables. The distance from each parcel to the nearest turbine on Wolfe Island was calculated using ArcGIS. Previous studies that
have accounted for turbine impacts on property values based on distance measures have used inverse distance (e.g., Heintzelman and Tuttle [2012],Vyn and
Mccullough [2014]) and distance bands (e.g.,Hoen et al., 2011, 2015). Both of
these measures have potential shortcomings: the use of inverse distance involves
estimating impacts at the mean distance from the turbines and, as such, may
underestimate the potentially greater impacts in very close proximity to turbines, while the use of distance bands, which involves dividing up observations
into bins of specied distances from turbines, can result in estimated impacts
for each band that may be based on relatively few observations.
Due to the
relatively low number of observations in our datasets in close proximity to the
turbines, the shortcoming associated with the use of distance bands is of greater
concern. As a result, we use the inverse distance measure in our analysis. Given
the distance-decaying nature of the visual and aural impacts of turbines, any
impacts on property values are expected to be greater in closer proximity to
the turbines, which would be reected by a negative coecient for the inverse
distance variable. Since the data includes sales up to 71 miles from the turbines
on the Canadian side and up to 44 miles away on the American side, there are a
considerable number of sales that would not be impacted by the turbines, which
comprise a control group.
Since proximity does not necessarily correspond with visual impact, as view
may be obstructed by landscape features, we also conducted eld visits to all
parcels that were potentially within visibility of the wind turbines (this potential
was determined based on a combination of viewshed modelling in ArcGIS and
distance to the nearest turbine). On these visits, it was determined whether the
parcel (as viewed from the road) had a partial or full view of one or more of
the turbines. The eld observations of the visual impacts of every transacted
parcel with a potential view is a strength of this paper, similar to that of Hoen
et al. [2011] and Vyn and Mccullough [2014]. It is superior to relying solely on
viewshed analysis in ArcGIS, which generally relies on a number of assumptions
about the height of dierent forms of land cover (trees) to estimate views, and
cannot possibly include every potential obstruction. Because of this, viewshed
analysis will tend to overestimate turbine views, and we avoid this potential
error with the eld observations.
10
Figure 1: Study Area
11
Since the data includes sales that occurred both before and after the wind
farm was developed, it is necessary to account for the time period during which
turbine impacts on property values are expected to occur. We specify a postturbine period (
Post-Con ) to account for sales that occurred after turbine con-
struction on Wolfe Island was completed in June 2009. In addition, we account
for potential announcement eects by specifying an announcement period between April 2007, when the wind farm was rst announced, and June 2009
(
Announcement ). Each of these time period variables is interacted with the visView ) and turbine distance (InvDistance ) variables in order to specify
ibility (
the variables that account for the impacts of turbines on property values. However, given the diculty in specifying the precise point in time at which impacts
may begin to occur, we test the robustness of our results with alternate specications of these time periods. Rather than using the post-construction date for
the specication of the post-turbine period, we use the date that construction
began: May 2008. During the construction period, while all turbines were not
yet fully erected, the locations were known and the visual eects were becoming
evident; hence, turbine impacts could conceivably have begun to occur during
this period. We also use an alternate announcement eect period that begins
following the approval of the zoning bylaw amendments in November 2006.
In the post-construction period, the Canadian data includes 47 parcels within
ve miles of the nearest turbine and 19 parcels within one mile, while the American data includes 58 parcels within ve miles, the nearest of which is 1.86 miles
from the turbines. There are 39 parcels in Ontario and 15 parcels in New York
with a view of the turbines that were sold in the post-construction period. Since
these numbers of observations are relatively low, we do not break down these
parcels between those that have a full view and those with a partial view; rather,
we only use the measure of whether a parcel has any view of a turbine. Any
eects that we estimate using this measure will be an average of eects across
these sub-categories of view, presumably over-estimating the eect for those
with only a partial view of one turbine and under-estimating the eect for those
with full views of multiple turbines.
To ensure consistency in the estimation approach between the two sides of
the border and to reduce the possbility of bias between the two sets of results,
the same set of explanatory variables representing the parcel and structural
characteristics are used. Variables accounting for parcel attributes include lot
size and categorical variables for waterfront and seasonal properties. The value
associated with the residence on each parcel is accounted for by a set of variables
that includes the numbers of bathrooms, bedrooms, and stories, the living area,
age of the house, and categorical variables for the existence of a replace, central
air conditioning, and a basement, quality ratings (1-5 on a 5-point scale), heat
types (electric, forced air, hot water, other, and no heat), and whether the
residence is a mobile home. In addition to distance to the nearest turbine, other
distance variables include the distances from each parcel to the nearest city and
town.
As mentioned above, sets of year and month xed eect variables are
also included. Most of these variables were included in both datasets, though in
some cases slight adjustments were made in the merging process. For example,
12
the variable representing house quality is based on a 5-point scale for the NY
data and on a 10-point scale for the ON data, which is accounted for by dividing
the quality index by two for parcels in Ontario.
Summary statistics for all variables for the two datasets are presented in
Table 1.
Notice that property values in our Ontario sample are considerably
larger than in our New York sample. A higher proportion of homes in our NY
sample are on the waterfront as compared to our ON sample, and a higher
proportion are also seasonal in our NY sample. Together, these facts suggest
that a higher proportion of our NY sample, and particularly those properties
aected by the turbines, are likely to be vacation or second homes. Otherwise,
our samples are quite comparable along most dimensions.
5
Results
Table 2 presents the results of our analysis for the regressions in which turbine
impacts are accounted for by visibility, while Table 3 presents the results for
the regressions based on distance to the nearest turbine. For the sake of
brevity, only the results for the variables on which we focus our discussion,
which include the turbine variables as well as the waterfront and seasonal
variables, are reported in these tables.
9
In Table 2, the key variables of interest are the interaction terms that indicate
how turbine view impacts property values in the announcement and
View*Announcement; View*Post-Con ). First, we
post-construction periods (
nd negative and signicant impacts from turbine construction for homes in
NY with a turbine view. The estimated coecient for this variable indicates
that the values of homes in NY with a view of the turbines have, on average,
been reduced by 13.4% following construction of the turbines
10
. Conversely, no
signicant impact is observed on the Canadian side in either period. It is also
evident from the results that sale prices across the NY sample decreased in the
post-construction period relative to prices prior to this period, but this
decrease was signicantly greater for parcels that had a view of the turbines.
A positive impact is found in NY during the announcement period, but
conceivably during this time home buyers in New York may not have been
aware of the proposed wind farm or the location of the turbines to be
constructed, or at the very least may not have been aware of the impending
impact on their viewshed. This positive impact may also be the result of an
omitted variable related to water view on the NY side.
The property type has a major impact on sale price in both NY and ON.
Waterfront properties sell at a substantial premium on both sides of the border,
9 Estimated
coecients for other variables, which include parcel and structural variables
(size of home, etc.) as well as xed eects variables, are largely consistent with expectations.
These results are available from the authors upon request.
10 This
gure is derived based on coecient interpretation for categorical variables in semi-
log equations (see Halvorsen and Palmquist, 1980)
13
14
= 1 if parcel is seasonal
= 1 if residence is mobile home
Size of the parcel, in acres
Number of bathrooms
Number of bedrooms
= 1 if at least one replace exists in the house
= 1 if house has central air conditioning
= 1 if house quality index is 1
= 1 if house quality index is 2
= 1 if house quality index is 3
= 1 if house quality index is 4
= 1 if house quality index is 5
Living area of the house, in square feet
Age of the house
Number of stories in the house
= 1 if the house has electric heat
= 1 if the house has forced air heat
= 1 if the house has hot water heat
= 1 if the house has other heat type
= 1 if the house has no heat
= 1 if the house has a basement
Distance to the nearest town, in miles
Distance to the nearest city (population >
Mobile
Lot Size
Bathrooms
Bedrooms
Fireplace
Air
Quality1
Quality2
Quality3
Quality4
Quality5
Living Area
Age
Stories
Electric
Forced Air
Hot Water
Other
No Heat
Basement
Town
City
Observations
50,000), in miles
= 1 if parcel is on the waterfront
Seasonal
turbine construction
= 1 if parcel sold following completion of
construction
announcement and completion of turbine
= 1 if parcel sold between project
miles
Distance to Nearest Wolfe Island Turbine, in
turbines
= 1 if parcel has a view (full or partial) of the
Waterfront
Post-Con
Announcement
Distance
View
Sale price of the parcel, in Canadian $ for
Sale Price
Ontario sales and in US $ for New York sales
Variable Description
Variable Name
17.02
13.80
0.33
0.046
0.04
0.02
0.72
0.18
1.26
42.45
1493.10
0.000
0.063
0.866
0.061
0.010
0.234
0.286
2.924
1.515
4.184
0.012
0.064
0.049
0.586
0.354
19.28
0.034
241,150.40
Mean
16.49
10.29
0.47
0.21
0.20
0.14
0.45
0.38
0.40
37.29
587.71
0.00
0.24
0.34
0.24
0.10
0.42
0.45
0.82
0.70
14.48
0.11
0.24
0.22
0.49
0.48
13.21
0.18
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.00
101.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
0.00
0.00
0.00
0.28
0.00
25,000.00
2,262
122,104.90
Min
Ontario
Std. Dev.
Table 1: Summary Statistics
81.32
57.02
1.00
1.00
1.00
1.00
1.00
1.00
3.00
190.00
4839.00
0.00
1.00
1.00
1.00
1.00
1.00
1.00
9.00
4.50
300.56
1.00
1.00
1.00
1.00
1.00
71.45
1.00
1,600,000.00
Max
2.90
1.61
0.09
0.08
0.00
0.08
0.70
0.15
1.46
67.50
1,557.52
0.001
0.095
0.763
0.124
0.017
0.021
0.170
2.990
1.433
6.538
0.037
0.102
0.117
0.410
0.211
25.75
0.004
142,004.90
Mean
3.03
1.51
0.28
0.27
0.00
0.27
0.46
0.35
0.44
52.45
598.32
0.03
0.29
0.43
0.33
0.13
0.14
0.38
0.94
0.58
25.41
0.19
0.30
0.32
0.49
0.41
9.02
0.07
Min
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.00
136.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
1.86
0.00
3,500.00
6,017
101,407.80
Std. Dev.
New York
17.88
8.25
1.00
1.00
0.00
1.00
1.00
1.00
3.00
225.00
6,074.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
9.00
5.50
391.43
1.00
1.00
1.00
1.00
1.00
44.38
1.00
2,000,300.00
Max
Table 2: Regression Results - Turbine View
Ontario
Variable
New York
coef
se
coef
se
View
0.008
0.017
0.045
0.048
Announcement
-0.010
0.021
-0.100*
0.059
Post-Con
0.043
0.046
-0.174**
0.069
View*Announcement
0.015
0.048
0.207***
0.056
View*Post-Con
-0.004
0.040
-0.144***
0.050
Waterfront
0.632***
0.026
0.646***
0.071
Seasonal
-0.093***
0.020
0.128***
0.045
Number of observations
2,262
6,017
Adjusted R2
0.711
0.423
Table 3: Regression Results - Turbine Distance
Ontario
Variable
InvDistance
New York
coef
se
coef
se
-0.189***
0.027
-0.661*
0.369
Announcement
-0.012
0.024
-0.104
0.065
Post-Con
0.034
0.049
-0.145*
0.074
InvDistance*Announcement
0.043
0.045
0.126
0.319
0.072*
0.039
-0.506**
0.242
Waterfront
0.559***
0.019
0.648***
0.069
Seasonal
-0.086***
0.018
0.123***
0.047
InvDistance*Post-Con
Number of observations
2,262
6,017
Adjusted R2
0.716
0.424
15
while seasonal homes sell for a premium in the NY sample but at a discount
11
in Ontario.
The negative coecient for ON may be due to the prevalence of
more rustic seasonal homes in the northern, mainland area of Frontenac County,
which are used more for hunting and shing purposes than as vacation homes.
In general, this tends to be the case for seasonal homes across NY, but in
this study area seasonal homes are more likely to be vacation homes located on
particularly attractive parcels, or in attractive locations.
An important caveat about the post-construction impact observed in NY is
that, while it is statistically signicant, it is based on a relatively small number
of parcels (15) with a view of the turbines that were sold after the turbines
were built. This suggests that this result should be interpreted cautiously. The
statistical signicance likely stems from the large number of control transactions
to which these transactions are being compared. The small number of treatment
transactions is unfortunately common in hedonic studies of wind turbines.
The results of the specication in which turbine impacts are accounted for
by distance to the nearest turbine are reported in Table 3. This specication
is less susceptible to the small numbers problem highlighted above since every
parcel has a distance to the nearest turbine and as such can contribute to the
estimation of turbine impacts. The key variables of interest are the interaction
terms between the inverse of the distance to the nearest turbine and each of
InvDistance*Announcement;
InvDistance*Post-Con ). We nd that the results of this specication are broadly
the announcement and post-construction periods (
consistent with those based on turbine view.
In particular, we nd, again, a
signicant negative impact in NY in the post-construction period, where parcels
in closer proximity to turbines have experienced reductions in value relative to
those at greater distances from the turbines. A positive impact is observed in
the ON sample in the post-construction period, which is unexpected, though it
is only signicant at the 10% level. Inverse distance to turbine is negative and
signicant in both samples, possibly lling in for a measure of proximity to the
water, particularly in the NY sample. Seasonal and waterfront homes have the
same eects as seen above.
Overall, the results of these two specications indicate that negative impacts
associated with the Wolfe Island turbines have only occurred to any observable
extent on the NY side, but not in Ontario despite the fact that many parcels on
Wolfe Island are located in amongst the turbines.This is somewhat surprising
given that concerns were raised on both sides of the border, but may reect the
cross-border dierence in the level of involvement in the project development
process. In addition, as indicated in Fast et al (2015), there are a fair number
of residents, both long-time residents and newcomers, on Wolfe Island that are
quite supportive of the wind farm. Those who support the wind farm are less
likely to be concerned about impacts on property values and may not reduce
their willingness-to-pay for properties with a view of or in close proximity to the
turbines. If a large enough proportion of residents and potential homebuyers
11 NYSORPTS
denes seasonal homes as those Dwelling units generally used for seasonal
occupancy; not constructed for year-round occupancy (inadequate insulation, heating, etc.).
MPAC is less specic about this denition for seasonal homes.
16
support the wind farm and believe it benets the community, this may reduce
the likelihood or magnitude of impacts on property values. It is also true that
the township of Frontenac Islands receives an annual payment of C$650,000
from the developer that should also be working counter to negative impacts.
Due to the uncertainty around the time at which turbine impacts could conceivably begin to occur, we test an alternate specication of the post-turbine
period.
In this specication we use the date that turbine construction be-
gan (May 2008) as the date following which impacts would be expected to
occur. As evident in Tables 4 and 5, the results are consistent with those of the
post-construction specication. We also test an alternate announcement period
where, as described above, we specify this period to begin following approval of
zoning bylaw amendments rather than using the date of announcement. The
results of this specication are again similar to those of our base specication,
but for the distance model the negative impact in the post-construction period
12
for the NY sample is not statistically signicant.
One of the underlying assumptions of the hedonic method is that the sample
area represents a single market. However, in our study areas this assumption
may not necessarily hold due to dierences in the composition of residential
property types and usage across each area. For example, on both sides of the
border, the areas that would be most aected by the wind turbines (i.e., Wolfe
Island and Cape Vincent), have proportionally greater waterfront properties relative to the other areas of their respective counties, and are also used to a greater
extent for vacation purposes. Such dierences could potentially introduce bias
into our estimates of the turbine impacts.
To address this potential bias, we
consider a specication that restricts our samples to parcels within 5 miles of
the turbines.
13
The results for the NY sample for both the view and distance
specications indicate similar results as with the full sample, where signicant
negative impacts are found in the post-construction period. Conversely, significant positive impacts are found for the ON sample in the post-construction
period, which coincides with the full sample nding for the distance specication but not with the view specication.
However, as with our full sample
specications, a negative impact is not found for the ON sample.
6
Conclusions
The main contributions of this paper are that it provides evidence that impacts
of wind turbines on property values vary depending on the context and it identies some factors that may contribute to the occurrence of signicant impacts.
This paper adds to the growing amount of evidence that wind facilities do have
signicant economic impacts on local communities. In our particular context,
with a unique scenario in which two communities separated by an international
12 We
nd similar results when we combine the alternate specications for both the an-
nouncement and post-turbine periods.
13 We
also tested a 10-mile restriction and found similar results to that of the 5-mile restric-
tion.
17
18
2,262
0.040
0.002
View*Post-Turbine
0.711
0.055
0.031
View*Announcement
Adjusted R2
0.043
-0.061
Post-Turbine
Number of observations
0.018
0.017
0.003
-0.021
se
coef
Announcement
View
Variable
Ontario
0.423
6,017
-0.146***
0.210***
-0.074
-0.093
0.047
coef
se
0.049
0.060
0.083
0.060
0.048
New York
0.711
2,262
0.054
0.118***
-0.194***
-0.249***
-0.040**
coef
Ontario
0.039
0.044
0.074
0.069
0.016
se
0.423
6,017
-0.142***
0.196***
-0.085
-0.017
0.044
coef
se
0.049
0.054
0.103
0.085
0.048
New York
Table 4: Results for Robustness Checks - Turbine View
Pre-construction
Announcement
0.774
110
0.258***
0.271***
-0.357***
-0.225***
-0.150***
coef
0.064
0.053
0.116
0.066
0.012
se
0.551
129
-0.797**
-0.308
0.020
-0.336
0.706**
coef
New York
se
0.360
0.401
0.661
0.368
0.282
Within 5 Miles of Turbines
Ontario
19
0.716
InvDistance*Post-Turbine
Adjusted R2
0.051
0.039
0.034
0.070*
InvDistance*Announcement
2,262
0.041
-0.063
Post-Turbine
Number of observations
0.021
0.027
-0.191***
-0.021
se
coef
Announcement
InvDistance
Variable
Ontario
0.424
6,017
-0.549**
0.395
-0.045
-0.111*
-0.669*
coef
se
0.242
0.321
0.084
0.067
0.368
New York
0.716
2,262
0.066*
0.032
-0.085
-0.131
-0.183***
coef
Ontario
0.035
0.037
0.092
0.080
0.024
se
0.424
6,017
-0.357
0.446
-0.074
-0.047
-0.829**
coef
se
0.271
0.345
0.100
0.079
0.399
New York
Table 5: Results for Robustness Checks - Turbine Distance
Pre-construction
Announcement
0.781
110
0.198***
0.128***
-0.514***
-0.269***
-0.178***
coef
0.038
0.014
0.070
0.010
0.001
se
0.550
129
-4.706*
0.932
2.284**
-0.341
2.139
coef
New York
se
2.578
2.723
1.092
1.037
2.511
Within 5 Miles of Turbines
Ontario
border are aected by the same wind farm, we nd evidence that property values
are negatively impacted by wind turbines, but only in our NY sample, which is
at a greater distance from the turbines. However, many of the aected homes
in that sample are likely to be vacation homes (we, unfortunately, cannot positively identify which ones) and homes with a view of the turbines over the water
- a view that may have been a main amenity of those properties when last transacted. In addition, these American homeowners had no ability to participate in
the siting process, nor do they recieve any compensation from the development.
Hence, these may be some contextual factors that determine whether property
values in a local area are negatively impacted by wind turbines.
There are limitations in this study that should be acknowledged. First, as
noted by Hoen et al. [2015], the use of inverse distance to account for turbine
impacts estimates this impact at a mean distance from the turbines, which
can hamper the ability to generate accurate impacts in close proximity to the
turbines.
This issue could potentially be avoided through the use of discrete
distance bands.
However, this approach may be unable to detect signicant
impacts within specied bands if the number of aected sales is relatively low,
which is the case in our study.
Interestingly, the nding of signicant negative impacts on property values
on only the U.S. side occurred despite the fact that there were fewer sales with
a view of the turbines than on the Ontario side. This suggests that the lack of
evidence of negative impacts on the Ontario side is not necessarily due to a lack
of observations. However, the possibility remains that parcels that are impacted
the most by the turbines were not sold or were unable to be sold. Unfortunately,
data is not available to examine this issue; as such, this represents an important
caveat to our study results.
This study furthers the conclusion from a read of the literature on wind
turbines that the specic context of each facility has the potential to make a large
dierence in the nature of the economic impacts. In our case, we have impacts
that dier across two communities impacted by the same facility. This implies
that, in general, researchers should not expect there to be one single answer
to the question of how wind farms aect property values, and that the lack of
consensus in the literature is not necessarily problematic. Instead, making any
forecast of impacts will require a more careful comparison to communities that
have already been studied. As in any benets transfer process, nding a proper
comparison site is the critical task when using results from one community to
predict outcomes for another.
20
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