Understanding the Amenity Impacts of Wind

Development on an International Border

Martin D. Heintzelman, Richard J. Vyn, and Sarah Guth∗

January 13, 2016

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Abstract

Wind energy developments are often controversial. Concerns are oftenraised about negative impacts on local communities, including impactson property values. Some of these negative impacts may be o�set bycompensatory payments made by wind developers. Communities oftenalso have a say in approving the development or setting parameters whichmight dictate terms such as the density of towers or minimum setbacksfrom property borders or homes. However, if the development is near aborder between municipalities, states, or even countries, it is often the casethat one or more jursidictions will not have an opportunity to set suchrules or demand compensation, but will, nonetheless, face some costs orimpacts from the development. We explore exactly this situation at theborder between Canada and the United States in the Thousand Islandsregion where a wind farm is currently operating on the Canadian borderisland of Wolfe Island. Using a parcel-level hedonic analysis of propertysales transactions, we �nd that properties in NY with a view of and/orin close proximity to the turbines signi�cantly depreciated in value afterconstruction while parcels in Canada saw no signi�cant change in theirvalue.

∗Heintzelman (corresponding author) is Associate Professor and Fredric C. Menz Scholarof Environmental Economics, School of Business, Clarkson University, P.O. Box 5790, Pots-dam, NY 13699, mheintze@clarkson.edu. Vyn is Associate Professor, University of Guelph,Ridgetown Campus. Sarah Guth is Research Assistant, Harvard University. This researchwas supported by the Fredric C. Menz endowment fund for Environmental Economics atClarkson University. Background research was conducted while Guth was participating inClarkson University's Research Experience for Undergraduates (REU) program, supportedby NSF Grant No. EEC-1359256. Additional research assistance was provided by BrittanyBerry at the University of Guelph and Chuan Tang at Clarkson University. Some GIS analysiswas provided by Adam Bonnycastle, also of the University of Guelph. We are indebted toseminar respondents at the University of New Hampshire and the 2015 Biennial Meeting of theAssociation for Canadian Studies in the United States for helpful comments and suggestions.

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1 Introduction

Renewable energy sources are a steadily increasing portion of our global energymix. Such energy sources are a global public good - by substituting for morepollution-intensive fossil-fuel sources they reduce global pollution of both crite-ria pollutants, such as NOx, SOx, Mercury, and others, as well as greenhousepollutants like CO2.

1 The bene�ts of these reductions are generally spread overa large area and, in the case of greenhouse gases, over the entire planet. Thecosts of these reductions, however, are more likely to fall on a much smaller ge-ographic area. In some cases, in fact, renewable energy facilities can be thoughtof simultaneously as global public goods and local public bads. As evidence ofthis, siting new renewable energy facilities, particularly wind farms, is often con-troversial, with local governments and/or activists putting up sti� resistance.This can be true even in areas with populations which are generally supportiveof environmental issues, as in the case of the Cape Wind development o� thecoast of Massachusetts2.

Common local concerns about wind developments include visual and auraldisamenities, potential human health impacts, and impacts on wildlife. Thevisual disamenities cited by wind opponents stem from the introduction of largeman-made structures into the landscape. At the most basic level, such a changeto the landscape can be o�-putting to residents of an area, who may havebought their property under the assumption that the landscape would remainunchanged. More speci�cally, opponents often cite the impacts of the large(depending on the facility) array of blinking red lights which sit atop the windturbine hubs, as well as shadown �icker which occurs when the turning bladesare between the sun and a home, resulting in rhythmically moving shadows.The �rst a�ect may be ampli�ed when the view of the turbines is over water, asthis clears the visual �eld of other features which would otherwise obstruct orreduce such views. Noise impacts are also often cited by homeowners opposed towind development. Of particular concern is low frequency noise which dissipatesmore slowly over distance (Bolin et al., 2011). Many people have complainedabout serious health impacts in relation to this low frequency noise. There havebeen some studies which �nd evidence of health impacts, in particular sleepdeprivation and psychological stress, but larger summary studies have foundlimited evidence linking the noise itself to these impacts (Council of CanadianAcademies, 2015). Instead, as found by a recent Health Canada study, windturbine noise is more likely to cause increased levels of annoyance rather thanhealth impacts3. Regardless of scienti�c evidence, the perception of healthimpacts remains, and these perceived amenity and health impacts are likelyto be re�ected in property values as bids for properties in close proximity to

1The exact emissions reductions from a wind facility depends very much on what otherenergy sources are displaced, the focus of Ka�ne et al. [2013].

2The Cape Wind project is a proposed o�shore wind facility in Nantucket Sound. It hasmet strict opposition from many local landowners and, most famously, the Kennedy family.

3Health Canada. 2014. Wind turbine noise and health study: Summary of results. Avail-able at: http://www.hc-sc.gc.ca/ewh-semt/noise-bruit/turbine-eoliennes/summary-resume-eng.php, accessed December 11, 2015.

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wind turbines will be reduced.Acting counter to these negative impacts are some bene�ts which often ac-

crue to local landowners and communities. First, individual landowners who areable to let their land out to wind energy companies stand to gain from streamsof rental payments from developers. These contracts vary across both sitesand landowners in magnitude and the allocation of risk (some contracts include�xed payments without regard to electricity production whereas others includepayments which are a function of electricity output, transferring some risk tolandowners). In addition, developers often compensate communities throughpayments-in-lieu-of-taxes (PILOTs). These payments help to mitigate negativeimpacts in the local community and, again, vary in size and structure acrosssites. Payments to communities will also be re�ected in property values as thesepayments could result in either lower local taxes or increased local services, ora combination of both (Kahn, 2013). Payments to landowners are unlikely tobroadly impact property values, but, if transferable, would certainly increase thevalue of parcels that include wind leases. Weber et al. [2013] note that energyroyalties, including wind turbine lease payments, are resulting in substantialpayments to farms across the United States

It is also possible that the development of wind energy will result in in-creased economic activity through direct employment impacts and indirectly aspayments made to individual landowners and the municipality �ow through thelocal economy. Brown et al. [2012] �nd signi�cant and positive economic im-pacts of wind facilities on personal income and employment, while Munday et al.[2011] �nd less de�nitive results on economic impacts. Any positive economicimpacts would likely also be re�ected in property values.

Another issue in public acceptance of wind development is public involve-ment in the development/approval process. If a community feels that they havenot had su�cient input to the approval process, they are more likely to opposeand �ght the development. In addition, if the project moves forward despitesubstantial opposition, this may lead to bad feelings and drive landowners outof 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 havea stronger hand in negotiating better compensation for their community in ad-dition to a�ecting details about the design of the project to their advantage.4

As an extreme form of this, if the development happens near a border betweencommunities, but is wholly contained within one community, the communitywithout the development is likely to be held out of the approval process, andwill also not receive compensation from the developer. These neighboring com-munities, in other words, will bear some of the cost of the project with littleprospect to receive any bene�ts.

All of these issues may be further complicated by the presence of second-home or vacation home owners. These owners are likely to have more elasticpreferences regarding changes in the amenities surrounding their property. Since

4See 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 andstrategies for social acceptance of wind energy.

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they are not tied as closely to a particular region because of concerns aboutemployment, they are more likely to sell their property, or less likely to buy anew property due to an adverse change in amenities. In addition, to the extentthat additional local services, such as improved school systems, are providedthrough the use of PILOTs, they are less likely to be able to take advantage ofthese improvements. Finally, they may also not have as much of a voice in theapproval process, and may feel like an outsider as a result. All of these factorsmake it possible that properties that are primarily used as second or vacationhomes, or communities with a large share of parcels in this use, will face evenlarger changes in property values as a result of wind farm developments.

This paper explores the issues discussed above using a unique scenario inwhich a large wind farm was constructed on the Canadian island of Wolfe Islandin 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, issituated at the entrance of the St. Lawrence River in Lake Ontario, directlyacross 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 thewestern half of the island in July 2005 (Keating, 2006). The o�cial plan andzoning bylaw amendments necessary to allow this project to move forward werepassed by council in November 2006, and the project was o�cially announcedon the Wolfe Island website in April 2007. Construction of the 86-turbine, 197.8MW facility began in May 2008 and was completed in June 2009, at which timethe wind farm became operational (Ontario Power Authority). We examinefor impacts of this wind farm on property values on both sides of the borderusing data from both Canada and the United States on property sales in closeproximity to the wind turbines.

On the American side, we focus on Je�erson County, which sits at the north-ern edge of New York and borders both the St. Lawrence River and Lake On-tario. Throughout the past decade, wind energy has divided public opinion inthe county. The region has been the targeted site for several recent Americanwind facility proposals, including in the Town of Cape Vincent and in the Townof Houns�eld, on Galloo Island in Lake Ontario, all of which have been highlycontroversial.

Newspaper coverage and letters to the editor in the New York media re-garding the Wolfe Island facility clearly expressed Je�erson County residents'opposition to turbines due to negative aesthetic impacts. A Cape Vincent jour-nalist feared that the turbines �will take away [the] image and. . . beauty of [his]township�, deterring prospective seasonal residents who �contribute so much intaxes and expertise� (Radley, 2009). A Chaumont resident characterized thewind farm as �blight on landscape� (Lynne, 2009). Finally, a seasonal residentof Chippewa Bay described the waterfront view of facility nighttime lightingas �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, wherethe construction of this wind farm has generated considerable controversy and

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public opposition. Opponents of the wind turbines have expressed concerns re-garding �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 resi-dences on Wolfe Island, many of which are waterfront properties. According toFast et al (2015), summer cottages comprise about one-third of all residenceson the island. As such, visual amenities play a signi�cant role in the value ofthese properties, and owners of these properties have expressed concerns re-garding potential negative impacts on property values arising due to the visualdisamenities associated with wind turbines. As one seasonal resident stated,�why would I want to live there [with the turbines]?� (Fast et al 2015). Inone case, property owners brought an appeal to Ontario's Assessment ReviewBoard to have the assessed value of their waterfront property reduced due to thedevaluation caused by the wind turbines. This appeal was ultimately rejecteddue to a lack of evidence of negative impacts. But this case highlights the un-derlying concerns that exist among residents of Wolfe Island regarding impactsof wind turbines. However, not all residents of Wolfe Island were opposed tothis project. As evident from interviews conducted by Fast et al (2015), thereare a considerable number of area residents that were supportive of the projectand of wind energy in general.

It is interesting to note that similar concerns and issues were raised by resi-dents on both sides of the border despite the fact that public meetings and openhouses were held for residents on the Canadian side throughout the applicationand development process. Public open houses for this project were �rst heldin 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 by-law amendment applicable to all Wolfe Island lands optioned for a wind plantzone (this amendment was passed by council the following month). In March2007, public open houses were held that included maps indicating 86 turbinelocations. Overall, public consultation focused only on residents of Wolfe Island,while residents of Cape Vincent had little to no involvement in this process. Thisdi�erence in the level of involvement could potentially contribute to a di�erencein 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 res-idents had some knowledge of and, perhaps, some input into the project beforeit was built. The Township of Frontenac Islands, which includes Wolfe Island,also receives C$645,000 per year in payments from the developer, TransAlta5.In addition, we also have American homes with substantial views of the tur-bines, but from a distance, whose owners had no input to the project and whosecommunities receive no compensation from the developer. Many of the Ameri-can homes with views of the turbines, in particular, are also vacation or secondhomes rather than primary residences.

We use a hedonic analysis to examine and compare how property values on

5TransAlta acquired Canadian Hydro Developers in 2009.

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both sides of the border have been impacted by the Wolfe Island wind turbines.In this analysis, a di�erence-in-di�erences approach is used to compare trans-action prices before and after approval or construction of the project as wellas between homes which can and cannot view the turbines, or are at varyingdistances to the turbines. We employ �xed e�ects to mitigate potential omittedvariables bias as well as controls for property market trends and seasonality ofprices. We �nd robust evidence of negative property value impacts on the NYside after construction of the turbines for homes in close proximity to the tur-bines and/or with a view of the turbines. In contrast, we do not �nd evidenceof signi�cant negative impacts on the Canadian side.

2 Literature Review

There is a substantial and growing literature that examines property value im-pacts of wind development, focusing primarily on Canada, the United States,and Europe. This literature reveals mixed conclusions, with some studies �ndingno signi�cant impacts on property values, while others �nd signi�cant negativeimpacts. Ben Hoen and his co-authors, in a series of studies looking at a largenumber of wind facilities across the United States, has consistently found noimpact of wind turbines on property values (Hoen et al., 2011, 2015). Thesestudies are impressive because of the large sample sizes which come from study-ing multiple sites around the country. However, by estimating an average impactacross all included sites, this may obscure signi�cant impacts at individual sites.In fact,Heintzelman and Tuttle [2012] �nd signi�cant negative impacts at twosites in northern New York but no impacts at a third site, which suggests thatthe impacts of turbines on property values can vary signi�cantly across di�erentsites. Hence, combining multiple sites in one study can avoid the issue of lownumbers of observations that has plagued prior wind turbine studies but doesnot permit observing di�erences in impacts from one site or region to another.

There are a number of other studies that also �nd no signi�cant impacts ofwind turbines on property values. Most recently, Lang et al. [2014] use a largedataset of transactions in Rhode Island to �nd no signi�cant property valueimpacts of small wind facilities with either only a single turbine or a few smallturbines. Laposa and Mueller [2010] �nd no e�ects from the announcement of aproposed wind farm in Colorado, but that facility was never built so it di�cultto interpret these results in the context of completed wind facilities. Shultzet 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 assessedvalues rather than transaction values. As assessed values re�ect only the expertopinion of the local assessor, it is hard to know how accurate this analysis wouldbe in assessing relatively new wind facilities. Sims and Dent [2007] and Simset al. [2008] �nd very limited evidence of turbine impacts, but use very smallsamples from sites with other confounding factors. Finally, Vyn and Mccullough[2014] study a large wind farm in rural Ontario and �nd no evidence of signi�cantimpacts on nearby property values. They do however point to the possibility

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that impacts may occur in other contexts. This may particularly be the case inthe province of Ontario where wind energy development has become increasinglycontroversial in recent years, and the concerns of impacts on health and propertyvalues have become prominent issues in the public forum.

Heintzelman and Tuttle [2012] is the �rst study that found signi�cant neg-ative impacts from wind turbines. Importantly, however, they �nd impacts inonly 2 of 3 counties in their study area, suggesting that local context matters agreat deal in determining the impact. Subsequently, a number of other studieshave found signi�cant negative impacts. Sunak and Madlener [2012] implementa combination of spatial �xed e�ects methods and Geographically Weighted Re-gression (GWR) and �nd consistent evidence that proximity to wind turbinesnegatively impacts property values in a region of Germany. The GWR �nds sub-stantial variation in these impacts over space. Likewise, Jensen et al. [2014] andGibbons [2015] both �nd signi�cant negative impacts of wind turbines and, inthe case of Jensen et al. [2014], both visual and aural disamenities are separatelyfound to have negative impacts.

Taken together, this growing literature suggests that there may not be anysingle, global answer to the impact of wind turbines on property values. In-stead, the literature strongly suggests that the speci�c context of each facilitywill drive outcomes for that community. This paper takes the next step of try-ing to determine what some factors might be by examining for impacts in theborder region where part of our sample is closer to the facility, but also receivescompensatory payments from the developer, while the rest of our sample viewsthe turbines from a distance and does not receive any compensation.

3 Methodology

Hedonic analysis is a well-accepted form of revealed preference (as opposed tostated 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 attributesof a compound good, like housing. In this case, he derives a hedonic pricefunction showing that, under certain assumptions, the market price of a housewill be a function of its attributes. One can then estimate this hedonic functionusing data on a set of homes with varying prices and attributes, where theestimated coe�cients represent the marginal willingness-to-pay for changes inthese individual attributes. The strength of this approach is that it allows forthe estimation of the value of marginal changes in attributes that are otherwisenot bought and sold on markets, such as environmental amenities.

The hedonic method is quite powerful because any amenity which is valuedby consumers and associated in some way with housing markets can, in theory,be valued through this method under the right conditions. A limitation of thisapproach, however, is that the hedonic method can only estimate what are

6See III et al. [2014] and Taylor [2003]for comprehensive treatments of the hedonic methodin environmental economics.

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called �use� values of homeowners or renters, and not broader societal values.For instance, the hedonic method could estimate the value of having a healthyecosystem to residents in a local area, but cannot estimate the value to society-at-large of a particular ecosystem.

As in any econometric exercise, there are a number of empirical issues thatare common in hedonic analyses. First, one must take care in choosing thefunctional form. Traditionally, based on Cropper et al. [1988], the log-linearor log-log forms are preferred. However, more recently, Kumino� et al. [2010]advocate for testing the appropriateness of this functional form using the Box-Cox formulation (Box and Cox, 1964). Subsequent applications, including recentstudies on the impacts of wind turbines on property values (Heintzelman andTuttle [2012], Vyn and Mccullough [2014]), have found that the Box-Cox analysissupports the log-linear form in this context. Similarly, our robustness testingin this study indicates that the results based on a Box-Cox functional form areconsistent with those of the log-linear form. As a result, we primarily use thelog-linear form in our analysis.

Kumino� et al. [2010] also advocate for the inclusion of spatial �xed e�ects,temporal controls, and quasi-experimental identi�cation to control as best aspossible for omitted variables bias, which is endemic to applications of the he-donic method. Omitted variables bias arises when one or more factors thatare correlated with both the dependent variable and one or more included ex-planatory variables are omitted from the speci�cation. In this case, the analysiswill assign explanatory power which properly belongs with the omitted variableto included variables, resulting in biased estimates of the e�ect of those vari-ables. Given the large number of factors which are both unobservable to theanalyst and correlated with property values (local neighborhood attributes, forinstance), this has serious implications for hedonic analysis. Similarly, giventhat 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 adequatelycontrol for trends over time, we may similarly bias our estimates of the turbineimpacts.

Fixed e�ects approaches help overcome these issues. By implicitly includinga large number of spatial dummy variables, �xed e�ects allows each area (town-ship or census block, for instance) to have its own intercept term, accounting fortime invariant factors which a�ect property values in these areas. The smallerthe level of the �xed e�ects, the less likely one is to have an omitted variablesproblem (as more and more spatial factors will be subsumed in the �xed e�ect).However, �xed e�ects also rely on within unit variation to identify the e�ectsof remaining variables. This often means that as the level of the �xed e�ectsgets smaller, the analyst loses power to identify e�ects. Thus, it is important tocarefully balance these e�ects when interpreting results and choosing a level of�xed e�ects. In our case, we found that the use of �xed e�ects at the townshiplevel provided an appropriate balance, as our preliminary analysis indicatedthat the ability to identify e�ects was diminished with the use of �xed e�ects atthe census block level. In addition, di�erences exist in the speci�cation of theAmerican census blocks and the closest Canadian equivalent, the homogeneous

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neighborhood which would undermine our ability to compare results across theborder.

We include year �xed e�ects to account for sample wide trends as well asmonth �xed e�ects to account for seasonality in prices. This is particularlyimportant given the turbulent real estate markets of the late 2000s.7 Our quasi-experimental identi�cation stems from the fact that we have transactions takingplace both before and after the turbines were built and in areas that are at vary-ing distances and with varying views of the turbines. We include clustered errorterms at the same level as our �xed e�ects. This allows error terms to be corre-lated across transactions in the same local area (i.e., townships) while requiringthem to be independent across local areas. This generalization helps to controlfor spatial autocorrelation and is a simpli�ed form of spatial econometrics.

With all of this in mind, our speci�cation follows Heintzelman and Tuttle[2012] and takes the general form:

ln(Pijt) = λt + αj + β1windi + β2windt + β3windit + β4xit + ηjt + εijt (1)

where λt represents the time (year and month) �xed e�ects, αj represents localarea �xed e�ects, windi represents the proximity to or view of wind turbinesfor parcel i, windt represents the time period, t, in which turbine impacts areexpected to occur, windit represents the interaction term between proximityto or view of wind turbines for parcel i with time period t, xit is a vector ofother parcel and structural characteristics, and ηjt and εijt represent spatialarea and individual speci�c error terms. 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 di�ered between thetwo locations.

4 Data

We use data on 8,279 single-family residential property transactions on bothsides of the border in our analysis: 6,017 in Je�erson County, NY8, and 2,262across the border in Frontenac County, Ontario. 1 provides a map of the studyarea and transaction locations. Data on NY transactions between January2004 and July 2013, inclusive, come from the New York State O�ce of RealProperty Taxation Services (NYSORPTS). This data includes sale price, saledate, and parcel identi�ying information. This transaction data is then mergedwith parcel and home characteristics data from the asseessment process, alsofrom NYSORPTS. We bring in parcel shape�le (GIS) data which we acquired

7It is important to note that real estate markets in Northern New York and in Ontariowere relatively insulated from this turmoil, and there is no dramatic crash in prices followingthe �nancial crisis in our sample. Nonetheless, it is important to control for these possibleimpacts.

8There were an additional 11 transactions that had to be omitted from the dataset due toincomplete information.

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from the Je�erson County Assessor's O�ce. With this spatial data we calcu-late a number of distance and spatial variables in ArcGIS. Data on Canadiantransactions comes from Ontario's Municipal Property Assessment Corporation(MPAC). This detailed data includes all open-market sales of residential prop-erties in Frontenac County between September 2004 and July 2013, inclusive.An extensive set of property and structural variables is included in the MPACdata, while additional distance and spatial variables are calculated using Ar-cGIS. There are no sales in this dataset of properties on which a turbine islocated.

The key variables that capture the visual and aural impacts of the wind tur-bines include both visibility and distance variables, and we estimate separatemodels using each of these variables. The distance from each parcel to the near-est turbine on Wolfe Island was calculated using ArcGIS. Previous studies thathave accounted for turbine impacts on property values based on distance mea-sures have used inverse distance (e.g., Heintzelman and Tuttle [2012],Vyn andMccullough [2014]) and distance bands (e.g.,Hoen et al., 2011, 2015). Both ofthese measures have potential shortcomings: the use of inverse distance involvesestimating impacts at the mean distance from the turbines and, as such, mayunderestimate the potentially greater impacts in very close proximity to tur-bines, while the use of distance bands, which involves dividing up observationsinto bins of speci�ed distances from turbines, can result in estimated impactsfor each band that may be based on relatively few observations. Due to therelatively low number of observations in our datasets in close proximity to theturbines, the shortcoming associated with the use of distance bands is of greaterconcern. As a result, we use the inverse distance measure in our analysis. Giventhe distance-decaying nature of the visual and aural impacts of turbines, anyimpacts on property values are expected to be greater in closer proximity tothe turbines, which would be re�ected by a negative coe�cient for the inversedistance variable. Since the data includes sales up to 71 miles from the turbineson the Canadian side and up to 44 miles away on the American side, there are aconsiderable number of sales that would not be impacted by the turbines, whichcomprise a control group.

Since proximity does not necessarily correspond with visual impact, as viewmay be obstructed by landscape features, we also conducted �eld visits to allparcels that were potentially within visibility of the wind turbines (this potentialwas determined based on a combination of viewshed modelling in ArcGIS anddistance to the nearest turbine). On these visits, it was determined whether theparcel (as viewed from the road) had a partial or full view of one or more ofthe turbines. The �eld observations of the visual impacts of every transactedparcel with a potential view is a strength of this paper, similar to that of Hoenet al. [2011] and Vyn and Mccullough [2014]. It is superior to relying solely onviewshed analysis in ArcGIS, which generally relies on a number of assumptionsabout the height of di�erent forms of land cover (trees) to estimate views, andcannot possibly include every potential obstruction. Because of this, viewshedanalysis will tend to overestimate turbine views, and we avoid this potentialerror with the �eld observations.

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Figure 1: Study Area

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Since the data includes sales that occurred both before and after the windfarm was developed, it is necessary to account for the time period during whichturbine impacts on property values are expected to occur. We specify a post-turbine period (Post-Con) to account for sales that occurred after turbine con-struction on Wolfe Island was completed in June 2009. In addition, we accountfor potential announcement e�ects by specifying an announcement period be-tween April 2007, when the wind farm was �rst announced, and June 2009(Announcement). Each of these time period variables is interacted with the vis-ibility (View) and turbine distance (InvDistance) variables in order to specifythe variables that account for the impacts of turbines on property values. How-ever, given the di�culty in specifying the precise point in time at which impactsmay begin to occur, we test the robustness of our results with alternate speci�-cations of these time periods. Rather than using the post-construction date forthe speci�cation of the post-turbine period, we use the date that constructionbegan: May 2008. During the construction period, while all turbines were notyet fully erected, the locations were known and the visual e�ects were becomingevident; hence, turbine impacts could conceivably have begun to occur duringthis period. We also use an alternate announcement e�ect period that beginsfollowing 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 Amer-ican data includes 58 parcels within �ve miles, the nearest of which is 1.86 milesfrom the turbines. There are 39 parcels in Ontario and 15 parcels in New Yorkwith a view of the turbines that were sold in the post-construction period. Sincethese numbers of observations are relatively low, we do not break down theseparcels 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. Anye�ects that we estimate using this measure will be an average of e�ects acrossthese sub-categories of view, presumably over-estimating the e�ect for thosewith only a partial view of one turbine and under-estimating the e�ect for thosewith full views of multiple turbines.

To ensure consistency in the estimation approach between the two sides ofthe border and to reduce the possbility of bias between the two sets of results,the same set of explanatory variables representing the parcel and structuralcharacteristics are used. Variables accounting for parcel attributes include lotsize and categorical variables for waterfront and seasonal properties. The valueassociated with the residence on each parcel is accounted for by a set of variablesthat includes the numbers of bathrooms, bedrooms, and stories, the living area,age of the house, and categorical variables for the existence of a �replace, centralair conditioning, and a basement, quality ratings (1-5 on a 5-point scale), heattypes (electric, forced air, hot water, other, and no heat), and whether theresidence is a mobile home. In addition to distance to the nearest turbine, otherdistance variables include the distances from each parcel to the nearest city andtown. As mentioned above, sets of year and month �xed e�ect variables arealso included. Most of these variables were included in both datasets, though insome cases slight adjustments were made in the merging process. For example,

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the variable representing house quality is based on a 5-point scale for the NYdata and on a 10-point scale for the ON data, which is accounted for by dividingthe quality index by two for parcels in Ontario.

Summary statistics for all variables for the two datasets are presented inTable 1. Notice that property values in our Ontario sample are considerablylarger than in our New York sample. A higher proportion of homes in our NYsample are on the waterfront as compared to our ON sample, and a higherproportion are also seasonal in our NY sample. Together, these facts suggestthat a higher proportion of our NY sample, and particularly those propertiesa�ected 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 turbineimpacts are accounted for by visibility, while Table 3 presents the results forthe regressions based on distance to the nearest turbine. For the sake ofbrevity, only the results for the variables on which we focus our discussion,which include the turbine variables as well as the waterfront and seasonalvariables, are reported in these tables.9

In Table 2, the key variables of interest are the interaction terms that indicatehow turbine view impacts property values in the announcement andpost-construction periods (View*Announcement; View*Post-Con). First, we�nd negative and signi�cant impacts from turbine construction for homes inNY with a turbine view. The estimated coe�cient for this variable indicatesthat the values of homes in NY with a view of the turbines have, on average,been reduced by 13.4% following construction of the turbines10. Conversely, nosigni�cant impact is observed on the Canadian side in either period. It is alsoevident from the results that sale prices across the NY sample decreased in thepost-construction period relative to prices prior to this period, but thisdecrease was signi�cantly greater for parcels that had a view of the turbines.A positive impact is found in NY during the announcement period, butconceivably during this time home buyers in New York may not have beenaware of the proposed wind farm or the location of the turbines to beconstructed, or at the very least may not have been aware of the impendingimpact on their viewshed. This positive impact may also be the result of anomitted 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,

9Estimated coe�cients for other variables, which include parcel and structural variables(size of home, etc.) as well as �xed e�ects variables, are largely consistent with expectations.These results are available from the authors upon request.

10This �gure is derived based on coe�cient interpretation for categorical variables in semi-log equations (see Halvorsen and Palmquist, 1980)

13

Table1:Summary

StatisticsOntario

NewYork

VariableName

VariableDescription

Mean

Std.Dev.

Min

Max

Mean

Std.Dev.

Min

Max

SalePrice

Saleprice

oftheparcel,in

Canadian$for

Ontariosalesandin

US$forNew

York

sales

241,150.40

122,104.90

25,000.00

1,600,000.00

142,004.90

101,407.80

3,500.00

2,000,300.00

View

=1ifparcelhasaview(fullorpartial)ofthe

turbines

0.034

0.18

0.00

1.00

0.004

0.07

0.00

1.00

Distance

Distance

toNearest

WolfeIslandTurbine,in

miles

19.28

13.21

0.28

71.45

25.75

9.02

1.86

44.38

Announcement

=1ifparcelsold

betweenproject

announcementandcompletionofturbine

construction

0.354

0.48

0.00

1.00

0.211

0.41

0.00

1.00

Post-Con

=1ifparcelsold

follow

ingcompletionof

turbineconstruction

0.586

0.49

0.00

1.00

0.410

0.49

0.00

1.00

Waterfront

=1ifparcelisonthewaterfront

0.049

0.22

0.00

1.00

0.117

0.32

0.00

1.00

Seasonal

=1ifparcelisseasonal

0.064

0.24

0.00

1.00

0.102

0.30

0.00

1.00

Mobile

=1ifresidence

ismobilehome

0.012

0.11

0.00

1.00

0.037

0.19

0.00

1.00

LotSize

Sizeoftheparcel,in

acres

4.184

14.48

0.06

300.56

6.538

25.41

0.01

391.43

Bathrooms

Number

ofbathrooms

1.515

0.70

0.00

4.50

1.433

0.58

0.00

5.50

Bedrooms

Number

ofbedrooms

2.924

0.82

0.00

9.00

2.990

0.94

0.00

9.00

Fireplace

=1ifatleast

one�replace

exists

inthehouse

0.286

0.45

0.00

1.00

0.170

0.38

0.00

1.00

Air

=1ifhouse

hascentralairconditioning

0.234

0.42

0.00

1.00

0.021

0.14

0.00

1.00

Quality1

=1ifhouse

quality

index

is1

0.010

0.10

0.00

1.00

0.017

0.13

0.00

1.00

Quality2

=1ifhouse

quality

index

is2

0.061

0.24

0.00

1.00

0.124

0.33

0.00

1.00

Quality3

=1ifhouse

quality

index

is3

0.866

0.34

0.00

1.00

0.763

0.43

0.00

1.00

Quality4

=1ifhouse

quality

index

is4

0.063

0.24

0.00

1.00

0.095

0.29

0.00

1.00

Quality5

=1ifhouse

quality

index

is5

0.000

0.00

0.00

0.00

0.001

0.03

0.00

1.00

LivingArea

Livingareaofthehouse,in

square

feet

1493.10

587.71

101.00

4839.00

1,557.52

598.32

136.00

6,074.00

Age

Ageofthehouse

42.45

37.29

0.00

190.00

67.50

52.45

0.00

225.00

Stories

Number

ofstories

inthehouse

1.26

0.40

1.00

3.00

1.46

0.44

1.00

3.00

Electric

=1ifthehouse

haselectricheat

0.18

0.38

0.00

1.00

0.15

0.35

0.00

1.00

ForcedAir

=1ifthehouse

hasforced

airheat

0.72

0.45

0.00

1.00

0.70

0.46

0.00

1.00

HotWater

=1ifthehouse

hashotwaterheat

0.02

0.14

0.00

1.00

0.08

0.27

0.00

1.00

Other

=1ifthehouse

hasother

heattype

0.04

0.20

0.00

1.00

0.00

0.00

0.00

0.00

NoHeat

=1ifthehouse

hasnoheat

0.046

0.21

0.00

1.00

0.08

0.27

0.00

1.00

Basement

=1ifthehouse

hasabasement

0.33

0.47

0.00

1.00

0.09

0.28

0.00

1.00

Town

Distance

tothenearest

town,in

miles

13.80

10.29

0.00

57.02

1.61

1.51

0.00

8.25

City

Distance

tothenearest

city

(population>

50,000),in

miles

17.02

16.49

0.00

81.32

2.90

3.03

0.00

17.88

Observations

2,262

6,017

14

Table 2: Regression Results - Turbine ViewOntario New York

Variable 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 DistanceOntario New York

Variable coef se coef se

InvDistance -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

InvDistance*Post-Con 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

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 discountin Ontario.11 The negative coe�cient for ON may be due to the prevalence ofmore 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 inthis study area seasonal homes are more likely to be vacation homes located onparticularly attractive parcels, or in attractive locations.

An important caveat about the post-construction impact observed in NY isthat, while it is statistically signi�cant, it is based on a relatively small numberof parcels (15) with a view of the turbines that were sold after the turbineswere built. This suggests that this result should be interpreted cautiously. Thestatistical signi�cance likely stems from the large number of control transactionsto which these transactions are being compared. The small number of treatmenttransactions is unfortunately common in hedonic studies of wind turbines.

The results of the speci�cation in which turbine impacts are accounted forby distance to the nearest turbine are reported in Table 3. This speci�cationis less susceptible to the small numbers problem highlighted above since everyparcel has a distance to the nearest turbine and as such can contribute to theestimation of turbine impacts. The key variables of interest are the interactionterms between the inverse of the distance to the nearest turbine and each ofthe announcement and post-construction periods (InvDistance*Announcement;InvDistance*Post-Con). We �nd that the results of this speci�cation are broadlyconsistent with those based on turbine view. In particular, we �nd, again, asigni�cant negative impact in NY in the post-construction period, where parcelsin closer proximity to turbines have experienced reductions in value relative tothose at greater distances from the turbines. A positive impact is observed inthe ON sample in the post-construction period, which is unexpected, though itis only signi�cant at the 10% level. Inverse distance to turbine is negative andsigni�cant in both samples, possibly �lling in for a measure of proximity to thewater, particularly in the NY sample. Seasonal and waterfront homes have thesame e�ects as seen above.

Overall, the results of these two speci�cations indicate that negative impactsassociated with the Wolfe Island turbines have only occurred to any observableextent on the NY side, but not in Ontario despite the fact that many parcels onWolfe Island are located in amongst the turbines.This is somewhat surprisinggiven that concerns were raised on both sides of the border, but may re�ect thecross-border di�erence in the level of involvement in the project developmentprocess. In addition, as indicated in Fast et al (2015), there are a fair numberof residents, both long-time residents and newcomers, on Wolfe Island that arequite supportive of the wind farm. Those who support the wind farm are lesslikely to be concerned about impacts on property values and may not reducetheir willingness-to-pay for properties with a view of or in close proximity to theturbines. If a large enough proportion of residents and potential homebuyers

11NYSORPTS de�nes seasonal homes as those �Dwelling units generally used for seasonaloccupancy; not constructed for year-round occupancy (inadequate insulation, heating, etc.).�MPAC is less speci�c about this de�nition for seasonal homes.

16

support the wind farm and believe it bene�ts the community, this may reducethe likelihood or magnitude of impacts on property values. It is also true thatthe township of Frontenac Islands receives an annual payment of C$650,000from the developer that should also be working counter to negative impacts.

Due to the uncertainty around the time at which turbine impacts could con-ceivably begin to occur, we test an alternate speci�cation of the post-turbineperiod. In this speci�cation we use the date that turbine construction be-gan (May 2008) as the date following which impacts would be expected tooccur. As evident in Tables 4 and 5, the results are consistent with those of thepost-construction speci�cation. We also test an alternate announcement periodwhere, as described above, we specify this period to begin following approval ofzoning bylaw amendments rather than using the date of announcement. Theresults of this speci�cation are again similar to those of our base speci�cation,but for the distance model the negative impact in the post-construction periodfor the NY sample is not statistically signi�cant.12

One of the underlying assumptions of the hedonic method is that the samplearea represents a single market. However, in our study areas this assumptionmay not necessarily hold due to di�erences in the composition of residentialproperty types and usage across each area. For example, on both sides of theborder, the areas that would be most a�ected by the wind turbines (i.e., WolfeIsland and Cape Vincent), have proportionally greater waterfront properties rel-ative to the other areas of their respective counties, and are also used to a greaterextent for vacation purposes. Such di�erences could potentially introduce biasinto our estimates of the turbine impacts. To address this potential bias, weconsider a speci�cation that restricts our samples to parcels within 5 miles ofthe turbines.13 The results for the NY sample for both the view and distancespeci�cations indicate similar results as with the full sample, where signi�cantnegative impacts are found in the post-construction period. Conversely, signif-icant positive impacts are found for the ON sample in the post-constructionperiod, which coincides with the full sample �nding for the distance speci�-cation but not with the view speci�cation. However, as with our full samplespeci�cations, a negative impact is not found for the ON sample.

6 Conclusions

The main contributions of this paper are that it provides evidence that impactsof wind turbines on property values vary depending on the context and it iden-ti�es some factors that may contribute to the occurrence of signi�cant impacts.This paper adds to the growing amount of evidence that wind facilities do havesigni�cant economic impacts on local communities. In our particular context,with a unique scenario in which two communities separated by an international

12We �nd similar results when we combine the alternate speci�cations for both the an-nouncement and post-turbine periods.

13We also tested a 10-mile restriction and found similar results to that of the 5-mile restric-tion.

17

Table4:ResultsforRobustnessChecks-TurbineView

Pre-construction

Announcement

Within

5Miles

ofTurbines

Ontario

New

York

Ontario

New

York

Ontario

New

York

Variable

coef

secoef

secoef

secoef

secoef

secoef

se

View

0.003

0.017

0.047

0.048

-0.040**

0.016

0.044

0.048

-0.150***

0.012

0.706**

0.282

Announcement

-0.021

0.018

-0.093

0.060

-0.249***

0.069

-0.017

0.085

-0.225***

0.066

-0.336

0.368

Post-Turbine

-0.061

0.043

-0.074

0.083

-0.194***

0.074

-0.085

0.103

-0.357***

0.116

0.020

0.661

View*Announcement

0.031

0.055

0.210***

0.060

0.118***

0.044

0.196***

0.054

0.271***

0.053

-0.308

0.401

View*Post-Turbine

0.002

0.040

-0.146***

0.049

0.054

0.039

-0.142***

0.049

0.258***

0.064

-0.797**

0.360

Number

ofobservations

2,262

6,017

2,262

6,017

110

129

Adjusted

R2

0.711

0.423

0.711

0.423

0.774

0.551

18

Table5:ResultsforRobustnessChecks-TurbineDistance

Pre-construction

Announcement

Within

5Miles

ofTurbines

Ontario

New

York

Ontario

New

York

Ontario

New

York

Variable

coef

secoef

secoef

secoef

secoef

secoef

se

InvDistance

-0.191***

0.027

-0.669*

0.368

-0.183***

0.024

-0.829**

0.399

-0.178***

0.001

2.139

2.511

Announcement

-0.021

0.021

-0.111*

0.067

-0.131

0.080

-0.047

0.079

-0.269***

0.010

-0.341

1.037

Post-Turbine

-0.063

0.041

-0.045

0.084

-0.085

0.092

-0.074

0.100

-0.514***

0.070

2.284**

1.092

InvDistance*Announcement

0.034

0.051

0.395

0.321

0.032

0.037

0.446

0.345

0.128***

0.014

0.932

2.723

InvDistance*Post-Turbine

0.070*

0.039

-0.549**

0.242

0.066*

0.035

-0.357

0.271

0.198***

0.038

-4.706*

2.578

Number

ofobservations

2,262

6,017

2,262

6,017

110

129

Adjusted

R2

0.716

0.424

0.716

0.424

0.781

0.550

19

border are a�ected by the same wind farm, we �nd evidence that property valuesare negatively impacted by wind turbines, but only in our NY sample, which isat a greater distance from the turbines. However, many of the a�ected homesin that sample are likely to be vacation homes (we, unfortunately, cannot posi-tively 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 trans-acted. In addition, these American homeowners had no ability to participate inthe siting process, nor do they recieve any compensation from the development.Hence, these may be some contextual factors that determine whether propertyvalues in a local area are negatively impacted by wind turbines.

There are limitations in this study that should be acknowledged. First, asnoted by Hoen et al. [2015], the use of inverse distance to account for turbineimpacts estimates this impact at a mean distance from the turbines, whichcan hamper the ability to generate accurate impacts in close proximity to theturbines. This issue could potentially be avoided through the use of discretedistance bands. However, this approach may be unable to detect signi�cantimpacts within speci�ed bands if the number of a�ected sales is relatively low,which is the case in our study.

Interestingly, the �nding of signi�cant negative impacts on property valueson only the U.S. side occurred despite the fact that there were fewer sales witha view of the turbines than on the Ontario side. This suggests that the lack ofevidence of negative impacts on the Ontario side is not necessarily due to a lackof observations. However, the possibility remains that parcels that are impactedthe 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 importantcaveat to our study results.

This study furthers the conclusion from a read of the literature on windturbines that the speci�c context of each facility has the potential to make a largedi�erence in the nature of the economic impacts. In our case, we have impactsthat di�er across two communities impacted by the same facility. This impliesthat, in general, researchers should not expect there to be one single answerto the question of how wind farms a�ect property values, and that the lack ofconsensus in the literature is not necessarily problematic. Instead, making anyforecast of impacts will require a more careful comparison to communities thathave already been studied. As in any bene�ts transfer process, �nding a propercomparison site is the critical task when using results from one community topredict outcomes for another.

20

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