The Final Dataset Consists of 7,459 Valid Residential Transactions Occurring between Pre Announcement Post Announcement Pre Construction 1st Year after Construction 2nd Year after Construction 2ϩ Years after Construction Total

Increasing numbers of communities are considering wind power developments. One concern within these communities is that proximate property values may be adversely affected, yet there has been little research on the subject. The present research investigates roughly 7,500 sales of single-family homes surrounding 24 existing wind facilities in the United States. Across four different hedonic models, and a variety of robustness tests, the results are consistent: neither the view of the wind facilities nor the distance of the home to those facilities is found to have a statistically significant effect on sales prices, yet further research is warranted. Wind power development has expanded dramatically in recent years (WEC, 2010) and that expansion is expected to continue (Global Wind Energy Council, 2008; Wiser and Hand, 2010). The U.S. Department of Energy, for example, published a report that analyzed the feasibility of meeting 20% of electricity demand in the United States with wind energy by 2030 (U.S. DOE, 2008). Approximately 3,000 wind facilities would need to be sited, permitted, and constructed to achieve a 20% wind electricity target in the U.S. Although surveys show that public acceptance is high in general for wind energy (e.g., Firestone and Kempton, 2006), a variety of local concerns exist that can impact the length and outcome of the siting and permitting process. One such concern is related to the views of and proximity to wind facilities and how these might impact surrounding property values. Surveys of local communities considering wind facilities have frequently found that adverse impacts on aesthetics and property values are in the top tier of concerns relative to other matters such as impacts on wildlife habitat and mortality, radar and communications systems, ground 2 8 0 H o e n , W i s e r , C a p p e r s , T h a y e r , a n d S e t h i transportation, and historic and cultural resources (e.g., BBC Research & Consulting, 2005; Firestone and Kempton, 2006). Concerns about the possible impacts of wind facilities on residential property values can be categorized into three potential effects: Scenic Vista Stigma: A perception that a home may be devalued because of the view of a wind energy facility, and the potential impact of that view on an otherwise scenic vista. Area Stigma: A perception that the general area surrounding a wind energy facility will appear more developed, which may adversely affect home values in the local community regardless of whether any individual home has a view of the wind turbines. Nuisance Stigma: A perception that factors that may occur in close proximity to wind turbines, such as sound and shadow flicker, will have an adverse influence on home values. Any combination of these three potential stigmas might affect a particular home. Consequently, each of the three potential impacts must be considered when analyzing the effects of wind facilities on residential sales prices. This paper uses several hedonic pricing models to analyze a sample of 7,459 armslength residential real estate transactions occurring between 1996 and 2007 for homes located near 24 existing wind facilities spread across nine U.S. states. In so doing, the paper investigates the degree to which views of and proximity to wind facilities affect sales prices. The remainder of the paper is organized as follows. The next section contains a summary of the existing literature that has investigated the effects of wind energy on residential property values. Then the data used in the analysis are described. Following that, a set of four hedonic models are described and estimated to test for the existence of property value impacts associated with the wind energy facilities. The findings regarding the existence and magnitude of the three stigmas mentioned above are described, as are a series of robustness tests intended to assess the reliability of the model results. The paper ends with a brief discussion of future research possibilities. P r e v i o u s R e s e a r c h A variety of methods, including surveys of homeowners and real estate experts, simple analysis of sales transactions (e.g., t-test), and sophisticated empirical analysis of sales transactions (e.g., multiple regression), have been used to explore the relationship between residential property values and views of and proximity to wind facilities. One of the overall conclusions that can be drawn from this literature is that wind facilities are often predicted to negatively impact residential property values in pre-construction surveys (Haughton, Giuffre, Barrett, and W i n d E n e r g y F a c i l i t i e s & R e s i d e n t i a l P r o p e r t i e s 2 8 1 J R E R V o l . 3 3 N o . 3 – 2 0 1 1 Tuerck, 2004; Khatri, 2004; Firestone, Kempton, and Krueger, 2007; Kielisch, 2009), but negative impacts have largely failed to materialize post-construction when actual transaction data become available for analysis (Jerabek, 2001; Sterzinger, Beck, and Kostiuk, 2003; Hoen, 2006; Poletti, 2007; Sims, Dent, and Oskrochi, 2008). In the only study using transaction data that did find a statistically significant adverse effect, the authors contend that the result was likely driven by variables omitted from their analysis, and not by the presence of wind facilities (Sims and Dent, 2007). Other studies that have relied on market data have sometimes found the possibility of negative effects, but the statistical significance of those results has not been reported (e.g., Kielisch, 2009). Potentially more important, the existing literature leaves much to be desired. First, many of the studies have relied only on surveys of homeowners or real estate professionals, rather than trying to quantify impacts based on market data (e.g., Haughton, Giuffre, Barrett, and Tuerck, 2004; Goldman, 2006). Second, a number of the studies that used market data conducted rather simplified analyses of those data, potentially not controlling for the many drivers (e.g., size and/or condition of the home and lot size) of residential sales prices (e.g., Sterzinger, Beck, and Kostiuk, 2003; McCann, 2008; Kielisch, 2009). Third, many of the studies have relied upon a very limited number of residential sales transactions, and therefore may not have had an adequate sample to statistically discern any property value effects, even if effects did exist (e.g., Jerabek, 2001). Fourth, and perhaps as a result, many of the studies did not conduct, or at least have not published, the statistical significance of their results. Fifth, when analyzed, there has been some emphasis on area stigma, and none of the studies has simultaneously investigated all three possible stigmas listed above. Sixth, only a few of the studies (Hoen, 2006; Sims and Dent, 2007; Sims, Dent, and Oskrochi, 2008; Kielisch, 2009) conducted field visits to the homes to assess the quality of the scenic vista from the home, and the degree to which the wind facility might impact that scenic vista. Finally, with two exceptions (Sims and Dent, 2007; Sims, Dent, and Oskrochi, 2008), none of the studies were peer-reviewed in the academic literature. D a t a O v e r v i e w The methods applied in the present work are intended to overcome many of the limitations of the existing literature. First, a large amount of residential real estate transaction data was collected from within ten miles of 24 different existing wind facilities in the U.S., allowing for a robust statistical analysis across a pooled dataset that includes a diverse group of wind facility sites. Second, all three potential stigmas were investigated by exploring the potential impact of wind facilities on home values based both on the distance to and view of the facilities from the homes. Third, field visits were made to every home in the sample, allowing for a reliable assessment of the scenic vista enjoyed by each home and the degree to which the wind facility was visible from the home, and to collect other value-influencing data from the field (e.g., if the home is situated on a cul2 8 2 H o e n , W i s e r , C a p p e r s , T h a y e r , a n d S e t h i Exhibi t 1 Summary of Study Areas Study Area Code Study Area Counties, States Facility Names # of Turbines # of MW Max Hub Height (meters) WAOR Benton and Walla Walla Counties, WA and Umatilla County, OR Vansycle Ridge, Stateline, Nine Canyon I & II, Combine Hills 582 429 60 TXHC Howard County, TX Big Spring I & II 46 34 80 OKCC Custer County, OK Weatherford I & II 98 147 80 IABV Buena Vista County, IA Storm Lake I & II, Waverly, Intrepid I & II 381 370 65 ILLC Lee County, IL Mendota Hills, GSG Wind 103 130 78 WIKCDC Kewaunee and Door Counties, WI Red River, Lincoln 31 20 65 PASC Somerset County, PA Green Mountain, Somerset, Meyersdale 34 49 80 PAWC Wayne County, PA Waymart 43 65 65 NYMCOC Madison and Oneida Counties, NY Madison 7 12 67 NYMC Madison County, NY Fenner 20 30 66