The Impact of Wind Power Projects on Residential Property Values ...

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Figure 1: Map <strong>of</strong> Study Areas and Potential Study Areas Table 2: Summary <strong>of</strong> Study Areas Study Area Code Study Area Counties, States Facility Names Number <strong>of</strong> Turbines Number <strong>of</strong> MW Max Hub Height (meters) Max Hub Height (feet) WAOR Benton and Walla Walla Counties, WA and Umatilla County, OR Vansycle Ridge, Stateline, Nine Canyon I & II, Combine Hills 582 429 60 197 TXHC Howard County, TX Big Spring I & II 46 34 80 262 OKCC Custer County, OK Weatherford I & II 98 147 80 262 IABV Buena Vista County, IA Storm Lake I & II, Waverly, Intrepid I & II 381 370 65 213 ILLC Lee County, IL Mendota Hills, GSG <strong>Wind</strong> 103 130 78 256 WIKCDC Kewaunee and Door Counties, WI Red River, Lincoln 31 20 65 213 PASC Somerset County, PA Green Mountain, Somerset, Meyersdale 34 49 80 262 PAWC Wayne County, PA Waymart 43 65 65 213 NYMCOC Madison and Oneida Counties, NY Madison 7 12 67 220 NYMC Madison County, NY Fenner 20 30 66 218 TOTAL 1345 1286 <strong>The</strong>se 10 study areas and 24 projects are located in nine separate states, and include projects in the Pacific Northwest, upper Midwest, the Northeast, and the South Central region. <strong>The</strong> wind projects included in the sample total 1,286 MW, or roughly 13% <strong>of</strong> total U.S. wind power capacity installed at the time (the end <strong>of</strong> 2005). Turbine hub heights in the sample range from a 12
minimum <strong>of</strong> 164 feet (50 meters) in the Washington/Oregon (WAOR) study area, to a maximum <strong>of</strong> 262 (80 meters) (TXHC, OKCC and PASC), with nine <strong>of</strong> the ten study areas having hub heights <strong>of</strong> at least 213 feet (65 meters). <strong>The</strong> sites include a diverse variety <strong>of</strong> land types, including combinations <strong>of</strong> ridgeline (WAOR, PASC, and PAWC), rolling hills (ILLC, WIKCDC, NYMCOC, and NYMC), mesa (TXHC), and windswept plains (OKCC, IABV). 23 3.2. Data Collection In general, for each study area, residential transaction data in as close proximity to the wind turbines as possible was sought, from both before and after wind facility construction. To balance the cost and quantity <strong>of</strong> data collection in each study area with the desire to cover as many study areas as possible, the research effort sought to collect data on 400 to 1,250 transactions in each study area. 24 In some instances, this meant including all residential transactions within ten miles <strong>of</strong> the wind turbines. In others, only transactions within five miles were included. In some extreme instances, when the number <strong>of</strong> transactions inside <strong>of</strong> five miles far exceeded the 1,250 limit, all transactions in close proximity to the wind turbines (e.g., inside three miles) were included in combination with a random sample <strong>of</strong> transactions outside <strong>of</strong> that distance band (e.g., between three and five miles). 25 <strong>The</strong> data selection processes for each Study Area are contained in Appendix A. Three primary sets <strong>of</strong> data are used in the analysis: tabular data, GIS data, and field data, each <strong>of</strong> which is discussed below. Following that, this subsection highlights the two qualitative variables that are essential to this analysis and that therefore require special attention, scenic vista and views <strong>of</strong> turbines, and then discusses the field data collection process. 3.2.1. Tabular Data Berkeley Lab obtained tabular transaction data from participating counties 26 containing 7,459 “valid” 27 transactions <strong>of</strong> single family residential homes, on less than 25 acres, 28 which were 23 Some areas, such as PASC, had both a ridgeline and rolling hills on which wind facilities were located. 24 This range was chosen to ensure that a minimum <strong>of</strong> data were present in each study area to allow for a robust analysis, and yet not too much so as to make data collection (e.g., the visiting <strong>of</strong> each home) inordinately time and resource consuming in any individual study area. 25 An alternative method would have been to collect data on every sale that occurred. Although in most cases this would be preferred, in ours it would not have added one additional transaction within close proximity or with dramatic views <strong>of</strong> wind turbine, the focus <strong>of</strong> the study. Rather, it would have added an overwhelming majority <strong>of</strong> transactions <strong>of</strong> homes without views and at distances outside <strong>of</strong> three miles from the turbines, all <strong>of</strong> which would have come at considerably cost and, more importantly, would not likely have influenced the results significantly while perhaps necessitating a reduction in the total number <strong>of</strong> study areas that could be included in the sample. 26 In some cases, the county <strong>of</strong>ficials, themselves, extracted data from their database, and in some cases a company engaged to manage a county’s data provided the necessary information. In either case the provider is referred to as “county.” Detailed descriptions <strong>of</strong> the providers are presented in Appendix A. 27 Validity was determined by each individual county data provider. A sale that is considered “valid” for county purposes would normally meet the minimum requirements <strong>of</strong> being arm’s length; being a transfer <strong>of</strong> all rights and warrants associated with the real estate; containing an insignificant amount <strong>of</strong> personal property so as not to affect the price; demonstrating that neither party in the sale acting under duress or coercion; not being the result <strong>of</strong> a liquidation <strong>of</strong> assets or any other auction, a mortgage foreclosure, a tax sale, or a quit claim; and being appropriate for use in calculating the sales price to assessed value ratios that are reported to the state. Due to the formal requirements associated with this calculation, “validity” is <strong>of</strong>ten defined by a state’s Department <strong>of</strong> Revenue, as shown, for example, here: http://www.orps.state.ny.us/assessor/manuals/vol6/rfv/index.htm. In addition, though the 13
Page 1 and 2: LBNL-2829E ERNEST ORLANDO LAWRENCE
Page 3 and 4: LBNL-2829E The <st
Page 5 and 6: Table of Contents
Page 7 and 8: List of Tables Tab
Page 9 and 10: Acknowledgements The</stron
Page 11 and 12: This report builds on the previous
Page 13 and 14: Table ES-2: Impact
Page 15 and 16: Figure ES-3: Base Model Results: Sc
Page 17 and 18: In the Distance Stability Model, fo
Page 19 and 20: 1. Introduction Wind</stron
Page 21 and 22: model 7 and uses various forms <str
Page 23 and 24: A particularly useful application <
Page 25 and 26: Using different statistical methods
Page 27 and 28: Table 1: Summary of</strong
Page 29: By using a variety of</stro
Page 33 and 34: 3.2.2. GIS Data GIS data on parcel
Page 35 and 36: In addition to the qualitative VIEW
Page 37 and 38: with the rest of t
Page 39 and 40: Figure 4: Frequency of</str
Page 41 and 42: 4. Base Hedonic Model This section
Page 43 and 44: impose the least structure on the u
Page 45 and 46: of the home, home
Page 47 and 48: etween the reference study area (WA
Page 49 and 50: Figure 7: Results from the Base Mod
Page 51 and 52: 5. Alternative Hedonic Models <stro
Page 53 and 54: concentrated inside of</str
Page 55 and 56: all other components are as defined
Page 57 and 58: the distance from them might not oc
Page 59 and 60: explanation is that the additional
Page 61 and 62: 6) More than four years after const
Page 63 and 64: Table 19: Results from Temporal Asp
Page 65 and 66: Turning to the coefficient differen
Page 67 and 68: Table 21: Frequency Crosstab <stron
Page 69 and 70: 5.6.1. Dataset and Model Form Data
Page 71 and 72: with the scenic vista. In other wor
Page 73 and 74: 6. Repeat Sales Analysis In general
Page 75 and 76: Table 27: List of
Page 77 and 78: adjustment, and represent another t
Page 79 and 80: that are located within one mile <s
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7. Sales Volume Analysis Th
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Figure 11: Sales Volumes by PERIOD
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three years before announcement), a
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8. Wind Pr
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Drawing from the previous literatur
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Taken together, the results from al
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9. Conclusions Though surveys gener
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Davis, L. W. (2008) The</st
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LeSage, J. P. (1999) The</s
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Watson, M. (2005) Estimation <stron
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Figure A - 1: Map of</stron
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all very small communities with lit
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A.2 TXHC Study Area: Howard County
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Census Statistics Name Type 2007 Po
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Data Collection and Summary County
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A.4 IABV Study Area: Buena Vista Co
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Variables of Inter
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facilities than the towns o
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A.6 WIKCDC Study Area: Kewaunee and
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Variables of Inter
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elatively high elevations o
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A.8 PAWC Study Area: Wayne County (
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Census Statistics Name Type 2007 Po
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ural landscapes, with the largest p
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A.10 NYMC Study Area: Madison Count
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Variables of Inter
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account, combined with the determin
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Appendix C: Field Data Collection I
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Figure A - 14: Field Data Collectio
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ABOVE AVERAGE VISTA PREMIUM VISTA 1
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EXTREME VIEW 6 turbines visible fro
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5 is a vector of d
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values, that effect seems likely to
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unrestricted Home and Site Characte
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with coefficients above zero and th
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Figure A - 15: Histogram of
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The M Distance his
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sold within the preceding six month
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Appendix H: Alternative Models: Ful
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Table A - 8: Full Results for the C
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Table A - 10: Full Results for the
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Table A - 11: Full Results for the
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