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

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<strong>The</strong> coefficients for the variables <strong>of</strong> interest ( 6 ) do not meet the a priori expectations. <strong>The</strong> estimated effect for SIDE ORIENTATION, instead <strong>of</strong> being close to zero, is -3% (p value 0.36), while BACK and FRONT, instead <strong>of</strong> being negative and larger, are estimated at 3% (p value 0.37) and -1% (p value 0.72), respectively. None <strong>of</strong> these variables are found to be even marginally statistically significant, however, and based on these results, it is concluded that there is no evidence that a home’s orientation to a wind facility affects property values in a measurable way. Further, as with previous models, no statistical evidence <strong>of</strong> a Scenic Vista Stigma is found among this sample <strong>of</strong> sales transactions. Table 23: Results from Orientation Model Base Model Orientation Model Variables <strong>of</strong> Interest Coef SE p Value n Coef SE p Value n No View Omitted Omitted Omitted 4207 Omitted Omitted Omitted 4207 Minor View -0.01 0.01 0.39 561 -0.01 0.06 0.88 561 Moderate View 0.02 0.03 0.57 106 0.00 0.06 0.96 106 Substantial View -0.01 0.07 0.92 35 -0.01 0.09 0.85 35 Extreme View 0.02 0.09 0.77 28 0.02 0.17 0.84 28 Inside 3000 Feet -0.05 0.06 0.31 67 -0.04 0.07 0.46 67 Between 3000 Feet and 1 Mile -0.05 0.05 0.20 58 -0.05 0.05 0.26 58 Between 1 and 3 Miles 0.00 0.02 0.80 2019 0.00 0.02 0.83 2019 Between 3 and 5 Miles 0.02 0.01 0.26 1923 0.02 0.01 0.26 1923 Outside 5 Miles Omitted Omitted Omitted 870 Omitted Omitted Omitted 870 Front Orientation -0.01 0.06 0.72 294 Back Orientation 0.03 0.06 0.37 280 Side Orientation -0.03 0.06 0.36 253 "Omitted" = reference category for fixed effects variables. "n" = number <strong>of</strong> cases in category when category = "1" Model Information Model Equation Number 1 8 Dependent Variable LN_SalePrice96 LN_SalePrice96 Number <strong>of</strong> Cases 4937 4937 Number <strong>of</strong> Predictors (k) 37 40 F Statistic 442.8 410.0 Adjusted R Squared 0.77 0.77 5.6. Overlap Model <strong>The</strong> Orientation Model, presented above, investigated, to some degree, how the potential effects <strong>of</strong> wind turbines might be impacted by how a home is oriented to the surrounding environment. In so doing, this model began to peel back the relationship between VIEW and VISTA, but stopped short <strong>of</strong> looking at the relationship directly. It would be quite useful, though, to understand the explicit relationship between the VISTA and VIEW variables. In particular, one might expect that views <strong>of</strong> wind turbines would have a particularly significant impact on residential property values when those views strongly overlap (“OVERLAP”) the prominent scenic vista from a home. To investigate this possibility directly, and, in general, the relationship between VIEW and VISTA, a parameter for OVERLAP is included in the model. 50
5.6.1. Dataset and Model Form Data on the degree to which the view <strong>of</strong> wind turbines overlaps with the prominent scenic vista from the home (OVERLAP) were collected in the course <strong>of</strong> the field visits to each home. 87 <strong>The</strong> categories for OVERLAP included NONE, BARELY, SOMEWHAT, and STRONGLY, and are described in Table 24: 88 Table 24: Definition <strong>of</strong> OVERLAP Categories OVERLAP - NONE OVERLAP - BARELY OVERLAP - SOMEWHAT OVERLAP - STRONGLY <strong>The</strong> scenic vista does not contain any view <strong>of</strong> the turbines. A small portion (~ 0 - 20%) <strong>of</strong> the scenic vista is overlapped by the view <strong>of</strong> turbines, and might contain a view <strong>of</strong> a few turbines, only a few <strong>of</strong> which can be seen entirely. A moderate portion (~20-50%) <strong>of</strong> the scenic vista contains turbines, and likely contains a view <strong>of</strong> more than one turbine, some <strong>of</strong> which are likely to be seen entirely. A large portion (~50-100%) <strong>of</strong> the scenic vista contains a view <strong>of</strong> turbines, many <strong>of</strong> which likely can be seen entirely. A crosstab describing the OVERLAP designations and the VIEW categories is shown in Table 25. As would be expected, the more dramatic views <strong>of</strong> wind turbines, where the turbines occupy more <strong>of</strong> the panorama, are coincident with the OVERLAP categories <strong>of</strong> SOMEWHAT or STRONGLY. Nonetheless, STRONGLY are common for all VIEW categories. Similarly, SOMEWHAT is well distributed across the MINOR and MODERATE rated views, while BARELY is concentrated in the MINOR rated views. <strong>The</strong> same dataset is used as in the Base Model, focusing on post-construction transactions (n = 4,937). To investigate whether the overlap <strong>of</strong> VIEW and VISTA has a marginal impact on residential property values, over and above that <strong>of</strong> the VIEW and VISTA impacts alone, the following hedonic model is estimated: 89 0 1 2 3 4 5 6 ln P N S X VIEW DISTANCE VISTA p 7 s k v d t OVERLAP where VIEW is a vector <strong>of</strong> v categorical view variables (e.g., MINOR, MODERATE, etc.), VISTA is a vector <strong>of</strong> t categorical scenic vista variables (e.g., POOR, BELOW-AVERAGE, etc.), OVERLAP is a vector <strong>of</strong> p categorical overlap variables (e.g., BARELY, SOMEWHAT, etc.), 87 Scenic vista was rated while taking into account the entire panorama surrounding a home. But, for each home, there usually was a prominent direction that <strong>of</strong>fered a preferred scenic vista. Often, but not always, the home was orientated to enjoy that prominent scenic vista. Overlap is defined as the degree to which the view <strong>of</strong> the wind facility overlaps with this prominent scenic vista. 88 “…can be seen entirely” refers to being able to see a turbine from the top <strong>of</strong> the sweep <strong>of</strong> its blade tips to below the nacelle <strong>of</strong> the turbine where the sweep <strong>of</strong> the tips intersects the tower. 89 Although VISTA appears in all models, and is usually included in the vector <strong>of</strong> home and site characteristics represented by X, it is shown separately here so that it can be discussed directly in the text that follows. (9) 51
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LBNL-2829E ERNEST ORLANDO LAWRENCE
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LBNL-2829E The <st
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Table of Contents
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List of Tables Tab
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Acknowledgements The</stron
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This report builds on the previous
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Table ES-2: Impact
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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 and 30: By using a variety of</stro
Page 31 and 32: minimum of 164 fee
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: Table 21: Frequency Crosstab <stron
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
Page 81 and 82: 7. Sales Volume Analysis Th
Page 83 and 84: Figure 11: Sales Volumes by PERIOD
Page 85 and 86: three years before announcement), a
Page 87 and 88: 8. Wind Pr
Page 89 and 90: Drawing from the previous literatur
Page 91 and 92: Taken together, the results from al
Page 93 and 94: 9. Conclusions Though surveys gener
Page 95 and 96: Davis, L. W. (2008) The</st
Page 97 and 98: LeSage, J. P. (1999) The</s
Page 99 and 100: Watson, M. (2005) Estimation <stron
Page 101 and 102: Figure A - 1: Map of</stron
Page 103 and 104: all very small communities with lit
Page 105 and 106: A.2 TXHC Study Area: Howard County
Page 107 and 108: Census Statistics Name Type 2007 Po
Page 109 and 110: Data Collection and Summary County
Page 111 and 112: A.4 IABV Study Area: Buena Vista Co
Page 113 and 114: Variables of Inter
Page 115 and 116: facilities than the towns o
Page 117 and 118: 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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