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

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5.1.1. Dataset and Model Form <strong>The</strong> same dataset is used as in the Base Model, focusing again on post-construction transactions (n = 4,937). To investigate DISTANCE effects alone the following model is estimated: (2) 0 1 2 3 5 ln P N S X DISTANCE s k d where P represents the inflation-adjusted sales price, N is the spatially weighted neighbors’ predicted sales price, S is the vector <strong>of</strong> s Study Area fixed effects variables (e.g., WAOR, OKCC, etc.), X is a vector <strong>of</strong> k home and site characteristics (e.g., acres, square feet, number <strong>of</strong> bathrooms, condition <strong>of</strong> the home, age <strong>of</strong> home, VISTA, etc.), DISTANCE is a vector <strong>of</strong> d categorical distance variables (e.g., less than 3000 feet, between one and three miles, etc.), 0 is the constant or intercept across the full sample, 1 is a parameter estimate for the spatially weighted neighbor’s predicted sales price, 2 is a vector <strong>of</strong> s parameter estimates for the study area fixed effects as compared to transactions <strong>of</strong> homes in the WAOR study area, 3 is a vector <strong>of</strong> k parameter estimates for the home and site characteristics, 5 is a vector <strong>of</strong> d parameter estimates for the DISTANCE variables as compared to transactions <strong>of</strong> homes situated outside <strong>of</strong> five miles, and is a random disturbance term. <strong>The</strong> parameters <strong>of</strong> primary interest are 5 , which represent the marginal differences between home values at various distances from the wind turbines as compared to the reference category <strong>of</strong> homes outside <strong>of</strong> five miles. <strong>The</strong>se coefficients can then be compared to the same coefficients estimated from the Base Model. Alternatively, to investigate the VIEW effects alone, the following model is estimated: (3) 0 1 2 3 4 ln P N S X VIEW s k v where VIEW is a vector <strong>of</strong> v categorical view variables (e.g., MINOR, MODERATE, etc.), 4 is a vector <strong>of</strong> v parameter estimates for the VIEW variables, and all other components are as defined in equation (2). <strong>The</strong> parameters <strong>of</strong> primary interest in this model are 4 , which represent the marginal differences between home values for homes with varying views <strong>of</strong> wind turbines at the time <strong>of</strong> sale as compared to the reference category <strong>of</strong> homes without a view <strong>of</strong> those turbines. Again, these coefficients can then be compared to the same coefficients estimated from the Base Model. Our expectation for both <strong>of</strong> the models described here is that the results will not be dramatically different from the Base Model, given the distribution <strong>of</strong> VIEW values across the DISTANCE values, and vice versa, as shown in Table 11. Except for EXTREME view, which is 34
concentrated inside <strong>of</strong> 3000 feet, all view ratings are adequately distributed among the distance categories. Table 11: Frequency Crosstab <strong>of</strong> VIEW and DISTANCE Parameters Inside 3000 Feet Between 3000 Feet and 1 Mile Between 1 and 3 Miles Between 3 and 5 Miles Outside 5 Miles No View 6 12 1653 1695 841 4207 Minor View 14 24 294 202 27 561 Moderate View 8 13 62 21 2 106 Substantial View 11 9 10 5 0 35 Extreme View 28 0 0 0 0 28 Total TOTAL 67 58 2019 1923 870 4937 5.1.2. Analysis <strong>of</strong> Results Summarized results for the variables <strong>of</strong> interest from the Base Model and the two Alternative Stability Models are presented in Table 12. (For brevity, the full set <strong>of</strong> results for the models is not shown in Table 12, but is instead included in Appendix H.) <strong>The</strong> adjusted R 2 for the View and Distance Stability Models is the same as for the Base Model, 0.77. All study area, spatial adjustment, and home and site characteristics are significant at or above the one percent level and are similar in magnitude to the estimates presented earlier for the Base Model. <strong>The</strong> DISTANCE and VIEW coefficients, 5 and 4 , are stable, changing no more than 3%, with most (7 out <strong>of</strong> 8) not experiencing a change greater than 1%. In all cases, changes to coefficient estimates for the variables <strong>of</strong> interest are considerably less than the standard errors. Based on these results, there is confidence that the correlation between the VIEW and DISTANCE variables is not responsible for the findings and that these two variables are adequately independent to be included in the same hedonic model regression. As importantly, no evidence <strong>of</strong> Area, Scenic Vista, or Nuisance Stigma is found in the sample, as none <strong>of</strong> the VIEW or DISTANCE variables are found to be statistically different from zero. Table 12: Results from Distance and View Stability Models Base Model Distance Stability View Stability Variables <strong>of</strong> Interest n Coef SE p Value Coef SE p Value Coef SE p Value No View 4207 Omitted Omitted Omitted Omitted Omitted Omitted Minor View 561 -0.01 0.01 0.39 -0.02 0.01 0.24 Moderate View 106 0.02 0.03 0.57 0.00 0.03 0.90 Substantial View 35 -0.01 0.07 0.92 -0.04 0.06 0.45 Extreme View 28 0.02 0.09 0.77 -0.03 0.06 0.58 Inside 3000 Feet 67 -0.05 0.06 0.31 -0.04 0.04 0.25 Between 3000 Feet and 1 Mile 58 -0.05 0.05 0.20 -0.06 0.05 0.17 Between 1 and 3 Miles 2019 0.00 0.02 0.80 -0.01 0.02 0.71 Between 3 and 5 Miles 1923 0.02 0.01 0.26 0.01 0.01 0.30 Outside 5 Miles 870 Omitted Omitted Omitted Omitted Omitted Omitted "Omitted" = reference category for fixed effects variables. "n" indicates number <strong>of</strong> cases in category when category = "1" Model Information Model Equation Number 1 2 3 Dependent Variable Number <strong>of</strong> Cases LN_SalePrice96 4937 LN_SalePrice96 4937 LN_SalePrice96 4937 Number <strong>of</strong> Predictors (k) 37 33 33 F Statistic 442.8 496.7 495.9 Adjusted R Squared 0.77 0.77 0.77 35
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 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: 5. Alternative Hedonic Models <stro
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
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
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Census Statistics Name Type 2007 Po
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Data Collection and Summary County
Page 111 and 112:
A.4 IABV Study Area: Buena Vista Co
Page 113 and 114:
Variables of Inter
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facilities than the towns o
Page 117 and 118:
A.6 WIKCDC Study Area: Kewaunee and
Page 119 and 120:
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
Page 135 and 136:
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
Page 147 and 148:
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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