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

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un with White’s heteroskedasticity correction to the parameter estimates’ standard errors (which will not adversely influence the errors if they are homoskedastic). Serial Autocorrelation: A standard formal test for the presence <strong>of</strong> serial autocorrelation in the error term is the Durbin-Watson statistic (Durbin and Watson, 1951). Applying this test as proposed by Durbin and Watson to the full panel dataset was problematic because the test looks at the error structure based on the order that observations are included in the statistical regression model. Any ordering choice over the entire panel data set invariably involves mixing home transactions from various study areas. Ideally, one would segment the data by study area for purposes <strong>of</strong> calculating this test, but that method was not easily implemented with the statistical s<strong>of</strong>tware package used for this analysis (i.e., SAS). Instead, study area specific regression models were run with the data chronologically ordered in each to produce twelve different Durbin-Watson statistics, one for each study area specific model. <strong>The</strong> Durbin-Watson test statistics ranged from 1.98–2.16, which are all within the acceptable range. 126 Given that serial autocorrelation was not found to be a significant concern for each study area specific model, it is assumed that the same holds for the full dataset used in the analysis presented in this report. Spatial Autocorrelation: It is well known that the sales price <strong>of</strong> a home can be systematically influenced by the sales prices <strong>of</strong> those homes that have sold nearby (Dubin, 1998; LeSage, 1999). Both the seller and the buyer use information from comparable surrounding sales to inform them <strong>of</strong> the appropriate transaction price, and nearby homes <strong>of</strong>ten experience similar amenities and disamenities. <strong>The</strong>refore, the price for any single home is likely to be weakly dependent <strong>of</strong> the prices <strong>of</strong> homes in close temporal and spatial proximity. This lack <strong>of</strong> independence <strong>of</strong> home sale prices could bias the hedonic results (Dubin, 1998; LeSage, 1999), if not adequately addressed. A number <strong>of</strong> techniques are available to address this concern (Case et al., 2004; Espey et al., 2007), but because <strong>of</strong> the large sample and computing limits, a variation <strong>of</strong> the Spatial Auto Regressive Model (SAR) was chosen (Espey et al., 2007). Specifically, an independent variable is included in the models: the predicted values <strong>of</strong> the weighted nearest neighbor’s natural log <strong>of</strong> sales price in 1996 dollars. 127 To construct this vector <strong>of</strong> predicted prices, an auxiliary regression is developed using the spatially weighted average natural log <strong>of</strong> sales price in 1996 dollars as the independent variable and the spatially weighted average set <strong>of</strong> home characteristics as the dependent variables. This regression was used to produce the predicted weighted nearest neighbor’s natural log <strong>of</strong> sales price in 1996 dollars that is then included in the Base and Alternative Models. This process required the following steps: 1) Selecting the neighbors for inclusion in the calculation; 2) Calculating a weighted sales price from these neighbors’ transactions; 3) Selecting and calculating the weighted neighbors home characteristics; and 4) Forecasting the weighted average neighbor’s sales price. Selecting the neighbors: To select the neighbors whose home transactions would most likely have affected the sales price <strong>of</strong> the subject home under review, all <strong>of</strong> the homes that 126 <strong>The</strong> critical values for the models were between 1.89 and 2.53, assuming 5% significance, greater than 20 variables, and more than 200 cases (Gujarati, 2003). 127 <strong>The</strong> predicted value was used, instead <strong>of</strong> the actual value, to help correct for simultaneity or endogeneity problems that might otherwise exist. 136
sold within the preceding six months <strong>of</strong> a subject home’s sale date in the same study area are identified and, from those, the five nearest neighbors based on Euclidian distance are selected. <strong>The</strong> inverse <strong>of</strong> each selected nearest neighbors’ distance (in quarter miles) to the subject home was then calculated. Each <strong>of</strong> these values was then divided by the sum <strong>of</strong> the five nearest neighbor’s inverse distance values to create a neighbor’s distance weight (NDW) for each <strong>of</strong> the five nearest neighbors. 128 Creating the weighted sales price: Each <strong>of</strong> the neighbor’s natural log <strong>of</strong> sales price in 1996 dollars (LN_Saleprice96) is multiplied by its distance weight (NDW). <strong>The</strong>n, each weighted neighbor’s LN_Saleprice96 is summed to create a weighted nearest neighbor LN_Saleprice96 (Nbr_LN_Saleprice96). Selecting and calculating the weighted neighbors home characteristics: Nine independent variables are used from each <strong>of</strong> the neighbor’s homes: square feet, age <strong>of</strong> the home at the time <strong>of</strong> sale, age <strong>of</strong> the home at the time <strong>of</strong> sale squared, acres, number <strong>of</strong> full baths, and condition (1-5, with Poor = 1, Below Average = 2, etc.). A weighted average is created <strong>of</strong> each <strong>of</strong> the characteristics by multiplying each <strong>of</strong> the neighbor’s individual characteristics by their NDW, and then summing those values across the five neighbors to create the weighted average nearest neighbors’ home characteristic. 129 <strong>The</strong>n each <strong>of</strong> the independent variables is interacted with the study area to allow each one to be independently estimated for each study area. Forecasting the weighted average neighbors sales price: To create the final predicted neighbor’s price, the weighted nearest neighbor LN_Saleprice96 is regressed on the weighted average nearest neighbors’ home characteristics to produce a predicted weighted nearest neighbor LN_Saleprice96 (Nbr_LN_SalePrice96_hat). <strong>The</strong>se predicted values are then included in the Base and Alternative Models as independent variables to account for the spatial and temporal influence <strong>of</strong> the neighbors’ home transactions. In all models, the coefficient for this spatial adjustment parameter meets the expectations for sign and magnitude and is significant well above the 99% level, indicating both the presence <strong>of</strong> spatial autocorrelation and the appropriateness <strong>of</strong> the control for it. Multicollinearity: <strong>The</strong>re are several standard formal tests for detecting multicollinearity within the independent variables <strong>of</strong> a regression model. <strong>The</strong> Variance-Inflation Factor and Condition Index is applied to test for this violation <strong>of</strong> OLS assumptions. Specifically, a Variance-Inflation Factor (VIF) greater than 4 and/or a Condition Index <strong>of</strong> greater than 30 (Kleinbaum et al., 1988) are strong indicators that multicollinearity may exist. Multicollinearity is found in the model using both tests. Such a result is not uncommon in hedonic models because a number <strong>of</strong> characteristics, such as square feet or age <strong>of</strong> a home, are <strong>of</strong>ten correlated with other characteristics, such as the number <strong>of</strong> acres, bathrooms, and fireplaces. Not surprisingly, age <strong>of</strong> the home at the time <strong>of</strong> sale (Age<strong>of</strong>Home) and the age <strong>of</strong> the home squared (AgeatHome_Sqrd) 128 Put differently, the weight is the contribution <strong>of</strong> that home’s inverse distance to the total sum <strong>of</strong> the five nearest neighbors’ inverse distances. 129 Condition requires rounding to the nearest integer and then creating a dummy from the 1-5 integers. 137
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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
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In the Distance Stability Model, fo
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1. Introduction Wind</stron
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model 7 and uses various forms <str
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A particularly useful application <
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Using different statistical methods
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Table 1: Summary of</strong
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By using a variety of</stro
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minimum of 164 fee
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3.2.2. GIS Data GIS data on parcel
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In addition to the qualitative VIEW
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with the rest of t
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Figure 4: Frequency of</str
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4. Base Hedonic Model This section
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impose the least structure on the u
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of the home, home
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etween the reference study area (WA
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Figure 7: Results from the Base Mod
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5. Alternative Hedonic Models <stro
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concentrated inside of</str
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all other components are as defined
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the distance from them might not oc
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explanation is that the additional
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6) More than four years after const
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Table 19: Results from Temporal Asp
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Turning to the coefficient differen
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Table 21: Frequency Crosstab <stron
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5.6.1. Dataset and Model Form Data
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with the scenic vista. In other wor
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6. Repeat Sales Analysis In general
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Table 27: List of
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adjustment, and represent another t
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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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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
Page 121 and 122: elatively high elevations o
Page 123 and 124: A.8 PAWC Study Area: Wayne County (
Page 125 and 126: Census Statistics Name Type 2007 Po
Page 127 and 128: ural landscapes, with the largest p
Page 129 and 130: A.10 NYMC Study Area: Madison Count
Page 133 and 134: account, combined with the determin
Page 135 and 136: Appendix C: Field Data Collection I
Page 137 and 138: Figure A - 14: Field Data Collectio
Page 139 and 140: ABOVE AVERAGE VISTA PREMIUM VISTA 1
Page 141 and 142: EXTREME VIEW 6 turbines visible fro
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Page 145 and 146: values, that effect seems likely to
Page 147 and 148: unrestricted Home and Site Characte
Page 149 and 150: with coefficients above zero and th
Page 151 and 152: Figure A - 15: Histogram of
Page 153: The M Distance his
Page 157 and 158: Appendix H: Alternative Models: Ful
Page 159 and 160: Table A - 8: Full Results for the C
Page 161 and 162: Table A - 10: Full Results for the
Page 163 and 164: Table A - 11: Full Results for the
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