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

It should be emphasized that in the Base Hedonic Model - equation (1) - and in all subsequent
models presented in Section 5, all variables <strong>of</strong> interest, spatial adjustments, and home and site
characteristics are pooled, and therefore their estimates represent the average across all study
areas. Ideally, one would have enough data to estimate a model at the study area level - a fully
unrestricted model - rather than pooled across all areas. This fully unrestricted model form,
along with 15 other model forms (with some variables restricted and others not), are discussed in
detail in Appendix F. In total, these 16 different models were estimated to explore which model
was the most parsimonious (had the fewest parameters), performed the best (e.g., had the highest
adjusted R 2 and the lowest Schwarz information criterion 60 ), and had the most stable coefficients
and standard errors. <strong>The</strong> basic pooled model described by equation (1) is found to fit that
description, and that model is therefore chosen as the Base Model to which others are compared.
By making this choice the effort concentrates on identifying the presence <strong>of</strong> potential property
value impacts across all <strong>of</strong> the study areas in the sample as opposed to any single study area. 61
Finally, to assure that the model produces the best linear unbiased parameter estimates, the
underlying assumptions <strong>of</strong> Ordinary Least Squares (OLS) regression techniques must be
verified:
1) Homoskedastic error term;
2) Absence <strong>of</strong> temporal serial correlation;
3) Reasonably limited multicollinearity; and
4) Appropriate controls for outliers and influencers. 62
<strong>The</strong>se assumptions, and the specific approaches that are used to address them, are discussed in
detail in Appendix G.
4.3. Analysis <strong>of</strong> Results
Table 10 (on page 32) presents the results <strong>of</strong> the Base Model (equation 1). 63 <strong>The</strong> model
performs well, with an adjusted R 2 <strong>of</strong> 0.77. 64 <strong>The</strong> spatial adjustment coefficient ( 1 ) <strong>of</strong> 0.29 (p
value 0.00) indicates that a 10% increase in the spatially weighted neighbor’s price increases the
subject home’s value by an average <strong>of</strong> 2.9%. <strong>The</strong> study-area fixed effects ( 2 ) variables are all
significant at the one percent level, demonstrating important differences in home valuations
60 <strong>The</strong> Schwarz information criterion measures relative parsimony between similar models (Schwarz, 1978).
61 Because effects might vary between study areas, and the models estimate an average across all study areas, the
full range <strong>of</strong> effects in individual study areas will go undetermined. That notwithstanding, there is no reason to
suspect that effects will be completely “washed out.” For that to occur, an effect in one study area would have to be
positive while in another area it would have to be negative, and there is no reason to suspect that sales prices would
increase because <strong>of</strong> the turbines in one community while decreasing in other communities.
62 <strong>The</strong> absence <strong>of</strong> spatial autocorrelation is <strong>of</strong>ten included in the group <strong>of</strong> assumptions, but because it was discussed
above (and in Appendix G), and is addressed directly by the variable (N i ) included in the model, it is not included in
this list.
63 This model and all subsequent models were estimated using the PROC REG procedure <strong>of</strong> SAS Version 9.2
TS1M0, which produces White’s corrected standard errors.
64 <strong>The</strong> appropriateness <strong>of</strong> the R 2 <strong>of</strong> 0.77 for this research is validated by the extensive hedonic literature that
precedes it (see e.g., Kroll and Priestley, 1992; Boyle and Kiel, 2001; Simons, 2006b).
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