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

unrestricted Home and Site Characteristics model (Model 4) makes the largest impact on model
performance, at least with respect to the Adjusted R 2 (0.80), but this comes with the addition <strong>of</strong>
151 estimated parameters a slight improvement in the Modified R 2 (0.78) and a worsening SIC
(0.095). Adding unrestricted Study Area delineations (Model 2), on the other hand, adversely
affects performance (Adj. R 2 = 0.74, Modified R 2 = 0.73) and adds 74 estimated parameters (SIC
= 0.110). Similarly, unrestricting the Spatial Adjustments (Model 3) <strong>of</strong>fers little improvement in
performance (Adj. R 2 = 0.77, Modified R 2 = 0.76) despite adding nine additional variables (SIC
= 0.088). Finally, unrestricting the Variables <strong>of</strong> Interest (Model 5) does not increase model
performance (Adj. R 2 = 0.77, Modified R 2 = 0.76) and adds 51 variables to the model (SIC =
0.093). This pattern <strong>of</strong> little model improvement yet considerable increases in the number <strong>of</strong>
estimated parameters (i.e., less parsimony) continues when pairs or trios <strong>of</strong> variable groups are
unrestricted. With an Adjusted R 2 <strong>of</strong> 0.77, the fully restricted equation (1) performs more than
adequately, and is, by far, the most parsimonious.
Standard Error Magnitudes
Table A - 3 summarizes the standard errors for the variables <strong>of</strong> interest for all <strong>of</strong> the 16 models,
grouped into restricted and unrestricted model categories. <strong>The</strong> table specifically compares the
medians, minimums, and maximums <strong>of</strong> the standard errors for the models with restricted
variables <strong>of</strong> interest (1, 2, 3, 4, 6, 7, 9 and 12) to those with unrestricted variables <strong>of</strong> interest (5, 8,
10, 11, 13, 14, 15 and 16). 120 <strong>The</strong> table demonstrates that the unrestricted standard errors for the
variables <strong>of</strong> interest are significantly larger than the restricted standard errors. In fact, the
minimum standard errors in the unrestricted models are <strong>of</strong>ten higher than the maximum standard
errors produced in the restricted models. For example, the maximum standard error for an
EXTREME VIEW in the restricted models is 0.09, yet the minimum in the unrestricted models is
0.12, with a maximum <strong>of</strong> 0.34. To put this result in a different light, a median standard error for
the unrestricted EXTREME VIEW variable <strong>of</strong> 0.25 would require an effect on house prices
larger than 50% to be considered statistically significant at the 90% level. Clearly, the statistical
power <strong>of</strong> the unrestricted models is weak. 121 Based on other disamenities, as discussed in
Section 2.1, an effect <strong>of</strong> this magnitude is very unlikely. <strong>The</strong>refore, based on these standard
errors, there is no apparent reason to unrestrict the variables <strong>of</strong> interest.
120 For the restricted models, the medians, minimums, and maximums are derived across all eight models for each
variable <strong>of</strong> interest. For the unrestricted models, they are derived across all study areas and all eight models for each
variable <strong>of</strong> interest.
121 At 90% confidence a standard error <strong>of</strong> 0.25 would produce a confidence interval <strong>of</strong> roughly +/- 0.42 (0.25 *
1.67). An effect <strong>of</strong> this magnitude represents a 52% change in sales prices because sales price is in a natural log
form (e ^ 0.42-1 = 0.52).
129