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

with coefficients above zero and those with coefficients below zero. 122 It should be emphasized
here that it is the a priori expectation that, if effects exist, all <strong>of</strong> these coefficients would be less
than zero, indicating an adverse effect on home prices from proximity to and views <strong>of</strong> wind
turbines. Despite that expectation, when the variables <strong>of</strong> interest are unrestricted it is found that
they are as likely to be above zero as they are below. 123 In effect, the small numbers <strong>of</strong> cases
available for analysis at the study area level produce unstable results, likely because the
estimates are being unduly influenced by either study area specific effects that are not captured
by the model or by a limited number <strong>of</strong> observations that represents a larger fraction <strong>of</strong> the
overall sample in that model. 124
Table A - 5: Summary <strong>of</strong> Significant VOI Above and Below Zero in Unrestricted Models
Significant Variables
Unrestricted Models
Total
Below
Zero
Above
Zero
Minor View 32% 14% 18%
Moderate View 23% 11% 13%
Substantial View 4% 4% 0%
Extreme View 0% 0% 0%
Inside 3000 Feet 23% 15% 8%
Between 3000 Feet and 1 Mile 30% 14% 16%
Between 1 and 3 Miles 56% 32% 24%
Between 3 and 5 Miles 45% 3% 43%
F.3 Selecting a Base Model
To conclude, it was found that all three concerns related to the estimation and use <strong>of</strong> an
unrestricted model form are borne out in practice. Despite experimenting with 16 different
combinations <strong>of</strong> interactions, little overall improvement in performance is discovered. Where
performance gains are found they are at the expense <strong>of</strong> parsimony as reflected in the lack <strong>of</strong>
increase in the Modified R 2 and the relatively higher Schwartz information criterion. Further,
divergent and spurious coefficients <strong>of</strong> interest and large standard errors are associated with those
coefficients. <strong>The</strong>refore the fully restricted model, equation (1), is used in this report as the “Base
Model”.
122 <strong>The</strong> “Total” percentage <strong>of</strong> significant coefficients is calculated by counting the total number <strong>of</strong> significant
coefficients across all 8 unrestricted models for each variable <strong>of</strong> interest, and dividing this total by the total number
<strong>of</strong> coefficients. <strong>The</strong>refore, a study area that did not have any homes in a group (for example, homes with
EXTREME VIEWS) was not counted in the “total number <strong>of</strong> coefficients” sum. Any differences between the sum
<strong>of</strong> “above” and “below” zero groups from the total are due to rounding errors.
123 <strong>The</strong> relatively larger number <strong>of</strong> significant variables for the MINOR rated view, MODERATE rated view, Mile 1
to 3, and Mile 3 to 5 parameters are likely related to the smaller standard errors for those categories, which result
from larger numbers <strong>of</strong> cases.
124 Another possible explanation for spurious results in general is measurement error, when parameters do not
appropriately represent what one is testing for. In this case though, the VIEW variables have been adequately
“ground truthed” during the development <strong>of</strong> the measurement scale, and are similar to the VISTA variables, which
were found to be very stable across study areas. DISTANCE, or for that matter, distance to any disamenity, has
been repeatedly found to be an appropriate proxy for the size <strong>of</strong> effects. As a result, it is not believed that
measurement error is a likely explanation for the results presented here.
131