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

Nuisance Stigma, it is assumed that Nuisance effects are concentrated within one mile <strong>of</strong> the
nearest wind turbine, while Area effects will be considered for those transactions outside <strong>of</strong> one
mile. Any property value effects discovered outside <strong>of</strong> one mile and based on the DISTANCE
variables are therefore assumed to indicate the presence <strong>of</strong> Area Stigma, while impacts within a
mile may reflect the combination <strong>of</strong> Nuisance and Area Stigma.
<strong>The</strong> second set <strong>of</strong> variables in the Base Model - spatial adjustments - correct for the assumed
presence <strong>of</strong> spatial autocorrelation in the error term (). 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.
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. This lack <strong>of</strong> independence <strong>of</strong> home sale prices could bias hedonic regression
results and, to help correct for this bias, a spatially (i.e., distance) weighted neighbors’ sales price
(N) is included in the model. Empirically, the neighbors’ price has been found to be a strong
(and sometimes even the strongest) predictor <strong>of</strong> home values (Leonard and Murdoch,
forthcoming), and the coefficient 1 is expected to be positive, indicating a positive correlation
between the neighbors’ and subject home’s sales price. A more-detailed discussion <strong>of</strong> the
importance <strong>of</strong> this variable, and how it was created, is contained in Appendix G.
<strong>The</strong> third group <strong>of</strong> variables in the Base Model - study area fixed effects - control for study area
influences and the differences between them. <strong>The</strong> vector’s parameters 2 represent the marginal
impact <strong>of</strong> being in any one <strong>of</strong> the study areas, as compared to a reference category. In this case,
the reference category is the Washington/Oregon (WAOR) study area. 54 <strong>The</strong> estimated
coefficients for this group <strong>of</strong> variables represent the combined effects <strong>of</strong> school districts, tax
rates, crime, and other locational influences across an entire study area. Although this approach
greatly simplifies the estimation <strong>of</strong> the model, because <strong>of</strong> the myriad <strong>of</strong> influences captured by
these study-area fixed effects variables, interpreting the coefficient can be difficult. In general,
though, the coefficients simply represent the mean difference in sales prices between the study
areas and the reference study area (WAOR). <strong>The</strong>se coefficients are expected to be strongly
influential, indicating significant differences in sales prices across study areas.
<strong>The</strong> fourth group <strong>of</strong> variables in the Base Model are the core home and site characteristics (X),
and include a range <strong>of</strong> continuous (“C”), 55 discrete (“D”), 56 binary (“B”), 57 and ordered
categorical (“OC”) variables. <strong>The</strong> specific home and site variables included in the Base Model
are listed in Table 9 along with the direction <strong>of</strong> expected influence. 58 Variables included are age
54 Because there is no intent to focus on the coefficients <strong>of</strong> the study area fixed effect variables, the reference case is
arbitrary. Further, the results for the other variables in the model are completely independent <strong>of</strong> this choice.
55 See discussion in footnote 52 on previous page.
56 Discrete variables, similar to continuous variables, are ordered and the distance between the values, such as X 1
and X 2 , have meaning, but for these variables, there are only a relatively small number <strong>of</strong> discrete values that the
variable can take, for example, the number <strong>of</strong> bathrooms in a home (BATHROOMS).
57 Binary variables have only two conditions: "on" or "<strong>of</strong>f" (i.e., "1" or "0" respectively). Examples are whether the
home has central air conditioning ("CENTRAL_AC") or if the home is situated on a cul-de-sac ("CUL_DE_SAC").
<strong>The</strong> coefficients for these variables are interpreted in relation to when the condition is "<strong>of</strong>f."
58 For those variables with a "+" sign it is expected that as the variable increases in value (or is valued at "1" as
would be the case for fixed effects variables) the price <strong>of</strong> the home will increase, and the converse is true for the
variables with a "-" sign. <strong>The</strong> expected signs <strong>of</strong> the variables all follow conventional wisdom (as discussed in
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