A Nail for Hoen’s Coffin

Ben Hoen was the lead author of the LBNL studyThe Impact of Wind Power Projects on Residential Property Values in the United States: A Multi-Site Hedonic Analysis“.  This Hoen 2009 study remains the largest ever undertaken, with nearly 7500 properties.  It found:  “Specifically, neither the view of the wind facilities nor the distance of the home to those facilities is found to have any consistent, measurable, and statistically significant effect on home sales prices“.  Hoen is the primary study quoted by wind energy proponents to rebut the common-sense notion that the nuisance created by nearby wind turbines will lower home prices.  This study is not without its problems;  I’ve previously posted on some of them.

Recently I read over another property value study, this one from Germany, “The Impact of Wind Farms on Property Values: A Geographically Weighted Hedonic Pricing Model“, by Yasin Sunak, 2012.  The main purpose behind it was to study the effect of pooling together properties from different locations and running regressions against the entire pool, compared with analyzing different neighborhoods separately.  Many of the studies routinely quoted by proponents pooled their data.  As an example, Hoen’s 7500 properties came from within 10 miles of 24 projects in 9 states and were all pooled together before he did any analyses.  I’ll cover the pooling issues first, but this study contained additional results that are equally important.

Pooling Example

To give you an example of how this might be a problem I created the following simplified example.  Below are shown close-in (<2km) and more distant prices in two separate neighborhoods.  Neighborhood #1’s price averaged $100,000 and #2 averaged $300,000.  After the project goes in the close-in neighborhoods suffer a 25% decline in prices for homes within 2km of the project.

Given that the means in both neighborhoods are over 2 standard deviations apart,  no statistician would suggest that the turbines had no effect.  A t-test shows that the chance of either of these distributions occurring by chance is 5 in a million.  But if we simply combine (“pool”) the neighborhoods and then run the analysis we get quite a different answer:

This is the same data as before, with the same declines as before, but now the standard deviation is large enough that most statisticians (especially those who are serving the interests of their sponsors) would declare there is no statistically significant effect of wind turbines on home prices.

Although my sample just considered the effect of pooling on standard deviations, the same principle applies to the effects on regression analysis: similar computational techniques are used to calculate both.  Sunak provides an analogous regression example at the bottom of page 7, figure 2.

Sunak

Sunak’s study involved several neighborhoods around just one project in Germany.  Although it was just one project he was still working with a surprisingly large number of sales, slightly over 1400.  Even more surprising, he used just vacant residential lots, so any complications from the number of bathrooms etc are avoided.  This large a sample in this small an area is unprecedented, and provides what is arguably the most accurate assessment of the effects of wind turbines on property values to date.

Sunak first pooled all the sales and ran traditional regressions against them.  For Sunak’s purposes this was just a prelude, but his results are extraordinary.  Lot prices decreased 0.209% for every 1% closer a property was to the project, a result that was statistically significant.  Put another way, prices of lots within 2km of the project decreased an average of 25.2%, again statistically significant.  Another surprising result was that being subject to shadow flicker also reduced the price enough that it was almost statistically significant as well.

Sunak then tested these results for something called spatial autocorrelation and finds some amount of it, which indicates that prices are influenced not just by the distance to the project, but also by where they are.  He then runs the regression using Geographically Weighted Regression and finds that the spatial autocorrelation has decreased markedly.  The single result that was previously applied across the entire area was not indicative of what happened within each neighborhood.  Sunak writes: “The application of the GWR revealed a more complex picture of the influencing effects through the weighting of spatial relationships and local variations in the data. Based on local estimates, the GWR revealed negative, wind-farm-related effects that are attributable to strong local influences of the wind farm site. Therefore, predominantly biased by local clustering, global estimations are not appropriate in capturing the impacts of wind farm proximity on property prices.

 Conclusion

There were two major results of this study.  First, the practice of pooling sales data from disparate areas is inappropriate and would tend to downplay the effect of the wind turbines on prices.  In the case of Hoen, one has to wonder if he was aware of this effect and went ahead with the pooling anyway, knowing that doing so served the interests of his sponsor.  Second, when enough data is collected from one area the effect of wind turbines is unmistakeable and irrefutable.

My thanks to Michael McCann, who alerted me to this study, and whose own studies indicate a loss of, guess what, 25% (or more) for properties within 2 miles of a project.

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