Do Wind Turbines Affect Property Values? No — or at Least “No Statistical Evidence” — Says New Hedonic Study

, Senior energy analyst | October 22, 2013, 10:35 am EDT
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I noted in my recent post on wind turbines and tourism that a related issue, wind farms and property values, was also important to consider. A new study does just that, and finds “no statistical evidence” of effects.

The study is A Spatial Hedonic Analysis of the Effects of Wind Energy Facilities on Surrounding Property Values in the United States, from Lawrence Berkeley National Laboratory. The author team included LBL experts, researchers from Texas A&M and San Diego State, and others.

The “hedonic” in the study title refers not to hedonism in the colloquial sense (think college students living it up during spring break), but to the amount of pleasure — or displeasure — that comes from a certain amenity, like a nearby park, or disamenity, like a garbage dump.

In this case, the question is where wind farms sit on the amenity-disamenity spectrum.

What the Research Shows

Spatial Hedonic Analysis is not the first study to look at these issues. This work builds on reports of analyses by some of the same team members in recent years, and, as the study’s impressive literature review attests, work by others.

Analyzing wind turbines and property values (Hoen et al. 2013)

Analyzing wind turbines and property values (States and counties studied in Hoen et al. 2013)

Still, this effort included analysis of an impressive and unprecedented level of data. It covered more than 51,000 sales of homes near wind turbines, in 27 counties in nine states — wherever they could get at least 250 relevant transactions. All of the homes were within 10 miles of wind farms (67 different facilities), and 1,200 of them were within a mile of a wind turbine.

And the upshot? Here:

Across all model specifications, we find no statistical evidence that home prices near wind turbines were affected in either the post-construction or post-announcement/preconstruction periods. (emphasis added)

Note that they can’t and don’t say definitively that there is no effect; absence of proof isn’t proof of absence. But, as the study puts it:

…if effects do exist, either the average impacts are relatively small (within the margin of error in the models) and/or sporadic (impacting only a small subset of homes).

Different states also have different characteristics, including demographics, population densities, and experience with energy development or natural resource use. But the research incorporated a wide variety, from Washington State to New York and New Jersey, and plenty of places in between.

What It All Means

No study is the last word, and these researchers may continue to find ways to increase understanding and “model fidelity.” But when we’re trying to make decisions about our energy future, it’s good to know what the best available research tells us about different facets of wind farms. Environmental costs and benefits, for example. Effects on electricity costs and rates. And, in this case, what it means for those who live closest, but aren’t benefiting directly from lease payments. (The research excluded “participating” landowners.)

Like tourists, neighbors will have different takes on wind turbines. But good, hard data on what a proposed wind project might mean when it comes to selling or buying in the neighborhood is a valuable contribution to the discussion.


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  • I’d love to see wind turbines off the coast of Georgia and maybe even a min-one in my own backyard one day!

    • Thanks, Stephanie. I’m glad to hear your enthusiasm for wind. And I’m looking forward to offshore wind in the U.S., too.



    If you piss in the ocean you will still only taste salt. If you piss in a glass the effect will be more than marginally different!

    • Ron, I’m a little more careful than either of those examples would suggest. But my response to Alec below may help address your concern, at least with regard to the study findings.

  • It is notable that the study considered homes within a 10 mile radius of turbines.
    The area of the region between 9 and 10 miles from the turbines (59 square miles) is larger than the entire region up to 4 miles from the turbines (50 square miles)
    The study merely demonstrates that if you dilute the influence of turbines on property prices by including as large an area as possible, you can guarantee you will not find a significant effect.
    Try repeating the analysis only for homes within 2 miles of turbines. The result will be considerably different.

    • Actually, Alec, they were thinking along the same lines; sorry that I wasn’t clear about that. They actually break down the data much more finely than that — <1 mile, 1-3 miles, and 3-10 miles in Chapter 4; then 3 miles in Section 5.1.2; and within 1/2 mile or 1 mile elsewhere. It’s worth a read.

  • Mike McCann

    I have studied wind project impacts from a “market value” perspective since 2005, with an understanding of the definition of Market Value ingrained through over 30 years of appraising and evaluating all types of property. I can tell you that the “statistical significance” or lack thereof of this latest report from LBNL does not meet any of the professional appraisal standards (USPAP)and just presumes to substitute their untested, unproven regression model for the Industry Standards for professional appraisers. (Google: wind Farms and Rubber Rulers)

    To illustrate the disparate results that hinge on the difference between Market Value and statistical significance, refer to table 7 of the LBNL report. What you will see is that post-construction of the turbines, homes within 1 mile of the turbines sold on average for 28% less than homes over 3 miles from where turbines were constructed.

    One does not need to be a scientist to understand that with over 1,000 sales within 1 mile, the average price is an entirely relevant mathematical result. Yet, the black box of the LBNL regression somehow is able to reduce 28% prima facie evidence of impact to a finding that tells the reader there is no statistically significant impact.

    This means the study conclusions do not comport with reality,….or the balance of the 50,000 sales used as background/control for the regression analysis must have been largely far superior properties, like lakefront homes, upscale subdivisions, or some other combination of features that invalidates their use for measuring impact of turbines.

    Further, even when one ignores the funding sponsor of that report being the USDOE and that they are open advocates for pursuing the policy of wind energy, you really can’t ignore what is stated on report page 5, 2nd paragraph, last sentence, wherein the authors admit that their analysis assumes that the value impact could not be higher than 4%. This is clearly research bias. Period.

    I suggest that LBNL and other academic research be conducted within the framework of the established and accepted methodology for the issue in question, and not reinvent the wheel to suit the policy goals of their funding sponsors. After all, a student with a few biology classes completed is similarly not qualified to render expert medical diagnosis or treatment.

    • Mike, I appreciate your attention to this important issue. Your citing of the 28% figure from Table 7, though, leaves out both the circumstances and the study’s rigorous subsequent treatment of that issues. The circumstances include the differences in home values “pre-announcement”, which are clearly visible in Table 7 itself, and which are a large part of that 28% difference; that is, there were appreciable differences even before wind turbines showed up. And the authors clearly note the Table 7 results, then explain why, even if you do the math correctly, the numbers aren’t the whole story:

      “In summary, focusing solely on trends in home price (or price per square foot) alone, and for only the PC period, as might be done in a simpler analysis, might incorrectly suggest that wind turbines are affecting price when other aspects of the markets, and other home and sites characteristic differences, could be driving the observed price differences. This is precisely why researchers generally prefer the hedonic model approach to control for such effects, and the results from our hedonic OLS and spatial modeling detailed in the next section account for these and many other possible influencing factors.” (p. 24)

      They then go on to apply those models to account for just such issues. The crux of their analysis appears in the subsequent section, the results chapter.

      As for the statement you point to on maximum effects, the authors use that in the very next paragraph solely to potentially “help explain why effects have not been discovered consistently in previous research,” and use that to explain why a larger dataset, such as they used, may be important. They come back to the maximum-effects issue in the final paragraph of the report, explaining that “If effects of this size are to be discovered in future research, even larger samples of data may be required.”

      And on the issue of funders, that’s something we very much share your interest in, and pay a lot of attention to, as through our helpful “Disinformation Playbook.” Works such as the LBL study, funded by the U.S. government and engaging a broad range of peer reviewers, are very different from ones funded by parties with strong commercial interests in retaining the status quo. Much of the basic science in this country is made possible by U.S. government funding, and the network of national labs play a huge role in advancing our country’s understanding of a whole range of issues. Though UCS doesn’t get (or seek) government or lab funding, we are very grateful for what they do provide us and the public at large, in the form of solid analysis about issues and options surrounding our energy choices.