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Relationship between Wind Turbines and Residential Property Values in Massachusetts - Frequently Asked Questions
Why did the Massachusetts Clean Energy Center (MassCEC) commission the University of Connecticut and Lawrence Berkley National Laboratory to do this report?
In recent years, concern has been raised over the potential impact on home values given proximity to wind turbines. While previous studies have been conducted, no studies have explored the specific relationship between wind turbines and home prices in Massachusetts.
What are the objectives of the report?
The research conducted evaluated five key questions about the relationship between wind turbines and home values in Massachusetts:
- Have wind facilities in Massachusetts been located in areas where average home prices were lower than prices in surrounding areas?
- Are home values affected by the construction of wind turbines?
- Are home values affected by the announcement of plans to construct wind turbines?
- How does the relationship between wind turbines and home values compare to those of factors previously shown to affect home prices, including high-voltage transmission lines, landfills, highways, protected open space and proximity to beachfront?
- Do homes that are close to turbines sell at the same rate as homes farther away from turbines?
How do the authors measure the relationship between home prices and wind turbines?
The authors employed a type of statistical model frequently used by economists and real estate professionals to assess the impacts of house and community characteristics on property values by investigating the sales prices of homes. This is known as a hedonic regression model.
For example, a house can be thought of as a bundle of characteristics (e.g., number of square feet, number of bathrooms, the size of the parcel). When a price is agreed upon by a buyer and seller, there is an implicit understanding that those characteristics have value. When data from a large number of residential transactions are available, the individual marginal contribution to the sales price of each characteristic for an average home can be estimated using the hedonic regression model. Such a model can statistically estimate, for example, how much an additional bathroom adds to the sale price of an average home. In this case, the model was used to isolate the impact that proximity to wind turbines might have on home prices as distinct from the other characteristics that affect home prices.
What are the findings of the report?
The analysis of over 122,000 Massachusetts home sales between 1998 and 2012 found no-statistically significant evidence that proximity to wind turbines affects home values.
What does “statistically significant” mean and what difference does it make whether the value is statistically significant or not?
Statistical significance is a measure of the likelihood that the observed patterns occurred as a direct result of a particular characteristic (in this case, proximity to a wind turbine) or by chance. Statistical significance is indicated by a probability value referred to as a “p-value.” The +0.5 percent net effect on house prices has a p-value of 0.85 which indicates that there is an 85 percent probability that this difference in price occurred by chance.
Why does the report include transactions that are far away from turbines if the objective is to measure the effects that turbines have on houses close to turbines?
Houses far from the turbines were included to provide a basis of comparison to homes close to turbines. These homes are equivalent to a “control” that are not subject to a particular “treatment” (in this case the construction of turbines).
Have you accounted for the possibility that homes close to turbines didn’t sell because no one would buy them?
Yes. If a home was on the market but could not attract a buyer, it is assumed that that the seller would reduce the price. Therefore, if a house near a wind turbine was considered to be affected, it would be reflected in the data as a lower sales price. In the case that no buyers could be found at any price, one would expect to see these homes sell less frequently. The authors specifically tested to see if this had occurred and found that homes close to turbines sold at the same rate as homes farther away from turbines. In fact, the data included 1,503 sales that actually occurred between 1998 and 2012 within one mile of turbines after the turbines were constructed.
In light of the results, what is a realtor supposed to tell someone who wants to put their house on the market if it is located near a turbine?
The findings of the study suggest that a house’s price should not be discounted because it is near a turbine. As with all characteristics of a particular property, a house’s proximity to a turbine may dissuade some buyers, but the fact that people do buy houses near turbines suggests that not all potential buyers share the same view about turbines.
How can the results inform a particular community that has a wind turbine?
The results of the study suggest that the construction of wind turbines in Massachusetts has had no discernible effect on home prices. However, we cannot say whether the effects of any specific location in the study area were above or below that average because there was not sufficient data to run tests for just the observations in one particular community.
Did the researchers measure the sound at these homes? If not, then how are they accounting for the effects that sound from turbines might have on homes?
The focus of the study is on proximity to wind turbines. Sound is only one aspect affected by proximity; others include visibility and shadow flicker. The researchers did not measure sound or any other impacts at the homes. Such studies would have been far beyond the scope and budget of the research. This study solely focused on the distance of home from operating turbines, which is commonly used as a determinant to assess property value impacts.
How did the researchers ensure their results were rigorous and conform to high academic standards?
The study underwent a peer review process similar to what other studies undergo prior to publication in academic journals. During the peer review process, a team of independent experts from multiple universities and with diverse backgrounds reviewed all aspects of the report. In particular, they focused on the report’s methods, results and conclusions to determine that all statistical tests have been applied properly and the report’s conclusions are appropriate in light of the results. The report was reviewed by the following experts:
- Dr. Thomas Jackson, Texas A&M University
- Dr. Corey Lang, University of Rhode Island
- Dr. Mark Thayer, San Diego State University
- Dr. Jeffrey Zabel, Tufts University