Spatial Hedonic Analysis of the Effects of US Wind Energy Facilities on Surrounding Property Values

  • Ben HoenEmail author
  • Jason P. Brown
  • Thomas Jackson
  • Mark A. Thayer
  • Ryan Wiser
  • Peter Cappers


Rapid, large-scale U.S. deployment of wind turbines is expected to continue in the coming years. Because some of that deployment is expected to occur in relatively populous areas, concerns have arisen about the impact of turbines on nearby home values. Previous research on the effects of wind turbines on surrounding home values has been limited by small home-sale data samples and insufficient consideration of confounding home-value factors and spatial dependence. This study examines the largest set of turbine-proximal sales data to date: more than 50,000 home sales including 1,198 within 1 mile of a turbine (331 of which were within a half mile). The data span the periods well before announcement of the wind facilities to well after their construction. We use ordinary least squares and spatial-process difference-in-difference hedonic models to estimate the home-value impacts of the wind facilities, controlling for value factors existing prior to the wind facilities’ announcements, the spatial dependence of home values, and value changes over time. A series of robustness models provide greater confidence in the results. We find no statistical evidence that home values near turbines were affected in the turbine post-construction or post-announcement/pre-construction periods.


Turbines Wind Property Value Price Hedonic Spatial 



This work was supported by the Office of Energy Efficiency and Renewable Energy (Wind and Water Power Technologies Office) of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. For funding and supporting this work, we especially thank Patrick Gilman, Cash Fitzpatrick, and Mark Higgins (U.S. DOE). For providing the data that were central to the analysis contained herein, we thank Cameron Rogers (Fiserv) and Joshua Tretter (CoreLogic Inc.), both of whom were highly supportive and extremely patient throughout the complicated data-acquisition process. Finally, we would like to thank the many external reviewers for providing valuable comments on an earlier draft version of the report. Of course, any remaining errors or omissions are our own. The views expressed herein are those of the authors and may not be attributed to the Lawrence Berkeley National Laboratory, the Federal Reserve Bank of Kansas City, Texas A&M University or San Diego State University.


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Copyright information

© Springer Science+Business Media New York (outside the USA) 2014

Authors and Affiliations

  • Ben Hoen
    • 1
    Email author
  • Jason P. Brown
    • 2
  • Thomas Jackson
    • 3
  • Mark A. Thayer
    • 4
  • Ryan Wiser
    • 5
  • Peter Cappers
    • 6
  1. 1.Lawrence Berkeley National LaboratoryMilanUSA
  2. 2.Federal Reserve Bank of Kansas CityKansas CityUSA
  3. 3.Texas A&M University and Real Property Analytics, Inc.College StationUSA
  4. 4.San Diego State UniversitySan DiegoUSA
  5. 5.Lawrence Berkeley National LaboratoryBerkeleyUSA
  6. 6.Lawrence Berkeley National LaboratoryFayettevilleUSA

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