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

The Journal of Real Estate Finance and Economics volume 51, pages 22–51 (2015)Cite this article

Abstract

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.

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Fig. 1

Notes

  1. Assuming 2-MW turbines, the 2012 U.S. average (AWEA 2013), and 5.5 GW of annual capacity growth.

  2. Disamenities and amenities are defined respectively as disadvantages (e.g., a nearby noxious industrial site) and advantages (e.g., a nearby park) of a location.

  3. Throughout this report, the terms “announced/announcement” and “constructed/construction” represent the dates on which the proposed wind facility (or facilities) entered the public domain and the dates on which facility construction began, respectively. Home transactions can either be pre-announcement (PA), post-announcement/pre-construction (PAPC), or post-construction (PC).

  4. Heintzelman and Tuttle do not appear convinced that the effect they found is related to the PAPC period, yet the two counties in which they found an effect (Clinton and Franklin Counties, NY) had transaction data produced almost entirely in the PAPC period.

  5. This analysis is available upon request from the authors.

  6. For example, Sunak and Madlener (2012) find larger effects related to the turbines in a city that is farther from the turbines than they find in a town which is closer. Additionally, they find stronger effects in the center of a third town than they do on the outskirts of that town, which do not seem related to the location of the turbines.

  7. See Jackson (2003) for a further discussion of the Hedonic Pricing Model and other analysis methods.

  8. Because it is assumed that nuisance effects from turbines come in the form of, for example, views of, sounds from and/or shadow flicker from turbines, and that the models do not test for these effects directly, the one-mile and half-mile models, therefore, act as a proxy. Previous research has shown that distance is a good proxy for these effects, that these effects are likely to fade beyond one half mile, and that, therefore, the half-mile models are more likely to coincide with these effects than the one-mile models (e.g., Hoen et al. 2009; Hoen et al. 2011).

  9. A “block group” is a US Census Bureau geographic delineation that contains a population between 600 to 3,000 persons.

  10. The dataset does not include “participating” landowners, those that have turbines situated on their land, but does include “neighboring” landowners, those adjacent to or nearby the turbines. One reviewer notes that the estimated average effects also include any effects from payments “neighboring” landowners might receive that might transfer with the home. Based on previous conversations with developers (see Hoen et al. 2009), we expect that the frequency of these arrangements is low, as is the right to transfer the payments to the new homeowner. Nonetheless, our results should be interpreted as “net” of any influence whatever “neighboring” landowner arrangements might have.

  11. Unlike the vector of home, site, and neighborhood characteristics, sale price inflation/deflation and seasonal changes were not expected to vary substantially across various counties in the same states in our sample and therefore the interaction was made at the state level. This assumption was tested as part of the robustness tests though, where they are interacted at the county level and found to not affect the results.

  12. In part because of the rural nature of many of the study areas included in the research sample, these census tracts are large enough to contain sales that are located close to the turbines as well as those farther away, thereby ensuring that they do not unduly absorb effects that might be related to the turbines. Moreover each tract contains sales from throughout the study periods, both before and after the wind facilities’ announcement and construction, further ensuring they are not biasing the variables of interest.

  13. SARAR refers to a “spatial-autoregressive model with spatial autoregressive residuals”.

  14. The most recent sale weights the transactions to those occurring after announcement and construction that are more recent in time. One reviewer wondered if the frequency of sales was affected near the turbines, which is also outside the scope of the study, though this “sales volume” was investigated in Hoen et al. (2009), where no evidence of such an effect was discovered. Another correctly noted that the most recent assessment is less accurate for older sales, because it might overestimate some characteristics of the home (e.g., sfla, baths) that might have changed (i.e., increased) over time. This would tend to bias those characteristics’ coefficients downward. Regardless, it is assumed that this occurrence is not correlated with proximity to turbines and therefore would not bias the variables of interest.

  15. As discussed in more detail in the Data Section, approximately 60 % of all the data obtained for this study (that obtained from CoreLogic) used the most recent assessment to populate the home and site characteristics for all transactions of a given property.

  16. See the EV Energy Map, which is part of the Velocity Suite of products at www.ventyx.com.

  17. See www.corelogic.com.

  18. The 15 acre screen was used because of a desire to exclude from the sample any transaction of property that might be hosting a wind turbine, and therefore directly benefitting from the turbine’s presence (which might then increase property values). To help ensure that the screen was effective, all parcels within a mile of a turbine were also visually inspected using satellite and ortho imagery via a geographic information system.

  19. See www.orps.state.ny.us

  20. Baths was calculated in the following manner: full bathrooms + (half bathrooms x 0.5). Some counties did not have baths data available, so for them baths was not used as an independent variable.

  21. The distribution of sfla1000 is skewed, which could bias OLS estimates, thus lsfla1000 is used instead, which is more normally distributed. Regression results, though, were robust when sfla1000 was used instead.

  22. This variable allows the separate estimations of the 1st acre and any additional acres over the 1st.

  23. Age and agesqr together account for the fact that, as homes age, their values usually decrease, but further increases in age might bestow countervailing positive “antique” effects.

  24. See footnote 14.

  25. Before the distances were calculated, each home inside of 1 mile was visually inspected using satellite and ortho imagery, with x/y coordinates corrected, if necessary, so that those coordinates were on the roof of the home.

  26. Cleaning involved the removal of all data that did not have certain core characteristics (sale date, sale price, sfla, yrbuilt, acres, median age, etc.) fully populated as well as the removal of any sales that had seemingly miscoded data (e.g., having a sfla that was greater than acres, having a yrbuilt more than 1 year after the sale, having less than one bath) or that did not conform to the rest of the data (e.g., had acres or sfla that were either larger or smaller, respectively, than 99 % or 1 % of the data). OLS models were rerun with those “nonconforming” data included with no substantive change in the results in comparison to the screened data presented in the report.

  27. Age could be as low as−1 (for a new home) for homes that were sold before construction was completed.

  28. The OLS models are estimated using the areg procedure in Stata with robust (White’s corrected) standard errors (White 1980). The spatial error models are estimated using the gstslshet routine in the sphet package in R, which also allows for robust standard errors to be estimated. See: http://cran.r-project.org/web/packages/sphet/sphet.pdf

  29. The controlling variables’ coefficients were similar across the base models, so only the one-mile results are summarized here.

  30. The possible adverse effects of these collinearities were fully explored both via the removal of the variables and by examining VIF statistics. The VOI results are robust to controlling variable removal and have relatively low (<5) VIF statistics.

  31. The removal of this, as well as the other block group census variables, however, did not substantively influence the results of the VOI.

  32. p-values are not shown in the table can but can be derived from the standard errors, which are shown.

  33. All DD estimates for the OLS models were calculated using the post-estimation “lincom” test in Stata, which uses the stored results’ variance/covariance matrix to test if a linear combination of coefficients is different from 0. For the SEM models, a similar test was performed in R.

  34. All differences in coefficients are converted to percentages in the table as follows: exp(coef)-1.

  35. Although not discussed in the text, this trend continues with homes between 1 and 2 miles being less negative/more positive than homes closer to the turbines (e.g., those within 1 mile).

  36. Results were also estimated for the one-mile OLS models for each of the robustness tests and are available upon request: the results do not substantively differ from what is presented here for the half-mile models. Because of the similarities in the results between the OLS and SEM “base” models, robustness tests on the SEM models were not prepared as we assumed that differences between the two models for the robustness tests would be minimal as well.

  37. This trend also continues outside of 1 mile, with those coefficients being less negative/more positive than those within 1 mile.

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Acknowledgments

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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Authors and Affiliations

  1. Lawrence Berkeley National Laboratory, c/o 20 Sawmill Road, 12571, Milan, NY, USA

    Ben Hoen

  2. Federal Reserve Bank of Kansas City, 1 Memorial Drive, 64198-0001, Kansas City, MO, USA

    Jason P. Brown

  3. Texas A&M University and Real Property Analytics, Inc., 4805 Spearman Drive, 77845-4412, College Station, TX, USA

    Thomas Jackson

  4. San Diego State University, 5500 Campanile Dr, 92182-4485, San Diego, CA, USA

    Mark A. Thayer

  5. Lawrence Berkeley National Laboratory, 1 Cyclotron RoadMS 90R4000, 94720-8136, Berkeley, CA, USA

    Ryan Wiser

  6. Lawrence Berkeley National Laboratory, c/o 7847 Karakul Lane, 13066, Fayetteville, NY, USA

    Peter Cappers

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  1. Ben Hoen
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Hoen, B., Brown, J.P., Jackson, T. et al. Spatial Hedonic Analysis of the Effects of US Wind Energy Facilities on Surrounding Property Values. J Real Estate Finan Econ 51, 22–51 (2015). https://doi.org/10.1007/s11146-014-9477-9

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Keywords

  • Turbines
  • Wind
  • Property Value
  • Price
  • Hedonic
  • Spatial