A Spatial Hedonic Analysis of the Effects of Wind Energy Facilities on Surrounding Property Values in the United States

Abstract

Previous research on the effects of wind energy facilities on surrounding home values has been limited by small samples of relevant home-sale data and the inability to account adequately for confounding home-value factors and spatial dependence in the data. This study helps fill those gaps. We collected data from more than 50,000 home sales among 27 counties in nine states.These homes were within 10 miles of 67 different wind facilities, and 1,198 sales were within 1 mile of a turbine-many more than previous studies have collected. The data span the periods well before announcement of the wind facilities to well after their construction. We use OLS and spatial-process difference-in-difference hedonic models to estimate the home-value impacts of the wind facilities; these models control for value factors existing before the wind facilities' announcements, the spatial dependence of unobserved factors effecting home values, and value changes over time. A set of robustness models adds confidence to our results. Regardless of model specification, we find no statistical evidence that home values near turbines were affected in the post-construction or post-announcement/pre-construction periods. Previous research on potentially analogous disamenities (e.g., high-voltage transmission lines, roads) suggests that the property-value effect of wind turbines is likely to be small, on average, if it is present at all, potentially helping to explain why no evidence of an effect was found in the present research.iv

The changing cultural and economic values of wind energy landscapes

“…Seasonally occupied residences also appear to have higher frequencies of reductions post turbine construction than do farms and other permanently held residences. Thus our tentative findings disagree with results of the large sample hedonic analyses (Hoen et al 2009;Hunt 2010;Hoen et al 2013;Vyn and McCullough 2014) that find no significant effect of turbines on property values. A more complete data set is required to confirm these tentative findings.…”

“…Hedonic regression analyses are generally favoured and typically assess distance to turbines as the variable to isolate for any price effect; "hedonic" refers to the pleasure or utility of a property ultimately measured in its price. North American studies of this type have generally found no significant effects (Hoen et al 2009;McCullough 2009;Hoen et al 2013;MPAC 2014). However, price comparison studies (Lansink Appraisals and Consulting 2012) and contingent valuation surveys (Landenburg and Dubgaard 2007) sometimes find property value reduction associated with turbines.…”

Hydrogen import pathway comparison framework incorporating cost and social preference: Case studies from Australia to Japan

“…The second impact assessed is the effect on real estate values imparted by new deployment of wind and solar generation plants. Three spatial analyses of housing price impacts of wind were considered, at the local and national level in the United States and in the Netherlands in urban and rural contexts . In both US case studies, no significant impact on house prices was discovered.…”

Housing market detections

“…For wind turbines, negative external effects range between 5 percent within 0.5 miles (Lang & Opaluch, 2013), 2 to 16 percent within 3 miles (Heintzelman & Tuttle, 2012b), 1.2 to 2.6 percent within 2 kilometers (Dröes & Koster, 2016) and 5 to 6 percent within 2 kilometers (Gibbons, 2015). Other studies find no evidence of significant effects (Carter, 2011;Hoen, 2014;Sims et al, 2008). Since the methodology, number of observations, research area and control variables differ widely between studies, it is not possible to directly compare these findings and to draw firm conclusions regarding the relative externality costs of different forms of electricity generation.…”

“…11 As time-fixed effects we use year dummies, measuring general house price dynamics over time (see e.g. Hoen, 2010Hoen, , 2014. 12 Standard errors are clustered by municipality and year.…”