Anti-wind NIMBYs grossly exaggerate their effect
Wei Guo, Leonie Wenz and Maximilian Auffhammer
It has been argued that wind turbines are a visual disamenity. We statistically estimate the impact of having at least one wind turbine within sight on home values, using data from more than 300 million home sales and 60,000 wind turbines in the United States from 1997 to 2020.
We find robust evidence of a 1% drop of home values within a wind turbine’s viewshed. The effect is larger for homes closer to more wind turbines, but is no longer detectable by the end of the 20-y period covered by our data.
Renewable power generation is the key to decarbonizing the electricity system. Wind power is the fastest-growing renewable source of electricity in the United States. However, expanding wind capacity often faces local opposition, partly due to a perceived visual disamenity from large wind turbines.
Here, we provide a US-wide assessment of the externality costs of wind power generation through the visibility impact on property values. To this end, we create a database on wind turbine visibility, combining information on the site and height of each utility-scale turbine having fed power into the U.S. grid, with a high-resolution elevation map to account for the underlying topography of the landscape.
Building on hedonic valuation theory, we statistically estimate the impact of wind turbine visibility on home values, informed by data from the majority of home sales in the United States since 1997.
We find that on
average, wind turbine visibility negatively affects home values in an
economically and statistically significant way in close proximity (<5
miles/8 km). However, the effect diminishes over time and in distance and is
indistinguishable from zero for larger distances and toward the end of our
sample.
Investment in renewable power generation capacity has gained significant momentum in the United States and globally in recent years (1). This is driven by massive drops in the cost of wind and solar and by concerns over the negative local and global externalities stemming from a fossil fuel–based energy system.
While renewable technologies address the issue of pollution externalities, their rollout poses different challenges (e.g., intermittency) (2, 3). Wind power is the fastest-growing source of renewable electricity in the United States. In 2020, wind power accounted for more than 7% of total electricity generation, and it is projected to continue to grow in the coming years (4).
Wind turbines, in particular, have also been a source of controversy as they may create low-frequency noise, cast shadows, create flickering, and visually degrade the landscape (5–8).
Understanding the visual disamenity value of wind turbines is becoming increasingly policy relevant as already large wind turbines are growing in height and are often located on high-elevation areas with extensive visibility (9, 10).
They are widely perceived as unattractive and disruptive to the landscape, with some polls suggesting that 8 to 25% of respondents strongly dislike seeing wind turbines (11, 12). Homeowners and developers may be negatively affected by the proximity of wind turbines through depressed home values (13–15).
These “NIMBY” (Not In
My BackYard) concerns, which in many places have manifested in vocal local
public opposition to new projects, can have an impact on the siting decision of
wind power infrastructure (16–18).
This paper presents a
comprehensive national-level analysis to causally estimate the visual
externality costs of wind power generating capacity in the United States. We
utilize the universe of re-geocoded home transactions listed in the ZTRAX
database for the years 1997 to 2020 and match these to the installation of wind
turbines nearby
We rely on the broadly applied theory of hedonic valuation to reveal local residents’ preferences for views of wind turbines (19, 20).
Previous studies have either focused on wind facilities outside of cities in Europe or on selected areas across the United States, making their results difficult to generalize to the entire USA (18, 21–27).
Further, unlike previous studies, we do not only consider mere proximity of a wind turbine to a home, but compute whether a wind turbine can actually be seen from each home (visibility).
To this end, we combine digital elevation models of the landscape with the location and height information of turbines, utilizing advanced geospatial tools from geomorphometry and computer science (28–30).
We can thus create a geospatial database on wind turbine visibility, comprising a high-resolution viewshed for every single wind facility in the USA (see Data & Methods for detail).
This database allows us to characterize whether and when a location can actually see a wind turbine or whether it is hidden from view by the natural landscape.
To investigate the causal effect of wind turbines on housing prices, we employ a spatial difference-in-difference (DiD) design that takes advantage of both temporal variation in turbine installations and spatial variation in visibility induced by the underlying topography of the landscape.
Our analysis estimates the average change in housing prices for homes with a wind turbine in their viewshed when it becomes operational, relative to the average change in housing prices for homes not visible to the same facility, within a 10-km (6.2-mile) range from the wind facility.
The high-resolution housing transaction data, which include precise property locations, allow us to relax the statistical identification assumption, as the exact location and installation of wind turbines is assumed to be exogenous to the evolution of nearby housing markets.
This is because the visibility of wind turbines is primarily determined by the underlying landscape topology, which is exogenous to changes in property values over time. Our examination of the parallel property value trends assumption pre-installation supports this statement.
We also control for other confounding
variables, such as location, general economic trends, and housing quality
(see Data & Methods for detail).
Results: Property Value Impacts.
We find that having at least one wind turbine in a home’s viewshed (10 km radius) reduces the sales price of such a property on average by 1.12%, which is statistically different from zero Table 1, column (1).
To put
this in perspective, this amounts to a US $24.5 billion reduction in the
property value for all houses affected by the visibility disamenity effect
nationwide, which is small when compared to the total value of US homes
(≈ US$45 trillion).
Additionally, we estimate that prior to the installation of a wind facility, there is a significant gap of 1.01% in the average property value between those areas that will later have a wind turbine in their viewshed (treated areas) and those that will not (control areas).
This gap cannot be explained by differences in observed property characteristics or disparities in neighborhood factors and housing market changes. Our best explanation for this gap is that wind turbines are more likely to be sited in areas where their visual disamenity affects communities with lower housing values.
Since the visibility of wind turbines is primarily determined by the nearby geographic landscape, this cross-sectional gap reflects only subtle decisions on wind turbine location within a small area rather than across entire neighborhoods.
The finding regarding
the disamenity effect remains robust across multiple ways of specifying the
regression model. These include limiting the sample to only properties that
have experienced repeated sales over the research period, excluding
nondisclosure states on property transactions, incorporating interaction terms
between property characteristics and yearly indicators, as well as interacting
treatment with state indicators. These checks are reported in SI Appendix, Table S1.
We investigate whether the visual impact of wind turbines varies with the intensity of visibility using two measures: The number of wind turbines in view and the intensity classified by whether there are more than 20 turbines in sight.
We find that the capitalization of the visual disamenity increases with the treatment intensity, with every additional 10 wind turbines in view reducing the property value by an additional 0.2% [Table 1, column (2)].
Furthermore, wind farms with more than 20 turbines reduce the property value in
visible areas by an average of 2.48%, whereas those with less than 20 turbines
have a reduction effect of only 1.02% on visible areas [Table 1, column (3)]. These
findings suggest that the density of wind turbines in view plays a role in
driving the magnitude of the visual disamenity valuation.
The impact of visual disamenity created by wind turbines may also vary depending on the distance from the nearest visible turbine. To test how the effect varies by distance, we re-run the baseline specification with the indicators of interest interacted with 500-m (∼ 0.3-mile) distance bin indicators for the proximity of the closest wind turbine.
The effect of wind turbine visibility decreases as distance increases (Fig. 2). The effect is largest in immediate proximity of wind turbines—with the visual disamenity reducing property values by up to 8% within a neighborhood range of 1.5 km (∼ 0.9 miles).
Even though this number is economically large, there are two noteworthy caveats. First, the CI is sizable including reductions in property value between 3 and 13%. Second, the number of properties within this distance bin is small.
Nationally, there are fewer than 250,000 transactions within 1.5 km of the nearest wind turbine, as opposed to approximately 8.5 million transactions within 10 km. The effect for the full sample is statistically indistinguishable from zero 8 km (5 miles) away from the nearest wind turbine.
To put this in perspective, if one
stretched out an average-sized arm and held up an aspirin tablet, this would
equate the perceived size of an average wind turbine five miles away. Were the
same wind turbine one mile away, it would appear to be roughly the size of a
golf ball.
To read the entire article: https://doi.org/10.1073/pnas.2309372121