London School of Economics - Gone with the Wind: Valuing the visual impacts of wind turbines through house prices

London School of Economics and Political Sciences & Spatial Economics Research Centre|Stephen Gibbons|April 11, 2014
United Kingdom (UK)Property Values


This study provides quantitative evidence on the local benefits and costs of wind farm developments in England and Wales, focussing on their visual environmental impacts. In the tradition of studies in environmental, public and urban economics, housing costs are used to reveal local preferences for views of wind farm developments. Estimation is based on quasiexperimental research designs that compare price changes occurring in places where wind farms become visible, with price changes in appropriate comparator groups. These comparator groups include places close to wind farms that became visible in the past, or where they will become operational in the future and places close to wind farms sites but where the turbines are hidden by the terrain. All these comparisons suggest that wind farm visibility reduces local house prices, and the implied visual environmental costs are substantial. The conclusions of the report are provided below. The full report can be accessed by clicking the link(s) on this page.


The analysis in this paper provided estimates of the effects of wind farm visibility on housing prices in England and Wales. The fairly crowded geographical setting, with numerous wind farms developed within sight of residential - 29 -roperty, provides a unique opportunity to examine the visual impacts of wind farms through hedonic property value methods. The analysis used a microaggregated postcode-by-quarter panel of housing transactions spanning 12 years, and estimated difference-in-difference effects using a quasi-experimental, postcode fixed effects methodology. Comparisons were made between house price changes occurring in postcodes where nearby wind farms become operational and visible, with the price changes occurring where nearby wind farms become operational but are hidden from view. All the results point in the same direction. Wind farms reduce house prices in postcodes where the turbines are visible, and reduce prices relative to postcodes close to wind farms where the wind farms are not visible. Averaging over wind farms of all sizes, this price reduction is around 5-6% within 2km, falling to less than 2% between 2 and 4km, and less than 1% by 14km which is at the limit of likely visibility. As might be expected, small wind farms have no impact beyond 4km, whereas the largest wind farms (20+ turbines) reduce prices by 12% within 2km, and reduce prices by small amounts right out to 14k (by around 1.5%). There are small (~2%) increases in neighbouring prices where the wind farms are not visible, although these are only statistically significant in the 4-8km band. This price uplift may indicate some local benefits from wind farms, for example due to spillovers from rents to landowners from wind farm operation, or from community grants. However these price increases could also be explained by displacement of demand by those seeking housing in these areas towards places where the wind farms are hidden. These offsetting price effects in neighbouring places where wind farms are visible and where they are not may explain, in part, why previous studies that focus only on distance to wind farms fail to find significant effects.

The these [sic] headline findings are comparable to the effects of coal power plants in the US found in Davis (2011) who finds up to 7% reduction within 2 miles (3.2 km). Of course, it takes many geographically dispersed wind farms to generate the same power as a single coal (or nuclear) plant, so the aggregate effects of wind farms and the number of households affected by their visual impact is likely to be considerably larger. The results are also in line with existing literature that suggests that other tall power infrastructure has negative impacts on prices (e.g. high voltage power lines, Sims and Dent 2005). The point estimates are comparable to the repeat sales estimates of the effects of wind farms in in Lang et al (2014) for Rhode Island, although their estimates are not statistically significant.

The paper presents a number of robustness tests, but even so the findings should be interpreted with some ‘health warnings’. The information on wind farm location and visibility is limited by lack of data on the precise location of individual turbines, so the classification of postcodes in terms of visibility is subject to measurement error. This is most likely to result in some attenuation of the estimated effects. Steps were taken to minimise this problem by eliminating postcodes where visibility is ambiguous. More importantly, the data lacks historical information on the timing of events leading up to wind farm operation (announcement, approval, construction etc.) so the price effects reported here relate to the average difference between the post-operation and preoperation periods for the periods spanned by the data (a gap of just under 6 years). However, the wind farm development cycle can last a number of years, and price changes evolve fairly slowly over time in response to events. Again the most likely consequence of this is that the results underestimate the full impact between the pre-announcement and post-construction phase.

Well established theories (Rosen 1974) suggest that we can interpret price differentials emerging between places where wind farms are visible and comparable places where they are not, as household marginal willingness to pay to avoid the dis-amenity associated with wind farm visibility. If we take the figures in the current paper seriously as estimates of the mean willingness to pay to avoid wind farms in communities exposed to their development, the implied costs are quite substantial. For example, a household would be willing to pay around £600 per year to avoid having a wind farm of small-average size visible within 2km, around £1000 to avoid a large wind farm visible at that distance and around £125 per year to avoid having a large wind farm visible in the 8-14km range.9 The implied amounts required per wind farm to compensate households for their loss of visual amenities is therefore fairly large: about £14 million on average to compensate households within 4km.10 The corresponding values for large wind farms will be much higher than this, as their impact is larger and spreads out over much greater distances.

These per-household figures are somewhat higher than the highest estimates from the stated preference literature, although there are no directly comparable figures. The figures cited in Bassi, Bowen and Fankhauser (2012) are typically much less than £100 per year, though this is per individual, so household willingness to pay could be higher.

The findings of the paper are relevant on a number of policy levels. The estimates provide potential inputs into cost-benefit analyses related to the siting of wind turbines, and the net benefits of wind power relative to other forms of low carbon energy. It should be noted, however, that the price effects reflect the valuation of home buyers in locations where wind farms are visible, so may not represent the mean valuation of wind farm visibility in the general population. The estimates could also inform policy on compensation for home owners for the loss of value in their homes arising from views of new wind farms. Interestingly, the evident increase in value of for houses where local wind farms are out of site suggests some scope, at least in theory, for these ‘winners’ to compensate the ‘losers’ in places where the turbines are visible e.g. through adjusting council taxes or introducing property value taxes.



April 11, 2014

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