Excerpt from the Introduction
This paper provides quantitative evidence on the local benefits and costs of wind farm developments. In the tradition of studies in environmental, public and urban economics, housing costs are used to reveal local preferences for wind farm development in England and Wales. This is feasible in England and Wales because wind farms are increasingly encroaching on rural, semi-rural and even urban residential areas in terms of their proximity and visibility, so the context provides a large sample of housing sales that potentially affected (at the time of writing, around 2.5% of residential postcodes are within 4 km of operational or proposed wind farm developments). Estimation is based on quasi experimental, difference-in-difference based research designs that compare price changes in postcodes close to wind farms when wind farms become operational with postcodes various comparator groups. These comparator groups include: places close to wind farms that became operational in the past, or where they will become operational in the future; places close to wind farms sites that are in the planning process but are not yet operational; places close to where wind farms became operational but where the turbines are hidden by the terrain; and places where wind farm proposals have been withdrawn or refused planning permission. The postcode fixed effects design implies that the analysis is based on repeat sales of the same, or similar housing units within postcode groups (typically 17 houses grouped together).
All these comparisons suggest that operational wind farm developments reduce prices in locations where the turbines are visible, and that the effects are causal. This price reduction is around 5-6% for housing with a visible wind farm of average size (11 turbines) within 2km, falling to 3% within 4km, and to 1% or less by 14km which is at the limit of likely visibility. Evidence from comparisons with places close to wind farms, but where wind farms are less visible suggests that most if not all of these price reductions are directly attributable to turbine visibility.
The paper has estimated the effects of visible wind farm turbines on housing prices in England and Wales. The study used a micro-aggregated postcode-by-quarter panel of housing transactions spanning 12 years, and estimated difference-in-difference effects using a postcode fixed effects based methodology. Comparisons were made between postcodes in which turbines became operational and visible with various control groups. All the results point in the same direction, regardless of the specific research design. Wind farms reduce house prices in postcodes where the turbines are visible. This price reduction is around 5-6% for housing with a visible wind farm of average size (11 turbines) within 2km, falling to 3% within 4km, and to 1% or less by 14km which is at the limit of likely visibility.
Evidence from comparisons with places close to wind farms, but where wind farms are less visible suggests that most if not all of these price reductions are directly attributable to turbine visibility. The effects of wind farms on the prices of locations with limited visibility are statistically insignificant or even positive – providing some indication that wind farms generate some local benefits, though these are more than offset by the dis-amenity associated with visibility. This may be why previous studies that have failed to distinguish between places where nearby turbines are visible and places where they are not, have failed to find effects. As might be expected, the effects are bigger and have greater geographical spread for larger wind farms. Wind farms with 20 or more turbines reduce prices by 3% at distances between 8-14km, and by up to 12% within 2km.
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 difference between the post-operation and pre-operation periods, for the periods spanned by the data. 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. Results based on comparison of operational sites and those refused planning permission suggest that these full impacts could be much bigger – the upper-bound estimate is about 15% within 2km of the average wind farm. Further data collection effort is required to fully address these issues.
Well established theories (Rosen 1974) suggest that these price effects can be interpreted as marginal willingness to pay to avoid the dis-amenity associated with wind farm proximity and visibility, net of any benefits provided by the wind farms in terms of economic opportunities, community payments or other financial compensation. 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 average size visible within 2km, or would be willing to pay around £200 per year to avoid having a large wind farm visible within 8-14km. The implied amounts required per wind farm to compensate households for their loss of visual amenities is therefore fairly large: about £12 million for a typical 11 turbine wind farm, based on the average numbers of households with turbines currently visible within 4km.8 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 comparable to the highest estimates from the stated preference literature. 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. It is worth noting, however, that the revealed preference method based on housing markets elicits the preferences of marginal home owners in the areas close to wind farms, which may differ from the mean willingness to pay amongst all households in the population.