In this article we quantify the marginal external effects of nearby land based wind turbines on property prices capitalized through traded residential properties located within 2,500 meters or less. We succeed in separating the effect of noise and visual pollution from wind turbines. This was achieved by using a dataset covering 21 municipalities and consisting of 12,640 traded residential properties sold in the period 2000-2011. We model the hedonic price function in two steps. First we detrend data across municipalities using a pooled cross sectional model which allows for different price trends across municipalities. Second we control for spatial autocorrelation by using explicit spatial models. Properties affected by noise and visual pollution from wind turbines are identified using Geographical Information Systems. Our results show that wind turbines have a significant negative impact on the price schedule of neighboring residential properties. The visual pollution accounts for 3.15% of the residential sales price. The price premium declines with distance by about 0.242% of the sales price for every 100 meters. The effect of noise depends on the noise level emitted and ranges from 3% to 7% of the sale price for residential properties.
In this paper we succeeded in separating and identifying the visual and audible externalities arising from wind turbines. We identified a negative price premium of around 3.15% of the sales price for having a view of at least one wind turbine. The price premium declines as distance to the turbine increases at a rate of 0.242% of the sales price per 100 meters. Furthermore, we find that noise provides an additional negative price premium, which in terms of impact mirrors that of having a view. Approximately 3% to 7% of the sales price can be explained by the exposure to noise.
The effect of view and distance changes to a positive externality at around 1,300 meters. Use of a specification of the turbine distance which allowed for a non-constant effect may have been more appropriate. On the other hand, the variable might capture the duality of the wind turbine in the rural landscape. While a turbine in the immediate surroundings is experienced as a negative externality, a wind turbine on the horizon may be a reminder of sustainable energy production and be perceived positively. The public in general is positive towards wind-energy although they dislike having it as a neighbor (Devine-Wright 2005). As such, the specification may capture both the ‘not in my back yard’ aspect and the general positive attitude towards wind energy. Another interpretation might simply be that properties with a view of a turbine which is 1,300 meters or more away have a view - which in itself is positive.
The results presented in this article can be applied in cost-benefit analysis especially because we succeed in modeling view and noise as two separate parameters. Note that the results of the hedonic house price model only represent marginal willingness to pay and that such results will not usually be used in scenarios with non-marginal changes. Still, Bartik (1988) argues that the estimates of non-marginal localized changes based on the hedonic house price model can be used as estimates of benefits or costs, given that the non-marginal change is restricted to a local area, thus not affecting the global housing market. We regard setting up a wind turbine in the landscape to be both non-marginal and localized. Based on this assumption, our results are directly applicable in the planning process and could be used to compensate those living close to wind turbines, or as part of a welfare economic cost-benefit analysis that includes the negative effects of noise and visual pollution.
In the analyses we do not account for a possible accumulation effect of wind turbines. The effect of having one wind turbine as opposed to having several turbines or an entire wind farm may be very different. We only account for the nearest turbine and disregard a possible accumulation effect. In addition, information on manufacture and turbine production capacity has been ignored. Such information might have provided further relevant results.
The analysis covers a large number of spatially detached areas. Recall that the hedonic price schedule is assumed to be generated in an equilibrium market. We essentially assume that the supply and preference structure are stable across the spatially detached areas and recognize that this might not be a fully valid assumption. Parameter estimates of noise and view between municipalities in the survey areas were tested by an ANOVA test. Based on this, we cannot reject that parameter estimates between municipalities are different. Previous hedonic studies on wind turbines have very likely suffered from lack of spatial variation due to a small dataset (Heintzelman and Tuttle 2012). The number of survey areas chosen in this analysis ensures a reasonable variation in the wind turbine variables.
Neither of the model estimations fully resolves the problem of spatial autocorrelation. Both explicit spatial models retain a significant spatial structure in the error term. This indicates that the models still suffer from omitted spatial processes such as misspecification of the functional form, mis-measurement of spatial covariates or from omitted spatial covariates. If the omitted spatial processes are not correlated with the turbine variables, the estimate of the impact of wind turbines remains trustworthy. In addition, the model estimates are robust across models.
We conclude that noise and visual pollution from wind turbines have a considerable impact on nearby residential properties. When Don Quijote was tilting at windmills he was fighting imaginary giants. At present, wind turbines are a symbol of sustainable energy, the way of the future. However, local residents who live in close proximity to these sustainable giants experience some very real negative externalities in the form of noise and visual pollution.