ABSTRACT. This paper discusses the findings from a UK study to determine the likely impact of a wind farm on house prices using a hedonic pricing model. The Government’s commitment to wind power has resulted in a massive increase in the number of wind farms sited in the UK. This has led to concerns that their visual and aural presence could have a negative impact on proximate house prices. This paper presents an analysis of 201sales transactions from houses situated within half a mile of a 16 turbine wind farm in Cornwall, UK. Whilst no causal link was established between the presence of the wind farm and house price, there was some evidence to suggest that both noise and flicker from the turbine blades could blight certain property and that the view of countryside enjoyed by the occupier had some value which may be affected by a wind farm.
CONCLUSION. Whilst the conclusions drawn relate specifically to this location, where the wind turbine height is relatively small (60m compared to modern turbines with heights of 100m or above), they support the findings from other studies (Hoen 2006, Poletti, 2005) which have found no apparent relationship between the presence of a wind farm and value diminution.
However, whilst there seems little evidence to suggest that wind farms reduce house prices (one exception to this was observed within the case study location; a farm where the rateable value had been reduced by one rating band due to the problem of flicker from the turbine blades), these results do raise a number of questions relating to the value or perceived value of the "vista” and the possible effect of an increase in turbine height.
As the results suggest, certain vistas can inflate or diminish house price and therefore landscape may have some intrinsic value to either community or the individual which has not been captured by the variables included in this analysis. This may become more obvious as data on properties within close proximity to wind farms increases and more analysis is undertaken. Clearly there are factors that will effects the price but cannot be measured or observed.
Unmeasured or unobserved variables are responsible for additional variation in the analysis. One way to tackle this problem is through a random effect modelling approach where the unmeasured or unobserved variables for each case (house price) are consider to be a realisation of a distribution. An appropriate mixed effects modelling approach, at least for this data, will also address the problem of spatial autocorrelation by assigning a specific area random effect to each homogenous geographical area. This type of mixed effect modelling for hedonic analysis would be of interest for further analysis.