# RECORD 4f4475636117754066fcc0578aecfcf69e646566 # TITLE: MODELLING THE IMPACT OF WIND FARMS ON HOUSE PRICES IN THE UK # YEAR: 2008.0 VENUE: nan DOI: 10.3846/1648-715x.2008.12.251-269 # AUTHORS: S. Sims; P. Dent; G. Oskrochi # PAGES: 19 CANDIDATE TABLE PAGES (1-based): [13] # 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. ===== PAGE 1 (score 6, numeric-paren 0, table-words 0, var-words 6) ===== MODELLING THE IMPACT OF WIND FARMS ON HOUSE PRICES IN THE UK Sally SIMS 1 , Peter DENT 2 and G. Reza OSKROCHI 3 1 Department of Real Estate and Construction, School of the Built Environment, Oxford Brookes University, Headington, Oxford, UK Tel: +44 (0) 1865483459; Fax: +44 (0) 1865 383927; E-mail: ssims@brookes.ac.uk 2 Department of Real Estate and Construction, School of the Built Environment, Oxford Brookes University, Headington, Oxford, UK E-mail: prdent@brookes.ac.uk 3 Department of Mathematical Sciences, School of Technology, Oxford Brookes University, Wheatley, Oxford, UK E-mail: roskrochi@brookes.ac.uk Received 28 January 2008; accepted 12 August 2008 ABSTRACT. This paper discusses the fi ndings 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 im- pact 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 fl icker 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. KEYWORDS: Property stigma; Residential values; Wind-power; Renewable energy; Hedonic 1. INTRODUCTION The 2007 Energy White Paper, published in May 2007, sets out the Government’s interna- tional and domestic energy strategy to respond to Climate Change with the main aim of cutting, “CO2 emissions [in the UK] by some 60% by about 2050, with real progress by 2020” (DTI, 2007). In order to meet these targets, the government ex- pects “20% of our energy to come from renewable sources…by 2020” (BBC Radio 4, 2007). According to the Sustainable Development Commission (SDC, 2005), wind is a prime can- didate since the UK has the “best and most geo- graphically diverse wind resources in Europe, more than enough to meet current renewable energy targets” (ibid). However, although wind power is now the fastest growing renewable en- ergy sector in Britain (BBC Radio 4, 2007), “ex- perts interviewed on ‘Costing the Earth,’ claim that the power of the wind to deliver electricity is being overestimated” (Country Guardian, 2007). They argue that although the Government has already invested, “half a billion pounds so far” wind power has “as yet…failed to deliver half of one per cent of our electricity needs” (ibid). International Journal of Strategic Property Management (2008) 12, 251–269 International Journal of Strategic Property Management ISSN 1648-715X print / ISSN 1648-9179 online © 2008 Vilnius Gediminas Technical University http://www.ijspm.vgtu.lt DOI: 10.3846/1648-715X.2008.12.251-269 ===== PAGE 13 (score 22, numeric-paren 0, table-words 7, var-words 5) ===== ingV”), was found to be signifi cant and positive at the 5% level, but “sidrfi nt” which is the in- teraction between side and front views (“FsideV & FfacingV”) of the wind farm are negatively, marginally signifi cant at the 10% level. All year of sale (“year”) dummies are posi- tively signifi cant compared to ‘sales completed in the year 2000, and they show the yearly in- fl ation of the log of house price (“lnprice”). The dummies for the postcode representing the top location (e.g. Halifax Road), signifi cantly in- creases the log of house price; note that the in- clusion of this variable partly controls the spa- tial covariance between similar house prices. The above regression model (Table 6) can explain 97.5% (R2 = 0.975, shown in Table 7) of the variation in house price. The homogeneity test of the residuals shows that the residuals are homogenously distributed for 10 ordered categories of predicted values (hetroskedastic- ity test, Table 8). The independence of the error term and the independent variables were also tested by regressing the residuals on the independent variables (Hoen, 2006). The coeffi cient of in- dependent values were close to zero and were therefore not signifi cant (f-value 0.002, p-value 1.000, R2 = 0.000). Table 5. Regression Model - including all independent variables Variables Unstandardized coeffi cients Standardized coeffi cients Sig. Collinearity statistics B Std. Error Beta Tolerance VIF 10.585 .254 .000 Terrace -.063 .015 -.064 .000 .624 1.603 offroad .011 .020 .012 .585 .318 3.143 FVista2 .045 .039 .043 .253 .107 9.325 FVista3 -.132 .075 -.028 .079 .587 1.704 RVista2 .069 .051 .029 .175 .325 3.081 RVista3 -.004 .036 -.002 .910 .731 1.367 RVista4 .041 .038 .026 .281 .261 3.836 DistMetre .000 .000 .019 .559 .146 6.857 fWF1 -.024 .065 -.007 .714 .394 2.539 fWF3 -.007 .032 -.006 .813 .201 4.984 FscreenV .100 .046 .051 .033 .266 3.755 FsideV .160 .040 .096 .000 .262 3.824 RfacingV -.042 .035 -.037 .233 .153 6.517 newH -.015 .026 -.015 .560 .235 4.264 Bed3&4 .055 .043 .058 .204 .072 13.878 year01 .310 .030 .195 .000 .427 2.342 year02 .407 .025 .289 .000 .481 2.080 year03 .683 .024 .374 .000 .850 1.177 year04 .963 .027 .491 .000 .772 1.295 year05 .973 .031 .654 .000 .355 2.814 year06 1.026 .025 .614 .000 .688 1.454 year07 1.026 .038 .376 .000 .777 1.286 toploction .139 .058 .099 .017 .089 11.185 medloction -.078 .052 -.070 .135 .068 14.613 time sold in months from 1/1/2000 .000 .000 -.015 .315 .643 1.554 Modelling the Impact of Wind Farms on House Prices in the UK 263 ===== PAGE 14 (score 11, numeric-paren 0, table-words 4, var-words 3) ===== Table 7. Model Summary Model R R Square Adjusted R square Std. Error of the estimate 1 .988a .975 .971 .07925 a. Dependent Variable: lnprice Table 8. ANOVA for studentized residual Sum of squares df Mean square F Sig. Between groups 5.100 9 .567 .539 .845 Within groups 197.519 188 1.051 Total 202.618 197 Table 6. Regression coeffi cients for model including independent variables and interactions Model Unstandardized coeffi cients Standardized coeffi cients Sig. B Std. Error Beta (Constant) 10.645 .249 .000 Terrace -.073 .017 -.074 .000 offroad .013 .020 .014 .523 FVista2 .040 .038 .038 .295 FVista3 -.038 .080 -.008 .636 RVista2 .024 .055 .010 .663 RVista3 -.011 .035 -.004 .749 RVista4 .022 .036 .014 .551 DistMetre 7.426E-5 .000 .012 .707 fWF1 -.026 .061 -.008 .675 FscreenV .100 .041 .051 .016 FsideV .261 .063 .156 .000 FfacingV .013 .031 .007 .685 RfacingV -.074 .036 -.066 .041 newH -.021 .024 -.020 .394 Bed3 .012 .028 .012 .684 time sold in months from 1/1/2000 .000 .000 -.016 .282 year01 .296 .029 .186 .000 year02 .399 .024 .283 .000 year03 .682 .024 .373 .000 year04 .958 .027 .489 .000 year05 .966 .030 .648 .000 year06 1.022 .024 .612 .000 year07 1.025 .037 .376 .000 toploction .190 .038 .135 .000 trsviewint .086 .037 .042 .021 sidrfi nt -.113 .070 -.062 .108 S. Sims, P. Dent and G. R. Oskrochi 264