1# RECORD 4f4475636117754066fcc0578aecfcf69e6465662# TITLE: MODELLING THE IMPACT OF WIND FARMS ON HOUSE PRICES IN THE UK3# YEAR: 2008.0 VENUE: nan DOI: 10.3846/1648-715x.2008.12.251-2694# AUTHORS: S. Sims; P. Dent; G. Oskrochi5# PAGES: 19 CANDIDATE TABLE PAGES (1-based): [13]6# 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.789===== PAGE 1 (score 6, numeric-paren 0, table-words 0, var-words 6) =====1011MODELLING THE IMPACT OF WIND FARMS 12ON HOUSE PRICES IN THE UK13Sally SIMS 1 , Peter DENT 2 and G. Reza OSKROCHI 3141 Department of Real Estate and Construction, School of the Built Environment, 15Oxford Brookes University, Headington, Oxford, UK 16 Tel: +44 (0) 1865483459; Fax: +44 (0) 1865 383927; E-mail: ssims@brookes.ac.uk172 Department of Real Estate and Construction, School of the Built Environment, 18Oxford Brookes University, Headington, Oxford, UK 19 E-mail: prdent@brookes.ac.uk 203 Department of Mathematical Sciences, School of Technology, Oxford Brookes University, 21Wheatley, Oxford, UK 22 E-mail: roskrochi@brookes.ac.uk23Received 28 January 2008; accepted 12 August 200824ABSTRACT. This paper discusses the fi ndings from a UK study to determine the likely impact 25of a wind farm on house prices using a hedonic pricing model. The Government’s commitment 26to wind power has resulted in a massive increase in the number of wind farms sited in the 27UK. This has led to concerns that their visual and aural presence could have a negative im-28pact on proximate house prices. This paper presents an analysis of 201sales transactions from 29houses situated within half a mile of a 16 turbine wind farm in Cornwall, UK. Whilst no causal 30link was established between the presence of the wind farm and house price, there was some 31evidence to suggest that both noise and fl icker from the turbine blades could blight certain 32property and that the view of countryside enjoyed by the occupier had some value which may 33be affected by a wind farm.34KEYWORDS: Property stigma; Residential values; Wind-power; Renewable energy; Hedonic351. INTRODUCTION36The 2007 Energy White Paper, published in 37May 2007, sets out the Government’s interna-38tional and domestic energy strategy to respond 39to Climate Change with the main aim of cutting, 40“CO2 emissions [in the UK] by some 60% by about 412050, with real progress by 2020” (DTI, 2007). In 42order to meet these targets, the government ex-43pects “20% of our energy to come from renewable 44sources…by 2020” (BBC Radio 4, 2007). 45According to the Sustainable Development 46Commission (SDC, 2005), wind is a prime can-47didate since the UK has the “best and most geo-48graphically diverse wind resources in Europe, 49more than enough to meet current renewable 50energy targets” (ibid). However, although wind 51power is now the fastest growing renewable en-52ergy sector in Britain (BBC Radio 4, 2007), “ex-53perts interviewed on ‘Costing the Earth,’ claim 54that the power of the wind to deliver electricity is 55being overestimated” (Country Guardian, 2007). 56They argue that although the Government has 57already invested, “half a billion pounds so far” 58wind power has “as yet…failed to deliver half of 59one per cent of our electricity needs” (ibid). 60International Journal of Strategic Property Management (2008) 12, 251–26961International Journal of Strategic Property Management62ISSN 1648-715X print / ISSN 1648-9179 online © 2008 Vilnius Gediminas Technical University63http://www.ijspm.vgtu.lt64DOI: 10.3846/1648-715X.2008.12.251-269656667===== PAGE 13 (score 22, numeric-paren 0, table-words 7, var-words 5) =====6869ingV”), was found to be signifi cant and positive 70at the 5% level, but “sidrfi nt” which is the in-71teraction between side and front views (“FsideV 72& FfacingV”) of the wind farm are negatively, 73marginally signifi cant at the 10% level.74All year of sale (“year”) dummies are posi-75tively signifi cant compared to ‘sales completed 76in the year 2000, and they show the yearly in-77fl ation of the log of house price (“lnprice”). The 78dummies for the postcode representing the top 79location (e.g. Halifax Road), signifi cantly in-80creases the log of house price; note that the in-81clusion of this variable partly controls the spa-82tial covariance between similar house prices. 83The above regression model (Table 6) can 84explain 97.5% (R2 = 0.975, shown in Table 7) of 85the variation in house price. The homogeneity 86test of the residuals shows that the residuals 87are homogenously distributed for 10 ordered 88categories of predicted values (hetroskedastic-89ity test, Table 8).90The independence of the error term and 91the independent variables were also tested by 92regressing the residuals on the independent 93variables (Hoen, 2006). The coeffi cient of in-94dependent values were close to zero and were 95therefore not signifi cant (f-value 0.002, p-value 961.000, R2 = 0.000).97Table 5. Regression Model - including all independent variables98Variables 99Unstandardized 100coeffi cients101Standardized 102coeffi cients103Sig.104Collinearity 105statistics106B107Std. Error108Beta109Tolerance110VIF11110.585112.254113.000114Terrace115-.063116.015117-.064118.000119.6241201.603121offroad122.011123.020124.012125.585126.3181273.143128FVista2129.045130.039131.043132.253133.1071349.325135FVista3136-.132137.075138-.028139.079140.5871411.704142RVista2143.069144.051145.029146.175147.3251483.081149RVista3150-.004151.036152-.002153.910154.7311551.367156RVista4157.041158.038159.026160.281161.2611623.836163DistMetre164.000165.000166.019167.559168.1461696.857170fWF1171-.024172.065173-.007174.714175.3941762.539177fWF3178-.007179.032180-.006181.813182.2011834.984184FscreenV185.100186.046187.051188.033189.2661903.755191FsideV192.160193.040194.096195.000196.2621973.824198RfacingV199-.042200.035201-.037202.233203.1532046.517205newH206-.015207.026208-.015209.560210.2352114.264212Bed3&4213.055214.043215.058216.204217.07221813.878219year01220.310221.030222.195223.000224.4272252.342226year02227.407228.025229.289230.000231.4812322.080233year03234.683235.024236.374237.000238.8502391.177240year04241.963242.027243.491244.000245.7722461.295247year05248.973249.031250.654251.000252.3552532.814254year062551.026256.025257.614258.000259.6882601.454261year072621.026263.038264.376265.000266.7772671.286268toploction269.139270.058271.099272.017273.08927411.185275medloction276-.078277.052278-.070279.135280.06828114.613282time sold in months from 1/1/2000283.000284.000285-.015286.315287.6432881.554289Modelling the Impact of Wind Farms on House Prices in the UK290263291292293===== PAGE 14 (score 11, numeric-paren 0, table-words 4, var-words 3) =====294295Table 7. Model Summary296Model297R298R Square299Adjusted R square300Std. Error of the estimate3011302.988a303.975304.971305.07925306a. Dependent Variable: lnprice307Table 8. ANOVA for studentized residual308Sum of squares309df310Mean square311F312Sig.313Between groups3145.1003159316.567317.539318.845319Within groups320197.5193211883221.051323Total324202.618325197326Table 6. Regression coeffi cients for model including independent variables and interactions327Model328Unstandardized coeffi cients329Standardized coeffi cients330Sig.331B332Std. Error333Beta334(Constant)33510.645336.249337.000338Terrace339-.073340.017341-.074342.000343offroad344.013345.020346.014347.523348FVista2349.040350.038351.038352.295353FVista3354-.038355.080356-.008357.636358RVista2359.024360.055361.010362.663363RVista3364-.011365.035366-.004367.749368RVista4369.022370.036371.014372.551373DistMetre3747.426E-5375.000376.012377.707378fWF1379-.026380.061381-.008382.675383FscreenV384.100385.041386.051387.016388FsideV389.261390.063391.156392.000393FfacingV394.013395.031396.007397.685398RfacingV399-.074400.036401-.066402.041403newH404-.021405.024406-.020407.394408Bed3409.012410.028411.012412.684413time sold in months from 1/1/2000414.000415.000416-.016417.282418year01419.296420.029421.186422.000423year02424.399425.024426.283427.000428year03429.682430.024431.373432.000433year04434.958435.027436.489437.000438year05439.966440.030441.648442.000443year064441.022445.024446.612447.000448year074491.025450.037451.376452.000453toploction454.190455.038456.135457.000458trsviewint459.086460.037461.042462.021463sidrfi nt464-.113465.070466-.062467.108468S. Sims, P. Dent and G. R. Oskrochi469264470