1. 1 Estimating the Impact of Floods on House Prices: An Application to the 2005 Carlisle Flood Paper presented at ERES 2009 Stockholm Gwilym Pryce, Yu Chen, Danny Mackay University of Glasgow

2. Structure of Presentation 1.Introduction 2.Theoretical framework 3.Proposed econometric model 4.Background to 2005 Carlisle Flood 5.Data 6.Estimation 7.Future work

3. 1. Introduction: the project Downscaled Climate Change & Flood Risk Estimates EWESEM Model (Based on impact of past floods) Simulate Socio- Economic Impacts for Case Study Area Digimap terrain, SWERVE 2008 Stakeholder Engagement (PP2) + Web Interface (WISP) From SWERVE

4. Introduction Aims of this paper: –To estimate the impacts of historical flood events on house prices –To capture spatial spill-over effects of floods Why house prices? –If heterogeneities in the type of dwelling can be controlled for, variation in house price across space offers a way of placing a monetary value on the variation in the desirability (and hence quality of life) of a location, and of the willingness to pay for avoiding flood risk. –house price is potentially a powerful measure of the impact on wellbeing of extreme weather

5. 2. Economic Theory Q1/ Why should house prices change at all in the event of a flood? –If markets are efficient, prices should already be fully risk adjusted. –Areas with higher perceived flood risk will have lower house prices, all else equal. –Yet, previous studies do indeed find: Temporary fall in house prices after a flood Followed by a gradual bounce back

6. A1/ The most plausible explanation is market amnesia –Market prices drift away from the risk adjusted level the longer the time lapse since last flood. –People actively cover up evidence of flood risk –Framing & herd behaviour (Zeckhauser 1996): tendency to underestimate risks that appear distant or global, or which others seem to accept without concern JARring Actions: Jeopardize Assets that are Remote (Zeckhauser 2006)

7. Q2/ Why do house prices bounce back? A2/ This is what you’d expect if the amnesia hypothesis is valid –Flood event is a reminder of the true risk –The more frequent the reminder, the less prices will diverge from the risk adjusted price So prices will not fall so much, and the bounce back effect will be correspondingly smaller. –Crucially, house prices observed in the aftermath of the flood reveal the true risk adjusted house price.

9. Whether floods are frequent or rare in an area, the price observed in the aftermath of a flood should be a good estimate of the risk adjusted price. This is important, because climate change will lead to more frequent flooding, and so prices in areas worst affected will eventually converge to their risk adjusted price as floods become more frequent. This allows us to estimate future house price impacts of flood risk.

10. 3. Proposed econometric model ln(price i ) = f(S i, Z, CBD, Green, Dep, year), where, price i =selling price of dwelling i S i  exp(ӨD ij H i ) = distance decay flood event variable captures the spill-over effect D ij = distance from dwelling i to nearest flooded postcode unit j H i =elevation Z = vector of dwelling characteristics CBD =distance to central business district Green =distance to woodland Dep =index of deprivation year =year dwelling sold Distance decay methods Explicit spatial econometrics models

11. 4. Background to Carlisle 2005 Floods Located in northwest England, capital of Cumbria A long historical record of flooding. –Over 50 flood events occurred from 1800 to 1979, with severe flooding every 11.4 years and major floods every 42.7 years January 2005: 15% of average annual rain fell in 36 hrs, once in 150 years Flood Defences were overwhelmed by the extreme flows. –1,925 properties were flooded up to two metres. –3 people died –Over 3000 people were made homeless for up to 12 months –Infrastructure was destroyed –An estimate of losses exceeded 450 million pounds

12. Source: EA 2005

13. 5. Data Housing transaction data –House prices, property attributes –Nationwide building society 2006-07 Location and accessibility measures –Elevation, distance to CBD, woodland. –Ordinance Survey Neighbourhood variable – Index of multiple deprivation Flood: –Flood outline overlayed with postcode boundaries in GIS Distance between each postcode and its nearest flooded postcode

14. 6. Estimation: functional form We incorporated distance to the nearest flooded postcode and height above sea level into the functional form of the flood variable: S i =exp(ӨD ij H i ) A Maximum likelikood grid search procedure on the following model, LnP = a 0 + a 1 Bathroom + a 2 Bedroom + a 3 lnfloorsize + a 4 Centralheating + a 5 Newproperty + a 6 bungalow + a 7 lnCBD +a 8 lnwoodland +a 9 imd + a 10 S i + a 11 year2007

15. Log likelihood values for different values of Ө T-values (based on White’s Standard Errors) for different values of Ө It reveals that the most appropriate value for Ө to be -0.005.

16. 6. Estimation: spatial econometrics Spatial Auto-Regressive Model SAR: –y = ρWy + Xβ + e –Correction for house price in place i depending on the weighted average of house prices nearby Spatial Error Model SEM: –y = Xβ + u; u = λ Wu + e –To adjust errors caused by omitted variables which vary spatially General Spatial Model GSM: –y = ρWy + Xβ + u; u = λ Wu + e –Both a spatially lag variable and a spatially weighted error term Estimation methods: –Maximum Likelihood: typically used but with problems, e.g. assuming normality –Generalised Moment Method as an alternative

17. A spatial weight matrix A square matrix measuring closeness in space Spatial contiguity matrix d ij =1/0 –where 1 denotes locations sharing the same boundary –only allow contiguous neighbours to affect each other K nearest neighbours: –where 1 denotes locations being one of the k nearest neighbours Defining a neighbour using a distance threshold Ways of calculating distance: – straight line distance – great circle distance – travel time – economic distance – trade costs, market access Row standardised

18. Selecting a spatial weight matrix Log-likelihood of SEM modelsPlot of W21 (783*783 ) SEM model using a spatial weight matrix with 21 nearest neighbours has the highest log- likelihood. Contiguity

19. List of models using spatial weight matrix with 21 nearest neighbours VariableOLSSAR_MLSEM_MLGSM_MLSEM_GMM constant10.278587***10.262238***9.504726***9.380543***9.606181*** bathroom0.107283***0.107987***0.065869***0.061220***0.068351*** bedroom0.055401***0.056796***0.070982***0.072790***0.070008*** lnfloorsize0.593950***0.586525***0.573139***0.569168***0.575033*** centralheating0.098258**0.099148**0.124554***0.126222***0.123285*** newproperty0.194032***0.197093***0.195552***0.194077***0.194811*** bungalow0.250270***0.246316***0.249200***0.248236***0.249605*** lnCBD-0.083740***-0.082627***-0.032512**-0.025310*-0.039321* lnwoodland-0.061884***-0.060543***-0.032726**-0.026478-0.036334** imd-0.014007***-0.014018*** -0.011483***-0.011129***-0.011690*** flood-0.310461***-0.307233***-0.151275**-0.126336**-0.172821** year20070.067920***0.068077***0.085071***0.087032***0.084149*** rho0.002657-0.016973 lamdba0.883962***1.123361***0.796783*** Rbar-squared0.7247000.7249000.7822000.7862000.772400 log-likelihood311.112610385.526510384.383000 N783 Normality of error term in SEM was rejected. SEM_GMM is more appropriate. OLS: Ordinary Least Squares SAR: Spatial Autoregressive Model SEM: Spatial Error Model GSM: General Spatial Model ML: Maximum Likelihood GMM: Generalised Moments Method

20. 7. Future work Estimate the location value impact of the flood: P A t-1 - P A t –Predict the CQP surface before the flood –Subtract the CQP surface after the flood Do the CIs overlap? –How will size of impact vary across space? Simulate house price impact of a hypothetical flood event due to climate change

21. Thank you!