Implications of Rising Flood Risk for Residential Real Estate Prices and Employment Location

Implications of Rising Flood Risk for Residential Real Estate Prices and the Location of EmploymentERES 15-18 June 2011, EindhovenYu Chen, Bernie Fingleton, Gwilym Pryce and the EPSRC CREW consortium

Plan: • Introduction • Literature • Econometric Strategy • Results • Summary

1. Introduction: Local Effects of Global Climate Change • Local implies an interest in the spatial • Existing approaches either non-spatial macro models or GIS layering • Important, but what will the market response to flood risk be? • How will employment change? • How will house prices change? • Current socio-economic geography could be transformed by shifts in the pattern of flood risk as climate changes • Should really be thinking about the future socio-econ geography

House prices important: • Measure of wellbeing • Major source of collateral for lenders • Major source of saving for retirement • Sorting effects • Employment important: • Generates HH income & local tax revenue • Agglomeration effects: • Firms locate near other firms • Virtuous circles and downward spirals Interaction

2. Existing Literature • What does the literature say about effects of flood events and flood risk on employment location? • Good news and bad news…

Good news and bad news… First the good news… P, E • No need to worry: • Existing research suggests: • Predicted  flood risk  little effect on house prices, employment etc. • When a flood occurs, prices, employment etc. bounce back f, s

E.g. Employment effects • Some studies find that floods reduce employment: • Sarmiento (2007) finds that floods decrease local employment by an average of 3.4% • Based on 1200 US municipalities during 1997 and 1999. • Others find the a +ive or bounce-back effect: • Skidmore and Toya (2002) • higher frequency of natural disasters is associated with increases in employment growth, output and total factor productivity in a cross-country analysis. • Ewing et al. (2003) • reported that employment levels increased and regional labour markets became more stable following the 2000 tornado in Fort Worth. • Ewing et al. (2009) • found negative effects on employment growth immediately after the 1999 tornado in Oklahoma but the coefficient on the extended period was significantly positive.

Good news and bad news… Now for the bad news… Problems with existing employment models of flood risk • (i) Measurement of Flood Risk • (ii) Spatial Scale of Employment • (iii) Agglomeration Effects • (iv) Endogenous house price effects:

(i) Measurement of Flood Risk • No study of employment impacts considers flood risk • Only particular flood events • But what about long run impact of flood risk? • Single event may be seen as a one-off event • Different prospect entirely if flood risk set to rise inexorably due to climate change… • studies of housing impacts that consider flood risk tend to use crude flood-plain categorisation • Problematic…

(i) Measurement of Flood Risk (cont’d) Crichton Risk Triangle • Flooding tends to be highly spatially specific • Risk not just about frequency: = f(severity, frequency) • Flood risk not just about fluvial floods: • E.g. London: pluvial flooding a major concern. • Climate Change  Simulation of future flood impacts a priority • Ideally would like to have a measure: • Of both current flood risk and future flood risk • computed using consistent methodology • Hi-resolution & includes pluvial • Captures severity not just frequency Nothing remotely like this in the employment literature

(ii) Spatial Scale of Employment • Previous studies tend to look at regional employment effects • Confuses wider benefits of investment from remediation & repair following a particular flood event with long term effects of flood risk • Fails to distinguish between impact on the specific locality worst affected and the wider economic impact. • Really need fine-grained employment data.

(iii) Agglomeration Effects • Previous studies do not always take into account the attraction of locating near other firms. • Local external benefits from locating near other firms • Wider urban agglomeration effects • Can both effects be captured in a single method or two-pronged approach better?

(iv) Endogenous house price effects: markets+ planning (a) Market Competition: • Theory very late to incorporate the competition for land between firms and households • Firm Location Theory: explain spatial patterns of supply given HH demand. • Urban Economic Theory: Explain spatial patterns of household demand for land & goods, given supply. • But now seen as central to urban formation: • E.g. Fajita (1986, RES); Liu & Fajita (1991, ARS) • Floor Space Equilibrium conditions: “each unit of floor space must be occupied by either a household or firm which bids a higher floor rent at that location” (Liu & Fajita 1991, p.88) • Firms will be attracted to areas with low land prices • Ceteris Paribus (i.e. holding constant good access to transport, proximity other firms etc.) • If marginal loss associated with flood risk is higher for households than firms, then firms will tend to locate in high flood risk areas.

E.g. Lucas & Rossi-Hansberg (Econometrica 2002) • “firms balance the external benefits from locating near other producers against the costs of longer commutes for workers” • Without agglomeration benefits, “producers would disperse from cities to areas where land for production and residential use is cheaper” • Possible flood risk effects in LRH model: • Affect HH demand for housing ( utility of housing) •  productivity of firms   density of employment • May distort agglomeration benefits at a given location by disrupting infrastructure

(iv) Endogenous house price effects: markets+ planning (b) Planning: • Zoning and planning permission decisions = f(flood risk) • E.g. Planners may view the impact on health and wellbeing of flooding to be less problematic for firms than HHs flooding. • Firm more likely to get planning permission to locate in high flood risk areas.

Implication for modelling? Competition for land between HHs & firms + Land Planning  Land Price Endogenous to firm location

Land Price Employment Flood risk • No existing employment model of flood risk allows for endogenous house price effects • Estimates likely to be inconsistent We take advantage of recent developments in spatial econometric modelling to estimate a variety of 2SLS spatial models…

3. Econometric Strategy • (i) Measurement of Flood Risk • High-resolution CREW estimates of pluvial flood risk, incorporates severity, & allows future projections. • (ii) Spatial Scale of Employment • Lower super output areas, location and number employed • (iii) Agglomeration Effects • (a) Distance decay gravity function plus lagged spatial effects • (b) Spatially Lagged Employment. • (iv) Endogenous house price effects: • Traditional 2SLS IV approach. • GMM Spatial 2SLS with WY and AR errors. • GMM Spatial 2SLS with WY and MA errors. • SHAC (Spatial Heteroscedasticity and Autocorrelation Consistent), With WY and endogenous House Price.

(i) Measurement of Flood Risk • Fluvial flood risk  zero due to Thames Barrier • Pluvial flood risk estimates for present and future (University of Exeter): • high resolution (10m2 grid squares) model of flood depth and frequency using data on rainfall, terrain, soil types and urban drainage. • Hazard Number (HN) is used to capture flood frequency and severity • As floods with Hazard Number lower than one have negligible consequences, we define flood risk as the frequency of floods with Hazard Number greater than one in a time period of 100 years. • About 70.6% of the LSOAs in Southeast London are subject to risk of floods with Hazard Number greater than one. • The frequency of such floods in a hundred years’ time ranges from 0 to 12. A map of flood risk and the study area is shown in Figure 1.

Current Flood Risk in Five London Boroughs (i) Measurement of Flood Risk (cont’d)

(ii) Spatial Scale of Employment • Employment data: UK official labour market statistics Nomis. • highly disaggregated Lower Layer Super Output Area (LSOA) level • LSOA has a minimum population of 1,000 and a mean population of 1,500. • An advantage of LSOA over postal sectors is that LSOA is of consistent size and boundary across England.

(iii) Agglomeration effects: (a) Gravity Model • Gi = Gravity-weighted effect of employment in surrounding areas: where: • dij = distance between LSOAi and LSOAj , includes LSOAs in 30km buffer round the edge of Greater London. • Ej = number of employees at LSOAj • b = functional form parameter estimated using ML, expected to be negative (i.e. negative distance effect: firms prefer to locate near other firms)

(iii) Agglomeration effects: (b) Gravity Model + WY • Include spatially lag of employment in the employment regression. • Provides a more flexible functional form for estimating the agglomeration effect • Local. • Wider urban effects. • We also consider whether the error term is best modelled as an AR or MA process: • AR  global diffusion of shock effects • Shock at location j transmitted to all other locations • MA  local diffusion of shock effects • Shock at location j only affect directly interacting locations

(iv) Endogenous house price effects: 2 equation model at LSOA level Where: • E = ln(employment / area) • P = ln(house price) • T = distance to roads & railway stations • D = location with underground • G = agglomeration • CBD = distance to CBD • W = population density • H = property density • F = flood risk • X = instrumental variables = distances to rivers and woodland, & their FO lags

(iv) Endogenous house price effects: • 3 approaches: • (a) Traditional 2SLS IV approach. • (b) GMM Spatial 2SLS with WY and AR errors. • (c) GMM Spatial 2SLS with WY and MA errors. • (d) SHAC With WY and endogenous House Price.

(a) Traditional 2SLS IV approach: IVs used are valid and strong – Regression (1) • We use distances to rivers and woodland and their first-order spatial lags as instrument variables (IVs) • correlated with house price but not with employment. • Validity and Weakness of Instruments: • Invalid if correlated with the error term • Weak if weak correlation with the explanatory variables • Sargan tests: • have shown that these instrumental variables are valid, i.e. correlated with the house price model but uncorrelated with the error term. • F-statistics and the Stock-Yogo test: • reject the weak instruments hypothesis.

Sargan, Stock-Yogo and Hausman tests: • Sargan Test for Valid Instruments: • The presence of more than one excluded instrument means that we have over-identification and therefore test the validity of the instruments via the Sargan test. • Sargan statistics (Table 3) do not reject the null hypothesis, showing that the IVs are orthogonal to the disturbances. • Stock and Yogo (2005) test for Weak/Strong Instruments: • F-statistics for the significance of the IVs in the first-stage regression is 19.22, which is larger than 10, indicating a significant statistical association between IVs and house price (Stock and Yogo, 2005). • F-statistic is also larger than the relevant weak IV critical value of 16.85, suggesting that the relevance of IVs to house price is strong enough to reject the weak IV hypothesis with less than 5 per cent maximal relative bias. • Hausman endogeneity test • rejects the null hypothesis that there is no systematic difference between the estimates from OLS and 2SLS. This suggests that house price is indeed endogenous, and the 2SLS estimator is required for consistent estimation.

(b) GMM Spatial 2SLS Model with WY and AR errors. • Y = ρWY +Pg + Xb + u • u = λWu + e • Y= E = ln(employment / area) • P = ln(house price) • W= contiguity spatial weight matrix

(c) GMM Spatial 2SLS Model with WY and MA errors Y = ρWY +Pg + Xb + u u = e - λWe • Where: Y= E = ln(employment / area) P = ln(house price) W= contiguity spatial weight matrix

(d) SHAC With WY and endogenous House Price • the non-parametric heteroscedasticity and autocorrelation consistent estimator of the variance-covariance matrix in a spatial context (SHAC), • as suggested by Kelejian and Prucha (2007), in the context of endogeneity due to system feedback • NB there is implicit endogeneity in all models with lagged spatial effects

House Price = interpolated constant quality at postcode level • Need to estimate “constant quality” average price for each postcode • Control for dwelling attributes • Otherwise variation in house prices may be simply due to variation in house size and type • We estimated a spatio-temporal interaction hedonic model that allows coefficients to vary over time & space: P = f(attributes, x, y, x2, y2, xy,x2y, xy2, x2y2, x3, y3, x3y, x3y2, x3y3, xy3, x2y3, month (t), interactions of x y and t, and their interactions with local authority dummies, year2007 and all attributes) (Adj R2 = 0.87) • Predict average CQ house price for each postcode unit

4. Results • Flood risk: • Statistically sig. negative effect on employment • Spatial autocorrelation: • H0: no SA rejected (sig. = 0.04) • Agglomeration effect: • Statistically sig. even when spatial lag included. • House Price: • sig. in 2SLS without spatial lag, • not sig. when include spatial lag in GMM

Results: preferred models = (3) & (6) • Flood risk has a statistically sig. negative effect on employment • Evidence of spatial autocorrelationH0: no SA rejected (sig. = 0.04) • House Price is sig. in 2SLS without spatial lag, but not sig. with spatial lag • Agglomeration effect statistically sig. even when spatial lag included.

5. Summary of Aim and Methods: Attempted to build a model that addresses 4 problems in existing studies: • Problem (i): Measurement of Flood Risk: • Solution: High-resolution CREW estimates of pluvial flood risk, incorporates severity, & allows future projections. • Problem (ii): Spatial Scale of Employment: • Solution: Lower super output areas, location and number employed • Problem (iii): Agglomeration Effects: • Solutions: (a) Distance decay gravity function plus lagged spatial effects (b) Spatially Lagged Employment. • Problem (iv): Endogenous house price effects: • Solutions: (a) Traditional 2SLS IV approach. (b) GMM Spatial Durbin with AR errors. (b) GMM Spatial Durbin with MA errors.

Summary of Results: (i) Flood risk: • Statistically sig. negative effect on employment (ii) Spatial autocorrelation: • H0: no SA rejected (sig. = 0.04) (iii) Agglomeration effect: • Statistically sig. even when spatial lag included. (iv) House Price: • sig. in 2SLS without spatial lag, • not sig. when include spatial lag in GMM

What Next? • Interact flood risk with agglomeration and lagged employment variables: • FR may distort agglomeration benefits at a given location by disrupting infrastructure • Simulate the impacts of anticipated changes to future flood risk • UKCP 09 • Adjust for likely increases in the sensitivity to flood risk of house prices and employment • people are likely to become more aware of flood risk as floods occur more frequently; • the availability and cost of insurance and mortgages are likely to become more sensitive to flood risk • Develop sorting models: allow for big shifts, new equilibrium

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

Thank You!

Implications of Rising Flood Risk for Residential Real Estate Prices and Employment Location