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EWE Socio-Economic Model (Winner of the ERES JPR Best Paper Prize in Real Estate Economics 2011)

The socio-economic consequences of EWE’s. EWE Socio-Economic Model (Winner of the ERES JPR Best Paper Prize in Real Estate Economics 2011). Estimate the impact on society of anticipated increases in the frequency & severity of floods: Impact on a range of sectors

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EWE Socio-Economic Model (Winner of the ERES JPR Best Paper Prize in Real Estate Economics 2011)

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  1. The socio-economic consequences of EWE’s EWE Socio-Economic Model(Winner of the ERES JPR Best Paper Prize in Real Estate Economics 2011) • Estimate the impact on society of anticipated increases in the frequency & severity of floods: • Impact on a range of sectors • - house prices, employment, deprivation • Grounded in empirical evidence from past EWEs • Updatable as more evidence becomes available • Incorporate potential spatial-spillovers and interactions • between sectors Prof. G.Pryce Dr Y.Chen University of Glasgow

  2. Stakeholder Engagement (PP2) + Web Interface (WISP) From SWERVE … a pioneering joined-up approach Simulate Socio-Economic Impacts for Case Study Area EWESEM Model (Based on impact of past floods & risk) Downscaled Climate Change & Flood Risk Estimates Digimap terrain, SWERVE 2008

  3. Local Effects of Global ClimateChange • Local implies an interest in the spatial • Existing approaches tend to be 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 • What will the future socio-econ geography be?

  4. 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

  5. Existing Evidence? Good news and bad news… Inertia & Bounce-Backs: • Major Problems & Omissions: • Measurement of Flood Risk • (ii) Spatial Scale of Employment • (iii) Agglomeration Effects • (iv) Feedback effects from house prices

  6. (i) Measuring Flood Risk • Employment Models: none consider flood risk effect • Only consider 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… • Housing Models: tend to use crude flood-plain categorisation • But in reality flooding tends to be highly spatially specific • Risk not just about frequency: = f(severity, frequency) • Flood risk not just about fluvial floods: pluvial flooding a major concern. • Climate Change Need to simulate of future flood impacts • Currently, no robust statistical models of either employment or housing that simulate future increases in flood risk at the local level.

  7. (ii) Spatial Scale • 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.

  8. (iii) Agglomeration Effects • If firms are repelled by flood risk • then we might conclude that those areas predicted to have increases in flood risk will gradually lose employment over time, other things being equal. • A critical qualifying factor, however, is the extent to which agglomeration economies mitigate these effects. • A big unknown in the literature on the socio-economic modelling of the effect of flood risk

  9. Land Price Employment Flood risk (iv) Feedback Effects • No existing employment model of flood risk allows for endogenous house price effects We take advantage of recent developments in spatial econometric modelling to estimate a variety of 2SLS spatial models…

  10. Advances by CREW/EWESEM: • (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

  11. Key Findings • Statistically significant negative impacts on employment location as a result of flood risk • Sensitivity to flood risk likely to increase over time due to: • improvements flood risk communication • Changes in insurance & mortgage risk premiums • Significant mitigating effects from agglomeration • agglomeration economies mitigate the effect of flood risk on employment location

  12. Important Implications • Flood risk may have a more deleterious effect on employment in areas where economic agglomeration is weak. • cannot assume a uniform effect of future changes to flood risk as a result of climate change. • Two areas could experience identical increases in flood risk but very different economic consequences because agglomeration economies are different.

  13. Important Implications • Complicates considerably the calculations for comparing the relative costs and benefits of flood defense interventions across different locations • agglomeration effects need to be taken into account

  14. Local House Prices Flood Risk Deprivation Jobs Important Implications • House price impacts also affected by feedback effects from employment and deprivation • Potentially important issues for housing wealth, equity withdrawal, mortgage default, council tax revenues etc.

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