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A Methodology to Estimate Post-disaster Unmet Housing Needs Using Limited Data

Application to the 2017 Californian Wildfire


  • Rodrigo Costa University of Waterloo
  • Jack Wesley Baker Stanford University



Risk Assessment, Wildfire, Societal Impacts, Disaster resilience, Simulation


In the US, assistance from the Department of Housing and Urban Development (HUD) plays an essential role in supporting the post-disaster recovery of states with unmet housing needs. HUD requires data on unmet needs to appropriate recovery funds. Ground truth data are not available for months after a disaster, however, so HUD uses a simplified approach to estimate unmet housing needs. State authorities argue that HUD’s simplified approach underestimates state’s needs. This paper presents a methodology to estimate post-disaster unmet housing needs that is accurate and relies only on data obtained shortly after a disaster. Data on the number of damaged buildings are combined with models for expected repair costs. Statistical models for aid distributed from the Federal Emergency Management Agency (FEMA) and the Small Business Administration (SBA) are then developed and used to forecast funding provided from those agencies. With these forecasts, the unmet need to be funded by HUD are estimated. The approach can be used for multiple states and hazard types. As validation, the proposed methodology is used to estimate the unmet housing needs following disasters that struck California in 2017. California authorities suggest that HUD’s methodology underestimated the state’s needs by a factor of 20. Conversely, the proposed methodology can replicate the estimates by the state authorities and provide accounts of losses, the amount of funding from FEMA and SBA, and the total unmet housing needs without requiring data unavailable shortly after a disaster. Thus, the proposed methodology can help improve HUD’s funding appropriation without delays.


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