A Probabilistic Approach to Estimating Post-disaster Unmet Housing Needs Under Limited Information

Abstract

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. Statistical models for aid distributed by the Federal Emergency Management Agency (FEMA) and the Small Business Administration (SBA) are developed and used to forecast funding provided by those agencies. With these forecasts, post-disaster unmet housing needs can be estimated shortly after a disaster, which can expedite the disbursal of financial aid. 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 the methodology employed by the Federal government 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 assists with timely and accurate funding appropriations.