A framework for estimating hurricane-induced housing demand: Enhancing disaster housing logistics planning

This paper introduces a framework designed to estimate post-hurricane disaster housing needs to enhance community resilience by streamlining the recovery process and facilitating logistics planning for emergency management agencies. The modeling approach commences with defining the study domain using housing data sourced from the National Structure Inventory and demographic information from the US Census, organized by Census Tracts. Next, a full-track hurricane impacting the study domain is selected from either a catalog of historical or simulated events. The peak wind speed at each Census Tract during the hurricane's passage is then calculated using a parametric wind field model. Building fragility curves and loss functions obtained from the FEMA HAZUS program are utilized to assess damages and losses to single-family residential dwellings, multi-family apartments, and multi-unit hospitality buildings. Demographic and socio-economic factors such as income, age, and insurance coverage combined with the estimated direct building losses are used to predict the number of short term and long-term displaced households. As part of the modeling framework validation study, the predicted numbers of long term displaced households for four prior hurricane events are compared and calibrated against the actual numbers of direct housing assistance or maximum amount of individual assistance grants provided by FEMA. While the total number of long-term displaced households predicted by the model does not align precisely with the actual assistance provided by FEMA, it was found that the model-predicted long-term displaced households by ZIP codes exhibit a high correlation with FEMA assistance figures. On average, FEMA extended assistance to approximately 20-60% of the model-predicted long-term displaced households. Discrepancies are attributed partly to factors such as eligibility requirements for FEMA assistance and availability of governmental funding, which are not accounted for in the current model.