Understanding US Housing Data in Relation to the 2017 Disasters

Abstract

Housing is a first line of defense, literally and figuratively. It is also a critical, although too-often overlooked, component of a community’s physical infrastructure. Variations in housing quality before a disaster strikes and in damages after directly define a family’s security, their ability to recover, and a community’s overall resilience. In a postdisaster scenario, data and other information about housing—which comprise the technological component of resilience—are murky; the multiple data collection, analysis, and interpretation processes are often blunt, inaccurate, and potentially unjust. A concentration of severe disasters can also overwhelm the system, as seen in 2017. The recent disaster season has brought needed attention to the gaps in housing damage data and assistance calculations. Disputes around damage assessments began in the early moments after Hurricane Harvey’s landfall and are likely to continue for years to come as jurisdictions transition to long-term recovery. Yet, there is surprisingly little scholarship on the technical procedures for collecting postdisaster housing data and on those data’s effects on aid appropriations and receipt. As such, the time to explore the data-collection infrastructure after damage is now. This paper seeks to anchor policy regarding physical recovery by describing how housing data and analyses of those data after the 2017 disasters have been collected and used to date, how those processes continue to evolve, and how they can be improved.