Panel:
Using Big Data to Identify, Build, and Preserve Affordable Housing
(Housing, Community Development, and Urban Policy)
*Names in bold indicate Presenter
This panel investigates the real causes and effects of the housing affordability crisis at the resident and property levels. All across the United States, renters are threatened with cost burdens, eviction, expiring affordability, and landlord harassment. Until recently, researchers have not been able to measure these challenges. The four analyses in this panel unpack exciting new "Big Data" sets with millions of records apiece, allowing researchers to identify the concentration of ownership, the propensity of tenant rights violations, the risk of eviction, and the variation of cost burdens across the city, neighborhood, and income distributions. In order to harness the full power of these data, the researchers employ sophisticated statistical methods, from machine learning to cluster analysis to Bayesian hierarchical modeling. Their presentations will illuminate not only critical levers for policymakers to address the needs of local residents but also methodological advances that can extend across sectors to extract the full value of Big Data for the broad future of public policy.
