Preventing Eviction-Caused Homelessness through ML-Informed Distribution
  of Rental Assistance
Catalina Vajiac Arun Frey Joachim Baumann Abigail Smith Kasun Amarasinghe Alice Lai Kit Rodolfa Rayid Ghani
Rental assistance programs provide individuals with financial assistance to
prevent housing instabilities caused by evictions and avert homelessness. Since
these programs operate under resource constraints, they must decide who to
prioritize. Typically, funding is distributed by a reactive or first-come-first
serve allocation process that does not systematically consider risk of future
homelessness. We partnered with Allegheny County, PA to explore a proactive
allocation approach that prioritizes individuals facing eviction based on their
risk of future homelessness. Our ML system that uses state and county
administrative data to accurately identify individuals in need of support
outperforms simpler prioritization approaches by at least 20% while being fair
and equitable across race and gender. Furthermore, our approach would identify
28% of individuals who are overlooked by the current process and end up
homeless. Beyond improvements to the rental assistance program in Allegheny
County, this study can inform the development of evidence-based decision
support tools in similar contexts, including lessons about data needs, model
design, evaluation, and field validation.