Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance

TL;DR

This paper presents a machine learning system that improves the prioritization of rental assistance to prevent homelessness, outperforming traditional methods and identifying at-risk individuals more effectively.

Contribution

The study introduces a proactive ML-based approach for allocating rental assistance that is more accurate and equitable than existing reactive methods.

Findings

ML system outperforms simpler approaches by at least 20%

Identifies 28% more individuals at risk of homelessness

Ensures fairness across race and gender

Abstract

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.