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

arXiv: https://arxiv.org/abs/2403.12599 · HTML: https://mlsanity.com/paper/2403.12599/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Risk Assessments, Evaluation, and Fairness](https://mlsanity.com/paper/1909.00066/) (2019)

## Built on by (most cited first, 3 of 3)

- [The Hardness of Achieving Impact in AI for Social Impact Research: A Ground-Level View of Challenges & Opportunities](https://mlsanity.com/paper/2506.14829/) (2025)
- [Optimising Temporary Accommodation Placement Across London with AI-Powered SaaS in E-Governance Systems](https://mlsanity.com/paper/2606.16652/) (2026)
- [Learning Who to Treat When Treatment is Missing](https://mlsanity.com/paper/2607.14346/) (2026)

## Abstract (arXiv)

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.