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

Authors

  • Catalina Vajiac Carnegie Mellon University
  • Arun Frey Stanford University
  • Joachim Baumann University of Zurich Zurich University of Applied Sciences
  • Abigail Smith NORC at the University of Chicago
  • Kasun Amarasinghe Carnegie Mellon University
  • Alice Lai Carnegie Mellon University
  • Kit T. Rodolfa Stanford University
  • Rayid Ghani Carnegie Mellon Uniiversity

DOI:

https://doi.org/10.1609/aaai.v38i20.30246

Keywords:

General

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 allocation process that does not systematically consider risk of future homelessness. We partnered with Anonymous County (PA) to explore a proactive and preventative allocation approach that prioritizes individuals facing eviction based on their risk of future homelessness. Our ML models, trained on state and county administrative data accurately identify at-risk individuals, outperforming simpler prioritization approaches by at least 20% while meeting our equity and fairness goals across race and gender. Furthermore, our approach would reach 28% of individuals who are overlooked by the current process and end up homeless. Beyond improvements to the rental assistance program in Anonymous 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.

Published

2024-03-24

How to Cite

Vajiac, C., Frey, A., Baumann, J., Smith, A., Amarasinghe, K., Lai, A., Rodolfa, K. T., & Ghani, R. (2024). Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance. Proceedings of the AAAI Conference on Artificial Intelligence, 38(20), 22393-22400. https://doi.org/10.1609/aaai.v38i20.30246