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.

使用机器学习系统，基于个人未来无家可归的风险，优先考虑面临驱逐的个人，从而提高了至少20%的准确性和公平性，并识别了当前过程中被忽视的28%的个人，此研究有助于改进Allegheny县的租赁援助计划，并促进类似上下文中基于证据的决策支持工具的发展。

通过机器学习信息化租赁援助的方式预防驱逐导致的无家可归