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.

翻译：租金援助项目为个人提供经济援助，以防止因驱逐导致的住房不稳定并避免 homelessness。由于这些项目受资源限制，必须决定优先援助对象。通常，资金通过被动或先到先得的分配流程发放，未系统考虑未来 homelessness 的风险。我们与宾夕法尼亚州阿勒格尼县合作，探索了一种基于个体未来 homelessness 风险优先考虑面临驱逐者的主动分配方法。我们的机器学习系统使用州和县级行政数据，准确识别需要支持的个人，其性能在公平性（涵盖种族和性别）上优于简单优先排序方法至少20%。此外，我们的方法能识别出当前流程忽视但最终陷入 homelessness 的28%个体。除改进阿勒格尼县的租金援助项目外，本研究可为类似情境下基于证据的决策支持工具开发提供指导，包括数据需求、模型设计、评估及实地验证方面的经验教训。