Local governments implement Rental Assistance Programs to provide temporary financial assistance to renters struggling to pay their rent to keep them stably housed and prevent homelessness. However, as the number of vulnerable households increases and funding for rental assistance programs remains limited, program administrators must prioritize households to help. Although identifying and prioritizing the most vulnerable individuals is a top concern, the typical practice is to prioritize individuals using simple heuristics such as “first-come-first-served”. This talk will describe a collaborative effort between our team at Carnegie Mellon University and the Allegheny County Department of Human Services, which examined the utility of using Machine Learning (ML) models to inform the prioritization of rental assistance resources to minimize eviction-caused homelessness.