Predictive Risk Modeling to Identify Homeless Clients at Risk for Prioritizing Services using Routinely Collected Data

Abstract For most homelessness service providers, the number of clients who are eligible for long-term housing outstrips the availability. This study uses a cohort of housing assessments taken from a mid-size county in the US and machine learning methods to train a Predictive Risk Model (PRM) that identifies clients who would experience multiple adversities in the future. The PRM outperforms the Vulnerability Index-Service Prioritization Decision Assistance Tool (VI-SPDAT) in flagging clients at the greatest risk of adversities. The proposed method can be readily used by any Continuum of Care (CoC) that holds electronic housing assessments and service records.