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.

摘要

对于大多数无家可归服务提供者来说，有资格获得长期住房的客户数量超过了可用性。本研究使用来自美国一个中等规模县的一组住房评估和机器学习方法来训练预测风险模型 (PRM)，该模型可识别未来将经历多重逆境的客户。PRM 在标记面临最大逆境风险的客户方面优于漏洞指数服务优先级决策辅助工具 (VI-SPDAT)。任何持有电子住房评估和服务记录的连续护理 (CoC) 都可以轻松使用建议的方法。