UCLA

Permanent supportive housing programs, which provide high-need homeless individuals with long-term housing and supportive services, are thought to be crucial for addressing chronic homelessness. However, many individuals who enroll into permanent supportive housing programs exit within a short period of time, often to unsuitable destinations. This paper utilizes a random survival forest model to predict the outcomes of permanent supportive housing programs in Los Angeles County.

The model demonstrates moderate success out-of-sample, with a concordance of 75% between expected risk of exit and observed length of stay. The identification of negative outcomes is similarly successful, with an AUC of 0.7. Organization-level covariates are found to be the most important predictors. Other important factors include age, previous homeless experience, and variables related to client income and benefits. On the other hand, most demographic variables, client health, and client disabilities are found to play relatively small roles in predicting outcomes.