Bayesian Network Modeling and Prediction of Transitions Within the Homelessness System

Administrative data collected by homeless service providers offer a unique opportunity to understand how homeless individuals navigate the homeless system towards securing stable housing. However, the literature on predictive models in the context of homeless service provision has neglected the sequential nature of services that an individual receives over time. Our work addresses this gap by learning, from administrative data, a Bayesian network, which in turn can be used to accurately predict whether an individual will exit the system, or alternatively, the service she would be assigned to the next time she experiences homelessness. Experimental evaluation shows that the proposed approach outperforms prior art not only at predicting exit, but also the less frequent services (and thus more challenging to predict).