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This paper uses rule search techniques for the early identification ofemergency homeless shelter clients who are at risk of becoming long term orchronic shelter users. Using a data set from a major North American sheltercontaining 12 years of service interactions with over 40,000 individuals, theoptimized pruning for unordered search (OPUS) algorithm is used to developrules that are both intuitive and effective. The rules are evaluated within aframework compatible with the real-time delivery of a housing program meant totransition high risk clients to supportive housing. Results demonstrate thatthe median time to identification of clients at risk of chronic shelter usedrops from 297 days to 162 days when the methods in this paper are applied.

A Rule Search Framework for the Early Identification of Chronic Emergency Homeless Shelter Clients