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Prioritizing Homeless Assistance Using Predictive Algorithms: An Evidence-Based Approach

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Abstract: This predictive analytic model prioritizes high-cost individuals for whom the solution of housing costs less than the problem of homelessness. Cost offsets from reduced service use after high-cost individuals are stably housed can be stretched across a larger pool of homeless people whose housing can be subsidized with those offsets. We assessed potential cost savings by comparing total housing and service costs ($17,000 annually) with the estimated 68 percent post-housing cost savings for true positives – those correctly identified as high-cost service users. The results confirmed that anticipated cost savings from true positives far exceed the total costs of housing, yielding net savings of $20,000 per person over the next two years after the total population with a probability score of 0.37 or higher enters permanent supportive housing.

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