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

30 Pages Posted: 17 Jul 2018

See all articles by Halil Toros

Halil Toros

Economic Roundtable

Daniel Flaming

Economic Roundtable

Date Written: April 3, 2018


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.

Keywords: Homelessness, Frequent Users of Public Services, Cost Avoidance, Predictive Analytic Model, Triage Tool, Permanent Supportive Housing, Public Costs, Prioritizing Access to Housing, High Cost Homeless, Cost Savings, Probability Scores

JEL Classification: I14, I18, I28 I31, I32, 138

Suggested Citation

Toros, Halil and Flaming, Daniel, Prioritizing Homeless Assistance Using Predictive Algorithms: An Evidence-Based Approach (April 3, 2018). Cityscape, Vol. 20, No. 1, 2018, Available at SSRN:

Halil Toros

Economic Roundtable ( email )

315 W. 9th Street, Suite 502
Los Angeles, CA California 90015
United States

Daniel Flaming (Contact Author)

Economic Roundtable ( email )

244 S. San Pedro St., Ste. 506
Los Angeles, CA 90012
United States
2138928104 (Phone)


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