Easy to use and validated predictive models to identify beneficiaries experiencing homelessness in Medicaid administrative data.

Large samples can be used to accurately predict homelessness in Medicaid administrative data if a validated homelessness indicator for a small subset can be obtained. In the absence of a validated indicator, the likelihood of homelessness can be calculated using county rate of homelessness, address- and claim-based indicators, and beneficiary age using a prediction model presented here. These approaches are needed given the rising prevalence of homelessness and the focus of Medicaid and other payers on addressing homelessness and its outcomes.