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In this article, we propose using network-based sampling strategies toestimate the number of unsheltered people experiencing homelessness within agiven administrative service unit, known as a Continuum of Care. We demonstratethe effectiveness of network sampling methods to solve this problem. Here, wefocus on Respondent Driven Sampling (RDS), which has been shown to provideunbiased or low-biased estimates of totals and proportions for hard-to-reachpopulations in contexts where a sampling frame (e.g., housing addresses) is notavailable. To make the RDS estimator work for estimating the total number ofpeople living unsheltered, we introduce a new method that leveragesadministrative data from the HUD-mandated Homeless Management InformationSystem (HMIS). The HMIS provides high-quality counts and demographics forpeople experiencing homelessness who sleep in emergency shelters. We thendemonstrate this method using network data collected in Nashville, TN, combinedwith simulation methods to illustrate the efficacy of this approach andintroduce a method for performing a power analysis to find the optimal samplesize in this setting. We conclude with the RDS unsheltered PIT count conductedby King County Regional Homelessness Authority in 2022 (data publicly availableon the HUD website) and perform a comparative analysis between the 2022 RDSestimate of unsheltered people experiencing homelessness and an ARIMA forecastof the visual unsheltered PIT count. Finally, we discuss how this method worksfor estimating the unsheltered population of people experiencing homelessnessand future areas of research.

Network Sampling Methods for Estimating Social Networks, Population Percentages, and Totals of People Experiencing Unsheltered Homelessness