Counting the Impossible: Sampling and Modeling to Achieve a Large State Homeless Count

Objective: Using inferential statistics, we develop estimates of the homeless population of a geographically large and economically diverse state -- Georgia. Methods: Multiple independent data sources (2000 U.S. Census, the 2006 Georgia County Guide, Georgia Chamber of Commerce) were used to develop Clusters of the 150 Georgia Counties. These clusters were used as strata to then execute traified sampling. Homeless counts were conducted within the sample counties, allowing for multiple regression models to be developed to generate predictions of homeless persons by county. Results: In response to a mandate from the US Department of Housing and Urban Development, the State of Georgia provided an estimate of its unsheltered homeless population of 12,058 utilizing mathematically validated estimation techniques. Conclusions: Utilization of statistical estimation techniques allowed the State of Georgia to meet the mandate of HUD, while saving the taxpayers of Georgia millions of dollars over a complete state homeless census