We present a predictive model for identifying homeless persons likely to have high future costs for public services. It was developed by linking administrative records from 2007 through 2012 for seven Santa Clara County agencies and identifying 38 demographic, clinical and service utilization variables with the greatest predictive value. 57,259 records from 2007 to 2009 were modelled, and the algorithm was validated using 2010 and 2011 records to predict high cost status in 2012. The model generated a good area under the ROC curve of 0.83. A business case scenario shows that two-thirds of the top 1,000 highcost users predicted by the model are true positives with estimated post-housing cost reductions of over $19,000 per person in 2011. The model performed very well in giving low scores to homeless persons with one-time cost spikes, achieving the desired result of excluding cases with single-year rather than ongoing high costs. Read more.
Prioritizing Which Homeless People Get Housing Using Predictive Algorithms
Economic Roundtable
Year: 2017