L.A. County turns to big data to solve homelessness

LOS ANGELES – Los Angeles County is developing predictive data models to more effectively allocate money and services in the fight against homelessness, and the Board of Supervisors asked Tuesday for an update on the work.

Supervisor Kathryn Barger recommended that county agencies find a way to integrate new data collection tools under development by researchers.

“It is critical that Measure H dollars are used efficiently by allocating funding for services and housing where they will have greatest impact,” Barger said.

The county currently relies on two tallies of homelessness. The Los Angeles Homeless Services Authority generates a point-in-time annual count that includes demographic data and designates populations as sheltered or unsheltered.

The office of the county’s chief executive officer makes another estimate based on administrative data from six county departments and Glendale and Pasadena, cities which independently receive funds from the U.S. Department of Housing and Urban Development.

Definitions of homelessness vary depending on the agency, creating inconsistencies in the data. For example, HUD, which dictates the definition used by LAHSA, does not recognize anyone who has spent most of the last 30 nights in an emergency shelter, transitional housing or youth shelter as homeless.

A count taken at a single point in time also has its limitations.

The Economic Roundtable annualized LAHSA data and estimated that nearly twice as many people were homeless at some time during 2017 than the 55,000 individuals reflected in the LAHSA tally.

Researchers told the Los Angeles Times the LAHSA data collection process, which depends on volunteers, was inconsistent and, therefore, unreliable.

The board wants a better read on what’s really happening on the street, including information on how people landed there.

“It is crucial that we get an accurate picture of who is suffering from homeless, cataloguing why they have lost their homes, and determining what they need to get back on their feet,” said Supervisor Hilda Solis, who co- authored a motion calling for a report — expected back in 90 days — on data tools and a plan for integration.

The CEO is working with the California Policy Lab at UCLA and Urban Labs at the University of Chicago to develop predictive statistical models to identify individuals who are most likely to become homeless.

The Homelessness Policy Research Institute at USC is also working on predictive analytic screening tools to identify newly homeless individuals at high risk of chronic homelessness.

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