Preventing Homelessness: The Tough Job of Predicting Who is at Real Risk

Introduction to Homelessness Prevention

When the unknown number popped up on her phone, Jocelyn Escanuela was in the checkout line at Walmart. She still can’t explain why she picked up and then listened to a cold-caller’s pitch that sounded a lot like a scam. She had been selected to receive a grant of $6,000, the caller told her. And she would have a personal assistant to help her get her through her “crisis.” How did they even know she was in a crisis? It turned out the caller was legitimate. She was from the Homelessness Prevention Unit, an experimental Los Angeles County program that is testing whether it is feasible to stop homelessness before it starts — one person at a time — by picking them out of mountains of data.

The Challenge of Predicting Homelessness

Escanuela’s crisis was detected not by a person but a predictive statistical model that was developed to solve a conundrum that has made homelessness prevention a tantalizing but underused strategy. Despite sound evidence that services such as eviction defense and financial assistance can prevent people from becoming homeless, it’s impossible to know after the fact whether any given person would have become homeless without the help. Research has shown that only a small percentage would. The elusive goal of prevention is to identify that small percentage. “With limited prevention resources to work with, there are real consequences to not getting them to the people who need them most,” said Steve Berg, chief policy officer for the National Alliance to End Homelessness, which has historically frowned on costly prevention programs.

The Role of Technology in Homelessness Prevention

But Berg said “it would be good news if these emerging technologies turned out to be effective at predicting who’s most likely to become homeless if they don’t get help.” Attaining that elusive precision will be increasingly important as both the city’s ULA “mansion tax” and the countywide Measure A sales tax begin to direct millions of dollars into homelessness prevention. The model that picked Escanuela as high risk is being tested to see how effective it is. It was created by the California Policy Lab at UCLA, a research institute that has access to data from county agencies such as the departments of health and social services, which interact with people at their most vulnerable. The Policy Lab sifts through all that data, evaluating some 500 markers to generate a list of individuals and families that its model predicts to be at high risk of becoming homeless.

How the Model Works

The Homelessness Prevention Unit analysts randomly work their way through the names on the high-risk list to come up with two groups of candidates. Half will be offered intervention — a cash stipend and a case manager for four months. The other half will receive nothing and never know they were chosen, but will be monitored through any contacts with county or homeless agencies they make. Escanuela landed in the target group — the fortunate half — of the random clinical trial. The holy grail of prevention would be a model that could pinpoint those who would become homeless, and avoid spending money on those who never would.

Effectiveness of Prevention Programs

In a 2023 report, Notre Dame University’s Lab for Economic Opportunities found that people served by a Santa Clara County prevention program were nearly 80% less likely than a control group to become homeless after receiving services. That’s not as impressive as it sounds because only 4.1% of those who got no help became homeless, suggesting that a lot of money was invested in people who wouldn’t have become homeless without the help. “Prediction is possible, even if it’s not great,” said Vanderbilt University research professor Beth Shinn, who studied New York City’s Homebase prevention program. Her research found that a model did moderately better than outreach workers at predicting.

Real-Life Examples of Success

Escanuela runs her own eyelash services business at the apartment she shares with her mother. (Allen J. Schaben / Los Angeles Times) Early findings are promising. In the data used to construct the model, about 47,000 people receiving county services, 24% of those predicted to be at high risk actually became homeless compared with only 7% of the whole sample. It’s also proved effective at finding people who are likely to become chronically homeless. “Our clients are living with really high levels of risk,” Vanderford said. “They have complex health and mental health conditions. They are meeting us at a real moment of crisis. The timing with which we reach out to our clients seems magical to me.”

Challenges in Implementing Prevention Programs

Full results of the trial will not be final until 2027 after a sufficient number of people have been tracked for 18 months after completing the four-month program. The Homelessness Prevention Unit was created with funding from the American Rescue Plan Act supplemented by county funds. It has about 250 active clients and, with a turnover of four to six months, can handle 750 a year. About 90% retained housing or found new homes, Vanderford said. It’s labor-intensive work. Four analysts go through the raw lists randomly screening out ineligible candidates. Because there is a delay before the Policy Lab obtains the county data, many on the final list are already homeless, proving the predictions accurate.

Overcoming Barriers to Success

“There is a real challenge in getting in touch with people,” Vanderford said. “Phones go off. A client may be hospitalized or in jail. Clients might be mistrustful of getting this call out of the blue that sounds a little too good to be true. Voicemails go unresponded to.” “I never answer calls like that,” Escanuela said. “I don’t know what compelled me to answer.” Neither Escanuela nor Vanderford know what specific factors placed her on the high-risk list except that she was accessing county services.

Conclusion

The story of Jocelyn Escanuela and her experience with the Homelessness Prevention Unit highlights the potential of using predictive statistical models to identify individuals at risk of homelessness and provide them with targeted support. While there are challenges in implementing such programs, the early findings are promising, and the approach shows great potential in reducing the risk of homelessness. As the city and county invest more in homelessness prevention, it is crucial to continue refining these models and strategies to ensure that resources are used effectively.

FAQs

1. What is the Homelessness Prevention Unit?

   • The Homelessness Prevention Unit is an experimental Los Angeles County program aimed at preventing homelessness by identifying individuals at high risk and providing them with targeted support.

2. How does the predictive model work?

   • The model, developed by the California Policy Lab at UCLA, sifts through data from county agencies to evaluate markers that indicate a high risk of becoming homeless.

3. What services does the Homelessness Prevention Unit offer?

   • The unit offers a cash stipend and a case manager for four months to those identified as high risk, along with referrals to health and mental health agencies and assistance with expenses.

4. What are the challenges in implementing prevention programs?

   • Challenges include the difficulty in getting in touch with potential clients, the labor-intensive nature of the work, and the delay in obtaining county data.
5. What are the early findings of the program’s effectiveness?
   • Early findings show that the model is effective in identifying individuals at high risk of becoming homeless and that those who receive intervention are less likely to become homeless.