Santa Clara County, California is one of the most affluent counties in the country. Encompassing the heart of Silicon Valley, it is home to many high-profile tech companies including Google, Apple, and Facebook. But this wealth comes with a cost; Santa Clara supports a growing number of people without a home. A 2019 study found nearly 10,000 people lacking housing, a 31 percent increase from 2017. The county had the fourth largest homeless population in the country in 2019, behind only New York, Los Angeles, and Seattle.
The problem of homelessness is well-known, but the solutions are far from obvious. Social workers who interact with families in precarious housing situations have identified one promising approach known as targeted homelessness prevention, a data-driven approach to identifying families who might most benefit from immediate but short-term financial assistance. The total amount of assistance is modest, equivalent to roughly one or two months of rent for the average recipient.
While this is popular among practitioners, there has been little rigorous research on the practice to date. The most compelling evidence comes from a 2016 study in Chicago conducted by the Wilson Sheehan Lab for Economic Opportunities at Notre Dame (LEO for short). While this study was encouraging, it had several limitations. It was non-experimental and retrospective, and conducted in a context (Chicago) with relatively low housing costs compared with places like New York and California.
People offered emergency financial assistance were 81 percent less likely to become homeless.
Seeking to more rigorously evaluate this approach, LEO researchers teamed up with a nonprofit organization in Santa Clara County, Destination:Home, that had been considering a targeted cash assistance approach. The study, published earlier this year in The Review of Economics and Statistics, evaluated individuals and families at imminent risk of being evicted or becoming homeless between July 2019 and December 2020. These families were ineligible for other prevention programs as they could not demonstrate the ability to pay rent in the future.
A randomly selected subset of households were offered emergency financial assistance as well as non-financial services such as credit counseling and landlord dispute resolution. The control group only received the nonfinancial services. During the period of study, the average treatment household received about $2,000 to pay rent, utilities, and other housing-related expenses.
So, what did they learn? The authors of the study, David Phillips, a research professor at LEO, and James Sullivan, a professor of economics and co-founder of LEO, found that people offered the emergency financial assistance were 81 percent less likely to become homeless within six months of enrollment and 73 percent less likely within 12 months.
In our exchange below, Phillips and Sullivan answer some questions about what they learned from their collaboration with Destination:Home, and how it fits into the broader efforts to tackle homelessness in the United States.
What sorts of interventions to alleviate homelessness have worked, and where do we still have gaps?
If we look at homelessness interventions on a spectrum, we’d have prevention on one end and permanent supportive housing on the other. Permanent supportive housing provides long-term rental subsidies to people who are already homeless and has considerable evidence behind it. A LEO study found that an expansion between 2008 and 2017 of the HUD-VASH program—a federal effort to reduce veteran homelessness through the distribution of housing vouchers—cut homelessness among veterans in half.
The lesson we can take from this is that these more intensive solutions are effective for chronically homeless people. But when we talk about this issue, we have to keep in mind that the experience of homelessness varies among the homeless population, so the solutions we offer need to reflect that. We now have evidence that targeted intervention for at-risk individuals works and we have evidence that permanent supportive housing for the chronically homeless works. So, where we really need to build evidence is the space between those two points: Things like rapid rehousing subsidies and shelter diversion [programs aimed at helping newly homeless populations identify housing and avoid entering the shelter system].
Why is it so important to focus on prevention in addition to other efforts?
The first and most important reason is the toll that homelessness takes on those who experience it. Once someone becomes homeless, they are immediately facing additional problems like finding housing, basic necessities, and healthcare. They are more likely to lose a job and have more frequent hospital visits. If we can prevent that suffering, we should.
Temporary financial assistance seems like an expensive but temporary band-aid. Is it really worth it in the long-term?
Financial assistance prevents homelessness rather than just delaying it and prevention is also less expensive. We conservatively estimate in our study that communities get $2.47 back in benefits per net dollar spent on emergency financial assistance. This frees up those dollars to go toward other supportive services, like those that help the chronically homeless.
If this intervention is effective, why are homeless numbers still rising and what can be done?
The challenge comes in offering a preventative measure to families that aren’t interacting with the system until it is too late. LEO is currently studying implementing a new risk assessment tool in Texas that could lead to a more equitable tool to use than the VI-SPDAT (the current tool used across much of the country). We know some factors increase one’s chance of becoming homeless, such as prior exposure to homelessness or being released from incarceration. If a public entity with access to that information and those populations were to take over an emergency financial assistance intervention, could they systematically and effectively offer the intervention to populations with a naturally higher risk of homelessness?
A longer version of this article was originally published in MIT Press Reader.
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