Using Data to Fight Homelessness

Using Data to Fight Homelessness

This article is the first in a series exploring data’s pivotal role in current national and local efforts to help the homeless, and how Cicero is joining the work.

On a given night in 2015, 564,708 individuals across the United States were identified as homeless.[i] It has been estimated that almost 2 million individuals experience homelessness each year. In 2016, Utah’s largest shelter, the Road Home, served 8,077 unique individuals.[ii]

While these numbers are significant, they paint only part of the picture: existing statistics don’t include the tens of thousands who are couch surfing, living out of cars or storage units, or being sheltered by family.

Beyond sheer body count, the staggering costs of homelessness serve as a forceful call to action. For example, a 1987–1989 University of Texas study calculated that almost $700,000 was spent on services for just 21 homeless men in San Antonio over the course of 1.5 years.[iii] This research suggests that each of the men studied cost the state approximately $33,000—that’s about $68,000 in 2017 dollars.[iv]

Who bears the brunt of these costs? You guessed it: taxpayers.

Regardless of the method used to scope the problem, the conclusion remains the same: homelessness is a significant nationwide issue.

Fortunately, business and data analytics are beginning to play a profound role in social impact efforts, as Cicero’s Jacob Allen has repeatedly shown.[v] The non-profit sector is transforming as organizations infuse data and analytics into homelessness response efforts across the nation. Government agencies and private sector groups alike are strategically innovating tools and systems that more effectively help those in need.

In 1983, the Department of Housing and Urban Development (HUD) began conducting nationwide Point-in-Time counts—comprehensive annual tallies of all homeless individuals on a given night in January.[vi] Shortly thereafter, cities began building local databases to better track basic statistics on their homeless populations. In 1999, Congress passed the HUD Appropriations Act which required that the Department continue these individual efforts and develop a national Homeless Management Information System (HMIS). Subsequent legislation standardized data collection procedures and made federal funding for related organizations contingent on contribution to the database.[vii] Using this information, state and federal HUD Departments allocate appropriations and publish regular reports on the status of homelessness, shelters, and services.

Private sector partners have also pioneered innovative and strategic approaches.[viii] For example, Dr. Jim O’Connell at Boston-based Healthcare for the Homeless developed the Vulnerability Index Service Prioritization Decision Assistance Tool (VI-SPDAT) to help healthcare providers identify homeless individuals most at risk of death.[ix] In New York City, digital media company SumAll partnered with the local government to identify families most likely to become homeless in the near future.[x] In California, Destination: Home developed the Silicon Valley Triage tool, a predictive model that identifies “the high cost users in our public safety net system and allows communities to prioritize them for supportive housing.”[xi]

The possibilities for data analytics in the social sector are endless, and Cicero is climbing on board. Over the next few months, our team will be joining forces with state and local leaders to craft actionable solutions and strategic recommendations. Cicero will soon begin initial analysis of historical HMIS data to identify trends and insights that can better direct state and local efforts to prevent and respond to homelessness.

While we are far from knowing the value and impact of this project, we are excited to invest our unique skills in bettering our community.

[i] Henry, Meghan, Azim Shivji, Tanya De Sousa, and Rebecca Cohen. The 2015 Annual Homeless Assessment Report (AHAR) to Congress. US Department of Housing and Urban Development: Office of Community Planning and Development. 2015. <https://www.hudexchange.info/resources/documents/2015-AHAR-Part-1.pdf>.

[ii] “Annual Report.” The Road Home. <https://www.theroadhome.org/about/AnnualReport/>.

[iii]Pamela, Diamond M., and Schnee B. Steven. Lives in the Shadows: Some of the Costs and Consequences of a “Non-System” of Care. San Antonio: Hogg Foundation for Mental Health, 1991. <https://repositories.lib.utexas.edu/bitstream/handle/2152/38802/1991LivesInTheShadows.pdf?sequence=2&isAllowed=y>.

[iv] “CPI Inflation Calculator.” U.S. Bureau of Labor Statistics. <https://data.bls.gov/cgi-bin/cpicalc.pl>.

[v] Allen, Jacob. “The Science of Social Impact: Measuring to Prove and Improve Your Theory.” Philanthropreneurship 2016. <https://www.philanthropreneurshipforum.com/social-impact-measuring-theory/> and —. “The Science of Social Impact: Measuring to Prove and Improve Your Theory.” Cicero Social Impact. Web. 30 March 2016. <https://cicerosocialimpact.org/articles/one-acre-fund/>.

[vi] “Using Data to Understand and End Homelessness.” Evidence Matters: Transforming Knowledge into Housing and Community Development Policy. US Department of Housing and Urban Development: Office of Policy Development and Research. <https://www.huduser.gov/portal/periodicals/em/summer12/highlight2.html>.

[vii] Poulin, Stephen R., Stephen Metraux, and Dennis P. Culhane. “The History and Future of Homeless Management Information Systems.” Homelessness in America. Ed. Robert Hartmann. McNamara. Vol. 3. Westport, CT: Praeger, 2008. 171-79. Print.

[viii] Sakaue, Lyell. “Data-driven Strategies for Reducing Homelessness.” Data-Smart City Solutions. Harvard Kennedy School, 6 Aug. 2014. <https://datasmart.ash.harvard.edu/news/article/data-driven-strategies-for-reducing-homelessness-516>.

[ix] Raeder, Jack. “An Algorithm for the Homeless.” Dataconomy. Dataconomy Media, 30 Jan. 2015. Web. 10 Mar. 2017. <https://dataconomy.com/2015/01/an-algorithm-for-the-homeless/>.

[x] Guerrini, Federico. “From Homelessness To Human Trafficking: How A NYC Nonprofit Is Using Big Data To Make A Difference.” Forbes. Forbes Magazine, 25 Apr. 2014. Web. 10 Mar. 2017. <https://www.forbes.com/sites/federicoguerrini/2014/04/24/from-syria-to-homelessness-and-human-trafficking-how-a-nyc-nonprofit-is-trying-to-make-a-difference-using-big-data/#6529ccf623fa>.

[xi] “The Silicon Valley Triage Tool.” Destination: Home. Destination: Home. <https://destinationhomescc.org/the-silicon-valley-triage-tool/>.