In this contemporary age, leveraging data analytics, like machine learning and AI, has become an integral part of our daily lives. It makes time-consuming tasks more efficient and assists people in the collection and organization of a wide range of datasets. These capabilities extend across numerous fields and provide unique value in the response to homelessness. For example, while it may seem like we often see individuals experiencing homelessness in communities across the nation, locating and following up with them for services remains a complex challenge for organizations assisting with housing and other stabilizing services. As such, data analytics can be a great tool to locate both individuals at risk and those currently experiencing homelessness. It can also be used to better understand their acute needs, to assess and provide resources quickly, and to prevent people from falling into homelessness again. Yet, its ultimate power lies in informing the policies that shape collective action in our communities.
People experience homelessness for a myriad of reasons: from the nationwide lack of affordable housing, unemployment, and discrimination in general – all of which are exacerbated by mental illness and addiction. During a single night in January 2024, there were more than 770,000 people in the United States who were experiencing homelessness, an 18% increase from 2023. 1 Individuals experiencing homelessness typically undergo the following three stages:
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- Stage 1: risk of homelessness
- Stage 2: literal homelessness
- Stage 3: return to homelessness
Many times, homelessness policies emphasize stage 2, yet stages 1 and 3 are just as crucial for effectively addressing the lifecycle of homelessness and ultimately require different policies and resources. Programs such as eviction prevention and crises stabilization focus on those who are at risk of homelessness or facing a return to homelessness. Data analytics yields the power to enhance our understanding of these stages and develop insights that shape policy. For example, by analyzing data through the Coordinated Entry System (CES), CoCs are better equipped to focus on the individual, understand their history and vulnerabilities, and determine what will improve the assistance available to them from entry, exit, and beyond. Predictive analysis is also very useful at the system level because it can identify which individuals are at a high risk of homelessness or a recurrence of homelessness. These tools combined allow individuals to be efficiently and carefully matched with services that are tailored to their needs, and then to course correct when needed.
Software developers have created tools to help address some of these challenges by equipping the local community to advocate for their homeless neighbors. For instance, the Los Angeles County Homeless Outreach Portal has equipped Los Angeles residents to request assistance and outreach services for individuals experiencing homelessness. Other software such as the WeCount app in Seattle enables those experiencing homelessness to request certain items such as clothing and allows community members to donate items as well. This not only uplifts the needs of those who are most vulnerable but also empowers the community with tangible ways to make a difference.
While we do not often think of data as a strategy for connecting with people who have experienced homelessness, data analytics is a form of interacting with and listening to their stories. By analyzing the services and assessments in data systems like the local Homeless Management Information System (HMIS), we can advocate for and amplify the voices of those whose stories are not yet heard. Therefore, as a network of nonprofits, private businesses, community members, and governmental officials, we must harness data analytics to make a difference in the fight against homelessness.
References:
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- U.S. Department of Housing and Urban Development, ” HUD Releases January 2024 Point-In-Time Count Report,” HUD Exchange, December 30, 2024, hudexchange.info.
- National Alliance to End Homelessness, “State of Homelessness: 2024 Edition,” National Alliance to End Homelessness, accessed February 19, 2025, endhomelessness.org.
- Deloitte Insights, “Addressing Homelessness with Data Analytics: A Data-driven Approach to Homelessness,” Deloitte Insights, 2019, Deloitte Insights.