How government data can be used as a road map to reduce homelessness

State and local governments, as they contend with rising numbers of those who are homeless or on the brink, must also focus on continuity of programs and protection of residents.

The critical first step is understanding who the vulnerable population is and getting them safely sheltered. The pandemic has changed our physical environments, with some facilities being temporarily or permanently closed, or completely repurposed. Cities’ efforts to increase capacity to shelter the growing number of homeless have run the gamut, from buying or commandeering hotels to building small communities on city land:

• King County, Washington — The county’s Health Through Housing initiative immediately creates housing units by acquiring former hotels, nursing homes and other single-room settings.
• Portland, Oregon — The city plans six “safe rest villages,” with locations across Portland. They are composed of sleeping pods and retrofitted shipping containers that will serve as laundry rooms, showers, bathrooms and kitchen facilities. The new sites join Portland’s growing network of outdoor village-style shelters.
• New York City, New York — New York’s homelessness is reportedly worse than at any time since the Great Depression, according to the New York Coalition for the Homeless.⁶ Mayor Eric Adams has announced a plan to convert the significant number of closed hotels (estimated at 200)⁷ to supportive housing for the homeless.⁸

Reduction of risk and cost are key concerns as cities seek solutions around shelter location and capacity strategy. The immediate nature of the need heightens the requirement for efficiency. Collaborating with economic development and community planning resources can help the team develop a strategy and action plan and, later, analyze progress and impact.

Similarly, cities that are turning to sites such as shuttered hotels and civic arenas to fill the immediate need for shelter may be treading unfamiliar ground. Real estate industry insights will help with key tasks, such as assessing contractual obligations; reviewing regulatory compliance; and developing solutions for maintaining functionality and maximizing efficiency.

As a city’s homeless shelter scenario begins to stabilize, residents should already be receiving the services they need to get back on their feet. By leaning into digital and focusing on unlocking their data, agencies are also unlocking the insights and agility that are essential for the next phase.

Applying a blanket approach across the entire population will achieve little. Each person is different, and there are myriad causes for homelessness. Some may be grappling with mental health, substance abuse or financial challenges; some are unable to work because of health conditions.

To deliver services that will get someone to a better place, governments must identify the specific factors that moved that person or family into homelessness. Data, specifically the powerful insights that it provides, makes this seemingly impossible task possible. And governments have a wealth of data at their disposal. Government programs receive and amass enormous stores of data covering a broad range of public services, from housing to health to childcare.

When agencies connect individuals’ many data points, they’re able to construct a “digital twin” of the residents they serve; user experiences drive improvements in this integrated service delivery model:

• Digital twin — A digital twin is a digital representation of a real-world entity or system. In this case, data is aggregated and analyzed to enable a holistic view around an individual so that agencies can determine how best to support them so that they experience improved outcomes.
• Experience-led transformation — Insights from user experiences will continue to give agencies a better view across needs and services so that residents can get the help that best fits their needs.

Without properly integrated data and with only a partial picture of the person who is in need, agencies won’t know how to help them. Without diving into the specifics and nuances of circumstances, an agency won’t be able to answer the two most foundational questions: what is their challenge, and can that government do anything to help?

Governments can go beyond merely providing shelter after someone becomes homeless. By accelerating their capabilities to unlock their data and analytics, agencies can do more than react — they can prevent.

In 2019, research by Ernst & Young, LLP and Imperial College London’s Institute of Global Health Innovation⁹ explored the use of data and analytics by five health and human services providers around the world. The findings highlighted the ways data and analytics can improve people’s lives. Results at an East London borough are just one example of what’s possible:

• London Borough of Barking and Dagenham (UK) — Local government officials wanted to address the borough’s challenges of unemployment, poverty and homelessness. Getting residents the right help and support in a timely way was challenging because their information was stored across case management systems. Caseworkers lacked the comprehensive overview necessary to keep risks from becoming crises.

“For too long, our residents faced a legacy of really poor outcomes,” said Rhodri Rowlands, the borough’s Deputy Director and Head of Programmes Community Solutions. “That, coupled with the challenges of rising demand and financial pressures, led us to a position where we wanted to think very differently about how we could better respond to address residents’ needs,” he said. 

The borough turned to data and analytics software to integrate its data. When COVID-19 struck, the agency was able to predict more than 90% of its at-risk population and connect them with support. Over 8,000 residents in the borough received rapid assistance.

In a separate initiative,¹⁰ a case study involving a borough in Kent, England, predictive modeling was applied:

• Maidstone Borough — In 2018, Maidstone had recorded a 58% increase in homelessness over the previous five years. Government funds had also been reduced. The Council sought a completely different approach to homelessness.

The borough transformed its service model from reactive to predictive, applying a proactive solution driven by data. Integrating data from multiple government systems helps agency workers understand what’s happening with an individual or family.

The results were nothing short of groundbreaking:

Because agency workers were able to identify a trend toward homelessness, they could intervene earlier, before individuals were actually on the street. The results were extraordinary by any standards: for the initial pilot group, in one year, the rate of homelessness fell from 40% to 0.4% — a 99% reduction in homelessness.

An early-intervention case study

1. The Maidstone Borough Council (MBC) was alerted that Mrs. A was at risk of eviction on Oct. 21, 2019.
2. On Oct. 29, MBC sent a text message to Mrs. A stating that she was eligible for a discretionary housing payment.
3. Mrs. A responded to the text message and was able to pay her rent, thus preventing a potential eviction and homelessness case.

Other examples of early intervention:

There are numerous ways agencies can intervene early to keep people from slipping into homelessness. These are real-life examples:

• Benefits support and budgeting advice
• Support to resolve tax debt
• Housing assistance
• Legal assistance
• Employment services
• Referral for mediation
• Disbursement of loan

Opportunities close to home

In cities across the nation, homelessness affects the entire community — not only those needing help but the people who live, work and visit there. Efforts may be more effective on the local level.

There are fewer constraints at the local level, while federal agencies must grapple with systemic challenges. For example:

• Federal funding programs are siloed. The funding stream for housing assistance is separate from the funding stream for food security — which is separate from the funding stream for medical services, and the one for mental health services, and so on.
• There is no incentive for these individual divisions to work together because they are held accountable to a metric that applies only to their specific area of focus.

But as funding from the federal government level trickles down through the state and local governments, flexibility increases. The very structure of funding programs expands opportunities to set a different governance model.

Turning the focus to data

Eradicating homelessness will require more than money. That is not to say that the emergency funds are not sorely needed. Digital tools and knowledge resources can provide the states with tailored solutions that help them take full advantage of funding and remove obstacles around technology, third-party vendors and application processes.

Data, not money, holds the key. Fortunately, data is an abundant government resource. Government, with its place at the head of the table, has the opportunity to know more and do more than the innumerable private organizations, online groups and nonprofits that are trying to end homelessness.

Sharing information across agencies provides valuable insights into specific needs. Determining how best to meet those needs, whether for an individual or a family, requires that the government know the data that is available. Both of these aspects, knowing the real need and knowing your data, will affect the ultimate goal: connecting people with the resources that keep them from sliding into homelessness.