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How data can prevent pandemic-related homelessness

In communities around the globe, low-income families have found themselves especially vulnerable to both the health and economic risks presented by COVID-19 and the resulting shutdowns.

Countless families face a looming eviction crisis because of lost jobs and income over the past several months. As part of our global response to the impacts of the pandemic, our Advanced Digital Engineering team has been working with an American NGO called New Story to target relief funds to those most in danger of losing their homes. The project has demonstrated how innovative data analytics can address community issues.  

Addressing homelessness at home

By New Story’s estimates, nearly a billion people worldwide have insufficient shelter. Although much of their work has been in Latin America, on their latest project, ‘The Neighborhood’ the team from New Story aimed to address housing issues in the US. The project has aimed to provide direct financial assistance to marginalised, often undocumented communities most in danger of losing their homes, those ineligible to receive federal support. 

Early action against homelessness has big impacts later. According to New Story, to prevent someone being evicted might require $2000-$5000, but one year of rapid rehousing support costs $14,000 and a year of homelessness incurs a cost of $100,000 to a community. Since New Story couldn’t build new housing during the lockdown, they chose to provide a safety net to those in need with direct funding.   

A data-driven process to identify the vulnerable

Our work was oriented around two questions: what communities are most at risk, and when does that risk occur? New Story had done an initial analysis to start to address these questions in the Atlanta area, but wanted our help to dig deeper into the sheer volume and variety of data related to this problem for the nine counties of the Bay Area in California. We assembled a small team with data engineering, data science, and GIS skills to manage and analyze the variety of data sources. 

Given the wave of unemployment hitting the USA, we knew we needed to move quickly. First, we needed to understand the existing policies on the federal, state, and local level and turn those into a timeline for each county in the Bay Area. These timelines acted like a countdown clock to show when people start to become at risk of eviction. We were able to get data about the percentage of households in poverty in each county, as well as other demographic and economic data, including in the overall use of homelessness shelters and a measure of how vulnerable each county was to COVID-19.

Using 11 datasets, we created a relative risk index to understand which counties were most vulnerable to an increase in evictions. We presented the risk for each county alongside its respective countdown clock to show which counties were most at risk related to the time left before policies expired. When these two metrics were combined, we were able to see which counties to prioritize based on their existing economic and demographic conditions and their own policy responses. 

Supporting society's most vulnerable

With unemployment numbers still at historic highs, it’s clear that the Coronavirus is disproportionately affecting poorer and minority groups within societies and many people are afraid of losing their homes. Addressing homelessness is a key area of global focus in our three-year Community Engagement Local Engagement strategy and a challenge we can all help to meet. We believe that any tool that helps society to look after its most vulnerable people should be shared.