An open source approach to preventing evictions
Throughout the US, many people have lost jobs and income because of COVID-19 and the resulting shutdowns in many states. At a time when people are being told to stay home to limit the spread of disease, many are at risk of losing their homes because they can’t work and therefore can’t pay the rent.
In early September, the Centers for Disease Control issued a national moratorium on evictions until December 31st. Under this order, tenants can provide a declaration to their landlord that shows that they meet certain conditions, which then prevents the landlord from evicting them. However, as the CDC notes in the order: “this Order does not relieve any individual of any obligation to pay rent, make a housing payment, or comply with any other obligation…”
So, while this order protects renters for the rest of the year, their rent payments continue to accumulate and the inevitable financial cliff that renters will face has simply been delayed. This makes the weeks up to December 31st a pivotal time to avoid a crisis of evictions and homelessness.
Homelessness charities have realized they need to act quickly in order to curb a rise in homelessness due to financial strain caused by COVID-19. In early May 2020, Arup’s Americas Advanced Digital Engineering team worked with New Story, a charity focused on housing and homelessness, to help with their initiative called The Neighborhood. This initiative intended to pay rents to keep people in their homes during the peak of the crisis.
When making decisions about how to distribute aid, there are many factors that can be considered and a multitude of open datasets available to address those factors. New Story enlisted Arup’s help to dig into the sheer volume and variety of data that would contribute to an analysis on who is most at risk for homelessness. Our initial case study location was the nine counties of the San Francisco Bay Area. The goal was to help New Story prioritize whose rent to pay by determining who was most at risk of losing their home. Over the course of just one week, we collected and analyzed socioeconomic, demographic, and policy data and presented a relative ranking of the counties based on their potential risk of eviction. This analysis ultimately helped guide their decision-making about how to distribute funds to over 350 families since its launch in summer 2020.
The success of that methodology makes clear to us that this data-driven approach would also be useful in other areas of the United States, so we have decided to open source our data analysis work for other organizations to use. We have published a repository with our analysis code and information about our database on GitHub.
The repository that is currently published has several key features. First, we published an open database for social and eviction-related datasets. This built off our work with New Story and includes up-to-date datasets from the Federal Reserve (FRED), the Department of Housing and Urban Development (HUD), the US Census, and others. This data is now available to anyone in a single database at the county level. This database can also be expanded to include more datasets that users may find useful.
Based on our work with New Story to specifically address direct relief to renters, we’ve updated our Python analysis and published workflows to evaluate the relative risk of eviction between counties. This analysis aims to give context around the socioeconomic indicators at the county level and show a direct comparison between counties to help decision makers prioritize how to direct aid. We’ve also documented our process to evaluate policies at a county level, using a methodology from EvictionLab, an organization devoted to publishing national eviction data, and provided a template for users.
Additionally, we built a new calculation to estimate the cost to prevent evictions at the county level with feedback from the Mayor’s office in Tulsa. This calculation uses fair market or median rents and an assumed distribution of housing stock in a county. In our database we provide data for the average national housing stock distribution as well as distributions of the top 15 metropolitan areas. We hope that by providing a transparent methodology to cities, they can form their own conclusions regarding how much funding is needed for rental assistance.
Like many other industries, the engineering industry does not traditionally publish work for others to use and build upon. However, open-sourcing socially valuable projects such as this are an opportunity to share what we’ve learned and allow others to use and improve upon what we’ve done. As people work with the code, their contributions make the project more secure, efficient, stable and usable. This is a great model for communities to address significant social issues, enabling them to take advantage of and build on tools that they may not have been able to develop on their own.
Since publishing, we have already begun adding new features and data to the repository, including demographic data and county shape files. Most recently, we published an interface that allows users to use our code and data without any coding skills whatsoever. We’ll be updating that app with more features going forward.
Our team has plans for new analyses, datasets, and visualizations that we would like to add to this repository, but we also hope that as people use the data for their problems, they will have new ideas to contribute. Our aim is that this repository serves as a resource to NGOs, municipalities, and other firms as we all try to help people remain in their homes.
You can use the first version of our web app to explore the data right now, no code required. If you’re interested in digging into the code and data in more detail, you can find documentation and instructions about how to report issues and contribute your own improvements at our GitHub repository.
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