Solving for new challenges to the housing crisis caused by COVID-19 with data
Jan 8, 2021
Q Foundation, a social sector non-profit providing technology solutions to deliver housing assistance primarily in San Francisco, implemented Looker in March 2020, just as the US issued shelter in place orders due to the COVID-19 pandemic. Prior to the pandemic, San Francisco was already dealing with the effects of a national homeless crisis. With new challenges COVID-19 was creating, the teams at Q Foundation knew that the housing crisis was about to get even worse.
By implementing Looker, the Q Foundation quickly gained the visibility needed to work with local governments on tackling the wave of housing requests coming from the thousands of people who’d lost their jobs and were being faced with losing their homes during the pandemic.
We recently spoke with Brian Basinger, Executive Director at Q Foundation, for more details around what Looker has enabled their teams to act quickly in the midst of this housing crisis.
Q: How has COVID-19 impacted the problems you aim to solve at Q Foundation?
Q Foundation began developing our vision for a national housing stabilization a couple of years ago. All of our emergency rent assistance has been online for 7 years to make them broadly and easily available. As the only agency in SF providing online emergency rent assistance, we knew we were going to be hit particularly hard as people began to lose their jobs as the pandemic started. We knew there was going to be an even greater increase in the number of people seeking emergency rent relief and that this would further stress an already overburdened system of care. Already being online, we were perfectly positioned to be able to assist households as they sheltered in place. Technology allows for democratic access to resources at a scale that is in a different universe compared to the old fashioned system of care. The downside to that accessibility is the profound level of need comes to light. To date, we have 10,000 applications for emergency rent assistance and nowhere near the resources to provide it.
Q: What’s one of the biggest challenges when it comes to gathering the necessary support to ultimately offer relief?
One of the struggles with homelessness prevention is generating votes and funding support. While it’s easy to see all of the people who were not able to get help in time, due to a lack of sufficient resources, most people are not able to “see” how many people did not become homeless because of the work of ours and similar agencies.
As a system of care, and as members of local and global communities, we need to do a better job of “showing” people the impacts of investments and showing stakeholders where and when to target resources most effectively.
Decision-makers need to be presented with accurate, actionable information which they can use to allocate resources, identify gaps in assistance, and most importantly, “see” the unintended consequences of different prioritization scenarios. I’ve witnessed how the absence of usable data results in poor planning and resource allocation. This undermines effectiveness with devastating consequences.
Q: How did Q Foundation get the necessary support to get started?
In 2004, Mayor Gavin Newsom gave Q Foundation our first funding after we created the first map of displacement in San Francisco. This one visualization led to us passing our first piece of legislation to keep people housed. Once we experienced the power of maps and visualizations, we were hooked.
From there, Q Foundation built an online rent assistance application to share our resources with every person in need, and every agency in San Francisco.
Q: You’ve recently built a new platform and accompanying dashboard extremely fast as a response to COVID-19. What did that process look like for you?
On February 21st, I finally opened my eyes to what was coming due to COVID-19 and realized that Q Foundation was about to bear the brunt of the coming wave. With our friends at Beezwax Consulting, we began doing the work to batten down the hatches of our current system while also implementing our plan to build the stack that was capable of withstanding the coming tidal wave of need and that could support any additional needs as they inevitably arose.
On a Sunday in mid-April, the Mayor's Office of Housing & Community Development asked if we could “actually build that idea of yours?” I replied, “Not only can we build it. It’s built,” and invited them to preview our first module the next morning. When I started talking about the dashboard, they said ‘That’s a nice idea, but we just need an online application’.
Using our own resources, we built the Equity Visualization & Optimization (EVO) application — in 9 days — along with a dashboard to provide clear, actionable insights from the requests.
Q: How did you accomplish building EVO in nine days?
We were motivated to help people with a solution as fast as possible. In fact, we wanted to be ready in time for May’s rent, which left us with two weeks to do the work. After some quick but detailed research with the Beezwax team, Looker made our short list. We submitted a demo request on a Friday morning and were on the demo that afternoon. After learning about what we were trying to build and the urgent timeline we were working with, the Looker team worked with our team of three engineers through the weekend to get something up by Monday morning.
In addition to a quick POC and pricing support (we are a nonprofit and no one had budgeted for COVID-19), the engineering team has gone above and beyond to drive this project through.
Q: Now that you’ve launched EVO and the accompanying Looker dashboard, how are they being used to help provide housing and resource support?
With EVO and Looker, funders can see in real-time where there is need today, track the daily provision of assistance to ensure intended targeting is occurring, and course-correct immediately. Some of the ways the Mayor’s Office and different organizations are using our data is to see real-time funding requests and allocations, allowing them to study unmet needs and track to ensure funds are being equitably distributed.
EVO has also provided more visibility into racial and ethnic granularity. Since EVO allows applicants to select up to 30 possible race and ethnicity categories — which calculates over 1 billion possible combinations of identities — we can synthesize with more complexity than ever before the uniqueness of each person.
One of the best things about data and data visualizations is not when it confirms what you think you already know. It is when it shows you that your assumptions are not always accurate. That is almost as satisfying as when the data shows that you are achieving your equity goals.
Q: What have you seen so far? How will this help solve the housing crisis magnified by COVID?
For starters, we’ve learned about points of failure in our process.
A surprising data point that had immediate effects on evolving public policy in response to the pandemic was that two-thirds of applicants indicated that they had not notified their landlord of their inability to pay rent due to COVID-19, potentially putting them at significant risk of eviction after the eviction moratorium was lifted.
With early access to this data, not only can we provide critical feedback to policy-makers, but we can also help solve this problem for people today. We can cross-reference the applications in the platform with our landlords that are in our database. Every month that the rent is unable to be paid, we can use our platform to automate notifications to the applicants via robocalls, text, emails. For example, we can send them a text message that says, “Do you want us to notify your landlord again, reply yes?”. At the most basic level, we can help people who didn't notify their landlord by helping them follow the current process and notify their landlords so they can be eligible for help.
We’ve also learned more about our communities.
The Southeast part of San Francisco is often proclaimed to not receive fair access to services — a point of view supported by massive, structural equity issues both past and present. Using Looker, we created a heat map which showed a surprising yet contextually-consistent significant number of applications were coming from the Southeast quadrant of the city, led by immigrant communities, especially monolingual communities, who clearly demonstrated an ability to successfully apply online.
And we’ve learned about the industries and people most impacted by COVID.
Despite all of the ink spilled about the tech industry in SF, hospitality is still the city’s #1 employer. But because of COVID-19, it has been decimated. Of the 3000+ applications filed for 1st Round funding assistance by organizations collected in just 9 days, 1200+ were in the hospitality industries (37%), representing the largest segment of job loss and amounting to twice the next highest categories. This is a critical insight that points to the hospitality industry likely being among those with the longest timeline for recovery post-COVID-19.
This kind of knowledge provides critical insight into what the future of emergency rent assistance levels may look like and for how long — data that might be helpful to the mayor’s Budget Director and Chief Economist.
Q: Are there plans to use the EVO application and insights from Looker to help solve problems beyond San Francisco?
Our vision is to provide government and nonprofit partners across the country with a single, end-to-end platform to help people keep their homes. This platform would include online applications, powerful real time visualizations, automation-assisted verification, and a dynamic EVO score. The platform would support rapid and efficient real-time approval decisions, a scalable payment processing platform, and dynamic reporting tools for funders. With the platform, our partners will be able to leverage our live agent call centers and access a state-of-the-art, online academy to train staff so they can start helping people quickly.
We are currently seeking donations to support the development of the full implementation of the platform and make it available to any community that asks. This is the clearest path I can see to rapidly scale the infrastructure needed over the coming months and years to help address the increased housing crisis challenges caused by the COVID-19 pandemic.