Help us deliver insights, understanding, and solutions to address the housing crisis in the United States.
A DataKit™ is a work-ready set of data, software, and innovation questions, curated by DataKind, in a domain of social good. As you engage with a DataKit, you will apply your skills for social impact while deepening your understanding of problems common in the space. All learnings, ideas, and insights resulting from this DataKit will be aggregated and shared with DataKind’s network of housing collaborators, making your DataKit solutions directly accessible to housing actors across the United States. Select ideas and prototypes may be highlighted for expansion through partnerships. Learnings will be shared throughout the DataKit, and in an early 2025 shareback once the DataKit concludes.
The United States faces a severe affordable housing crisis, with a national shortage of over 7 million affordable homes for more than 10.8 million extremely low-income families. There is no state or county in which a renter earning minimum wage can afford a two-bedroom apartment–even if working full time. As a result, 70% of these low-income families are severely cost-burdened, spending over half their income on rent, far above the recommended one-third threshold. Homeless camps have expanded, and "super commuters"–those who drive for 90 minutes or longer to work–have migrated well beyond the expensive coasts to smaller cities like Spokane, Wash., and fast-growing metropolitan areas like Dallas and Phoenix.
We’ve identified four challenges that stand to benefit from your data, software, and storytelling skills:
Housing actors need a better understanding of the housing need and availability in communities by income and household in order to take action to increase housing availability.
Housing displacement occurs when households are forced to leave their neighborhoods due to direct and indirect needs. Housing actors must be able to better anticipate the risk of displacement so as to support policies and programs to keep households in their homes.
New housing development is supported by a number of federal programs. Creating a better understanding of community eligibility and needs will help housing actors and communities better prepare for new development.
Natural disasters increasingly impact housing inventory and opportunities in the United States. Housing actors often lack the data tools necessary to engage quickly when disaster strikes. Better data and tools will help communities prepare for and respond to disasters.
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Ready to go? Head to the Challenges to dive in. Each comes with some initial questions and guidance for how to get started.
Join us to deliver insights, understanding, and solutions to address the housing crisis in the United States. As a volunteer, you can contribute to challenges in technical or nontechnical ways, as often as you’d like–there’s something for everyone. Here are some avenues to consider:
We’re working to understand the housing ecosystem in the United States, and there’s a lot of data and information out there. Help us find and evaluate data solutions from across all 50 states and expand access to the information.
Draw connections between datasets to help create a community-level understanding of housing needs. Visualize those relationships in interactive and interpretable ways so that we can answer key community questions.
Turn research and analysis into action by creating mock-ups, user flows, written documents, or experimental applications to help folks understand the potential of a really good solution.
DataKind has compiled initial socio-economic datasets for two states: Florida and California. This data covers 350+ variables at the county and Census tract levels. These datasets are sourced from DataKind’s own Economic Opportunity Datascape product which you can further explore and use for this DataKit as needed.
DataKit data can be found in this repository's Releases.
Every bit of information you contribute is a chance for DataKind and our community partners to chip away at the housing crisis. We want to see it all–not just right answers, but wrong answers and works in progress.
We ask that you share your work regularly on Slack or in the GitHub Discussions. When you share, please define the dataset(s) you used, how you did the work , and what you think the key takeaways are from the work.
Please add your work to this repository and engage with the discussions.
