Zoning, a topic usually buried in the real estate sections of local newspapers or debated at community planning meetings, has recently taken the national stage. Zoning figures prominently in the debates over the value that Amazon HQ2 will deliver to Arlington, Virginia, and New York City, while coverage of the affordable housing crisis in states like California blames local zoning restrictions. Cities like Minneapolis, which is radically rewriting its zoning codes, are being hailed as national models.
Growing political consensus indicates that local zoning is a national issue. In 2016, the Obama administration released a report (PDF) targeting zoning restrictions as a barrier to affordable housing. Secretary of housing and urban development Ben Carson made similar arguments about zoning’s effects.
Senators Elizabeth Warren and Cory Booker have each proposed legislation to provide localities incentives to ease local zoning restrictions, taking a page from former Republican congressman Jack Kemp’s never-played playbook.
Now that zoning is in the spotlight and gaining political traction, we must use evidence to drive policy and reform at all levels. But how much do we know about zoning, its variations across the US, and the effectiveness of various reform options? As it turns out, we don’t know as much as we would like.
It’s hard to make good decisions without good data
Even though there has been some promising research on local zoning and land-use regulations, most has been hampered by a lack of data. How has zoning changed? What reforms were implemented and where? How have various reforms affected housing supply, affordability, and climate change? We either don’t know or know very little because data are out of date, incomplete, or do not exist.
Our best national data sources consist of two surveys of local governments in the nation’s largest metropolitan areas dated 1994 and 2003, a regulatory index derived from a survey of local governments in 2007, and some recent papers that try to approximate zoning data using court decisions or construction costs (PDF). While some of these data are publicly available, many are not, and they all tell us different things about zoning or land-use regulations at different, specific times.
As our former colleague Sam Bieler wrote regarding gun violence data, this situation is the equivalent of trying to make investing decisions today using a 15-year-old interview with a CEO and a 10-year-old corporate prospectus. It’s hard to make good decisions without current, accurate information.
We are working to fill the gaps in zoning data to make better policy
Here at the Urban Institute, we’re working to fill these gaps in zoning data to elevate effective strategies to address our nation’s most pressing issues, such as climate change, affordable housing, and access to high-quality education.
We are working on several projects to update these older data sources while using fresh approaches to collect more timely zoning data. Several of these projects will be released in the coming year.
One of our first publications tests whether we can estimate density limits in local zoning codes using property assessment data. We manually created a dataset of density limits in every zone in Washington, DC, and trained a machine learning model to estimate these density characteristics based on various characteristics of the underlying properties within the zones, such as lot area and type of building.
We found that property assessment data are an accurate predictor of density information, and we plan to expand this model to additional jurisdictions.
We hope such a model could be used to create standard, comparable, and regularly updated zoning data in several US metropolitan areas, given the availability of nationally collected local assessment data. The data generated by the model could inform policymakers and local communities, spur innovative research, and drive better housing, environmental, and education policy.
Zoning is becoming a hot topic, and smart reform could address some of the most pressing policy issues of our time. But to solve these issues, we must first gain access to the data we need and learn more about what works.