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Local zoning codes affect a broad range of policies. For example, they can constrain housing supply and create affordability challenges (Ikeda and Washington 2015; Glaeser and Ward 2006), contribute to housing segregation (Greene at al. 2017; Pendall 2000), and determine how well a jurisdiction responds to changes in demographics and climate (Micklow and Warner 2014; Nolon 2013).

Despite the importance of local zoning regulations, research that measures the restrictiveness of zoning laws across places and the impact of restrictive zoning across a range of outcomes is remarkably thin. One reason is that zoning codes are long, technical, and difficult to access. Consequently, there is no up-to-date national dataset or reliable standard practice used to compare zoning codes.

To address this gap, we explore whether it is possible to merge property assessment records with granular data on zoning policies to generate a model that “predicts” zoning regulations. Using such a model, we could build an accurate, publicly accessible dataset of zoning regulations across the US.

In this paper, we test this hypothesis with a proof of concept in Washington, DC. We combine property assessment records provided by Zillow (ZTRAX) with data from the Washington, DC, Office of Zoning, and using a random forest regression, predict zoning characteristics in zones with residences.

We find that we are able to use property records to predict density limits with a relatively high degree of accuracy. Our model itself is exploratory and has many limitations: it uses a relatively small set of zones and depends greatly on how properties with missing zone designations are assigned to zones. Additionally, model predictions are largely directional, and vary in accuracy by zone type. We focus on one variable in the zoning code—density limits—and one city—Washington, DC—and thus more work is needed to determine whether the model can generalize to different jurisdictions and zoning regulations. Yet, preliminary directional success suggests that pursuing this work at a larger scale would be fruitful, and it would offer significant benefit to researchers seeking to study the effects of zoning across a wide array of policy domains.