Zoning in American Cities: Are Reforms Making a Difference? An AI-based Analysis

TL;DR

This study uses NLP to analyze over 2000 U.S. zoning documents, revealing that form-based codes promote sustainable urban forms, improve walkability, and increase multi-family housing, indicating positive impacts of zoning reforms.

Contribution

It introduces an NLP-based methodology to evaluate zoning codes at scale and provides empirical evidence of the benefits of form-based codes for urban sustainability.

Findings

Widespread adoption of FBCs across U.S. cities

FBCs correlate with higher density and walkability

FBCs are linked to increased multi-family housing

Abstract

Cities are at the forefront of addressing global sustainability challenges, particularly those exacerbated by climate change. Traditional zoning codes, which often segregate land uses, have been linked to increased vehicular dependence, urban sprawl, and social disconnection, undermining broader social and environmental sustainability objectives. This study investigates the adoption and impact of form-based codes (FBCs), which aim to promote sustainable, compact, and mixed-use urban forms as a solution to these issues. Using Natural Language Processing (NLP) techniques, we analyzed zoning documents from over 2000 U.S. census-designated places to identify linguistic patterns indicative of FBC principles. Our findings reveal widespread adoption of FBCs across the country, with notable variations within regions. FBCs are associated with higher floor-to-area ratios, narrower and more consistent street setbacks, and smaller plots. We also find that places with FBCs have improved walkability, shorter commutes, and a higher share of multi-family housing. Our findings highlight the utility of NLP for evaluating zoning codes and underscore the potential benefits of form-based zoning reforms for enhancing urban sustainability.