The Costs of Housing Regulation: Evidence From Generative Regulatory Measurement

Abstract

We introduce a new approach to decode and interpret statutes and administrative documents employing Large Language Models (LLMs) for data collection and analysis that we call generative regulatory measurement. We use this tool to construct a detailed assessment of U.S. zoning regulations. We estimate the correlation of these housing regulations with housing costs and construction. Our work highlights the efficacy and reliability of LLMs in measuring and interpreting complex regulatory datasets.

Keywords: housing regulation, zoning codes, large language models, natural language processing, artificial intelligence, municipal ordinances

JEL Classification: R52, R58, K11, O38, R31, C81