Large Language Models and Quantifying the Regulatory Expenses of Affordable Housing: A Thorough Examination Utilizing Generative Assessment

Abstract

This thesis presents an innovative methodology using Large Language Model-based methods to extract and quantify housing regulations from municipal zoning codes, making possible the most comprehensive examination of regulatory costs at the municipal level across California to date. A multi-staged extraction framework is devised that delivers 85-95% accuracy in the identification and standardization of complex regulatory requirements from legal documents. Applying this methodology to over twenty California cities over the period 2015-2025, it is estimated that regulatory constraints raise the cost of developing a housing unit by roughly between 5% to 10% (or $50,000 and $100,000+) per housing unit, with the most acute constraints in the state’s coastal metros. This method is used to find that factors such as regulation costs limit housing supply elasticity from 1.24 in low-regulation jurisdictions to 0.08 in high-regulation areas. The LLM-based framework allows us to conduct analyses at an unprecedented scale and granularity and to reveal, for example, that the relaxation of regulation by streamlining policies like the Los Angeles Transit Oriented Communities program boosts housing production in eligible zoned areas by 43%. This study makes significant contributions to the restructuring of California’s housing regulation system in response to the affordability crisis, and its methodology presents a replicable tool for regulatory analysis in other policy domains.