Regulatory Utilization: A Novel Measure of Public Land Use Controls Comparable Across Space and Time and Calculable from Open-Source Data

Over the course of the COVID-19 pandemic, housing prices have risen sharply and ubiquitously, with the highest jumps frequently occurring in previously sleepy markets like Boise City, Idaho (FHFA, 2021). One explanation touted in the media and in "YIMBY" activist circles is the restrictive effect of land use regulation on housing supply. Although economic theory generally accords with this explanation, attempts to quantify the effects of land use regulations on housing supply have faced significant conceptual and practical challenges. Conceptually, land use regulations are difficult to measure because regulations are multidimensional, dynamic and political, among other challenges. Practically, there is no national database of land use regulations, so researchers have typically gathered their own data and created their own measures of regulatory stringency, either directly—typically by reading and interpreting hundreds of pages of legalese per city or surveying thousands of urban planners—or indirectly—by connecting land use regulations to a different, more easily measured, quality like time required for a permit or percentage of permits accepted, or inferring effects from natural experiments. Methodological differences between time periods studied, types of regulations measured, numbers and types of jurisdictions included, and level of spatial analysis have frustrated efforts to unify the lessons of each study into a coherent whole (Gyourko and Molloy 2015). What is needed is a way to quantify and analyze land use regulations that is: a) Easily calculated from readily available open-source data b) Comparable within and across geographic areas at multiple scales c) Comparable within and across geographic areas over time This thesis explores an original measurement that meets the criteria above: regulatory utilization, which is the used proportion of a regulatory limit. It defines Ru and demonstrates its calculation from municipal GIS and administrative data. It explores the advantages and disadvantages compared to current approaches. And it demonstrates a method for combining many different Ru values into two aggregate metrics: density utilization and bulk utilization. The next section relates these aggregates to 3 important topics in real estate economics: real options, price elasticity of supply, and land leverage. It continues by suggesting applications in identifying and interpreting neighborhood change, calculating a "build score" (similar to a "walk score") for parcels, and estimating the impact of policy reforms. Directions for future research are outlined in the conclusion.