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. / Master of Urban and Regional Planning / Over the course of the COVID-19 pandemic, housing prices have risen sharply in many cities, with the highest jumps frequently occurring in previously sleepy markets like Boise City, Idaho (FHFA, 2021). One explanation given in the media and in activist circles is that local regulations are causing a shortage by making it very difficult to build more housing in popular areas. This is a sound economic argument in theory but proving it requires a way to measure how restrictive, a.k.a. "stringent", these regulations are so researchers can compare cities. But each city has its own unique code with hundreds of pages of regulations. These rules can change over time, and different cities may use the same word in different ways. Even compiling these rules can be challenging because there is no national source of information.
Researchers have been gathering their own data and inventing new measurements for decades. Some collect and read the regulations themselves, but this limits how many cities they can study at once. Others send out surveys to thousands of urban planners or real estate developers, but these provide a spotty and limited view. Still others tried to measure something simpler like the time or number of steps it takes for someone to get a building permit, but these might be different for many reasons (efficiency, number of staff, etc.) so these too are unreliable. Overall, the differences and disagreements between studies have prevented scholars from drawing definitive conclusions about the effects of these regulations on housing construction and prices (Gyourko and Molloy 2015).
I argue that an ideal measurement of regulatory stringency would be:
a) Easily calculated from open-source data available online b) Comparable within and across geographic areas c) Comparable over time in the same area(s) This thesis explores an original measurement that meets these three criteria that I call "regulatory utilization." I start by defining land use regulations and describing how economists think they affect markets for jobs, homes, and land. Next, I explain several challenges that researchers face when trying to measure these regulations and examine the main approaches that have been used in the past. Then I define my own measurement. I demonstrate how to calculate it from the open data that many cities publish on their websites, and I compare it to past approaches. I show how it relates to important topics in real estate economics and consider practical applications: to sense neighborhood change over time, inform homeowners about their redevelopment options, and help politicians and activists estimate the impacts of potential zoning changes. I conclude by summarizing and suggesting areas for further study.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/110404 |
| Date | 01 June 2022 |
| Creators | Gordner III, Gerald Marvin |
| Contributors | Urban Affairs and Planning, Bieri, David Stephan, Sanchez, Thomas W., Lee, Hyojung |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution-NonCommercial 4.0 International, http://creativecommons.org/licenses/by-nc/4.0/ |
Page generated in 0.0036 seconds