Property Tax Assessment Procedures and Housing Affordability
A new property tax revolt is taking shape across the United States. As rising housing costs and broader cost-of-living pressures strain household budgets in the post-COVID era, legislatures in a majority of states are considering policies that cap assessments, expand exemptions, sharply reduce property tax bills, or even eliminate major categories of property taxation. These measures promise immediate relief to homeowners, but they raise a fundamental question: do property tax cuts actually make housing more affordable, or are their benefits quickly absorbed into higher home prices?
This project will answer that question at a nationwide scale by building a novel database of property tax assessors and local reassessment practices. By documenting how often homes are revalued, when assessments reset, and how these rules change over time, this project will identify when and how property tax relief is capitalized into home values. The results will show who might ultimately gain from today’s tax revolt: existing homeowners, prospective buyers, renters, or the communities that depend on property taxes to fund local services.
Requisite Skills and Qualifications
The data acquisition portion of this project involves compiling records from municipal tax authorities, news articles, and related government websites. This may require a large number of web searches, web scraping (i.e. batch downloading files and text, or converting PDFs to tabular format in Excel), submitting written data requests via email to the relevant public official(s), and cleaning the raw data to ensure consistency across jurisdictions. Therefore, attention to detail and professional writing skills are crucial to the execution of the research.
Prior experience with web scraping is preferred. I am especially interested in candidates who have experience with SQL/Postgres and fuzzy string matching techniques. However, familiarity with these tools is not a requirement.
Basic proficiency in statistical/econometric software packages such as R/Python/Stata, and ideally in agentic AI, is required for aspects of the project which involve cleaning the source data collected. I welcome applicants with interests in other fields besides economics – including but not limited to – finance, urban studies, computer science, mathematics/statistics, and physics.