The adoption of land value taxes (LVTs) is theoretically promising for addressing urban sprawl, housing shortages, and inefficient tax systems, but real-world evidence of their impact remains fragmented. Existing research focuses on isolated cases like Pittsburgh or Hong Kong, leaving policymakers without robust, systematic comparisons. This gap makes it difficult to assess whether LVTs genuinely incentivize redevelopment or improve economic efficiency, limiting their broader adoption despite their potential benefits.

Evaluating the Impact of Land Value Taxes

One way to rigorously assess LVTs would involve a multi-phase approach:

• Literature Synthesis: Compile existing case studies to identify common outcomes and methodological gaps.
• Empirical Analysis: Use quasi-experimental methods such as difference-in-differences (comparing cities before/after LVT adoption) and border regression discontinuity (analyzing parcels on either side of taxing/non-taxing jurisdictions). These techniques could isolate LVT effects from external factors like zoning changes.
• Policy Simulation: Model how LVTs might perform in new settings—such as high-growth cities—based on empirical findings.

Key Stakeholders and Incentives

The findings could benefit multiple groups:

• Policymakers: Municipal governments need evidence to determine if LVTs could spur development without disrupting tax revenues.
• Urban Planners: Agencies tackling housing shortages or sprawl could refine zoning and tax policies based on insights.
• Economists: Researchers studying tax incidence or land markets would gain causal estimates rarely found in current literature.
• Advocacy Groups: Proponents of LVTs could use data to strengthen campaigns, while skeptics would gain empirical clarity.

Execution and Overcoming Barriers

To ensure credible results, the project could start with publicly available data (e.g., Pennsylvania's LVT records) in a pilot phase before expanding geographically. Potential challenges—like incomplete historical records—could be mitigated by focusing on recent adopters with digital archives. Another hurdle, political bias, might be addressed by pre-registering analysis plans to prevent cherry-picking of results.

By combining rigorous causal methods with practical policy modeling, this approach could provide much-needed clarity on LVTs while adapting to diverse urban contexts.