Urban development and housing policy currently assess the value of new housing at the metropolitan level, missing crucial differences between city centers and suburbs. For example, an apartment in Manhattan might provide greater economic benefits than one in Westchester, even though both are in the New York metro area. This oversight leads to inefficient resource allocation, affecting job access, income growth, and innovation potential. A more refined approach could help policymakers, developers, and researchers make data-driven decisions.

Understanding Hyper-Local Housing Value

Instead of treating whole metro areas as uniform, this project would measure housing value at a granular level—comparing downtown cores to suburban and exurban areas. The analysis would focus on two key economic drivers:

• Earnings potential: Do people earn more when they live closer to job centers, controlling for other factors?
• Innovation spillovers: Are densely populated urban centers more conducive to knowledge sharing and entrepreneurship?

To answer these questions, one approach could involve analyzing anonymized earnings data from tax records, tracking job access via LinkedIn or transit maps, and using land price differences to distinguish economic value from amenities like parks or school quality.

Policy and Practical Applications

For urban planners, the findings could guide zoning laws and infrastructure investments, helping cities prioritize high-impact housing developments. Developers might adjust projects based on long-term economic returns rather than short-term demand. Additionally, policymakers could design targeted subsidies or density bonuses to balance affordability with economic benefits.

Execution Strategies

A scaled approach could start with a pilot study of two major cities—such as New York and Chicago—using existing datasets like Census microdata and Zillow price trends. If successful, the methodology could expand to include:

1. Incorporating patent filings and business registrations to measure innovation.
2. Building econometric models to isolate the causal effects of location on earnings.
3. Simulating policy scenarios, such as tax incentives for central-city housing.

While challenges like data gaps and endogeneity (e.g., ambitious workers moving downtown) exist, instrumental variables (e.g., new subway lines) could help isolate true location effects.

This approach offers a way to refine urban development strategies, moving beyond broad metro-level assessments to pinpoint where new housing creates the most value.