The Possibilities and Perils of Data-Driven Preservation Research: Lessons from a Multi-Year Study of Federal Historic Tax Credits

Federal historic rehabilitation tax credits (HTCs) spur thousands of historic preservation projects around the nation. Wide-ranging arguments praise the power of HTCs to underpin urban revitalization. For example, the National Park Service (NPS), which oversees the program, claims that it is “the nation’s most effective program to promote historic preservation and community revitalization through historic rehabilitation” and that it “generates much needed jobs, enhances property values in older communities, creates affordable housing, and augments revenue for Federal, state and local governments.” Listokin, Listokin, & Lahr argue that “the most significant program involving historic preservation and the production of housing (including affordable units)…is the historic rehabilitation tax credit” and, after the housing crisis in 2008, NPS claimed that the HTC “continues to be a major stimulus for economic recovery in older communities throughout the nation.” Descriptive statistics and highly aggregated data underpin these compelling arguments. Recent studies quantify the program’s economic impact at the federal level, yet minimal scholarship examines the HTCs’ use and effects in cities, neighborhoods and downtowns. Urban policymakers have minimal understanding about where the private sector invests in historic buildings, how these projects shape cities and neighborhoods, and if and how HTCs interact with other urban revitalization efforts.To better understand the spatial dynamics, change over time, and sub-national effects of the HTC, we conducted a multi-year study of the federal HTC using a limited dataset provided by the Technical Preservation Services (TPS) division of NPS. The broad research question underpinning this work was: What is the impact of HTC activity on urban places? Furthermore, we explored methods for evaluating and analyzing community and economic development effects, spatial distribution and patterns of investment, and variations across different market contexts.