AI-driven Tax Increment Financing: Revolutionizing Transportation Infrastructure Funding

Local governments across the U.S. are facing significant challenges in funding critical transportation infrastructure improvements. While federal funding opportunities exist, communities often struggle with providing required matching funds for transportation projects. This requirement heightened the need for creative financing strategies, particularly value capture tools like Tax Increment Financing (TIF). TIF is a geographically targeted economic development tool that captures a portion of the increased property tax revenue generated by development spurred by public investments in infrastructure or other public assets and reallocates it to help finance those investments. This project introduces an innovative machine learning approach that combines advanced time series analysis with multi-factor economic modeling to generate 30-year property tax revenue forecasts. It will employ a hybrid model that integrates multiple data streams including historical land value trends, macroeconomic indicators, and planned development data. Building on TTI’s decade of expertise in TIF and value capture methodologies, this data-driven advanced AI model will enable local governments to make more informed decisions about infrastructure investments and expand the application of value capture strategies. The research aligns both Policy and Technology pillars of NCIT, leveraging artificial intelligence to enhance financial resource allocation decisions. By providing more reliable revenue predictions, this research will help reduce financial risks in infrastructure investments while promoting the broader adoption of value capture strategies. The project’s goal is to catalyze transportation infrastructure development, fostering improved mobility and economic growth across communities nationwide.

Language

• English

Project

• Status: Active
• Funding: $166,230.00
• Contract Numbers:

  69A3552344813

  69A3552348318

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  National Center for Infrastructure Transformation

  Prairie View A&M University
  Prairie View, TX  United States  77446
• Performing Organizations:

  Texas A&M Transportation Institute

  Texas A&M University System
  3135 TAMU
  College Station, TX  United States  77843-3135
• Principal Investigators:

  Gurbuz, Okan

• Start Date: 20260201
• Expected Completion Date: 20270131
• Actual Completion Date: 20270131
• USDOT Program: University Transportation Centers Program
• Source Data: 03-52-TTI

Subject/Index Terms

• TRT Terms: Artificial intelligence; Machine learning; Value capture
• Subject Areas: Data and Information Technology; Economics;

Filing Info

• Accession Number: 02001902
• Record Type: Research project
• Source Agency: National Center for Infrastructure Transformation
• Contract Numbers: 69A3552344813, 69A3552348318
• Files: UTC, RIP
• Created Date: Sep 10 2026 8:17AM