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https://nap.nationalacademies.org/catalog/27432/critical-issues-in-transportation-for-2024-and-beyond

Assessing the Impact of Light Rail Transit on Land Values and Tax Revenues

Light Rail Transit (LRT) has become an increasingly popular centerpiece of smart growth policies in the United States, yet considerable debate exists about its cost-effectiveness. The overarching question that we propose to address in this research is "What are the early returns to proposed and under-construction Light-Rail Transit (LRT) investment and how do these vary across neighborhoods?" It may take 10 or 20 years to generate substantial revitalization of a given neighborhood, but early increases in property value and tax collections may stimulate neighborhood change, and defray capital costs. A related question is "What is the best methodology to address the responses to LRT at the neighborhood level, while simultaneously measuring changes in property values at a regional scale rather than focusing solely on station area impacts?" We propose also to separate agglomeration effects (such as neighborhood commercial and residential development) from those associated with change in mobility (such as better access to downtown). Accordingly, a team of researchers with expertise in smart growth, spatial and temporal econometric modeling, investment decision making, and GIS/GIS-T , will build a series of models to estimate the effects of LRT on the land markets. Detailed GIS-based data on land-use, new construction, and residential transactions will be analyzed for the LRT system in Phoenix, Arizona, for three distinct phases--pre-approval, planning, and construction. Innovative independent variables include dynamic measures of accessibility at the metropolitan scale derived from GIS-T models, and a proxy for neighborhood change derived from data on new construction. Modeling techniques capable of disentangling neighborhood and regional effects include Geographically Weighted Regression (GWR) and a local Spatial and Temporal Autoregressive (STAR) model. Comparison of out-of-sample predictive ability and spatial autocorrelation in the models' residuals will inform on performance, and provide a more nuanced understanding of the dynamics set into motion by decisions to build LRT, quantify the spatial distribution and extent of early returns on investment at the metropolitan scale, and provide more effective decision-making on this smart growth tool.

    Language

    • English

    Project

    • Status: Active
    • Funding: $71490.00
    • Contract Numbers:

      DTRT06-G-0025

      CTLS 08-01

    • Sponsor Organizations:

      Center for Transportation and Urban Planning

      University of Connecticut, Storrs
      179 Middle Turnpike Unit 5202
      Storrs, CT  United States  06268-5202
    • Project Managers:

      Lownes, Nicholas

    • Principal Investigators:

      Cromley, Robert

      Lownes, Nicholas

      Clapp, John

      Atkinson-Palombo, Carol

    • Start Date: 20080822
    • Expected Completion Date: 0
    • Actual Completion Date: 0
    • Source Data: RiP Project 18413

    Subject/Index Terms

    Filing Info

    • Accession Number: 01483024
    • Record Type: Research project
    • Source Agency: Center for Transportation and Urban Planning
    • Contract Numbers: DTRT06-G-0025, CTLS 08-01
    • Files: UTC, RIP
    • Created Date: Jun 4 2013 1:00AM