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Determining Transit’s Impact on Seoul Commercial Land Values: An Application of Spatial Econometrics

Author

Listed:
  • Jin Kim

    () (Texas A&M University, Hensel Dr. #Y2C, College Station, TX 77840-3137)

  • Ming Zhang

    () (University of Texas at Austin, University Station B7500, Austin, TX 78712-0222)

Abstract

Literature regarding transit’s impact on land values reports mixed results concerning the economic benefits of accessibility to subway stations, specifically regarding commercial properties. After examining 731 commercial land values in Seoul, Korea, this study suggests a possible explanation for the mixed results: transit’s discrimination impact on land values by location in a built-up urban area. The regression coefficient for distance to station in the central business district is the highest, the subcenters are next, and other areas are lowest – apparently a strong correlation with higher centrality and development densities of submarkets. Also, the inclusion of spatial lag and error term variables greatly improves the goodness of fit of the regression equations lowering the spatial autocorrelation in the ordinary least squares residuals as well as reduces overestimation of value premiums in association with rail transit stations, which enables a regression model to produce a more accurate and efficient estimator for transit’s impact on commercial land values.

Suggested Citation

  • Jin Kim & Ming Zhang, 2005. "Determining Transit’s Impact on Seoul Commercial Land Values: An Application of Spatial Econometrics," International Real Estate Review, Asian Real Estate Society, vol. 8(1), pages 1-26.
  • Handle: RePEc:ire:issued:v:08:n:01:2005:p:1-26
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    Cited by:

    1. Xu, Tao & Zhang, Ming & Aditjandra, Paulus T., 2016. "The impact of urban rail transit on commercial property value: New evidence from Wuhan, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 223-235.
    2. Rodríguez, Daniel A. & Mojica, Carlos H., 2009. "Capitalization of BRT network expansions effects into prices of non-expansion areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(5), pages 560-571, June.
    3. Seungwoo Chin & Matthew E. Kahn & Hyungsik Roger Moon, 2017. "Estimating the Gains from New Rail Transit Investment: A Machine Learning Tree Approach," NBER Working Papers 23326, National Bureau of Economic Research, Inc.
    4. repec:eee:regeco:v:66:y:2017:i:c:p:74-90 is not listed on IDEAS

    More about this item

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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