Identification of the geographical extent of an area benefiting from a transportation project: A generalized synthetic control


  • Takara Kunimi
  • Hajime Seya Kobe University



In evaluating the benefits of an infrastructure project, it is essential to consider who is benefiting from the project and where benefits are located. However, there is no established way to accurately determine the latter. To fill this methodological gap, this study proposes an approach for the ex-post identification of the geographical extent of an area benefiting from a transportation project based on a generalized synthetic control method. Specifically, it allows comparing multiple treatment units with their counterfactuals in a single run—changes in land prices (actual outcome) at each treated site are compared to the counterfactual outcome, and the individual (i.e., unit-level) treatment effect on the treated site is then estimated. This approach is empirically applied to a large-scale Japanese heavy railway, the Tsukuba Express line project. Our approach enables the detection of 1) the complicated spatial shape of benefit incidence; 2) negative spillovers; and 3) the increase in options (train routes), typically not considered in a benefit evaluation system based on the hedonic approach, but which can be capitalized into land prices.


Abadie, A., Diamond, A., Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493–505.

Ahsan, H. M., Nakamura, H., & Ueda, T. (2001). A land use-transport model: The structure and applications. Journal of Civil Engineering, 29(2), 219–235.

Athey, S., Bayati, M., Doudchenko, N., Imbens, G., & Khosravi, K. (2018). Matrix completion methods for causal panel data models (No. w25132). Cambridge, MA: National Bureau of Economic Research.

Bai, J. (2009). Panel data models with interactive fixed effects. Econometrica, 77(4), 1229–1279.

Cameron, T. A. (2006). Directional heterogeneity in distance profiles in hedonic property value models. Journal of Environmental Economics and Management, 51(1), 26–45.

Cervero, R., & Landis, J. (1995). The transportation-land use connection still matters. Access, 7, 2–11.

Debrezion, G., Pels, E., & Rietveld, P. (2007). The impact of railway stations on residential and commercial property value: A meta-analysis. The Journal of Real Estate Finance and Economics, 35(2), 161–180.

Dubé, J., Thériault, M., & Des Rosiers, F. (2013). Commuter rail accessibility and house values: The case of the Montreal South Shore, Canada, 1992–2009. Transportation Research Part A, 54, 49–66.

Ewing, R. (2008). Highway-induced development: Research results for metropolitan areas. Transportation Research Record, 2067, 101–109.

Geng, B., Bao, H., & Liang, Y. (2015). A study of the effect of a high-speed rail station on spatial variations in housing price based on the hedonic model. Habitat International, 49, 333–339.

Gibbons, S., & Machin, S. (2005). Valuing rail access using transport innovations. Journal of Urban Economics, 57(1), 148–169.

Handy, S. (2005). Smart growth and the transportation-land use connection: What does the research tell us? International Regional Science Review, 28(2), 146–167.

Kawada, M., Okamoto, N., Ishida, H., & Tsutsumi, M. (2010). Effects of the Tsukuba Express Project on the residents’ travel behavior. Journal of the Eastern Asia Society for Transportation Studies, 8, 539–547.

Kuminoff, N. V., & Pope, J. C. (2014). Do “Capitalization effects” for public goods reveal the public’s willingness to pay? International Economic Review, 55(4), 1227–1250.

Laird, J., Geurs, K., & Nash, C. (2009). Option and non-use values and rail project appraisal. Transport Policy, 16(4), 173–182.

Mizutani, C. (2012). Construction of an analytical framework for polygon-based land use transition analyses. Computers, Environment and Urban Systems, 36(3), 270–280.

Morisugi, H., & Ohno, E. (1992). A benefit incidence matrix for urban transport improvement. Papers in Regional Science, 71(1), 53–70.

Morisugi, H., & Ohno, E. (1995). Proposal of a benefit incidence matrix for urban development projects. Regional Science and Urban Economics, 25(4), 461–481.

Padeiro, M., Louro, A., & da Costa, N. M. (2019). Transit-oriented development and gentrification: A systematic review. Transport Reviews, 39(6), 733–754.

Pior, M. Y., & Shimizu, E. (2001). GIS-aided evaluation system for infrastructure improvements: Focusing on simple hedonic and Rosen’s two-step approaches. Computers, Environment and Urban Systems, 25(2), 223–246.

Pior, M. Y., Shimizu, E., & Nakamura, H. (1998). GIS-aided benefit evaluation system for urban railway improvements: Focusing on hedonic approach. Theory and Applications of GIS, 6(2), 11–22.

Seya, H., & Timmermans, H. (2018) An overview of Asian studies on transport and land use. In Junyi Zhang and Cheng-Min Feng (Eds.), Routledge Handbook of Transport in Asia (pp. 314–336). London: Routledge.

Seya, H., Yoshida, T. & Tsutsumi, M. (2016). Ex-post identification of geographical extent of benefited area by a transportation project: Functional data analysis method. Journal of Transport Geography, 55, 1–10.

Shimizu, C., & Nishimura, K. (2006). Biases in appraisal land price information: The case of Japan. Journal of Property Investment & Finance, 24(2), 150–175.

Sorensen, A. (2001). Subcentres and satellite cities: Tokyo’s 20th century experience of planned polycentrism. International planning studies, 6(1), 9–32.

Tsutsumi, M., & Seya, H. (2008). Measuring the impact of large-scale transportation project on land price using spatial statistical models. Papers in Regional Science, 87(3), 385–401.

Tsutsumi, M., & Seya, H. (2009). Hedonic approaches based on spatial econometrics and spatial statistics: Application to evaluation of project benefits. Journal of Geographical Systems, 11(4), 357–380.

Xu, Y. (2017). Generalized synthetic control method: Causal inference with interactive fixed effects models. Political Analysis, 25(1), 57–76.

Yamagata, Y., & Seya, H. (Eds.). (2019). Spatial analysis using big data: Methods and urban applications. Cambridge, MA: Academic Press.




How to Cite

Kunimi, T. ., & Seya, H. (2021). Identification of the geographical extent of an area benefiting from a transportation project: A generalized synthetic control. Journal of Transport and Land Use, 14(1), 25–45.