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作者

Seungwoo Chin;Matthew E. Kahn;Hyungsik Roger Moon

来源

REAL ESTATE ECONOMICS,Vol.48,Issue3,P.886-914

语言

英文

关键字

作者单位

E-mail address: chinseun@usc.edu Department of Economics, University of Southern California, Los Angeles, CA, 90089 E-mail address: chinseun@usc.edu, E-mail address: kahnme@usc.edu, E-mail address: moonr@usc.edu;E-mail address: kahnme@usc.edu Department of Economics, University of Southern California, Los Angeles, CA, 90089 E-mail address: chinseun@usc.edu, E-mail address: kahnme@usc.edu, E-mail address: moonr@usc.edu;E-mail address: moonr@usc.edu Department of Economics, Dornsife INET, Lusk Center of Real Estate, University of Southern California, Los Angeles, CA 90089 and the School of Economics, Yonsei University, Seoul 03722, Korea E-mail address: chinseun@usc.edu, E-mail address: kahnme@usc.edu, E-mail address: moonr@usc.edu

摘要

Urban rail transit investments are expensive and irreversible. As people differ with respect to their demand for trips, their value of time, and the types of real estate they live in, such projects are likely to offer heterogeneous benefits to residents of a city. Defining the opening of a major new subway in Seoul as a treatment for apartments close to the new rail stations, we contrast hedonic estimates based on multivariate hedonic methods with a machine learning (ML) approach. This ML approach yields new estimates of these heterogeneous effects. While a majority of the “treated” apartment types appreciate in value, other types decline in value. We cross‐validate our estimates by studying what types of new housing units developers build in the treated areas close to the new train lines.