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Estimating the Gains from New Rail Transit Investment: A Machine Learning Tree Approach

Seungwoo Chin, Matthew E. Kahn, Hyungsik Roger Moon
Urban rail transit investments are expensive and irreversible. Since 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 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.