EconPapers    
Economics at your fingertips  
 

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

Seungwoo Chin, Matthew Kahn () and Hyungsik Roger Moon

Real Estate Economics, 2020, vol. 48, issue 3, 886-914

Abstract: 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.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.1111/1540-6229.12249

Related works:
Working Paper: Estimating the Gains from New Rail Transit Investment: A Machine Learning Tree Approach (2017) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:reesec:v:48:y:2020:i:3:p:886-914

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1080-8620

Access Statistics for this article

Real Estate Economics is currently edited by Crocker Liu, N. Edward Coulson and Walter Torous

More articles in Real Estate Economics from American Real Estate and Urban Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2022-06-24
Handle: RePEc:bla:reesec:v:48:y:2020:i:3:p:886-914