| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w23326 |
| 来源ID | Working Paper 23326 |
| Estimating the Gains from New Rail Transit Investment: A Machine Learning Tree Approach | |
| Seungwoo Chin; Matthew E. Kahn; Hyungsik Roger Moon | |
| 发表日期 | 2017-04-17 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | 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. Using the opening of a major new subway in Seoul, we contrast hedonic estimates based on multivariate hedonic methods with a machine learning approach that allows us to estimate these heterogeneous effects. While a majority of the "treated" apartment types appreciate in value, other types decline in value. We explore potential mechanisms. We also cross-validate our estimates by studying what types of new housing units developers build in the treated areas close to the new train lines. |
| 主题 | Regional and Urban Economics |
| URL | https://www.nber.org/papers/w23326 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/581000 |
| 推荐引用方式 GB/T 7714 | Seungwoo Chin,Matthew E. Kahn,Hyungsik Roger Moon. Estimating the Gains from New Rail Transit Investment: A Machine Learning Tree Approach. 2017. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w23326.pdf（826KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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