Machine learning for inference: using gradient boosting decision tree to assess non-linear effects of bus rapid transit on house prices
Document Type
Journal Article
Publication Date
2021
Subject Area
mode - bus rapid transit, place - asia, place - urban, land use - impacts
Keywords
House price, Bus Rapid Transit (BRT), Gradient Boosting Decision Tree (GBDT), machine learning, hedonic pricing model
Abstract
The adoption of bus rapid transit (BRT) systems has gained worldwide popularity over the past several decades. China is no exception as it has long been aiming at promoting public transportation. Prior studies have provided extensive evidence that BRT has substantial effects on house prices with traditional econometric techniques, such as hedonic pricing models. However, few of those investigations have discussed the non-linear relationship between BRT and house prices. Using the Xiamen data, this study employs a machine learning technique, namely the gradient boosting decision tree (GBDT), to scrutinize the non-linear relationship between BRT and house prices. This study documents a positive association between accessibility to BRT stations and house prices and a negative association between proximity to the BRT corridor and house prices. Moreover, it suggests a non-linear relationship between BRT and house prices and indicates that GBDT has more substantial predictive power than hedonic pricing models.
Rights
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Recommended Citation
Yang, L., Liang, Y., Zhu, Q., & Chu, X. (2021). Machine learning for inference: using gradient boosting decision tree to assess non-linear effects of bus rapid transit on house prices. Annals of GIS, Vol. 27(3), pp. 273-284.
