Housing market hedonic price study based on boosting regression tree

被引：8

作者：

Gu G. ^{[1
,2
,3
]}

Xu B. ^{[1
]}

机构：

[1] Research Institute of Quantitative Economics, Zhejiang Gongshang University, Hangzhou, Zhejiang

[2] School of Economics and Management, Zhejiang A and F University, Hangzhou, Zhejiang

[3] Center for China Farmers' Development of Zhejiang lin'An, Hangzhou, Zhejiang

来源：

Journal of Advanced Computational Intelligence and Intelligent Informatics | 2017年 / 21卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Gradient boosting; Machine learning; Regression tree; Residential hedonic price;

D O I：

10.20965/jaciii.2017.p1040

中图分类号：

学科分类号：

摘要：

Based on the purchase price data of new real estate markets three cities in China, Beijing, Shanghai, and Guangzhou, including architectural features, neighborhood property features, and location features, in this study a boosting regression tree model was built to study the factors and the influence path of housing prices from the microcosmic perspective. First, a classical hedonic price model was constructed to analyze and compare the significant effect factors on housing prices in the market segments of the three cities. Second, the gradient boosting regression tree method that is proposed in this paper was applied to the three markets in combination to analyze the influence paths and factors and the importance of the type of housing hedonic price. The influence paths of housing hedonic prices and decision tree rules are visualized. The significant housing features are effectively extracted. Finally, we present three main conclusions and several suggestions for policy makers to improve urban functions while stabilizing real estate prices.

引用

页码：1040 / 1047

页数：7

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