Factor Analysis of Housing Price Based on Boosting Regression Tree—Taking Boston as an Example
Abstract: Housing price is a very important index which can reflect the economic and social development level and situation of a certain region or city. It is of great theoretical value and practical meaning to study important factors influencing housing price as well as their influence patterns and magnitude. Boosting regression tree has been recently developed as one of the most prevalent nonparametric modeling methods in the fields of machine learning, which has desirable properties such as high efficiency as well as easy-interpretation. In this paper, we take the housing price data in Boston as an example and try to analyze factors determining housing price based on Boosting Regression Tree method. We identify some relatively significant factors by comparing their relative importance in the model and also investigate their influence patterns. Results in this paper could be reasonably extended to housing price researches of some Chinese first-tire cities.
文章引用: 盛佳 , 潘东东 (2016) 基于增强回归树的房价影响因素分析—以波士顿地区为例。 统计学与应用， 5， 299-304. doi: 10.12677/SA.2016.53030
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