﻿ 基于增强回归树的房价影响因素分析—以波士顿地区为例 Factor Analysis of Housing Price Based on Boosting Regression Tree—Taking Boston as an Example

Statistics and Application
Vol.05 No.03(2016), Article ID:18646,6 pages
10.12677/SA.2016.53030

Factor Analysis of Housing Price Based on Boosting Regression Tree

—Taking Boston as an Example

Jia Sheng, Dongdong Pan

School of Mathematics and Statistics, Yunnan University, Kunming Yunnan

Received: Sep. 6th, 2016; accepted: Sep. 22nd, 2016; published: Sep. 29th, 2016

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.

Keywords:Regression Tree, Boosting, Housing Price, Factor Analysis

—以波士顿地区为例

1. 引言

2. 回归树与增强法

3. 波士顿地区房价数据

4. 建模及结果分析

Table 1. Boston area house price data variable

Figure 1. The error changes with the training times of the curve, the top of the CV error curve, the bottom for the training error curve

Figure 2. The relative influence of the different factors in the model

Figure 3. Partial dependence curves of several important factors of the housing prices, from left to right from the top down factors that are: low level, the proportion of people per room to room number, the average weighted distance, the center of crime rate, the concentration of nitrogen oxides

5. 总结

Factor Analysis of Housing Price Based on Boosting Regression Tree—Taking Boston as an Example[J]. 统计学与应用, 2016, 05(03): 299-304. http://dx.doi.org/10.12677/SA.2016.53030

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