基于空间网络自回归变点模型的合肥市房地产价格影响因素分析

doi:10.7523/j.issn.2095-6134.2020.03.013

中国科学院大学学报 ›› 2020, Vol. 37 ›› Issue (3): 398-404.DOI: 10.7523/j.issn.2095-6134.2020.03.013

基于空间网络自回归变点模型的合肥市房地产价格影响因素分析

周佳琪, 金百锁

中国科学技术大学管理学院统计与金融系, 合肥 230026

收稿日期:2018-06-15 修回日期:2019-01-17 发布日期:2020-05-15
通讯作者: 周佳琪
基金资助:
国家自然科学基金（71873128，11571337）资助

Analysis for influencing factors of real estate price in Hefei based on spatial network auto-regressive transformation model

ZHOU Jiaqi, JIN Baisuo

Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China

Received:2018-06-15 Revised:2019-01-17 Published:2020-05-15

摘要/Abstract

摘要： 基于合肥市普通住宅价格2016-2017年的交易数据，利用空间插值法和趋势分析法，对住宅价格的空间变化进行分析，发现合肥市住宅价格有从南到北逐渐递减、在东西方向上由中心向边缘递减的趋势，拓展了Jin等的两阶段变点估计方法。运用新的变点检测算法，找到一个变点，从而将住宅价格分成两个区间，分别建立空间网络自回归模型。研究结果表明，合肥市住宅价格空间自相关显著，在空间上有明显的集聚特点。对比模型效果，发现找出房价的变点再分别建立空间网络自回归模型的效果更好。影响房价的因素有很多，商业区、地铁、学区、容积率和总建筑面积等均对房价有一定的影响。

关键词: 空间自相关, 空间网络自回归变点模型, 变点

Abstract: The transaction data of ordinary residential house prices in Hefei City from 2016 to 2017 was considered. By using the spatial interpolation method and trend analysis method, the spatial changes in residential prices were analyzed. It was found that the house prices in Hefei gradually decreased from south to north and decreased from the center to the edge districts in the east-west direction. Expanding the two-phase change-point estimation method of Jin et al. and using the new change-point detection algorithm we found a change point which divided the residential price into two intervals, and we analyzed separately to establish a spatial lag model. The research results show that the residential prices in Baohe District show a strong spatial auto-correlation, and there are obvious spatial agglomeration characteristics. It is better to build a spatial lag model by finding out the change points and then separately building the spatial network auto-regressive models. There are many factors that affect house prices. Business districts, subway stations, school districts, plot ratios, and total floor area all have certain impacts on prices.

Key words: spatial auto-correlation, spatial network auto-regressive change point model, change point

中图分类号:

F299.23
TP39

周佳琪, 金百锁. 基于空间网络自回归变点模型的合肥市房地产价格影响因素分析[J]. 中国科学院大学学报, 2020, 37(3): 398-404.

ZHOU Jiaqi, JIN Baisuo. Analysis for influencing factors of real estate price in Hefei based on spatial network auto-regressive transformation model[J]. , 2020, 37(3): 398-404.

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基于空间网络自回归变点模型的合肥市房地产价格影响因素分析

Analysis for influencing factors of real estate price in Hefei based on spatial network auto-regressive transformation model

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