房价空间关联网络结构实证分析  

The Empirical Analysis of the Spatial Correlation Network Structure of Housing Price

作　　者：方大春 裴梦迪[1] 

机构地区：[1]安徽工业大学商学院,243032 [2]安徽工业大学安徽创新驱动发展研究院,243032

出　　处：《上海经济研究》2018年第1期63-73,共11页Shanghai Journal of Economics

基　　金：国家社会科学基金青年项目:“包容性增长实现路径研究”(批准编号:11CJL001);安徽省2018年“高校学科(专业)拔尖人才学术资助项目”资助

摘　　要：该文基于2005-2015年中国35个主要城市的房地产开发企业住宅商品房平均销售价格数据,利用社会网络分析方法对35个城市的房价空间关联网络结构特征进行研究。研究发现：从整体网络结构特征看,城市房价联动呈现显著的网络结构形态,网络密度和网络关联度呈现上升趋势,网络等级度处在较高水平,网络效率逐渐稳定;中心性分析结果显示,北京、上海、深圳、广州等城市中心度较高,处于网络中心位置,具有房价＂引领＂作用,全国房价空间关联网络中心性均值呈上升趋势,说明区域间房价关联程度逐步加强;北京、天津、石家庄、太原、呼和浩特、沈阳、大连、济南、青岛等9个城市属于＂双向溢出＂板块,长春、哈尔滨、郑州、南昌、合肥、兰州、西宁、乌鲁木齐、重庆、西安、银川等11个城市构成＂净溢出板块＂,杭州、宁波、南京、武汉、厦门、上海等6个城市属于＂经纪人＂板块,深圳、广州、长沙、成都、福州、昆明、海口、贵阳、南宁等9个城市构成＂净收益板块＂;QAP分析结果显示,城市间空间邻接关系、人口数量、经济发展水平和产业结构差异对城市房价联动关系具有显著影响。房价空间关联的网络结构为房价调控政策制定和实施带来严峻挑战,同时也为房价跨城市协同调控创建有利条件。Based on the average selling price data of commercial housing in China＇s 35 major cities＇ real estate development enterprises during the period from 2005 to 2015,this thesis focuses on the network features of spatial correlation in 35 cities＇ house price by the method of social network analysis.The result shows that：firstly,from the angle of overall characteristics of network structure,there exists obvious network structure in housing price co-movement,with the increasing trend of network density and network correlation;network hierarchy is on higher level and network efficiency is increasingly stable.Secondly,the result of centrality demonstrates that Beijing,Shanghai,Shenzhen and Guangzhou have a higher centrality degree,and are located in the heart area of network and play a leading role in housing price,and the mean centrality of spatial correlation＇s network of housing price tend to rise,which indicates that the correlation degree in regional house price is gradually strengthened.Thirdly,members of bidirectional spillover sector are mainly found in Beijing,Tianjin,Shijiazhuang,Taiyuan, Huhehaote,Shenyang,Dalian,Jinan, Qingdao; Changchun, Harbin,Zhengzhou, Nanchang, Hefei,Lanzhou,Xining,Urumqi,Chongqing,Xi＇an,Yinchuan consist of the net spillover sector;the agent sector is made up by cities like Hangzhou,Ningbo,Nanjing,Wuhan,Xiamen,Shanghai;members of net benefit sector belongs to Shenzhen,Guangzhou,Changsha,Chengdu,Fuzhou,Kunming,Haikou,Guiyang,Nanning.Forth,the result of QAP analysis reveals that the relationship of urban spatial adjacency,the size of population,the level of economic development,the difference of industrial structure have a significant influence on correlation of housing price.The structure of spatial collection network in housing price brings a great challenge to the formulation and implementation of housing price policy,but it also creates favorable conditions for coordinated regulation of the house price across different cities.

关 键 词：房价 空间关联 网络结构 影响因素 社会网络分析 

分 类 号：F830.59[经济管理—金融学]