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基础设施与城乡房价、房租:基于贝叶斯模型平均的微观研究
引用本文:欧阳艳艳,陈浪南,李子健.基础设施与城乡房价、房租:基于贝叶斯模型平均的微观研究[J].系统工程理论与实践,1981,40(11):2825-2838.
作者姓名:欧阳艳艳  陈浪南  李子健
作者单位:1. 中山大学 国际金融学院, 广州 510275;2. 中山大学 岭南(大学)学院, 广州 510275
基金项目:教育部人文社科项目(17YJC790112);广东省自然科学基金(2017A030313423)
摘    要:本文基于中国劳动力动态调查(CLDS)数据,采用贝叶斯模型平均法(BMA)实证考察了基础设施对我国城乡住宅价格和租金的影响.实证结果表明:交通、通讯和能源三类基础设施均是房价的重要影响因素,但交通基础设施对房租的影响不显著;房价的决定因素要比房租的决定因素多.从城乡异质性分析来看,三类基础设施对城市房价房租均有重要作用,但是通讯基础设施对农村房价不显著,交通、通讯基础设施对农村房租不显著.基础设施建设可以提高服务业就业比例,两者共同促进了城乡房价、城市房租的增长;而完善的基础设施加大了人口的流动性,促进了城市和农村的房价水平,但人口密度并不是影响房租的有效机制.在考虑内生性、变换不同的模型先验概率和不同的参数先验概率情况下,本文得到的实证结果始终是稳健的.本文深化了基础设施与房价房租互动关系的研究,为促进我国房地产市场的健康发展提供了不同的思路和政策建议.

关 键 词:基础设施  房价房租  贝叶斯模型平均法  
收稿时间:2020-03-04

Infrastructure,urban and rural housing prices,rents:A micro-study based on Bayesian model averaging
OUYANG Yanyan,CHEN Langnan,LI Zijian.Infrastructure,urban and rural housing prices,rents:A micro-study based on Bayesian model averaging[J].Systems Engineering —Theory & Practice,1981,40(11):2825-2838.
Authors:OUYANG Yanyan  CHEN Langnan  LI Zijian
Institution:1. International School of Business and Finance, Sun Yat-sen University, Guangzhou 510275, China;2. Lingnan(University) College, Sun Yat-sen University, Guangzhou 510275, China
Abstract:Based on data from the Chinese Labor Force Dynamics Survey (CLDS), this paper uses Bayesian model averaging (BMA) to empirically investigate the impact of infrastructure on urban and rural housing prices and rents in China. The empirical results show that transportation, communications and energy are all three important factors influencing housing prices, but transportation infrastructure does not have a significant impact on housing rents; and housing prices have more determinants than housing rents. From the analysis of the heterogeneity of urban and rural areas, the three types of infrastructure have an important effect on urban housing prices and rents, but communication infrastructure is not significant on rural housing prices, and transportation and communications infrastructure are not significant on rural rents. Infrastructure construction can increase the employment ratio of the service industry, both of which jointly promote the growth of urban and rural housing prices and urban rents while infrastructure increases the mobility of population, thus promotes housing prices in urban and rural areas. However, the increase in population density is not the effective mechanism on rents. The empirical results obtained in this paper are always robust in consideration of endogeneity, different model prior probabilities and different parameter prior probabilities. This paper deepens the research on the interactive relationship between infrastructure and housing prices and rents, which provides different ideas and policy suggestions for promoting the healthy development of China's real estate market.
Keywords:infrastructure  housing price and rent  BMA model  
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