Is It a Curse or a Blessing to Live Near Rich Neighbors? Spatial Analysis and Spillover Effects of House Prices in Beijing

K. Vergos, Hui Zhi
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引用次数: 1

Abstract

This study investigates the spatial statistics of house prices in Beijing, China. We examine whether the house prices in one region is affected by the house price in neighbouring regions. We also investigate how the house prices in one region is affected by unknown characteristics of the neighbouring regions. Moreover, we analyse whether the explanatory factors of house prices in one region are affected by explanatory factors of house prices in neighbouring regions. Subsequently, we attempt to investigate the spatial spill-over effects of explanatory factors. Initially, we use Lagrange Multiplier (LM) test to examine the significance of spatial autocorrelation. After this we apply the spatial autoregressive model (SAR), spatial Durbin model (SDM), spatial autoregressive model with autoregressive disturbances (SAC) and spatial error model (SEM) into spatial regression methods. The paper overcomes the shortcomings of the previous studies by extending the range of examining spatial models, providing reasonable spatial model selection procedures, and employing improved spatial weights to analysing spillover effects of explanatory factors. On the aspect of analysing direct and indirect (spill-over) effects, this study examines the partitioning of direct and indirect effects and finds out the impacts of the neighbouring factors. Evidence is found for spatial dependence of house prices: house prices in one region are influenced by the house prices in neighbouring regions positively and significantly. Evidence is found for spatial heterogeneity of house prices across the space: house prices in neighbouring regions spill over more in times of increasing neighbouring house prices, then when neighbouring house prices are declining. Evidence is found for spatial spillover effects of explanatory factors: increases of average wage of real estate staff, income , tax, urban population and the house prices of the previous year increases the house prices positively in neighbouring regions; a decrease of unemployment drives down the house prices in neighbouring regions.
与富人为邻是祸还是福?北京房价的空间分析与溢出效应
本研究考察了中国北京房价的空间统计。我们考察一个地区的房价是否受到邻近地区房价的影响。我们还研究了一个地区的房价如何受到邻近地区未知特征的影响。此外,我们还分析了一个地区的房价解释因素是否受到邻近地区房价解释因素的影响。随后,我们试图探讨解释因素的空间溢出效应。首先,我们使用拉格朗日乘数(LM)检验来检验空间自相关的显著性。在此基础上,我们将空间自回归模型(SAR)、空间Durbin模型(SDM)、空间自回归扰动自回归模型(SAC)和空间误差模型(SEM)应用到空间回归方法中。本文通过扩大空间模型的考察范围、提供合理的空间模型选择程序以及采用改进的空间权重来分析解释因素的溢出效应,克服了前人研究的不足。在分析直接和间接(溢出)效应方面,本研究考察了直接和间接效应的划分,并找出了邻近因素的影响。房价的空间依赖性:一个地区的房价受到邻近地区房价的显著正影响。我们发现了整个空间中房价的空间异质性的证据:邻近地区的房价在邻近房价上涨时溢出更多,然后在邻近房价下跌时溢出更多。解释因素的空间溢出效应有证据表明:房地产从业人员平均工资、收入、税收、城市人口和前一年房价的增长对邻近地区的房价有正向的推动作用;失业率的下降使邻近地区的房价下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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