“ style='font-family:Verdana;'>adjust measures to local conditions and classifying macro-'/> Spatial Differentiation of Urban Housing Prices in Guangdong Province and Its Influencing Factors
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Spatial Differentiation of Urban Housing Prices in Guangdong Province and Its Influencing Factors

机译:广东省城市房屋价格的空间分异及其影响因素

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In the context of the central government’s policies on housing prices themed on style="font-family:Verdana;">“ style="font-family:Verdana;">adjust measures to local conditions and classifying macro-control style="font-family:Verdana;">” style="font-family:Verdana;">, it is of great significance to study the convergence and the differences of housing prices. Taking the urban area of Guangdong province as an economic unit, this paper explores the spatial heterogeneity of housing price in Guangdong Province during 1995-2014 and the spatial heterogeneity of its impact factors by means of ESDA and GWR models. The study found that the spatial structure of housing prices in the region shows a certain circle structure, while the housing prices in the Pearl River Delta region are relatively high, there is a big difference in housing prices between Zhongshan, Huizhou and other cities. In most of the cities in eastern, western and northern Guangdong, the price of housing is low, which is extremely different from the price of housing of cities in the Pearl River Delta, there is also a huge difference between the housing price of Shantou and that of its surrounding cities. The influencing factors of housing prices in different cities show a localized characteristic of unbalanced linkage style="font-family:Verdana;">. style="font-family:Verdana;"> The difference between the impact of GDP per capita and the floor space of buildings completed on housing prices is the largest in the two time sections. In the middle and late time of the real estate development, the style="font-family:""> style="font-family:Verdana;">relevancy degree between housing prices and GDP per capita is no longer obvious, and is more affected by the urbanization rate and population migration, especially in western Guangdong. The completion of housing area and loan balance ha style="font-family:Verdana;">s style="font-family:Verdana;"> the greatest sensitivity to urban housing prices in western Guangdong. The former has the least sensitivity to the Pearl River Delta region. The latter has the least sensitivity to housing price in eastern Guangdong Province. The amount of the investment in real estate development has the greatest sensitivity to Meizhou, Chaozhou, Shantou and Jieyang prices produced the largest inhibition effect, the inhibition effect of the western part of the smallest. Therefore, we should combine the current situation of housing prices in different regions with the spatial effect mechanism of impact factors and promote the healthy development of real estate economy according to local conditions. The main contribution of this paper lies in: Firstly, for the first time we use ESDA-GWR model to systematically analyze the evolution of city level characteristics and spatial correlation of Guangdong Province and the spatial difference of impact factor driving prices fluctuation, as well as to solve the problem of regional differentiation regulation policies provide the basis. Secondly, we use the theory of real estate cycle to analyze the difference between demand and supply factors of housing prices in driving the fluctuation of housing prices in different cities, which provides the basis for adopting differentiated short-term and long-term regulatory policies and regional regulation and control policies. But there are also the limitations of Guangdong Province.
机译:在以 style =“ font-family:Verdana;”>“ style =” font-family:Verdana;“>调整措施为主题的中央政府的房价政策范围内 style =“ font-family:Verdana;”>” style =“ font-family:Verdana;”>,这对研究房价的趋同和差异。以广东省市区为经济单位,通过ESDA和GWR模型探讨了1995-2014年广东省房价的空间异质性及其影响因素的空间异质性。研究发现,该地区房价的空间结构呈一定的圆形结构,而珠江三角洲地区的房价相对较高,中山,惠州等城市的房价差异较大。在广东东部,西部和北部的大多数城市,住房价格低廉,这与珠江三角洲城市的住房价格有很大的不同,汕头市与广东省的房价之间也存在巨大差异。它周围的城市。不同城市房价的影响因素表现出局部的联系不平衡的特征 style =“ font-family:Verdana;”>。 style =“ font-family:Verdana;” >人均GDP与已建成建筑物的建筑面积对房价的影响之间的差异在两个时段中最大。在房地产开发的中后期, style =“ font-family:”“> style =” font-family:Verdana;“>住房之间的关联度价格和人均GDP不再明显,并且受城市化率和人口迁移的影响更大,尤其是在粤西地区。住房面积和贷款余额的完成已经 style =“ font-family:Verdana ;“> s style =” font-family:Verdana;“>粤西地区对城市房价的敏感度最高,前者对珠三角地区的敏感度最低,后者对珠三角地区的敏感度最低。粤东地区对房价的敏感度。房地产开发投资额对梅州,潮州,汕头和揭阳的价格敏感度最大,西部的抑制作用最小。我们应该结合目前房价的不同情况结合影响因素的空间效应机理,根据地区实际,促进房地产经济健康发展。本文的主要贡献在于:首先,我们首次使用ESDA-GWR模型系统地分析了广东省城市水平特征和空间相关性的演变以及影响价格波动的影响因素的空间差异,以及为解决区域差异化问题提供了调控政策依据。其次,我们运用房地产周期理论分析了住房价格的供求因素在驱动不同城市住房价格波动中的差异,这为采取有区别的短期和长期监管政策提供了依据。区域调控政策。但是广东省也有局限性。

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