中国主要城市商品住宅价格时空特征及影响因素研究

发布时间：2017-12-27 05:03

本文关键词：中国主要城市商品住宅价格时空特征及影响因素研究　出处：《武汉大学》2017年硕士论文　论文类型：学位论文

【摘要】：改革开放以来,快速发展和崛起的房地产业成为了助推中国经济快速前进的重要引擎。2015年伴随着中央"去库存"的主基调,房价迎来新一轮的上涨,再次成为中国社会舆论关注的焦点。目前关于商品住宅价格的研究集中围绕单个区域核心城市或省会城市,关于全国层面商品住宅价格分析主要运用省级面板数据,空间相关分析也是省域尺度之间的相关性,研究全国范围地级城市的较少。本文以全国212个主要地级城市为研究对象,采用空间数据可视化、空间自相关分析及Paneldata形式的固定效应模型等方法分析全国主要城市商品住宅价格时空特征及影响因素。(1)在时间特征上:2009-2014年,全国百城住宅平均价格呈"S"型增涨,涨跌与政府出台的宏观调控政策关系密切。不同地区划分方式下房价整体保持逐年上涨趋势。其中东部高于全国均价、高于中西部地区。三个沿海经济综合区房价均高于全国均价,其他地区都低于全国平均水平。不同能级城市房价涨跌的拐点也基本在调控政策转向时点前后,总体呈上涨趋势。同时,一线城市房价高过全国均价及二、三线城市,增长幅度最大。(2)在空间特征上:对比2010年、2012年、2014年三期全国商品住宅价格地图,房价在空间分布上存在明显的区域差异性和集聚性,主要表现为东部沿海地区价格高、内陆地区均价低的特征。其次,省会城市商品住宅平均价格在省域范围内属于高值,在全国范围内成为了热点区域以外次极值的主要分布类型。从增涨幅度来看,长江中游的湖北、安徽、江西三省增长最为明显。利用空间相关性分析得出全国商品住宅价格在全局和局部空间相关性上都呈现明显正相关,房价具有显著的"外溢效应"。(3)在影响因素上:采用聚类分析把样本城市分为5个类型城市,通过分析发现社会经济发展水平不同和市场成熟度的显著分化,导致不同类型城市房价影响因素存在差异。具体而言,地区生产总值、人均可支配收入、第三产业占比同样与房价具有正关联性,而住宅销售面积与住房价格呈现负关联。利率调控具有地域差异性和时滞性,其与房价的关系会"因城而异"。从短期可调控的因素来看,主要依赖于不同程度的限购限贷政策从而影响成交销售面积以及利率调整。第一类、第二类城市维持当前严厉的调控政策,房价或将平稳。警惕第三类还没有出台调控政策的城市房价快速上涨的风险。第四类和第五类城市房价增长较缓,当前仍以"去库存"为主,需要进一步发展经济,提振房地产业。
[Abstract]:Since the reform and opening up, the rapid development and rise of the real estate industry has become an important engine to boost the rapid progress of China's economy. In 2015, with the main tone of "going to stock" in the Central Committee, the price of house price has come to a new round of rise, and it has become the focus of public opinion in China again. The current research on the price of commercial housing concentrated around a single regional core city or provincial capital city, a national level analysis of commercial housing price using provincial panel data, the correlation between the spatial correlation analysis is the provincial scale, study the country level city less. Taking 212 main cities in China as the research object, this paper uses spatial data visualization, spatial autocorrelation analysis and Paneldata's fixed effect model to analyze the spatial and temporal characteristics and influencing factors of commercial housing prices in major cities of China. (1) in time characteristics: in 2009-2014 years, the average price of residential houses in the whole country is "S", and the rise and fall are closely related to the macro regulation policy issued by the government. The price of house prices kept rising year by year in different regions. The East is higher than the national average, higher than the central and western regions. The price of the three coastal economic zones is higher than the national average, and the other regions are lower than the national average. The inflection point of the price rise and fall of different level cities is also basically in the trend of the turning point of the regulation policy to the time point. At the same time, the house price of the first tier cities is higher than the national average price and the two or three line city, and the growth rate is the biggest. (2) spatial characteristics: compared with the three phase of the national commercial housing price map in 2010, 2012 and 2014, the spatial distribution of house price has obvious regional difference and agglomeration. The main characteristics are the high price in the eastern coastal area and the low average price in the inland area. Secondly, the average price of commercial housing in provincial capital cities is high in the provincial level, which has become the main distribution type outside the hot spots in the whole country. In terms of the increase, the growth of Hubei, Anhui and Jiangxi provinces in the middle reaches of the Yangtze River is the most obvious. The spatial correlation analysis shows that the price of commodity housing is positively correlated with the overall and partial spatial correlation, and the housing price has a significant "spillover effect". (3) on the influencing factors: cluster analysis is used to divide the sample cities into 5 types of cities. Through the analysis, we find that the significant differentiation of social economic development level and market maturity leads to different factors of housing prices in different types of cities. Specifically, gross domestic product, per capita disposable income and the proportion of the third industries are also positively correlated with housing prices, while the area of residential sales is negatively correlated with housing prices. Interest rate regulation has regional difference and time delay, and its relationship with house price will be "different by city". From the short-term and controllable factors, it mainly depends on the limited purchase and loan limit policy to influence the sale area and the adjustment of interest rate. The first and second types of cities maintain the current stringent control policies, and the house prices will be smooth. Be vigilant to the risk that the third types of urban housing prices have not yet been regulated. The fourth types and the fifth types of urban housing prices are slowly increasing. At present, the "go to stock" is still the main way. It needs to further develop the economy and boost the real estate industry.
【学位授予单位】：武汉大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F299.23

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