房地产价格与银行信贷的关系研究

发布时间：2018-01-12 02:26

本文关键词：房地产价格与银行信贷的关系研究　出处：《山东大学》2017年硕士论文　论文类型：学位论文

【摘要】：从1998年房改至今,中国房地产市场经历了几次波动起伏,房地产价格在这18年间增幅高达262%,房贷成为一代人沉重的负担,不断变换的政策,不断累积的风险,不断跳动的房价,让房地产研究具有了现实意义。理论部分着重分析了房地产价格和银行信贷的互动机理,分析出银行信贷对房地产价格有正向和负向的推动作用,且房地产价格对银行信贷也有正向和负向的推动作用,但正向和负向推动作用的效果无法通过理论分析出,这部分需要通过实证部分得到实际的结果。现状部分分为两节,首先本文整理了 1998年至2016年房地产调控的政策,并根据政策效果分为五个周期,可以用"鼓励——抑制——鼓励——抑制——鼓励"来简单概括,政策拐点的时间点分别为2004年,2008年10月,2009年12月和2014年。并结合房地产调控政策分析房地产市场的各类指标,发现房地产价格和银行信贷的数据波动与房地产调控政策周期相吻合,但有长短不一的时滞性;其次,将1998-2016依据货币供应量M2划分为两个周期,拐点为2009年,2009年之前,M2增速缓慢,2009年之后M2增速加剧,结合数据发现2009年是房地产供给以及房地产开发资金拐点。2009年以后,房地产市场供过于求的趋势开始显现。房地产开发资金以2009年为分界点,2009年之前,增速缓慢,2009年后房地产开发资金增速较高。实证部分本文以全国30个省、市、自治区2002-2016年的数据,建立动态面板模型,分区域探究房地产价格和银行信贷的关系。指标选取方面,除房地产价格和银行信贷外,控制变量选取土地价格代表房地产市场供给层面,选取人均GDP代表需求层面。分析方法方面,先对房地产价格和银行信贷的关系进行理论分析,再使用数据进行实证分析,通过单位根检验、协整检验、格兰杰因果关系检验,随后构建了动态面板模型,先研究全国及东中西部三个区域银行信贷对房地产价格的影响,再研究全国及东中西三个区域房地产价格对银行信贷的影响,研究时使用GMM估计方法对模型进行估计,研究结果通过过度识别检验和序列相关性检验。论文得出结论如下:1.房地产价格和银行信贷间有长期稳定关系,且房地产价格和银行信贷是彼此的格兰杰原因。2.银行信贷对房价短期有正向推动作用,但各地区银行信贷对于房地产价格推动效果不一致,东部地区效果最强,西部次之,而中部地区房地产价格对于银行信贷的依赖性较低,但对经济基本面的依赖性较高;3.短期内房地产价格的增长对银行信贷的增长有正向推动作用,西部地区正向推动作用最强,中部次之,东部最弱。最后结合现状分析的结果以及实证分析结果,并考虑中国东中西部区域的异质性提出三条政策建议。
[Abstract]:From 1998 to now, China's real estate market has experienced several fluctuations, real estate prices in these 18 years as high as 262 increase, mortgage has become a heavy burden for a generation, constantly changing policies. The constant accumulation of risk, the beating house prices, so that the real estate research has practical significance. The theoretical part focuses on the analysis of real estate prices and bank credit interaction mechanism. The analysis shows that bank credit has a positive and negative impact on real estate prices, and real estate prices also have a positive and negative impact on bank credit. But the effect of positive and negative push can not be analyzed by theory, this part needs to get the actual results through the empirical part. The present situation part is divided into two sections. First of all, this paper arranges the real estate regulation policy from 1998 to 2016, and divides it into five cycles according to the effect of the policy. A brief summary can be made with "encouragement-inhibition-encouragement-inhibition-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-encouragement-. In December 2009 and 2014, combined with the real estate regulation policy analysis of various indicators of the real estate market, it is found that the real estate price and bank credit data fluctuations in line with the real estate regulatory policy cycle. But there are different time delay. Secondly, according to the money supply M2 divided into two cycles, the inflection point is 2009, before 2009, M2 growth rate is slow, after 2009 M2 growth rate intensified. Combined with the data found that 2009 is the real estate supply and real estate development capital inflection point. 2009. The trend of oversupply in the real estate market is beginning to show. The capital for real estate development is divided by 2009, and the growth rate was slow until 2009. After 2009, the real estate development fund growth rate is relatively high. The empirical part of this paper based on the data of 30 provinces, municipalities and autonomous regions from 2002 to 2016, establish the dynamic panel model. This paper explores the relationship between real estate prices and bank credit. In addition to real estate prices and bank credit, the control variables select land prices to represent the real estate market supply level. Choose per capita GDP to represent the level of demand. In terms of analysis methods, the relationship between real estate prices and bank credit is analyzed theoretically, and then the data is used for empirical analysis, through unit root test, cointegration test. Granger causality test followed by a dynamic panel model to study the impact of bank credit on real estate prices. Then study the impact of real estate prices on bank credit in the three regions of China, East and West, and use GMM estimation method to estimate the model. The conclusions are as follows: 1. There is a long-term stable relationship between real estate prices and bank credit. And real estate prices and bank credit are the Granger reasons for each other. 2. Bank credit has a positive impact on house prices in the short term, but the effect of bank credit on real estate prices in different regions is not consistent. The effect of real estate in the east is the strongest, followed by the west, while the price of real estate in the central region is less dependent on bank credit, but higher on the economic fundamentals. 3. In the short term, the growth of real estate price has a positive effect on the growth of bank credit, the western region has the strongest positive promotion effect, and the middle part is the second. In the end, three policy recommendations are put forward considering the heterogeneity of the eastern, central and western regions of China, combined with the results of the current situation analysis and the empirical analysis.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F299.23;F832.4

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