我国商业银行房地产信贷风险的识别与预测

发布时间：2018-01-05 13:34

本文关键词：我国商业银行房地产信贷风险的识别与预测　出处：《陕西师范大学》2012年硕士论文　论文类型：学位论文

【摘要】：房地产信贷是商业银行以房地产为服务对象,围绕房地产再生产各环节发放贷款的活动。作为经济发展拉动之一的房地产业,是一项高风险与高收益并存的经济活动,具有周期长、投资额大、影响因素多的行业特点。伴随着房地产行业的发展,房地产开发企业资金来源于商业银行的信贷支持、对商业银行形成较大依赖性。因而使得商业银行面临一定的信贷风险,一旦出现房地产开发企业与个人住房按揭贷款者违约,无法偿还银行信贷,那么商业银行所面临的信贷风险是巨大的。因此,本文有必要针对目前商业银行房地产信贷问题作以预测研究,降低商业银行面临的房地产信贷的风险性。本论文基于房地产信贷风险产生的理论基础,分析了房地产信贷风险的影响因素,并对房地产信贷风险因素进行了识别研究,从而在根源上降低信贷风险发生的概率。在对商业银行房地产信贷风险的预测上,随机选取了20家房地产上市企业的五大类指标因素,采用灰色预测法,通过Matlab软件实现灰色算法,对2005-2010年的20个房地产上市企业的23个指标进行数据处理,得到了2011-2012年对应的指标预测数据。在房地产信贷风险预测中也对2005-2010年数据进行了拟合性分析,得出计算残差图,表明该预测方法有较好的拟合性,能够较准确的预测各指标。最后根据预测到的2011-2012年的房地产上市企业指标数据,得出23个指标变化趋势图,说明房地产上市企业未来的成长能力、经营能力、偿债能力、盈利能力和现金流的状况,得出房地产企业对于商业银行所产生的信贷风险在2011-2012年间会有所降低,其在国家宏观调控的影响下和商业银行对信贷风险意识加强的基础上,房地产信贷将出现不同程度收缩的结论。论文分为四个部分组成。第一部分为绪论部分,主要内容为本研究的选题背景和研究意义、国内外房地产信贷风险预测的现状、本文的研究内容和思路。第二部分介绍了房地产信贷风险与风险识别的相关理论,为论文的进一步研究做了理论的铺垫。第三部分本文的实证部分,着重以影响房地产上市企业信贷风险的指标因素为切入点,运用灰色预测实证方法对房地产信贷风险进行预测研究。第四部分为主要结论及构建房地产信贷风险的防范体系。
[Abstract]:The real estate credit of commercial banks to the real estate as the service object, on various aspects of the real estate lending activities. As the reproduction of stimulating the development of economy the real estate industry is a high risk and high return coexist in economic activity, with a long cycle, large amount of investment, many influencing factors with the characteristics of the industry. With the development of the real estate industry, real estate development enterprise funds from the commercial bank credit support, the formation of large dependence on commercial banks. The commercial banks face certain credit risk, once the real estate development enterprises and the personal housing mortgage loan default, unable to repay the bank credit, the credit risk faced by commercial banks is huge. Therefore, it is necessary to predict the research aiming at the problems of commercial bank real estate credit, reduce the risks faced by commercial banks real estate credit.
The theoretical basis of real estate credit risk based on the analysis of the factors affecting the real estate credit risk, and credit risk of real estate factors were analyzed and identified, so as to reduce the occurrence probability of credit risk at the root. In the prediction of commercial bank real estate credit risk, randomly selected five categories the 20 index of listed real estate companies, using grey prediction method, gray algorithm was implemented by Matlab, on the 23 index 2005-2010 years of 20 real estate listed companies for data processing, obtained the corresponding forecast data. 2011-2012 index in the real estate credit risk prediction of 2005-2010 data fitting analysis, calculation of residual plot shows that the prediction method has better fitting, can accurately predict the parameters. Finally, according to the forecast to 2011-2012 years of real estate listed The enterprise index data, draw 23 index change trend chart shows that the real estate listed enterprise future growth ability, operation ability, solvency, profitability and cash flow situation, the real estate business for commercial banks from credit risk will be reduced in the period of 2011-2012, based on the national macro-control under the influence of and Commercial Banks to strengthen credit risk consciousness, real estate credit will appear different degree of contraction of the conclusion.
This paper is divided into four parts. The first part is the introduction, the main contents of the research background and research significance, status of domestic and foreign real estate credit risk prediction, research content and ideas of this paper. The second part introduces the related theories of real estate credit risk and risk identification, for further research of the thesis the theory. The empirical part of the third part of this paper, focuses on the influence factors of credit risk of listed real estate enterprises as the starting point, using the grey prediction method of empirical prediction of credit risk of real estate. The fourth part is the main conclusions and the construction of the real estate credit risk prevention system.

【学位授予单位】：陕西师范大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F832.4;F293.3;F224

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