Research On The Influencing Factors Of Individual Housing Loan Default In Nanjing X Branch Of G Bank

In the modern times,the real estate business occupies an increasingly important proportion in our national economy,and it is an important industry to strengthen the national economy and improve the people's lives.In recent years,China's real estate market is becoming more and more prosperous,as well as the housing loan business is also growing.Relevant experience at home and abroad shows that the personal real estate credit risk has a huge impact on banks and financial institutions.With the rapid development of personal mortgage business,the default risk of the housing loan is increasing day by day.China's non-performing loan ratio is significantly higher than the United States,Hongkong,China and other developed countries or regions,which has a trend to grow.At present,China's banking sector has generally recognized the importance of strengthening the management of personal loans' default risk,and the academic community has also raised some focus on the open issue.However,the empirical research on the risk of personal mortgage default is still relatively scarce,especially in a particular city or a sub branch of banks.Considering the above background,this paper is originated from the actual situation of the individual housing loan business of Nanjing X branch of G Bank —— my internship institution,and based on the characteristics of the real estate industry in Nanjing to filter and analyse the factors for risk default of Nanjing X sub branch of G Bank.Thus,I try to put forward some specific proposals for the X branch,in order to effectively prevent and manage default risk in the early personal loans issued.Referring to the literature review,the paper classifies the major determinants in three dimensions:borrower characteristics dimension,house characteristics dimension,and loan characteristics dimension.This practical study uses actual and credible mortgage loan data of Nanjing X sub branch of G Bank,from Jan 1st,2009 to Dec 30 th,2014.Using the method of combining rough set theory with two classification logistic regression model,I firstly attribute reduction of all the set variables by rough set algorithm,and then obtain seven main influencing factors: borrowers' age,years of education,family income,loan period,monthly repayment accounted for the proportion of household income,housing area,and housing downpayment.And then based on the filtered independent variables,logistic regression analysis is carried out,whose empirical results indicate that: Borrowers' age,education and housing downpayment are the determinants negatively related with default;monthly repayment accounted for the proportion of household income,loan period and housing area are the determinants positively related with default.By testing,the regression model performs well in forecasting.At the end of this dissertation,several constructive suggestions are raised for themanagement of default risk of Nanjing X sub branch of G Bank,including establishing a comprehensive credit rating system suitable for Nanjing local conditions,to achieve the goal of securitization of the residential mortgage assets for G Bank.