| Summary: | 碩士 === 南華大學 === 財務金融學系財務管理碩士班 === 105 === The housing loan customers in the period time of the Republic of China 95 years to 104 years of a selected L bank in Taichung city were the subjects for this study, by random sampling for the study 323 sample, of which 295 were normal housing loan customers and 28 for the abnormal ones .The 17 variable through literature review, practical experience selected (age, gender, education level, marital status, job title, job, years of service, repayment, the guarantor, income, property located in the region, property type, loan purpose, whether two or more housing loans, whether guaranteed debt, credit card or cash card have cycle balance, checked by other Financial institution) are entered into the logistic regression model, and set LR1, then install 17 variables in the logistic regression to filter a significant variable, and set LR2(significant variable are: education level , job title, years of service, repayment, property located in the region, guaranteed debt, debt, credit card or cash card have cycle. and set LR2. )
LR1 and LR2 model are processed by The logistic regression, which totle classification accuracy rates were 94.43% and 93.19%, and LR1 model to predict the correct rate of normal mortgage customers about 99.66%, correct prediction rate of abnormal mortgage customers about 39.29%, LR1 nodel shows better prediction capability, select the LR1 is the best credit risk assessment model.
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