| Summary: | 碩士 === 國立成功大學 === 統計學系碩博士班 === 95 === Housing mortgage plays a major role in the banking system, building a effective and correct leading evaluation models is very important. We can use it to reduce Non-Performing loan, provment performance and enhance profits. Typical research involves gathering information on the background of the applicant and how the applicant will make the loan payments. This model does not reflect how the bank rejects the applicant. This new research uses a different method which involves a two-stage model, which will gather more information to correct the errors found in the current method.
This research is based on the the information gathered from 2,890 out of 4,274 cases of mortgage loans from an unanimous bank inTainan, Taiwan between June 2004 and April 2006. There are two stages in this research, which are named Stage 1 and Stage 2. The research utilizes Logistic Regression Analysis, Cox Regression Analysis, and Discriminant Analysis. In the model using these analyses, we must take into account the risk as a factor, especially when the bank is involved. The credibility of the individual, the home, and ability to make payments were also accounted for in the research process. The results are as follow:
(1) The information gathered in stage 2 includes: amount applied for, applicant’s age, sex, and education, the condition of the house, the age of the house, and how many times within 3 months the house has been appraised. In addition, the bank will ask about the guarantor, the source of income, and the loan term. Additionally, how these factors will affect the non-performing loan.
(2) The Logistic Regression Analysis has a figure of , which shows it is better than the other analyses. From the trial samples, Logistic Regression Analysis has 52.4% of correctly evaluating the applicant, which doubles the percentage when using Discriminant Analysis, which is 24.83%. This research results in finding that Logistic Regression Analysis is the best method to be used. However, when compared to the Cox Regression Analysis, the samples’ results fluctuates between good and bad, mainly due to too many detailed information provided.
(3) This study use the type of graphics to show the models, we hope tha can provid a extension way to help the people who do not toch models before.
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