| Summary: | 碩士 === 臺中健康暨管理學院 === 經營管理研究所 === 92 === The main purpose of this Research is to analyze the risk evaluation of house loan credit. Granting credit is one of the essential practices for financial institutions, hence, the existence and development of a financial institution depend on whether its quality of granting credit is bad or good. In recent years, the national banks’ total over loan rates, from 7.48% of the end of 2001 to 4.33% of the end of 2003, has reduced by 3.15% for these three years, which apparently indicates the importance of having good control for granting credit risk in ordinary days.
The variables of this Research totally include 14 variables such as sex, age, family with double salary, education level, working place, occupation, working accumulating year, income, security area, security condition, house age, loans-to-value ratios, loan types, etc. The ways for the Research is to compare their differences between normal households and overdue households and construct a risk evaluation mode for house loan credit by using MFLN and analyze the feasibility of the modes in practice.
The results demonstrated by this Research can be concluded into several points as follows:
1.In the 14 variables which affect credit risks for house loans, known from the analysis of sample characteristics, overdue reimbursement is remarkably related to sex, family with double salary, education level, working place, occupation, working accumulating year, income, security area, and security condition.
2.By means of MFLN, the variables such as family with double salary, education level, working places, occupation, working accumulating year, and income are considered as input variables in this Research, and the condition of interests paying as an output variable. Convergence is good, but in sensitivity analysis there is an apparent positive relevance to family with double salary and occupation, and a weak negative relevance to working accumulating year. Further, it changes into C formula, which of correct rate is 81%.
Key Words: over loan rates, Multilayer Functional-Link Network (MFLN)
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