A Study of Refinancing for Prepayment on Mortgage Loan
碩士 === 中原大學 === 企業管理研究所 === 94 === Abstract The main purpose of this research is to discuss the variables attributing to the refinance of the early- repayment mortgage loan. 17 variables are defined as: 1. Gender 2. Education level 3. Marital status 4. Age 5. Monthly income 6. Working years for...
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ndltd-TW-094CYCU51210062016-06-03T04:13:38Z http://ndltd.ncl.edu.tw/handle/10377558345457717317 A Study of Refinancing for Prepayment on Mortgage Loan 房屋貸款提前還款再貸款之研究-以S銀行為例 Yen-Chi Kuo 郭雲啟 碩士 中原大學 企業管理研究所 94 Abstract The main purpose of this research is to discuss the variables attributing to the refinance of the early- repayment mortgage loan. 17 variables are defined as: 1. Gender 2. Education level 3. Marital status 4. Age 5. Monthly income 6. Working years for current job 7. Occupation 8. The period of repayments 9. The use of the property 10. Loan amount 11. Loan percentage of appraisal 12. Methods of repayment 13. With or without guarantee 14. With or without government subsidy 15.The proportion of monthly income to total income 16. The relationship between borrower and guarantee 17.The type of the property. The result of the research identities that seven variables which includes gender, monthly income, the use of the properties, loan amount, methods of repayment, with or without guarantee and with or without government subsidy have no significant relationship with the refinance of the early- repayment mortgage loan. However, three variables of marital status, occupation and the proportion of repayment to total income have marginally significant impact on the behavior of refinance. The rest of the variables including education level of borrowers, age, working years for current job, the period of repayments, loan percentage of appraisal, the relationship between borrower and guarantee and the type of the property have significant relationship with the behavior of refinance. The outcome of the analysis reveals that the overall accuracy rates are 79.82% and 96.9% in Logistic model and Back Propagation Artificial Neural Network model respectively while putting the seven significant factors as mentioned above into two models. Obviously, the latter model has higher accuracy rate. The research outcome also indicates that the characteristics of two types of early repayment mortgage loan borrowers- the refinance and non-refinance are shown as following: 1. Refinancing borrowers- over 30 years old, loan percentage of appraisal is below 80% and there are relationship between borrowers and guarantees (no guarantee or direct relatives). 2. Non-refinancing borrowers- below 30 years old, loan percentage of appraisal is higher than 80% and there are no relationship between borrowers and guarantees (with guarantees or non direct relatives). none 劉立倫 2006 學位論文 ; thesis 67 zh-TW |
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碩士 === 中原大學 === 企業管理研究所 === 94 === Abstract
The main purpose of this research is to discuss the variables attributing to the refinance of the early- repayment mortgage loan. 17 variables are defined as: 1. Gender 2. Education level 3. Marital status 4. Age 5. Monthly income 6. Working years for current job 7. Occupation 8. The period of repayments 9. The use of the property 10. Loan amount 11. Loan percentage of appraisal 12. Methods of repayment 13. With or without guarantee 14. With or without government subsidy 15.The proportion of monthly income to total income 16. The relationship between borrower and guarantee 17.The type of the property.
The result of the research identities that seven variables which includes gender, monthly income, the use of the properties, loan amount, methods of repayment, with or without guarantee and with or without government subsidy have no significant relationship with the refinance of the early- repayment mortgage loan. However, three variables of marital status, occupation and the proportion of repayment to total income have marginally significant impact on the behavior of refinance. The rest of the variables including education level of borrowers, age, working years for current job, the period of repayments, loan percentage of appraisal, the relationship between borrower and guarantee and the type of the property have significant relationship with the behavior of refinance.
The outcome of the analysis reveals that the overall accuracy rates are 79.82% and 96.9% in Logistic model and Back Propagation Artificial Neural Network model respectively while putting the seven significant factors as mentioned above into two models. Obviously, the latter model has higher accuracy rate.
The research outcome also indicates that the characteristics of two types of early repayment mortgage loan borrowers- the refinance and non-refinance are shown as following:
1. Refinancing borrowers- over 30 years old, loan percentage of appraisal is below 80% and there are relationship between borrowers and guarantees (no guarantee or direct relatives).
2. Non-refinancing borrowers- below 30 years old, loan percentage of appraisal is higher than 80% and there are no relationship between borrowers and guarantees (with guarantees or non direct relatives).
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author2 |
none |
author_facet |
none Yen-Chi Kuo 郭雲啟 |
author |
Yen-Chi Kuo 郭雲啟 |
spellingShingle |
Yen-Chi Kuo 郭雲啟 A Study of Refinancing for Prepayment on Mortgage Loan |
author_sort |
Yen-Chi Kuo |
title |
A Study of Refinancing for Prepayment on Mortgage Loan |
title_short |
A Study of Refinancing for Prepayment on Mortgage Loan |
title_full |
A Study of Refinancing for Prepayment on Mortgage Loan |
title_fullStr |
A Study of Refinancing for Prepayment on Mortgage Loan |
title_full_unstemmed |
A Study of Refinancing for Prepayment on Mortgage Loan |
title_sort |
study of refinancing for prepayment on mortgage loan |
publishDate |
2006 |
url |
http://ndltd.ncl.edu.tw/handle/10377558345457717317 |
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