To Examine Mortgage Loan Risk of Life Insurance Industry from the Viewpoint of Lender and Borrower: An Application of Nested Logit Model

碩士 === 國立高雄第一科技大學 === 風險管理與保險所 === 98 === The life insurance industry plays an increasingly important role in future funding intermediaries. Particularly, the real estate mortgage loan is around 90 percent of total mortgage loan. That is an essential tool in life insurance companies. Further, it has...

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Bibliographic Details
Main Authors: Wei-Ming Feng, 馮偉銘
Other Authors: Pai-lung Chou
Format: Others
Language:zh-TW
Published: 2010
Online Access:http://ndltd.ncl.edu.tw/handle/34284572479458090269
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Summary:碩士 === 國立高雄第一科技大學 === 風險管理與保險所 === 98 === The life insurance industry plays an increasingly important role in future funding intermediaries. Particularly, the real estate mortgage loan is around 90 percent of total mortgage loan. That is an essential tool in life insurance companies. Further, it has been concerned about selecting action of lenders on high-risk customers according to the potential default and prepayment factors of borrowers. Thus, lenders reduce the ratio of non-performing loans for the real estate mortgage loan in order to increase the operational performance and revenue. However, domestic studies have paid little attention to on-time repayment, default and prepayment households among the three types for original information of the real estate mortgage loan. Therefore, our sample in this study is collected from actual loan dataset of one leading life insurance company in south area. And then exploring what affect determinants of default and repayment which affect profitability of loan activities. According to the theoretical hypotheses of the nested logit model, there will be three alternatives which are the on-time repayment, default and prepayment adopting to establish empirical models from the different perspective of lender’s and borrower’s. The empirical results shows that the inclusive value estimates of lender’s modeling couldn’t fit the theoretical hypothesis of the nested logit model. Conversely, the borrower’s modeling which is built on consumer behavior matches the theoretical hypothesis exactly. In addition, this study demonstrates the default behavior is affected significantly by the occupation, seniority, loan-to-value ratio and house age. The prepayment behavior is influenced significantly by four factors, namely the marriage, age, income and loan-to-value ratio.