| 摘要: | 壽險業資金主要來自保戶所繳交保險費累積之責任準備金，其運用規範均設有相當嚴謹且明確的限制，以確保日後履行壽險契約無虞，並顧及公司股東權益。壽險業資金屬於中長期，較一般銀行資金來自於短期存款更適合運用於房屋抵押貸款。關於辦理不動產抵押貸款業務，在保險法等相關法令規定下，如同一般銀行辦理放款授信業務一樣，仍要遵守安全性、流動性、公益性、收益性、成長性等五項基本原則，並且在授信過程符合5P原則之標準，以降低可能面臨的風險。
房屋抵押放款業務可能面臨的風險有提前清償、逾期違約、流動性及利率等風險，其中以逾期違約及提前清償兩種因素對壽險業的投資收益影響最大。因此本研究以國內S金控公司所屬之壽險子公司於2002~2008年核准撥款之借款人為樣本資料，以逾期違約風險因子及提前清償風險因子作實證分析。資料分成北、中、南、東四個區域，選取之解釋變數有學歷、婚姻狀況、年收入、年齡、性別、是否有保證人、建物結構、抵押品屋齡、抵押品使用現況、抵押品座落地區、借款金額、借款用途、貸款契約期間、申貸案件種類、利率商品類別、相對貸款利率差距、是否寬限、貸款成數、負債支出佔所得比例等19項風險因子來分析。過去大部份文獻採用Logistic Regression迴歸模型作為研究方法，本文係採用Probit迴歸模型分析逾期違約與提前清償風險因子。經過實證瞭解這些風險因子對於違約或提前清償是否有顯著影響以及在不同區域其解釋變數的顯著性是否會有不同變化，進而建立違約與提前清償預測模型以客觀評估風險因子，降低房屋抵押放款風險之發生。
近來市場利率持續下跌，房貸獲利亦遭到侵蝕，因此壽險業者更應提升授信專業技巧與品質，對於從事房屋抵押貸款之各項隱含風險，有必要事先加以防範，方能使壽險資金之運用，達到安全與穩定獲利的雙重要求，為現今壽險業者經營之首要課題。
The working capital of a life insurance company mostly comes from the reserve accumulated by insurance premiums of applicants. In order to exercise the insurance contract without any problem, the way to run the reserve is strictly regulated and clearly restricted by the government to secure the policyholders’ benefit. As the working capital of insurance business belongs to a midterm or longterm ones, therefore, it is more suitable for insurance companies to conduct residential mortgage loan than banks whose working capital belong short term ones. Insurance companies, similar to banks conducting the lending business, have to comply with the basic credit policies of safety, liquidity, profitability and growth and comply with the 5P policies to decrease their credit risk under the regulations related to the insurance law.
The possible risks of residential mortgage loan are prepayment, default, mobility and interest rates; among those risks, the prepayment and default are the most critical risks affecting the return of insurance companies. This study conducts an empirical analysis based on the mortgage applicants’ dataset of one life insurance company during 2002 to 2008 in Taiwan to investigate the predictors of default and prepayment. The data is grouped into northern, eastern, southern and western areas. The selected explanation variables are educational level, marital status, annual income, age, gender, with/no guarantor, the structure of the collateral property, age of the collateral property, conditions of the collateral property, address of the collateral property, loan amount, loan year, the type of a loan, the type of interest rate, the current mortgage interest rates relative to the rate on an outstanding loan, loan to value ratio, and debt-to-income ratios.
While logistic regression method was used by most prior studies, this study uses probit regression method to analyze the factors of default and prepayment. To understand which factor is significantly related with default and prepayment, we try to establish the prediction models of default and prepayment in order to reduce the risk of residential mortgage loan for life insurance companies.
Since the market interest rate declines continually affected the profitability of mortgage loan, the insurance companies should upgrade the credit method and quality. It is necessary to adopt more useful and efficient mothod to reduce risk of the mortgage loan for life insurance companies reaching the purpose of safety and profitability of working capital. |
