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題名：房屋貸款違約與提前清償風險因素之研究
作者：吳政仲
作者(外文)：Cheng-Chung Wu
校院名稱：國立高雄第一科技大學
系所名稱：管理研究所
指導教授：許碩芬
學位類別：博士
出版日期：2011
主題關鍵詞：違約、提前清償、零膨脹卜瓦松模式、零膨脹負二項模式；Zero-Inflated Poisson；Prepayment；Defaults；Zero-Inflated Negative Binomial
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商業銀行的消費性貸款中，承作不動產抵押貸款業務一直是相當重要的業務。實務上會影響借款人風險行為的因素相當多，因此金融機構承做房屋貸款業務時，其所面臨貸款者的可能違約決策而言，是一種不確定性的概念。本研究藉由資料之分析，以找出影響借款人對於房貸行為違約的可解釋因素，同時與先前國內學者所做相關研究進行比較。以提出影響房屋抵押貸款行為中，有關房貸戶違約行為與提前清償的普遍主要影響風險因素。而這些因素事實上也是金融機構在承作相關房貸業務時所需要考量的風險因子，以提供其授信決策之參考。
本研究實證結果顯示，探討影響房貸違約與提前清償模型因素，藉由以Heckman兩階段迴歸模型與Tobit迴歸模型估計，其次根據相關文獻採用一般線性模式中的卜瓦松迴歸模式與負二項迴歸模式統計量之檢定結果，得出與所蒐集文獻所整理有關考慮之借款者個人屬性因素、借款契約條件內容相近。然資料具有過度分散問題，所以利用卜瓦松迴歸(poisson regression model)與負二項迴歸模式(negative binomial regression model)來預測結果，且所估計之參數具有一致、漸進有效及漸進常態之特性，偏誤也會隨著樣本的增加而減少。其結果在違約風險得知貸款剩餘期數、貸款年限、初貸金額、現欠餘額與財務狀況自變項是有意義的違約次數預測因子。寬限付款期限、貸款成數、年齡和性別皆是沒有意義的。結論可知貸款剩餘期數、貸款年限、初貸金額、現欠餘額與財務狀況能有較準確預估借款人違約次數的變數。但因為資料型態存在過多為零的資料分佈，最後利用零膨脹卜瓦松模式與零膨脹負二項模式的模型更是著力解決數據中“0”現象過多的問題，零膨脹卜瓦松模式之參數估計值，可得知貸款剩餘期數及貸款年限為顯著變數。零膨脹負二項模式之參數估計值，可得知貸款剩餘期數和現欠餘額為顯著變數。
Mortgage loans have been an important business in consumer finance for commercial banks. In fact, there are many factors concerning the risks of such loans. Financial institutions offering mortgage loans are confronted with the uncertainties of defaults by borrowers. This paper identifies the potential explanations of defaults by borrowers of mortgage loans with a data analysis, and clarifies the major risk factors concerning defaults and early repayments by comparing relevant studies of domestic scholars. These factors are also the key considerations for the credit policies of the financial institutions operating in the mortgage loan markets.
This paper performs an empirical study on the factors influencing mortgage loan defaults and prepayment using Heckman’s two-stage regression model and Tobit’s regression model. Based on the statistics tests of Poisson regression model and the negative binomial regression model, which are two linear models frequently seen in relevant literature, this paper derives a list of personal attributes of borrowers and contractual terms that should be taken into consideration. The findings are similar with existing literature; however, data disperse greatly. Poisson regression model and the negative binomial regression model are used applied to forecast the results. The estimated parameters are consistent, asymptotically efficient and asymptotically normal. Bias also decreases along with an increase of samples. The results suggest that independent variables, such as remaining repayment periods, mortgage years, initial loans, outstanding balances and financial statuses, are meaningful predictive factors of default risks; whereas grace periods, mortgage percentages, ages and sexes are meaningless. The conclusion shows that remaining payment periods, mortgage years, initial loans, outstanding balances and financial statuses can better predict the number of defaults by borrowers. However, as there is significant distribution of zeros in the data, this paper runs a zero-inflated poisson model and a zero-inflated negative binomial regression model to resolve this issue. The estimated parameters by the zero-inflated poisson model suggest that remaining repayment periods and mortgage years are the significant variables. The estimated parameters by the zero-inflated negative binomial regression model indicate that remaining repayment periods and outstanding balances are the significant variables.
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