台灣的銀行業目前呈現過度競爭的情況，加上政府極力推動金融改革，銀行為了拓展版圖，不惜降低徵、授信標準，擴張信用貸款，使得很多銀行的逾期放款增加。加上國內銀行多以消費性金融業務為主，造成雙卡風暴嚴重影響金融市場的秩序，導致銀行緊縮無擔保貸款並增加房屋貸款現象，加上96年初美國爆發次級房貸風暴，為降低銀行因過度擴張房貸業務對銀行經營造成負面影響，本研究以國內某商業銀行為主要研究對象，並以巴塞爾協定為基礎，針對消費者房屋貸款的授信評量做一研究與分析，試圖利用統計方法建構一信用風險內部評等模型，客觀評估房屋抵押貸款之違約風險因子。 本研究所得結論如下: 1.本研究所使用的模型為Logistic Regression，利用銀行實際之進件資料和交易行為資料進行分析，透過此模型找出和違約有關的風險因子，並計算每個客戶之違約率。 2.交叉分析顯示變數「3個月平均繳款率」、「觀察期帳戶逾期情形」、「地區」、「學歷」、「貸款成數指標」、「3個月貸款餘額比率」、「是否超過法定貸款額度」和違約間有顯著的差異，且在Logistic Regression模型中顯示這些因子對於違約有一定影響 3.經由本研究所建構的內部模型，可以及早發現高風險群組的客戶，並做適當處理，提升銀行房貸業務的授信品質，降低銀行整體的逾放比。

在傳統銀行放款的觀念中認為，借款人主導了還款的來源，關於貸款風險的研究大多集中在借款人行為因素的探討，但是房屋貸款的風險，除了借款人本身的特質外，應該還需要不同角度的探討，尤其是在擔保品方面。銀行在辦理放款時，對擔保品價值的評估僅以當時的市場價值作直接的判斷，並依判斷結果來決定貸款的成數，這樣的決策並未考慮擔保品本身所處的區域條件及其未來的發展性，因而產生了風險判斷的偏誤。 本研究將透過不動產的供需價量的關係，嘗試找出影響房屋貸款擔保品風險的因子，並對房屋貸款的風險因子給予適當的權重及評分，再運用劃分等級的模型，將研究區域依房屋貸款風險的大小劃分風險等級。最後利用不同的角度或方法檢驗各種模型對區域風險分類之異同及功能，以建立模型提供銀行於承做房屋貸款或制定放款政策時，作為決定貸款成數(LTV)的參考依據，避免銀行貸款日後遭受擔保品價格下跌所產生的風險。 本研究以分析層級程序法(AHP)及分析網路程序法(ANP)設計不同的問卷，在取得各風險因子的權重後，對各項風險因子時間序列的數據進行分析，最終取得台北縣市各區域的風險等級。實證結果AHP及ANP皆通過一致性分析，AHP與未權重化ANP間不具顯著差異；權重化ANP與極限化ANP間不具顯著差異。AHP權重與ANP未權重化矩陣兩種模型在區域房屋貸款風險等級的區分標準上較為寬鬆。ANP權重化矩陣及ANP極限化矩陣對區域房屋貸款風險等級的劃分較為嚴格。這兩類不同等級劃分標準的模型提供金融業者在制定房屋貸款政策時可以有多樣的選擇。 / People with traditional concept of bank lending believe that borrowers dominate the sources of repayment. Researches regarding to the credit risk of loan mainly focus on the behaviors of borrowers. Nevertheless, the risk of mortgage loan should be deliberated with different points of view, especially the collateral, besides considering the characteristics of borrowers. During the process of loan, banks evaluate the collateral with directly determine according to the prevailing market value and decide the proportion of loan. The decision is not considered with the regional factors and the future development of the collateral. A bias of risk determination therefore exists during the process. The research tries to find the factors that influence the collateral risk of mortgage loan through supply, demand, price and quantity of real estate. Also, it allocates the weight and evaluation of every risk factor of mortgage loan. The research then distinguishes the investigated areas into different risk levels according to the mortgage loan risk by applying appropriate model. The research stands at various points of view and utilizes different methods to determine whether the classification of area risk is appropriate or not and offering a banks model to be the reference basis of determining the loan to value(LTV) when executing mortgage loan or drawing up loan policy. Banks can avoid the risk of collateral depreciation in the future. The research designed various questionnaires with Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). After obtained the weights of different risk factors, an analysis was processed on time sequence data of every risk factor and results the risk level of every subarea of Taipei. The empirical results in consistency analysis by AHP and ANP are passed. The difference between AHP and un-weighted ANP is not significant. The difference between weighted ANP and limited ANP is also not significant. Both weighted AHP and un-weighted ANP matrix models are lenient on the classify criteria of area mortgage risk levels. In opposition, weighted ANP matrix models and ANP limited matrix are strict on the same criteria. The two models with different criteria offer financial corporations different choices when drawing up policies of mortgage loan

本文的研究目的為，在一個含有房屋及房屋相關貸款的動態隨機一般均衡模型的架構中，比較貨幣政策、財政政策以及總體審慎政策對於房屋價格及房屋相關貸款的穩定效果。本文建構一個經濟封閉體系，其中包含三種不同家計單位、商品生產部門、房屋建商、資本生產部門，並且由政府部門制定相關政策；此模型的特色為，不同家計單位中的借貸行為、名目價格僵固性以及透過房屋價格抵押貸款的限制來刻劃金融摩擦。我們考慮了一般緊縮貨幣政策、提高財產稅率以及緊縮貸款價值比；本文發現，在三種政策中，對於抑制房屋價格以及降低住房貸款對國內生產毛額的比例，財政政策及總體審慎政策比起緊縮貨幣政策擁有較好的效果。 / The main purpose in this paper is to compare the effect of monetary policy, fiscal policy and macroprudential policy on housing price and housing related loans using a micro-based dynamic stochastic general equilibrium (DSGE) model with housing and housing related loans. We equip a closed economy model with three types of infinitely-lived households (patient households, impatient households and renters), a goods firm, housing and capital producer and a government sector. The model features borrowing and lending between patient and impatient households, nominal rigidity in goods price and financial friction in the form of collateral constraints tied to price of house. We consider the contractionary monetary policy by raising the interest rate, fiscal policy by increasing property tax rate and the macroprudential policy through tightening the loan-to-value (LTV) ratio. We find that among these three policies, in terms of dampening the price of housing and lowering the loan-to-GDP ratio, raising the property tax and lowering the LTV ratio outperforms the contractionary monetary policy.

房屋貸款保證保險制度可移轉部分違約風險予保險公司。然而，保險公司與金融機構在共同承擔風險之際，因房貸保證保險制度之施行，於提高貸款成數後，產生違約風險提高之矛盾現象；而估計保險之預期損失時，以目前尚無此制度下之違約數據估計損失額，將有錯估之可能。 本研究以二元邏吉斯特迴歸模型(Binary Logistic Regression Model)與存活分析(Survival Analysis)估計違約行為，並比較各模型間資料適合度及預測能力，進而單獨分析變數-貸款成數對違約率之邊際機率影響。以探討房貸保證保險施行後，因其對借款者信用增強而提高之貸款成數，所增加之違約風險。並評估金融機構因提高貸款成數後可能之違約風險變動，據以推估違約率數據，並根據房貸保證保險費率結構模型，計算可能之預期損失額，估算變動的保險費率。 實證結果發現，貸款成數與違約風險呈現顯著正相關，貸款成數增加，邊際影響呈遞增情形，違約率隨之遞增，而違約預期損失額亦同時上升。保險公司因預期損失額增加，為維持保費收入得以支付預期損失，其保險費率將明顯提升。故實施房屋貸款保證保險，因借款者信用增強而提高之貸款成數，將增加違約機率並對保險費率產生直接變動。 / Mortgage insurance system may transfer part of the default risk to insurance companies. However, the implementation of mortgage insurance system, on increasing loan to value ratio, the resulting increase default risk. And literatures estimate the expected loss without the default data, there will be misjudge. Our study constructs the binary logistic regression model and survival analysis to estimate the mortgage default behavior, and compare the data between the model fit and the predictive power. Analyzes the effect of loan to value ratio on the marginal probability of default rate. Furthermore, assess the financial institutions in the risk of default due to loan to value ratio changes. According to the estimated default rate data, we employ the mortgage insurance rate structural model to calculate the expected amount of loss and the changes in premium rates. Empirical results found loan to value ratio have a significant positive effect on borrowers’ default. Loan to value ratio increase, the marginal effect progressively increase, along with increasing default rates and expected default losses. Due to the ascendant expected loss, insurance companies increase premiums to cover the expected loss, the premium rate will be significantly improved. Therefore, the implementation of mortgage insurance, credit enhancement for the borrower to improve loan to value ratio, will increase the probability of default and insurance rates.