臺灣博碩士論文加值系統

自1991 年政府開放新銀行設立後，國內銀行競爭過度激烈，不論是公營銀行或民營銀行，為爭取市場占有率，銀行授信原則有所鬆動，以致授信品質下降，造成金融機構逾期放款比率呈逐年增加的趨勢。復查近10年間本國銀行逾放比率從2008年1.54%逐年下降至2015年0.23%，2016~2017年間則緩步上升至0.27%~0.31%，截至2018年6月逾期放款比率為0.28%。面臨逾期放款似乎有逐漸惡化現象，金融機構如何健全銀行授信品質，減少逾期放款發生，儼然成為當前極需正視之課題。
2007 年美國發生次級房貸，引發全球金融風暴，次貸的核心問題，即是信用風險，意即借貸雙方資訊不對等，因而導致違約率的不斷攀升，進而造成全世界的金融風暴。為了探討違約放款之行為，本研究藉由探討個人房貸逾期放款所可能產生的影響變數，以某一不動產專業銀行南部某分行為範疇，就2013年至 2017年間曾發生應繳納貸款本息逾期一個月~三個月以上，及目前仍正常還本繳息之房貸戶抽樣案件，採用羅吉斯迴歸模式來進行實證研究。
本研究以銀行的貸放條件為自變數,應變數為個人房貸是否逾放違約。一些基本自變數如貸款利率、貸款成數(LTV)、借款金額及期限與擔保品座落區域，另外加入房地產景氣變化、貸款戶個人特徵之基本資料當作控制變數，如性別、負債負擔比率、學歷、婚姻狀況、職業、個人年收入、保證人之有無等，以控制其對應變數的影響，期望能找出與是否發生違約之間的關係。
實證結果發現，貸款金額大小、擔保品座落地點好壞、借款人教育程度等級及負債負擔比率高低與房貸逾放有顯著因素。貸款成數愈高，違約機率愈低，則與假設不符，個人職業愈穩定、收入愈高及負債負擔比率愈低，則貸款繳息能力佳，違約機率較低，銀行核貸成數相對較高。

實證結果，貸款金額愈高，違約機率愈低，與假設不符；銀行整體通盤考量借款人條件，輔以參酌金融聯合徵信中心個人票債信紀錄與信用卡繳款情形，如評估違約風險低，銀行債權可以確保，貸款金額當然會比較高。
由於個人房貸戶的不同特性，連帶影響銀行授信條件的品質，與是否產生逾期放款的違約。本研究藉由分析逾期放款的共同顯著變數，作為授信風險控管時的另一道防線，以增進銀行優質債權，減少逾期放款產生。

Abstract
Bank Credit Quality,Real Estate Variables and
Housing Loan Overdue Ratio-Evidence from L Bank
Chen- Kuo Ching
Hsuan-Chu Lin
Graduate Institute of Finance,National Cheng Kung University

SUMMARY
The competition of domestic banks has been excessively fierce. In order to gain market share, the principle of bank credit has been loosened, resulting in a decline in the quality of credit, resulting in the overdue ratio of financial institutions overdue. Increased trend. How to improve the quality of bank credit and reduce the occurrence of overdue loans has become a topic that needs to be addressed at present. This research Using the Rogers regression model for empirical research.The bank’s loan conditions are self-variant, and the number of strains is whether the personal mortgage is over-represented. In addition, the basic data of the changes in the real estate boom and the personal characteristics of the loan households are added as control variables .Expectation can find out the relationship between whether or not a default occurs
The empirical results show that the size of the loan, the location of the collateral, the level of the education level of the borrower and the debt burden ratio and the over-release of the mortgage have significant factors. The higher the number of loans, the lower the chance of default, and the assumptions do not match. The higher the loan amount, the lower the probability of default, which is inconsistent with the assumption. This study analyzes the common significant variables of overdue loans as another line of defense when credit risk is controlled to enhance the bank's quality claims and reduce overdue loans.
Keywords: personal mortgage, loan conditions, overdue loans, return to Rogers

INTRODUCTION
The over-release ratio of domestic banks decreased from 1.54% in 2008 to 0.23% in 2015, and gradually increased to 0.27%~0.31% between 2016 and 2017. The overdue loan ratio was 0.28% as of June 2018. This study explores the variables that affect overdue lending and the probability of overshooting, which is used as a credit risk factor. Relevant literatures at home and abroad pointed out the loan interest rate, the number of loans, the length of the loan, the amount of the loan, the collateral area, the changes in the real estate boom, and the personal basic information and conditions of the borrower. Through the analysis of the chi-square test and the return of the Rogers model, it affects the overdue factors and probability of housing loan cases. The empirical results show that there is a significant impact on the overdue of home loan credit cases.

MATERIALS AND METHODS
Based on the collected data, this study organizes the data of the sample data first, and then uses the Stata software to perform statistical analysis according to the hypothesis and structure. The methods used are Narrative, Mean Analysis, Correlations Analysis, Chi-Square Test, Logistic Regression, etc.
This study intends to verify the following hypothesis for the overdue default risk of home loan households:
(1)The longer the customer loan period, the higher the default rate
(2)In terms of where the real estate collateral is located, different geographical locations may affect the risk of credit.
(3)In terms of changes in the real estate environment, different time and space backgrounds will affect the risk of credit
(4)Gender affects the risk of credit
(5)Single or marriage affects the risk of credit
(6)The higher the education level, the lower the default rate
(7)If the service agency is a public official or a larger one, it is inferred that the default rate is lower.
(8)There are guarantors who infer that their default rate is low
(9)The higher the customer loan interest rate, the higher the default rate
(10)The higher the customer loan is, the higher the default rate is.
(11)The higher the customer loan amount, the higher the default rate
(12)The higher the income and the lower the debt ratio, the lower the default rate

RESULTS AND DISCUSSION
Through Logistic Regression, the hypothesis verification results proposed by the Institute are compiled as shown in the table.

This research hypothes is Whether it is established
1 Conforms to the hypothesis, but not significant
2 Conforms to the hypothesis
3 Conforms to the hypothesis
4 Conforms to the hypothesis, but not significant
5 Conforms to the hypothesis, but not significant
6 Conforms to the hypothesis
7 Conforms to the hypothesis
8 Conforms to the hypothesis
9 Conforms to the hypothesis, but not significant
10 Does not match
11 Does not match
12 Debt ratio, Conforms to the hypothesis
Income, Conforms to the hypothesis, but not significant

CONCLUSION
The empirical results show that the size of the loan, the location of the collateral, the level of the education level of the borrower and the debt burden ratio and the over-release of the mortgage have significant factors. The higher the number of loans and the lower the probability of default, the more inconsistent with the assumptions, the more stable the individual occupation, the higher the income and the lower the debt burden ratio, the better the interest rate of the loan, the lower the probability of default, and the higher the number of bank loans.
The empirical result shows that the higher the loan amount, the lower the probability of default, which is inconsistent with the assumptions; the bank considers the borrower's conditions as a whole, supplemented by the personal credit information record and credit card payment of the financial joint credit center.If the risk of default is assessed, bank claims can ensure that the loan amount will of course be higher.
The limitations of this study, the insufficient number of housing loan data, may affect the true prediction accuracy of the model. There is no analysis of the management style and personality traits of the operators, whether it indirectly affects the outcome of the default of the mortgage; the personal data filled by the borrowers cannot actually know the accuracy, so the adverse selection and moral hazard problems cannot be avoided.
The research proposal is based on personal mortgage as the research subject, and the follow-up researchers can conduct research and analysis on other credit granting services such as personal credit loans, credit card auditing and issuing cards. Comparing and analyzing different types of banks, if more samples can be obtained from private banks and public banks and foreign banks, the correctness of the empirical results of this study can be verified.

第一章 緒論.............................................1
第一節 研究背景與動機..................... .............. 1
第二節 研究目的.. .......... ..... ..................... 3
第三節 研究範圍.. ...................................... 4
第二章 文獻探討......................................... 5
第一節 銀行授信評估原則.................................. 5
第二節 逾期放款之定義.................................... 9
第三節 房地產變數對銀行逾放比的影響................. .... . 11
第四節 其他影響房貸逾放變數...... ........................ 13
第三章 研究方法................. ........... ........ ...19
第一節 研究架構及研究假設... ..............................19
第二節 研究變數及操作性定義.. ... .................. .......23
第三節 研究樣本...... .......... ..... ...................26
第四節 資料分析及統計分析方法 ................... ..........28
第四章 實證分析與結果. ...................................32
第一節 樣本結構分析.............................. ........32
第二節 平均數分析.......... ...... .......... ... ...... .42
第三節 各變數與房貸逾放違約差異性之檢定................ ......45
第四節 相關分析....... ..... .... ................ .......47
第五節 羅吉斯迴歸分析.......... . ................ ........50
第六節 研究假說與實證結果分析............... ...............54
第五章 結論與建議................ ......... ..... ........58
第一節 研究結論.............. ... ........... ..... ......58
第二節 研究限制及建議... ... ..................... ..... ..60
參考文獻..................... ... ......... ....... .....61
國內部分........... ......... ... ........... ..... .....61
國外部分........... ......... ... ......... ....... .....63

表目錄
表3.1 本研究變數之操作性定義... ... .............. .........24
表3.2 樣本變數分析彙整表............ .......... ...........27
表 4.1 貸款利率在正常戶、違約戶之間的交叉分析表...... .........33
表 4.2 貸款成數在正常戶、違約戶之間的交叉分析表.. .............33
表 4.3 貸款年限在正常戶、違約戶之間的交叉分析表.. .............34
表 4.4 貸款金額在正常戶、違約戶之間的交叉分析表.. .............35
表 4.5 擔保品區域在正常戶、違約戶之間的交叉分析表.. ...........35
表 4.6 房地產景氣變化在正常戶、違約戶之間的交叉分析表.. ........36
表 4.7 性別在正常戶、違約戶之間的交叉分析表.............. ....37
表 4.8 婚姻狀況在正常戶、違約戶之間的交叉分析表......... . ....37
表 4.9 教育程度在正常戶、違約戶之間的交叉分析表........ .......38
表 4.10職業別在正常戶、違約戶之間的交叉分析表............ ....39
表 4.11 所得水準在正常戶、違約戶之間的交叉分析表.......... . ..39
表 4.12 負債負擔比率在正常戶、違約戶之間的交叉分析表........ ...40
表 4.13 有無保證人在正常戶、違約戶之間的交叉分析表......... ....41
表 4.14 貸款利率平均數分析.......... .. ..... ...............42
表 4.15 貸款成數平均數分析... ...... .. ..... ...............43
表 4.16 貸款金額平均數分析... . .... .. ... ..... ...........43
表 4.17 借款人所得平均數分析... . .. .. .. ....... ..........44
表 4.18 負債負擔比率平均數分析... . ... .. .. ....... .......44
表 4.19 各變數對房貸是否違約差異性卡方檢定彙整表. ..............45
表 4.20 各變數之相關性分析(Pearson) ... . .. .. .......... ..47
表 4.21 各變數之相關性分析(Pearson) ... .... ........... ....47
表 4.22 羅吉斯迴歸分析彙整表(1) ... ....... .. ......... ....50
表 4.23 羅吉斯迴歸分析彙整表(2) ... ....... .. ......... ....52
表 4.24 本研究假說檢定結果彙整... . .... .. .... ......... ..56

圖目錄
圖3-1 研究架構........................ ... ......... ..... 20
圖3-2研究模型................... ....... ......... ..... ..20
圖3-3 Logistic迴歸函數曲線圖....... ...... ......... ... ...30

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