本文主要針對預售屋及新成屋、中古屋房價與 A 銀行 CAMELS 指標變化之關聯性研究，資料期間為 2007 年 3 月至 2021 年 12 月，共 60 季。首先，挑選於 2021 年 9 月底尚有購置住宅貸款餘額之 35 家銀行，並針對不動產放款市占率較高之 A 銀行作探討；再者，以 A 銀行 CAMELS 指標評分為被解釋變數，而以預售屋及新成屋價格指數、中古屋房價指數為解釋變數，控制變數含括貨幣總計數年增率、房價所得比、人口成長率、實質經濟成長率、通貨膨脹率、大盤加權指數、匯率及五大銀行新承做放款利率。透過實證結果發現，預售屋及新成屋房價與 A 銀行資產品質、獲利能力及流動性均具顯著負相關；中古屋房價與資本適足性及經營品質變化均具顯著正相關。其中，預售屋及新成屋房價與 A 銀行 CAMELS 指標變化具顯著負相關，中古屋房價則與 CAMELS 指標變化具顯著正相關。因此，本文認為當房價變化時，恐對 A 銀行 CAMELS 指標造成重大影響，若能及早採取因應措施，如調整授信配置等，將可減緩其對 A 銀行 CAMELS 指標之衝擊。

This paper assesses the performance of Bank A using the CAMELS model, for a period between the first quarter of 2007 to the fourth quarter of 2021 and explores the relationship between housing price and the CAMELS rating of Bank A in Taiwan. The present study uses a sample of 35 banks that have mortgage balance in Taiwan and have picked Bank A, which has the highest proportion of mortgage and construction loans. The CAMELS rating is used as the response variable; whereas, the housing price index acts as the explanatory variable. Other control variables include: money supply growth, ratio of house price to income, population growth rate, real GDP growth rate, and inflation. The results show a significant negative correlation between pre-sold house (including new house) and capital adequacy as well as earnings and liquidity, and a significant positive correlation between pre-owned house and capital adequacy, and management, and pre-owned house and the CAMELS rating system of Bank A. Therefore, the CAMELS rating of Bank A could be significantly hampered by a sharp fall in housing price; thus, it is necessary to taking action to avoid this problem.

1.前言---------------1
2.文獻回顧-----------4
3.研究方法-----------8
4.實證結果-----------23
5.結論---------------30
參考文獻--------------31

1. 中央存款保險公司 (2012)，「2008年全球金融危機紀要與改革趨勢」，《存款保險叢書之131》，264-283。
2. 白金安, 張金鶚 (1996)，「國內預售屋訂價模式之探討」，《國家科學委員會研究彙刊》，5，29-44
3. 沈中華 (2002)，「金控公司的銀行與獨立銀行CAMEL比較：1997~1998」，《台灣金融財務季刊》，3(2)，73-97。
4. 沈中華 (2006)，「全球100大銀行及100小銀行之績效—台灣的銀行與它們的相同與相異及台灣的銀行競爭」，會議論文，台北:行政院公平交易委員會。
5. 沈中華, 王建安, 林昆立 (2014)，「房價偏離值與銀行績效關係的研究：跨國的實證分析」，《證券市場發展季刊》，26，1-37。
6. 李光傲 (2006)，「台灣金控公司績效因子之實證」，碩士論文，台中:國立中興大學財務金融學系。
7. 林紋鈴 (2016)，「房價變化對CAMEL指標之影響：以臺灣銀行業為例」，碩士論文，台北:國立臺灣大學財務金融學系。
8. 金融管理委員會銀行局 (2022)，「消費者貸款餘額」，《金融業務統計輯要》，532，82-88。
9. 林國隆 (2018)，「總體經濟因素對CAMELS指標之影響：以台灣銀行業為例」，碩士論文，台北:國立臺灣大學經濟學系。
10. 陳柏鈞 (2019)，「房價變動對CAMELS評等系統之影響：以台灣銀行業為例」，碩士在職專班論文，台北:國立臺灣大學經濟系在職專班。
11. 陳薇如 (2015)，「房價對銀行CAMEL指標之影響---以中國大陸為例」，碩士論文，台北:國立臺灣大學經濟系。
12. 張金鶚、楊宗憲與洪御仁 (2008)，「中古屋及預售屋房價指數之建立、評估與整合-台北市之實證分析」，《住宅學報》，17(2)，13-34。
13. 張順全與唐瓔璋 (2016)，「海峽兩岸金融標竿銀行之CAMEL指標表現：視覺分析的新詮釋」，《中華管理評論國際學報》，19(1)，1-10。
14. 黃子耘 (2000)，「房價與銀行績效表現-跨國資料比較」，碩士論文，台北:國立臺灣大學財務金融學系。
15. Barker, D. and D. Holdsworth (1993), “The causes of bank failures in the 1980s”, Research Paper No. 9325.
16. Berger, A. N. and D.B. Humphrey (1992), Output Measurement in the Service Sectors, Chicago: University of Chicago Press.
17. Christopoulos, A. G., Mylonakis, J. and Diktapanidis, P. (2011), “Could Lehman Brothers’ Collapse Be Anticipated? An Examination Using CAMELS Rating System,” Journal of International Business Research, 4(2), 11-19.
18. Claessens, S., P. Bongini and G. Ferri (2001), “The Political Economy of Distress in East Asian Financial Institutions,” Journal of Financial Services Research, 19(1), 5-25.
19. Cohen, J., P. Cohen, S. G. West, and L. S. Aiken (2003), Applied Multiple Regression Correlation Analysis for the Behavioral Sciences (3rd ed.), New Jersey: Lawrence Erlbaum.
20. Granger, C.W.J. and Newbold, P. (1974), “Spurious Regressions in Econometrics,” Journal of Econometrics, 2(2), 111-120.
21. Gupta, R. CA. (2014), “An Analysis of Indian Public Sector Banks Using Camel Approach,” Journal of Business and Management, 16(1), 94-102.
22. He, L. T., F.C.N Myer, and J.R. Webb (1996), “The Sensitivity of Bank Stock Returns to Real Estate,” Journal of Real Estate Finance and Economics,12,203-220.
23. Hirtle, B. J. and J. A. Lopez (1999), “Supervisory Information and the Frequency of Bank Examinations”, Economic Policy Review, 5, 1-20.
24. Hofmann B. (2003), “Bank Lending and Property Prices：Some International Evidence,” Working Paper.
25. Hott, C. (2009), “Banks and Real Estate Prices,” Working Paper.
26. James, P.S. (2009), Applied Multivariate Statistics for the Social Sciences (4th ed.), New York: Routledge.
27. Kandemir, T. and N.D. Arıcı (2013), “A Comparative Study on CAMELS Performance Evaluation Model in Deposit Banks (2001-2010),” Journal of Faculty of Economics, 18(1), 61-87.
28. Kumar, S. and S. Arora (1995), “A Model for Risk Classification of Banks,” Managerial and Decision Economics, 16(2), 155-165.
29. Luuk, H. (2019), “Real Estate Prices and Bank Stability in the U.S. Market,” Master Thesis, Groningen: University of Groningen.
30. Makinen, M. and L. Solanko (2017), “Determinants of bank closures: Do changes of CAMEL variables matter?,” Journal of Money and Finance, 77(2), 3-21.
31. Nurazi, R. and M. Evans (1990), “An Indonesian Study of the Use of CAMEL(S) Ratios as Predictors of Bank Failure,” Journal of Economic and Social Policy, 10(1), 143-167.
32. Pedhazur, E.J. (1997), Multiple Regression in Behavioral Research: Explanation and Prediction (3rd ed.), Fort Worth: Harcourt Brace.
33. Persons, O.S. (1999), “Using Financial Information to Differentiate Failed vs. Surviving Finance Companies in Thailand: An Implication for Emerging Economies,” Journal of Multinational Finance, 3, 127-145.
34. Richard, J.H. and S.M. Wachter (1998), “Real Estate Booms and Banking Busts: An International Perspective,” The Wharton School Research Paper.
35. Schwartz, G. (1978), “Estimating the Dimension of a Model,” Annals of Statistics, 6, 461-464.
36.Thomson, J. B. (1991), “Predicting bank failures in the 1980s,” Federal Reserve Bank of Cleveland, Economic Review, 27, 9-20.
37. Türker, K.Y. (2001), “CAMELS Analysis of the Turkish Banking Sector, ”Journal of Banking Regulation and Supervision Agency,6, 1-25.
38. West, R. C. (1985), “A Factor-Analytic Approach to Bank Condition,” Journal of Banking and Finance, 9(2), 253-266.
39. Whalen, G. (1991), “A Proportional Hazards Model of Bank Failure: An Examination of Its Usefulness as an Early Warning Tool,” Federal Reserve Bank of Cleveland, Economic Review, 27(1), 21-31.