本研究旨在探討2012年8月至2022年5月中央銀行實施選擇性信用管制措施對於台灣房地產市場之影響。政府打擊房地產炒作之政策最主要是想抑制投機性炒房，然而政府對於房價飆漲迅速之地區並沒有明確的定義，因此根據Igan and Kang(2011)對於投機性地區(speculative zones)和非投機性地區(non-speculative zones)之定義，將台灣六都七十個行政區劃分為投機性地區和非投機性地區，並採用結構式自我向量迴歸和追蹤資料模型分析選擇性信用管制對於房價、交易量、購屋住宅貸款及個人房貸總人數之影響。
根據結構式自我向量迴歸得出之衝擊反應函數，發現選擇性信用管制對於六都部分房價指數會呈現正向顯著，對於六都部分交易量會呈現負向顯著，並會與購屋住宅貸款和個人房貸總人數在初期正相關且具統計顯著性。接著，進一步利用追蹤資料迴歸估計選擇性信用管制對於投機性和非投機性地區之影響，結果發現在兩個不同之區間段，針對投機性地區和非投機性地區實施選擇性信用管制，都無法達到抑制房價的成效，對於交易量影響則是顯著負向或不影響，且此結果在投機性地區更趨明顯。
此結果意味著選擇性信用管制並無法有效壓制房價的飆漲，並且在部分地區和區間段，房地產市場呈現價量背離的現象，即儘管交易量的萎縮，房價仍然走升，其對照現今的房地產市場走勢，會發現確實存在房市價量呈現負相關的情形。

The purpose of this paper is to investigate the impact of selective credit control on housing market in Taiwan from August 2012 to May 2022. The government's primary objective in combating real estate speculation is to curb speculative property investment. Based on the definition provided by Igan and Kang (2011) regarding speculative and non-speculative zones, the seventy administrative districts in the six major cities of Taiwan were categorized into speculative and non-speculative zones.
Based on the impulse response analysis, it was found that selective credit control has a significant positive impact on the housing price and a significant negative impact on the transaction volume in certain districts of Taiwan’s six major cities. Furthermore, there is an initial positive and statistically significant relationship between selective credit controls and the total mortgage credit and the total number of individual housing borrowers. Subsequently, according to the panel data regression, it shows that implementing selective credit controls in both speculative and non-speculative regions did not effectively suppress housing prices in either case. The impact on transaction volume was either significantly negative or inconsequential, with these effects being more pronounced in speculative regions.
In certain regions and time intervals, the real estate market exhibits a decoupling phenomenon between prices and transaction volume. When comparing this with the current trend in the real estate market, it is indeed observed that there is a negative correlation between prices and transaction volume.

摘要 ii
ABSTRACT iii
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 3
第三節 研究架構 4
第二章 文獻回顧 7
第一節 實施房屋貸款成數之依據 7
第二節 房屋貸款成數限制之實施效果 8
第三章 資料敘述 13
第一節 變數選擇 13
第二節 變數說明 13
第三節 資料來源與處理 15
第四節 資料走勢圖 16
第四章 使用方法 23
第一節 六都資料之實證模型：結構式自我向量迴歸模型 23
第二節 區分投機性與非投機性地區 25
第三節 投機性和非投機性地區：追蹤資料迴歸 27
第五章 實證結果 30
第一節 衝擊反應函數 30
第二節 全部地區、投機性和非投機性地區迴歸結果 39
第六章 結論 51
參考文獻 52

王泓仁、陳南光、林姿妤(2017)，房貸成數(LTV)對台灣房地產價格與授信之影響，中央銀行季刊，39(3)，頁5-39。

林秋瑾、黃佩玲(1995)，住宅價格與總體經濟變數關係之研究—以向量自我迴歸模式(VAR)進行實證，國立政治大學學報，第71期。

張金鶚、林哲群、楊屯山(2020)，總體審慎措施對於房地產價量變化影響，住宅學報，31(2)，頁29-62。

陳柏如(2018)，總體審慎政策工具與臺灣房價的關係－特定目標信用工具與房市相關租稅工具的影響，經濟研究，54(2)，頁P287-330。

陳旭昇(2013)，時間序列分析：總體經濟與財務金融之應用。

黃富櫻(2011)，中央銀行在金融穩定的角色與工具，國際金融參考資料，第六十二集，頁1-46。

楊金龍(2022)，央行在健全房市方案中扮演的角色，中央銀行季刊，44(1)，頁5-27。

Bruno, V. and H. S. Shin (2015). Capital flows and the risk-taking channel of monetary policy. Journal of Monetary Economics,71, 119-132.

Christiano, L. J., M. Eichenbaum and C. Evans (1998). Monetary Policy Shocks: What Have We Learned and to What End. NBER Working Paper, No. 6400.

Clement, P. (2010). The term “Macroprudential”: Origins and Evolution. BIS Quarterly Reviews.

Favara, G. and J. Imbs (2014). Credit Supply and the Price of Housing. The American Economics Review, 105(3), 958-982.

Follain, J. and O. Velz (1995). Incorporating the Number of Existing Home Sales into a Structural Model of the Market for Owner-Occupied Housing, Journal of Housing Economics, Elsevier, 4(2), 93-117.

Gelain, P., K. J. Lansing and C. Mendicino (2013). House Prices, Credit Growth, and Excess Volatility: Implications for Monetary and Macroprudential Policy. International Journal of Central Banking, 9(2), 219-276.

Iacoviello, M. (2005). House prices, borrowing constraints, and monetary policy in the business cycle. American economic review, 95(3), 739-764.

Igan, D. and H. Kang (2011). Do Loan-To-Value and Debt-To-Income Limits Work? Evidence From Korea. IMF Working Papers 2011/197.

Jacome, L. I. and S. Mitra (2015). LTV and DTI Limits—Going Granular. IMF Working Paper 2015/154.

Krznar, I. and J. Morsink (2014). With Great Power Comes Great Responsibility: Macroprudential Tools at Work in Canada. IMF Working Paper 2014/08.

Kuttner, K. and I. Shim (2013). Can non-interest rate policies stabilize housing markets? Evidence from a panel of 57 economics. Journal of Financial Stability, Elsevier, vol. 26(C), 31-44.

Rubio, M. and F. Yao (2016). Macroprudential Policies in a Low Interest Rate Environment. Journal of Money, Credit and Banking, 52(6), 1565-1591.

Singh, B. (2020). House Prices and Macroprudential Policies: Evidence from City-Level Data in India. IMF Working Paper 2020/291.

Stein, J. (1995). Prices and Trading Volume in the Housing Market: A Model with Down-Payment Effects. Quarterly Journal of Economics, 110, 379-406.

Sutton, G. D. (2002). Explaining changes in housing prices. BIS Quarterly Reviews.

Wheaton, W. (1990). Vacancy, Search, and Prices in a Housing Market Matching Model. Journal of Political Economy, 98(6), 1270-1292.

Wong, E., T. Fong, K.-F. Li, and H. Choi (2011). Loan-to-Value Ratio as a Macroprudential Tool: Hong Kong’s Experience and Cross-Country Evidence. Hong Kong Monetary Authority, Working Paper No. 01/2011.

Yao, F. and B. Lu (2020). The Effectiveness of Loan-to-Value Ratio Policy and its Interaction with Monetary Policy in New Zealand: An Empirical Analysis Using Supervisory Bank-Level Data. BIS paper No. 110e.

Zhang, L. and E. Zoli (2014). Leaning Against the Wind: Macroprudential Policy in Asia,” IMF Working Paper 2014/22.