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An Estimated DSGE Model to Analyze Housing Market Policies in Hong Kong SAR

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  • Mr. Pau Rabanal
Abstract
During the last decade, Hong Kong SAR has experienced a large increase in house prices and credit, prompting the authorities to respond with several rounds of tightening macroprudential rules and increasing stamp duty taxes. This paper provides a Dynamic Stochastic General Equilibrium (DSGE) model for Hong Kong SAR and analyzes the effectiveness of these measures, and finds that they have helped reduce house price appreciation and household leverage. A baseline small open economy real business cycle model is extended by including a housing sector, financial frictions, foreign demand for the domestic housing stock, and is estimated using Bayesian methods and data for Hong Kong SAR between 1996 and 2017. The paper finds that, without these policies, house prices would have been 10.5 percent higher, and the household credit-GDP ratio 14 percent higher.

Suggested Citation

  • Mr. Pau Rabanal, 2018. "An Estimated DSGE Model to Analyze Housing Market Policies in Hong Kong SAR," IMF Working Papers 2018/090, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2018/090
    as

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    References listed on IDEAS

    as
    1. Matteo Iacoviello, 2005. "House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle," American Economic Review, American Economic Association, vol. 95(3), pages 739-764, June.
    2. He, D., 2014. "The effects of macroprudential policies on housing market risks: evidence from Hong Kong," Financial Stability Review, Banque de France, issue 18, pages 105-120, April.
    3. Mr. R. S Craig & Mr. Changchun Hua, 2011. "Determinants of Property Prices in Hong Kong SAR: Implications for Policy," IMF Working Papers 2011/277, International Monetary Fund.
    4. International Monetary Fund, 2018. "People’s Republic of China-Hong Kong Special Administrative Region: Selected Issues," IMF Staff Country Reports 2018/017, International Monetary Fund.
    5. Andrew Bauer & Nicholas Haltom & Juan F. Rubio-Ramirez, 2003. "Using the Kalman filter to smooth the shocks of a dynamic stochastic general equilibrium model," FRB Atlanta Working Paper 2003-32, Federal Reserve Bank of Atlanta.
    6. Matteo Iacoviello & Stefano Neri, 2010. "Housing Market Spillovers: Evidence from an Estimated DSGE Model," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(2), pages 125-164, April.
    7. Sami Alpanda & Sarah Zubairy, 2016. "Housing and Tax Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 485-512, March.
    8. Mr. Malhar S Nabar & Mr. Ashvin Ahuja, 2011. "Safeguarding Banks and Containing Property Booms: Cross-Country Evidenceon Macroprudential Policies and Lessons From Hong Kong SAR," IMF Working Papers 2011/284, International Monetary Fund.
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    Cited by:

    1. Fabio Canova & Filippo Ferroni, 2022. "Mind the Gap! Stylized Dynamic Facts and Structural Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(4), pages 104-135, October.
    2. Mr. Damien Puy & Mr. Anil Ari & Ms. Yu Shi, 2020. "Foreign Demand and Local House Prices: Evidence from the US," IMF Working Papers 2020/043, International Monetary Fund.
    3. Wong, Siu Kei & Cheung, Ka Shing & Deng, Kuang Kuang & Chau, Kwong Wing, 2021. "Policy responses to an overheated housing market: Credit tightening versus transaction taxes," Journal of Asian Economics, Elsevier, vol. 75(C).
    4. Javier Ferri & Francisca Herranz-Baez, 2023. "Building on fiscal policy: government consumption and the residential sector. When helping hurts," Working Papers 2023-01, FEDEA.

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