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A Model of Housing and Credit Cycles with Imperfect Market Knowledge

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  • Pei Kuang

Abstract

The paper presents a model of housing and credit cycles featuring distorted beliefs and comovement and mutual reinforcement between house price expectations and price developments via credit expansion/contraction. Positive (negative) development in house price fuels optimism (pessimism) and credit expansion (contraction), which in turn boost (dampen) housing demand and house prices and reinforce agents' optimism (pessimism). Bayesian learning about house prices can endogenously generate self-reinforcing booms and busts in house prices and significantly strenthen the role of collateral constraints in aggregate fluctuations. The model can quantitatively account for the 2001-2008 U.S. boom-bust cycle in house prices and associated household debt and consumption dynamics. It also demonstrates that allowing for imperfect knowledge knowledge of agents, a higher leveraged economy is more prone to self-reinforcing fluctuations.

Suggested Citation

  • Pei Kuang, 2014. "A Model of Housing and Credit Cycles with Imperfect Market Knowledge," Discussion Papers 14-07, Department of Economics, University of Birmingham.
    • Handle: RePEc:bir:birmec:14-07
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    Cited by:

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    2. Patrick Pintus & Jacek Suda, 2019. "Learning Financial Shocks and the Great Recession," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 31, pages 123-146, January.
    3. Caines, Colin, 2020. "Can learning explain boom-bust cycles in asset prices? An application to the US housing boom," Journal of Macroeconomics, Elsevier, vol. 66(C).
    4. Margaret Jacobson, 2019. "Beliefs, Aggregate Risk, and the U.S. Housing Boom," 2019 Meeting Papers 1549, Society for Economic Dynamics.
    5. Pancrazi, Roberto & Pietrunti, Mario, 2019. "Natural expectations and home equity extraction," Journal of Housing Economics, Elsevier, vol. 46(C).
    6. Ma, Qiang & Khan, Zeeshan & Chen, Fuzhong & Murshed, Muntasir & Siqun, Yang & Kirikkaleli, Dervis, 2023. "Revisiting the nexus between house pricing and money demand: Power spectrum and wavelet coherence based approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 266-274.
    7. Natalia S. Nikitina, 2022. "Прогнозирование Индекса Цен На Недвижимость В России," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 6, pages 23-28, June.
    8. Deng, Yongheng & Girardin, Eric & Joyeux, Roselyne, 2018. "Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy," China Economic Review, Elsevier, vol. 48(C), pages 205-222.
    9. Martijn Dröes & Philip Koppels & Boris Ziermans, 2017. "Information Asymmetry, Lease Incentives, and the Role of Advisors in the Market for Commercial Real Estate," ERES eres2017_250, European Real Estate Society (ERES).
    10. Pei Kuang & Tong Wang, 2017. "Labor Market Dynamics With Search Frictions And Fair Wage Considerations," Economic Inquiry, Western Economic Association International, vol. 55(3), pages 1336-1349, July.
    11. Mérő, Bence & Vágó, Nikolett, 2018. "Keresletvezérelt lakáspiaci modell a lakáshitelezést szabályozó makro prudenciális eszközök tanulmányozására [A demand-led model of the housing market for studying the macro-prudential means of reg," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(11), pages 1115-1153.
    12. Yongheng Deng & Eric Girardin & Roselyne Joyeux, 2015. "Fundamentals and the Volatility of Real Estate Prices in China: A Sequential Modelling Strategy," Working Papers 222015, Hong Kong Institute for Monetary Research.
    13. Martijn I. Dröes & Marc K. Francke, 2018. "What Causes the Positive Price-Turnover Correlation in European Housing Markets?," The Journal of Real Estate Finance and Economics, Springer, vol. 57(4), pages 618-646, November.
    14. Kuang, Pei & Yao, Yao, 2017. "Are rational explosive solutions learnable?," Economics Letters, Elsevier, vol. 157(C), pages 62-66.
    15. Natalia S. Nikitina, 2022. "Forecasting the Real Estate Price Index in Russia [Прогнозирование Индекса Цен На Недвижимость В России]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 6, pages 23-28, June.
    16. Pauline Gandré, 2020. "Learning, house prices and macro-financial linkages," EconomiX Working Papers 2020-10, University of Paris Nanterre, EconomiX.
    17. Francisco Camões & Sofia Vale, 2020. "I feel wealthy: A major determinant of Portuguese households’ indebtedness?," Empirical Economics, Springer, vol. 58(4), pages 1953-1978, April.
    18. Gandré, Pauline, 2020. "US stock prices and recency-biased learning in the run-up to the Global Financial Crisis and its aftermath," Journal of International Money and Finance, Elsevier, vol. 104(C).
    19. Mérő, Bence & Borsos, András & Hosszú, Zsuzsanna & Oláh, Zsolt & Vágó, Nikolett, 2023. "A high-resolution, data-driven agent-based model of the housing market," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    20. Pedro Gete, 2015. "Housing demands, savings gluts and current account dynamics," Globalization Institute Working Papers 221, Federal Reserve Bank of Dallas.

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    More about this item

    Keywords

    Boom Bust; Collateral Constraints; Learning; Leverage Housing;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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