IDEAS home Printed from https://ideas.repec.org/p/cdf/wpaper/2020-3.html

Is there a National Housing Market Bubble Brewing in the United States?

Author

Listed:
  • Gupta, Rangan

    (Department of Economics, University of Pretoria)

  • Ma, Jun

    (Department of Economics, Northeastern University)

  • Theodoridis, Konstantinos

    (Cardiff Business School)

  • Wohar, Mark E

    (College of Business Administration, University of Nebraska at Omaha)

Abstract

We use a time-varying parameter dynamic factor model with stochastic volatility (DFM-TV-SV) estimated using Bayesian methods to disentangle the relative importance of the common component in FHFA house price movements from state-specific shocks, over the quarterly period of 1975Q2 to 2017Q4. We find that the contribution of the national factor in explaining fluctuations in house prices is not only critical, but also has been increasing and has become more important than the local factors since around 1990. We then use a Bayesian change-point vector autoregressive (VAR) model, that allows for different regimes throughout the sample period, to study the impact of aggregate supply, aggregate demand, (conventional) monetary policy, and term-spread shocks, identified based on sign-restrictions, on the national component of house price movements. We detect three regimes corresponding to the periods of Great Inflation , Great Moderation , and the zero-lower bound (ZLB). While the conventional monetary policy is found to have played an important role in the historical evolution of the national factor in the first-regime, other shocks are found to be quite dominant as well especially during the second regime, with monetary policy shocks playing virtually no role during this period. In the third-regime, unconventional monetary policy shock is found to have led to a (delayed) recovery in the housing market. But more importantly, we find evidence that the national housing factor has been detached from the identified macroeconomic shocks (fundamentals) since 2014, thus suggesting that a national bubble might be brewing again in the US housing market. Understandably, our results have important policy implications.

Suggested Citation

  • Gupta, Rangan & Ma, Jun & Theodoridis, Konstantinos & Wohar, Mark E, 2020. "Is there a National Housing Market Bubble Brewing in the United States?," Cardiff Economics Working Papers E2020/3, Cardiff University, Cardiff Business School, Economics Section.
  • Handle: RePEc:cdf:wpaper:2020/3
    as

    Download full text from publisher

    File URL: http://carbsecon.com/wp/E2020_3.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Goodness C. Aye & Christina Christou & Rangan Gupta & Christis Hassapis, 2024. "High-Frequency Contagion between Aggregate and Regional Housing Markets of the United States with Financial Assets: Evidence from Multichannel Tests," The Journal of Real Estate Finance and Economics, Springer, vol. 69(2), pages 253-276, August.
    2. Sheng, Xin & Marfatia, Hardik A. & Gupta, Rangan & Ji, Qiang, 2021. "House price synchronization across the US states: The role of structural oil shocks," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    3. Bouri, Elie & Gupta, Rangan & Kyei, Clement Kweku & Shivambu, Rinsuna, 2021. "Uncertainty and daily predictability of housing returns and volatility of the United States: Evidence from a higher-order nonparametric causality-in-quantiles test," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 200-206.
    4. Jinwoong Lee, 2024. "What factors drive house prices in the USA? Sign restricted VAR approach," Empirical Economics, Springer, vol. 66(6), pages 2533-2556, June.
    5. Rangan Gupta & Hardik A. Marfatia & Christian Pierdzioch & Afees A. Salisu, 2022. "Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty," The Journal of Real Estate Finance and Economics, Springer, vol. 64(4), pages 523-545, May.
    6. Oguzhan Cepni & Hardik A. Marfatia & Rangan Gupta, 2025. "The time-varying impact of uncertainty shocks on the co-movement of regional housing prices of the United Kingdom," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-22, December.
    7. Christophe Andre & David Gabauer & Rangan Gupta, 2020. "Time-Varying Spillovers between Housing Sentiment and Housing Market in the United States," Working Papers 202091, University of Pretoria, Department of Economics.
    8. André, Christophe & Gabauer, David & Gupta, Rangan, 2021. "Time-varying spillovers between housing sentiment and housing market in the United States☆," Finance Research Letters, Elsevier, vol. 42(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cdf:wpaper:2020/3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Yongdeng Xu (email available below). General contact details of provider: https://edirc.repec.org/data/ecscfuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.