IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v58y2023ipds1544612323010085.html
   My bibliography  Save this article

US housing prices and the transmission mechanism of connectedness

Author

Listed:
  • Roy Chowdhury, S.
  • Gupta, Kirti
  • Tzeremes, Panayiotis

Abstract

This research examines the connectedness effects of US house prices across different metropolitan areas, utilizing a quantile connectedness model. We identify MSAs that could play a significant role in the transmission of house prices, either as net contributors or recipients of these effects. The level of quantiles emerges as another crucial factor, influencing the behavior of various metropolitan areas across different house price ranges. Denver, Los Angeles, Seattle, Phoenix, San Diego, and San Francisco exhibit net-contributing behavior, whereas Chicago, Detroit, Las Vegas, Minneapolis, New York, and Atlanta show a net-receiving trend. Metropolitan areas like Portland, Boston, Charlotte, Cleveland, Dallas, Miami, Tampa, and Washington display roles that can either be contributing or receiving over the time period. Lastly, external factors such as economic crises or health events like the Covid-19 outbreak play a pivotal role in shaping MSAs' behavior.

Suggested Citation

  • Roy Chowdhury, S. & Gupta, Kirti & Tzeremes, Panayiotis, 2023. "US housing prices and the transmission mechanism of connectedness," Finance Research Letters, Elsevier, vol. 58(PD).
  • Handle: RePEc:eee:finlet:v:58:y:2023:i:pd:s1544612323010085
    DOI: 10.1016/j.frl.2023.104636
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323010085
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2023.104636?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gerhard Rünstler & Marente Vlekke, 2018. "Business, housing, and credit cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 212-226, March.
    2. Edward E. Leamer, 2007. "Housing is the business cycle," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 149-233.
    3. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2021. "A regional decomposition of US housing prices and volume: market dynamics and Portfolio diversification," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(2), pages 279-307, April.
    4. Robert J. Shiller, 2007. "Understanding recent trends in house prices and homeownership," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 89-123.
    5. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    6. Tomohiro Ando & Matthew Greenwood-Nimmo & Yongcheol Shin, 2022. "Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks," Management Science, INFORMS, vol. 68(4), pages 2401-2431, April.
    7. Montañés, A. & Olmos, L., 2013. "Convergence in US house prices," Economics Letters, Elsevier, vol. 121(2), pages 152-155.
    8. Robert J. Shiller, 2007. "Understanding recent trends in house prices and homeownership," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 89-123.
    9. Edward E. Leamer, 2015. "Housing Really Is the Business Cycle: What Survives the Lessons of 2008–09?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(S1), pages 43-50, March.
    10. Robert Shiller, 2007. "Understanding Recent Trends in House Prices and Home Ownership," Yale School of Management Working Papers amz2557, Yale School of Management, revised 01 Nov 2007.
    11. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    12. Thomas J. Fisher & Colin M. Gallagher, 2012. "New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 777-787, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Forecasting Growth-at-Risk of the United States: Housing Price versus Housing Sentiment or Attention," Working Papers 202401, University of Pretoria, Department of Economics.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2019. "A Regional Decomposition of US Housing Prices and Volume: Market Dynamics and Economic Diversification Opportunities," Working Papers in Economics & Finance 2019-06, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    2. Le, Trung H. & Pham, Linh & Do, Hung X., 2023. "Price risk transmissions in the water-energy-food nexus: Impacts of climate risks and portfolio implications," Energy Economics, Elsevier, vol. 124(C).
    3. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2021. "A regional decomposition of US housing prices and volume: market dynamics and Portfolio diversification," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(2), pages 279-307, April.
    4. Kyriazis, Nikolaos & Papadamou, Stephanos & Tzeremes, Panayiotis & Corbet, Shaen, 2024. "Quantifying spillovers and connectedness among commodities and cryptocurrencies: Evidence from a Quantile-VAR analysis," Journal of Commodity Markets, Elsevier, vol. 33(C).
    5. Rafiq, Shuddhasattwa, 2022. "How did house and stock prices respond to different crisis episodes since the 1870s?," Economic Modelling, Elsevier, vol. 114(C).
    6. Nyakurukwa, Kingstone & Seetharam, Yudhvir, 2023. "Quantile and asymmetric return connectedness among BRICS stock markets," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    7. Ozkan, Oktay & Sunday Adebayo, Tomiwa & Usman, Ojonugwa, 2024. "Dynamic connectedness of clean energy markets, green markets, and sustainable markets: The role of climate policy uncertainty," Energy, Elsevier, vol. 303(C).
    8. Patel, Ritesh & Gubareva, Mariya & Chishti, Muhammad Zubair & Teplova, Tamara, 2024. "Connectedness between healthcare cryptocurrencies and major asset classes: Implications for hedging and investments strategies," International Review of Financial Analysis, Elsevier, vol. 93(C).
    9. Gabauer, David & Gupta, Rangan, 2020. "Spillovers across macroeconomic, financial and real estate uncertainties: A time-varying approach," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 167-173.
    10. Helmut Herwartz & Fang Xu, 2020. "Low Mortgage Rates and Securitization: A Distinct Perspective on the US Housing Boom," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 164-190, January.
    11. Guangxi Cao & Fei Xie, 2024. "Extreme risk spillovers across energy and carbon markets: Evidence from the quantile extended joint connectedness approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2155-2175, April.
    12. Armah, Mohammed & Amewu, Godfred, 2024. "Quantile dependence and asymmetric connectedness between global financial market stress and REIT returns: Evidence from the COVID-19 pandemic," The Journal of Economic Asymmetries, Elsevier, vol. 29(C).
    13. Sala-Ríos, Mercé & Farré-Perdiguer, Mariona & Torres-Solé, Teresa, 2018. "How do Housing Prices and Business Cycles Interact in Spain? An Empirical Analysis/¿Cómo interactúan los precios de la vivienda y el ciclo económico en España? Un análisis empírico," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 36, pages 897-920, Septiembr.
    14. Cunado, Juncal & Chatziantoniou, Ioannis & Gabauer, David & de Gracia, Fernando Perez & Hardik, Marfatia, 2023. "Dynamic spillovers across precious metals and oil realized volatilities: Evidence from quantile extended joint connectedness measures," Journal of Commodity Markets, Elsevier, vol. 30(C).
    15. Bouri, Elie & Gabauer, David & Gupta, Rangan & Tiwari, Aviral Kumar, 2021. "Volatility connectedness of major cryptocurrencies: The role of investor happiness," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    16. Cagli, Efe Caglar, 2023. "The volatility spillover between battery metals and future mobility stocks: Evidence from the time-varying frequency connectedness approach," Resources Policy, Elsevier, vol. 86(PA).
    17. Giorgio Canarella & Stephen M. Miller & Stephen K. Pollard, 2010. "Unit Roots and Structural Change: An Application to US House-Price Indices," Working papers 2010-04, University of Connecticut, Department of Economics, revised Dec 2010.
    18. Benati, Luca, 2021. "Leaning against house prices: A structural VAR investigation," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 399-412.
    19. Lu, Xunfa & Huang, Nan & Mo, Jianlei & Ye, Zhitao, 2023. "Dynamics of the return and volatility connectedness among green finance markets during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 125(C).
    20. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2021. "The impact of Euro through time: Exchange rate dynamics under different regimes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1375-1408, January.

    More about this item

    Keywords

    House price indices; Metropolitan statistical areas; Quantile vector Auto rergression; Global financial crisis; Covid-19;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

    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:eee:finlet:v:58:y:2023:i:pd:s1544612323010085. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.