IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v52y2020i5p528-536.html

The role of real estate uncertainty in predicting US home sales growth: evidence from a quantiles-based Bayesian model averaging approach

Author

Listed:
  • Oğuzhan Çepni
  • Rangan Gupta
  • Mark E. Wohar

Abstract

This paper investigates the role of real estate-specific uncertainty in predicting the conditional distribution of US home sales growth over the monthly period of 1970:07 to 2017:12, based on Bayesian Model Averaging (BMA) to account for model uncertainty. After controlling for standard predictors of home sales (housing price, mortgage rate, personal disposable income, unemployment rate, building permits, and housing starts), and macroeconomic and financial uncertainties, our results from the quantile BMA (QBMA) model show that real estate uncertainty has predictive content for the lower and upper quantiles of the conditional distribution of home sales growth.

Suggested Citation

  • Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2020. "The role of real estate uncertainty in predicting US home sales growth: evidence from a quantiles-based Bayesian model averaging approach," Applied Economics, Taylor & Francis Journals, vol. 52(5), pages 528-536, January.
    • Handle: RePEc:taf:applec:v:52:y:2020:i:5:p:528-536
    DOI: 10.1080/00036846.2019.1654082
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2019.1654082
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2019.1654082?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 look for a different version below or

    for a different version of it.

    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. Stolbov, Mikhail & Shchepeleva, Maria, 2022. "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    2. Baek, Ingul & Liu, Jia & Noh, Sanha, 2024. "Real estate uncertainty and financial conditions over the business cycle," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 656-675.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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:taf:applec:v:52:y:2020:i:5:p:528-536. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    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.