IDEAS home Printed from https://ideas.repec.org/a/taf/repmxx/v26y2020i2p111-117.html
   My bibliography  Save this article

Mortgage Default Risks and High-Frequency Predictability of the U.S. Housing Market: A Reconsideration

Author

Listed:
  • Mehmet Balcilar
  • Elie Bouri
  • Rangan Gupta
  • Mark E. Wohar

Abstract

Recent evidence, based on a linear framework, tends to suggest that while mortgage default risks can predict weekly and monthly housing returns of the United States, the same does not hold at the daily frequency. We, however, indicate that the relationship between daily housing returns with mortgage default risks is in fact nonlinear, and hence a linear predictive model is misspecified. Given this, we use a k-th order nonparametric causality-in-quantiles test, which in turn allows us to test for predictability over the entire conditional distribution of not only housing returns, but also volatility, by controlling for misspecification due to nonlinearity. Based on this model, we show that mortgage default risks do indeed predict housing returns and volatility, barring at the extreme upper end of the respective conditional distributions.

Suggested Citation

  • Mehmet Balcilar & Elie Bouri & Rangan Gupta & Mark E. Wohar, 2020. "Mortgage Default Risks and High-Frequency Predictability of the U.S. Housing Market: A Reconsideration," Journal of Real Estate Portfolio Management, Taylor & Francis Journals, vol. 26(2), pages 111-117, December.
    • Handle: RePEc:taf:repmxx:v:26:y:2020:i:2:p:111-117
    DOI: 10.1080/10835547.2020.1854606
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10835547.2020.1854606
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10835547.2020.1854606?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 search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Nyakabawo, Wendy & Miller, Stephen M. & Balcilar, Mehmet & Das, Sonali & Gupta, Rangan, 2015. "Temporal causality between house prices and output in the US: A bootstrap rolling-window approach," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 55-73.
    2. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2014. "Housing and the Great Depression," Applied Economics, Taylor & Francis Journals, vol. 46(24), pages 2966-2981, August.
    3. Tim Bollerslev & Andrew J. Patton & Wenjing Wang, 2016. "Daily House Price Indices: Construction, Modeling, and Longer‐run Predictions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1005-1025, September.
    4. Bork, Lasse & Møller, Stig V., 2015. "Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection," International Journal of Forecasting, Elsevier, vol. 31(1), pages 63-78.
    5. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    6. Norman Miller & Liang Peng, 2006. "Exploring Metropolitan Housing Price Volatility," The Journal of Real Estate Finance and Economics, Springer, vol. 33(1), pages 5-18, August.
    7. Lasse Bork & Stig V. Møller, 2018. "Housing Price Forecastability: A Factor Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 46(3), pages 582-611, September.
    8. Chauvet, Marcelle & Gabriel, Stuart & Lutz, Chandler, 2016. "Mortgage default risk: New evidence from internet search queries," Journal of Urban Economics, Elsevier, vol. 96(C), pages 91-111.
    9. Mehmet Balcilar & Rangan Gupta & Duc Khuong Nguyen & Mark E. Wohar, 2018. "Causal effects of the United States and Japan on Pacific-Rim stock markets: nonparametric quantile causality approach," Applied Economics, Taylor & Francis Journals, vol. 50(53), pages 5712-5727, November.
    10. Furkan Emirmahmutoglu & Mehmet Bacilar & Nicholas Apergis & Beatrice D. Simo-Kengne & Tsangyao Chang & Rangan Gupta, 2016. "Causal Relationship between Asset Prices and Output in the United States: Evidence from the State-Level Panel Granger Causality Test," Regional Studies, Taylor & Francis Journals, vol. 50(10), pages 1728-1741, October.
    11. Li, Kui-Wai, 2011. "A study on the volatility forecast of the US housing market in the 2008 crisis," MPRA Paper 41033, University Library of Munich, Germany.
    12. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    13. Nishiyama, Yoshihiko & Hitomi, Kohtaro & Kawasaki, Yoshinori & Jeong, Kiho, 2011. "A consistent nonparametric test for nonlinear causality—Specification in time series regression," Journal of Econometrics, Elsevier, vol. 165(1), pages 112-127.
    Full references (including those not matched with items on IDEAS)

    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. Rangan Gupta & Chi Keung Marco Lau & Wendy Nyakabawo, 2018. "Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment," Working Papers 201866, University of Pretoria, Department of Economics.
    2. Rangan Gupta & Hardik A. Marfatia & Eric Olson, 2020. "Effect of uncertainty on U.S. stock returns and volatility: evidence from over eighty years of high-frequency data," Applied Economics Letters, Taylor & Francis Journals, vol. 27(16), pages 1305-1311, September.
    3. Elie Bouri & Rangan Gupta & Hardik A. Marfatia & Jacobus Nel, 2022. "Do Climate Risks Predict US Housing Returns and Volatility? Evidence from a Quantiles-Based Approach," Working Papers 202240, University of Pretoria, Department of Economics.
    4. Rangan Gupta & Damien Moodley, 2023. "Housing Search Activity and Quantiles-Based Predictability of Housing Price Movements in the United States," Working Papers 202335, University of Pretoria, Department of Economics.
    5. 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.
    6. Balcilar, Mehmet & Gupta, Rangan & Sousa, Ricardo M. & Wohar, Mark E., 2021. "Linking U.S. State-level housing market returns, and the consumption-(Dis)Aggregate wealth ratio," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 779-810.
    7. Bouras, Christos & Christou, Christina & Gupta, Rangan & Lesame, Keagile, 2023. "Forecasting state- and MSA-level housing returns of the US: The role of mortgage default risks," Research in International Business and Finance, Elsevier, vol. 65(C).
    8. Christou, Christina & Gupta, Rangan & Hassapis, Christis, 2017. "Does economic policy uncertainty forecast real housing returns in a panel of OECD countries? A Bayesian approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 50-60.
    9. Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2021. "High-Frequency Volatility Forecasting of US Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 62(2), pages 283-317, February.
    10. Aviral Kumar Tiwari & Rangan Gupta & Mark E. Wohar, 2020. "Is the Housing Market in the United States Really Weakly-Efficient?," Applied Economics Letters, Taylor & Francis Journals, vol. 27(14), pages 1124-1134, July.
    11. 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.
    12. Riza Demirer & Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2020. "Effect of Rare Disaster Risks on Crude Oil: Evidence from El Nino from Over 140 Years of Data," Working Papers 2020104, University of Pretoria, Department of Economics.
    13. Ugur Korkut Pata & Ojonugwa Usman & Godwin Olasehinde-Williams & Oktay Ozkan, 2024. "Stock Returns, Crude Oil and Gold Prices in Turkey: Evidence from Rolling Window-Based Nonparametric Quantile Causality Test," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(3), pages 779-797, September.
    14. Rangan Gupta, 2018. "Manager Sentiment and Stock Market Volatility," Working Papers 201853, University of Pretoria, Department of Economics.
    15. Demirer, Riza & Gabauer, David & Gupta, Rangan & Nielsen, Joshua, 2024. "Gold, platinum and the predictability of bubbles in global stock markets," Resources Policy, Elsevier, vol. 90(C).
    16. Godwin Olasehinde-Williams & Ifedola Olanipekun & Oktay Özkan, 2024. "Stock Market Response to Quantitative Easing: Evidence from the Novel Rolling Windows Nonparametric Causality-in-Quantiles Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 947-977, August.
    17. Gabauer, David & Gupta, Rangan & Marfatia, Hardik A. & Miller, Stephen M., 2024. "Estimating U.S. housing price network connectedness: Evidence from dynamic Elastic Net, Lasso, and ridge vector autoregressive models," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 349-362.
    18. 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).
    19. Mehmet Balcilar & Rangan Gupta & Ricardo M. Sousa & Mark E. Wohar, 2021. "What Can Fifty-Two Collateralizable Wealth Measures Tell Us About Future Housing Market Returns? Evidence from U.S. State-Level Data," The Journal of Real Estate Finance and Economics, Springer, vol. 62(1), pages 81-107, January.
    20. Aviral Kumar Tiwari & Rangan Gupta & Juncal Cunado & Xin Sheng, 2020. "Testing the white noise hypothesis in high-frequency housing returns of the United States," Economics and Business Letters, Oviedo University Press, vol. 9(3), pages 178-188.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

    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:repmxx:v:26:y:2020:i:2:p:111-117. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/repm20 .

    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.