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High-Frequency Predictability of Housing Market Movements of the United States: The Role of Economic Sentiment

Author

Listed:
  • Mehmet Balcilar

    (Department of Economics, Eastern Mediterranean University, Famagusta, via Mersin 10, Northern Cyprus, Turkey)

  • Elie Bouri

    (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Clement Kweku Kyei

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

Abstract

We analyse the ability of a newspaper-based economic sentiment index of the United States to predict housing market movements using daily data over the period 2nd August, 2007 to 19th June, 2020. For this purpose, we use a k-th order nonparametric causality-in-quantiles test, which 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 and structural breaks – both of which we show to exist between housing returns and the economic sentiment index. Our results show that economic sentiment does indeed predict housing returns (unlike the conditional mean-based, i.e., linear, Granger causality test and volatility), barring the extreme upper ends of the respective conditional distributions. Our results have important implications for academics, policymakers, and investors.

Suggested Citation

  • Mehmet Balcilar & Elie Bouri & Rangan Gupta & Clement Kweku Kyei, 2020. "High-Frequency Predictability of Housing Market Movements of the United States: The Role of Economic Sentiment," Working Papers 202066, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202066
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Gupta, Rangan & Sheng, Xin & van Eyden, Reneé & Wohar, Mark E., 2021. "The impact of disaggregated oil shocks on state-level real housing returns of the United States: The role of oil dependence," Finance Research Letters, Elsevier, vol. 43(C).
    5. 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.
    6. Bingdao Feng & Fangyu Cheng & Yanfei Liu & Xinglong Chang & Xiaobao Wang & Di Jin, 2024. "Community Detection on Social Networks With Sentimental Interaction," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 20(1), pages 1-23, January.
    7. 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.
    8. Hakan Yıldırım & Festus Victor Bekun, 2023. "Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models," Future Business Journal, Springer, vol. 9(1), pages 1-8, December.

    More about this item

    Keywords

    Economic Sentiment; Housing Returns and Volatility; Higher-Order Nonparametric Causality in Quantiles Test;
    All these keywords.

    JEL classification:

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

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