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Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment

Author

Listed:
  • Rangan Gupta

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

  • Chi Keung Marco Lau

    () (Huddersfield Business School, University of Huddersfield, Huddersfield, United Kingdom)

  • Wendy Nyakabawo

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

Abstract

This paper examines the predictive ability of housing-related sentiment on housing market volatility for 50 states, District of Columbia, and the aggregate US economy, based on quarterly data covering 1975:3 and 2014:3. Given that existing studies have already shown housing sentiment to predict movements in aggregate and state-level housing returns, we use a k-th order causality-in-quantiles test for our purpose, since this methodology allows us to test for predictability for both housing returns and volatility simultaneously. In addition, this test being a data-driven approach accommodates the existing nonlinearity (as detected by formal tests) between volatility and sentiment, besides providing causality over the entire conditional distribution of (returns and) volatility. Our results show that barring 5 states (Connecticut, Georgia, Indiana, Iowa, and Nebraska), housing sentiment is observed to predict volatility barring the extreme ends of the conditional distribution. As far as returns are concerned, except for California, predictability is observed for all of the remaining 51 cases.

Suggested Citation

  • 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.
  • Handle: RePEc:pre:wpaper:201866
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Housing sentiment; housing market returns and volatility; higher-order nonparametric causality-in-quantiles test; overall and regional US economy;

    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E7 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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