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The Role of Housing Sentiment in Forecasting US Home Sales Growth: Evidence from a Bayesian Compressed Vector Autoregressive Model

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)

  • Vasilios Plakandaras

    (Department of Economics, Democritus University of Thrace, Komotini, Greece)

  • Wing-Keung Wong

    (Department of Finance, Fintech Center, and Big Data Research Center, Asia University; Department of Medical Research, China Medical University Hospital, Taiwan; Department of Economics and Finance, Hang Seng Management College, Hong Kong, China; Department of Economics, Lingnan University, Hong Kong, China)

Abstract

Accurate forecasts of home sales can provide valuable information for not only policymakers, but also financial institutions and real estate professionals. Against this backdrop, the objective of our paper is to analyze the role of consumers’ home buying attitudes in forecasting quarterly US home sales growth. Our results show that the home sentiment index in standard classical and Minnesota prior-based Bayesian VARs fail to add to the forecasting accuracy of the growth of home sales derived from standard economic variables already included in the models. However, when shrinkage is achieved by compressing the data using a Bayesian compressed VAR (instead of the parameters as in the BVAR), growth of US home sales can be forecasted more accurately, with the housing market sentiment improving the accuracy of the forecasts relative to the information contained in economic variables only.

Suggested Citation

  • Rangan Gupta & Chi Keung Marco Lau & Vasilios Plakandaras & Wing-Keung Wong, 2018. "The Role of Housing Sentiment in Forecasting US Home Sales Growth: Evidence from a Bayesian Compressed Vector Autoregressive Model," Working Papers 201842, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201842
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    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.
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    1. Hamid Ahaggach & Lylia Abrouk & Eric Lebon, 2024. "Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions," Forecasting, MDPI, vol. 6(3), pages 1-31, July.

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    More about this item

    Keywords

    Home Sales; Housing Sentiment; Classical and Bayesian Vector Autoregressive Models;
    All these keywords.

    JEL classification:

    • 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
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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