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The Blessing Of Dimensionality In Forecasting Real House Price Growth In The Nine Census Divisions Of The Us

Author

Listed:
  • Sonali Das

    (CSIR, Pretoria)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • Alain Kabundi

    (Department of Economics and Econometrics, University of Johannesburg)

Abstract

This paper analyzes whether a wealth of information contained in 126 monthly series used by large-scale Bayesian Vector Autoregressive (LBVAR) models, as well as Factor Augmented Vector Autoregressive (FAVAR) models, either Bayesian or classical, can prove to be more useful in forecasting real house price growth rate of the nine census divisions of the US, compared to the small-scale VAR models, that merely use the house prices. Using the period of 1991:02 to 2000:12 as the in-sample period and 2001:01 to 2005:06 as the out-of-sample horizon, we compare the forecast performance of the alternative models for one- to twelve–months ahead forecasts. Based on the average Root Mean Squared Error (RMSEs) for one- to twelve–months ahead forecasts, we find that the alternative FAVAR models outperform the other models in eight of the nine census divisions.

Suggested Citation

  • Sonali Das & Rangan Gupta & Alain Kabundi, 2009. "The Blessing Of Dimensionality In Forecasting Real House Price Growth In The Nine Census Divisions Of The Us," Working Papers 200902, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:200902
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    Citations

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    Cited by:

    1. 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.
    2. John McDonald & Houston Stokes, 2013. "Monetary Policy and the Housing Bubble," The Journal of Real Estate Finance and Economics, Springer, vol. 46(3), pages 437-451, April.
    3. Tripti Sharma & Declan French & Donal McKillop, 2022. "Risk and Equity Release Mortgages in the UK," The Journal of Real Estate Finance and Economics, Springer, vol. 64(2), pages 274-297, February.
    4. Goodness C. Aye & Rangan Gupta, 2013. "Forecasting Real House Price of the U.S.: An Analysis Covering 1890 to 2012," Working Papers 201362, University of Pretoria, Department of Economics.
    5. Alain KABUNDI & Rangan GUPTA, 2009. "The Effect of Monetary Policy on House Price Inflation: A Factor Augmented Vector Autoregression (FAVAR) Approach," EcoMod2009 21500048, EcoMod.
    6. Rangan Gupta & Faaiqa Hartley, 2013. "The Role of Asset Prices in Forecasting Inflation and Output in South Africa," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 12(3), pages 239-291, December.
    7. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
    8. Dylan Gibson & Leslie A. Duram, 2020. "Shifting Discourse on Climate and Sustainability: Key Characteristics of the Higher Education Fossil Fuel Divestment Movement," Sustainability, MDPI, vol. 12(23), pages 1-17, December.
    9. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    10. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05r, Department of Economics, University of Birmingham.
    11. Nan-Kuang Chen & Han-Liang Cheng & Ching-Sheng Mao, 2014. "Identifying and forecasting house prices: a macroeconomic perspective," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2105-2120, December.
    12. Rangan Gupta, 2012. "Forecasting House Prices for the Four Census Regions and the Aggregate US Economy: The Role of a Data-Rich Environment," Working Papers 201214, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    Dynamic Factor Model; BVAR; Forecast Accuracy;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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