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Predicting Changes in Canadian Housing Markets with Machine Learning

Author

Listed:
  • Johan Brannlund
  • Helen Lao
  • Maureen MacIsaac
  • Jing Yang

Abstract

This paper examines whether machine learning (ML) algorithms can outperform a linear model in predicting monthly growth in Canada of both house prices and existing home sales. The aim is to apply two widely used ML techniques (support vector regression and multilayer perceptron) in economic forecasting to understand their scopes and limitations. We find that the two ML algorithms can perform better than a linear model in forecasting house prices and resales. However, the improvement in forecast accuracy is not always statistically significant. Therefore, we cannot systematically conclude using traditional time-series data that the ML models outperform the linear model in a significant way. Future research should explore non-traditional data sets to fully take advantage of ML methods.

Suggested Citation

  • Johan Brannlund & Helen Lao & Maureen MacIsaac & Jing Yang, 2023. "Predicting Changes in Canadian Housing Markets with Machine Learning," Discussion Papers 2023-21, Bank of Canada.
    • Handle: RePEc:bca:bocadp:23-21
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    References listed on IDEAS

    as
    1. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    4. Mikael Khan & Taylor Webley, 2019. "Disentangling the Factors Driving Housing Resales," Staff Analytical Notes 2019-12, Bank of Canada.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Econometric and statistical methods; Financial markets; Housing;
    All these keywords.

    JEL classification:

    • A - General Economics and Teaching
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • R2 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location
    • D2 - Microeconomics - - Production and Organizations

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