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Residential investment and recession predictability

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  • Knut Are Aastveit
  • Andr� K. Anundsen
  • Eyo I. Herstad

Abstract

We assess the importance of residential investment in predicting economic recessions for an unbalanced panel of 12 OECD countries over the period 1960Q1�2014Q4. Our approach is to estimate various probit models with di?erent leading indicators and evaluate their relative prediction accuracy using the receiver operating characteristic curve. We document that residential investment contains information useful in predicting recessions both in-sample and out-of-sample. This result is robust to adding typical leading indicators, such as the term spread, stock prices, consumer confidence surveys and oil prices. It is shown that residential investment is particularly useful in predicting recessions for countries with high home-ownership rates. Finally, in a separate exercise for the US economy, we show that the predictive ability of residential investment is robust to employing real-time data.

Suggested Citation

  • Knut Are Aastveit & Andr� K. Anundsen & Eyo I. Herstad, 2017. "Residential investment and recession predictability," Working Papers No 8/2017, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    • Handle: RePEc:bny:wpaper:0057
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    Cited by:

    1. André K. Anundsen, 2019. "Detecting Imbalances in House Prices: What Goes Up Must Come Down?," Scandinavian Journal of Economics, Wiley Blackwell, vol. 121(4), pages 1587-1619, October.
    2. Andr� Kall�k Anundsen & Bj�rnar Karlsen Kivedal & Erling R�ed Larsen & Leif Anders Thorsrud, 2020. "Behavioral changes and policy effects during Covid-19," Working Papers No 07/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    3. Christiansen, Charlotte & Eriksen, Jonas N. & Møller, Stig V., 2019. "Negative house price co-movements and US recessions," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 382-394.
    4. Mikhail Mamonov & Vera Pankova & Renat Akhmetov & Anna Pestova, 2020. "Financial Shocks and Credit Cycles," Russian Journal of Money and Finance, Bank of Russia, vol. 79(4), pages 45-74, December.
    5. Carlos Cañizares Martínez & Gabe J. de Bondt & Arne Gieseck, 2023. "Forecasting housing investment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 543-565, April.
    6. Zihao Wang & Kun Li & Steve Q. Xia & Hongfu Liu, 2021. "Economic Recession Prediction Using Deep Neural Network," Papers 2107.10980, arXiv.org.
    7. Ujjal Chatterjee, 2023. "Predicting economic growth: evidence from real-estate loans securitization," SN Business & Economics, Springer, vol. 3(3), pages 1-20, March.
    8. Garciga, Christian & Knotek II, Edward S., 2019. "Forecasting GDP growth with NIPA aggregates: In search of core GDP," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1814-1828.
    9. Anundsen, André Kallåk & Kivedal, Bjørnar Karlsen & Røed Larsen, Erling & Thorsrud, Leif Anders, 2023. "Behavioral changes in the housing market before and after the Covid-19 lockdown," Journal of Housing Economics, Elsevier, vol. 59(PB).

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    More about this item

    Keywords

    Recession predictability; Housing; Leading indicators; Real-time data;
    All these keywords.

    JEL classification:

    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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