Out-of-sample forecasting of housing bubble tipping points

Diego Ardila; Dorsa Sanadgol; Didier Sornette; Diego Ardila; Dorsa Sanadgol; Didier Sornette

doi:10.3934/QFE.2018.4.904

Quantitative Finance and Economics

2018, Volume 2, Issue 4: 904-930. doi: 10.3934/QFE.2018.4.904

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Out-of-sample forecasting of housing bubble tipping points

1.
ETH Zurich, Department of Management, Technology, and Economics, Scheuchzerstrasse 7, 8092 Zurich, Switzerland
2.
Swiss Finance Institute, Switzerland

Received: 22 April 2018 Accepted: 27 July 2018 Published: 26 November 2018
JEL Codes: C40, C52, C53, E31, E37, G01

This paper analyzes the information content of statistical tests for bubble detection in the context of international real estate markets. We derive binary indicators from the causal application of five statistical tests to log house prices, and via logit regressions we assess the indicators' out-of-sample performance in the forecasting of tipping points of housing bubbles and systemic financial crises. In our assessment, three of the indicators - two based on the identification of super-exponential trends and one based on the scaled ratio of the sum of squared forecast errors - exhibit significant out-of-sample results. Combining the indicators via simple threshold-rules yields the most robust and best results.
- housing bubbles,
- out-of-sample forecasting,
- house price forecasting,
- monitoring forecasting,
- OECD countries
Citation: Diego Ardila, Dorsa Sanadgol, Didier Sornette. Out-of-sample forecasting of housing bubble tipping points[J]. Quantitative Finance and Economics, 2018, 2(4): 904-930. doi: 10.3934/QFE.2018.4.904

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Abstract

This paper analyzes the information content of statistical tests for bubble detection in the context of international real estate markets. We derive binary indicators from the causal application of five statistical tests to log house prices, and via logit regressions we assess the indicators' out-of-sample performance in the forecasting of tipping points of housing bubbles and systemic financial crises. In our assessment, three of the indicators - two based on the identification of super-exponential trends and one based on the scaled ratio of the sum of squared forecast errors - exhibit significant out-of-sample results. Combining the indicators via simple threshold-rules yields the most robust and best results.

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