Construct comprehensive indicators through a signal extraction approach for predicting housing price crises

Abstract

(edit 5/22/24): RETRACTION: Following the publication of this article, concerns were raised regarding peer review integrity.

Upon review of the submission history for the manuscript, the editors found strong indications that the peer review process was compromised.

These concerns were not resolved following discussion with the authors.

In light of these concerns, the PLOS ONE Editors retract this article. We regret that the issues in this article were not identified prior to publication.

TL did not agree with the retraction and stands by the article’s findings. YX, YM, ZZ and JL either did not respond directly or could not be reached.

1 Mar 2023: The PLOS ONE Editors (2023) Retraction: Construct comprehensive indicators through a signal extraction approach for predicting housing price crises. PLOS ONE 18(3): e0282628. https://doi.org/10.1371/journal.pone.0282628

In this paper, a novel early warning system that has usually been applied to predict the financial stress events is established to predict the likelihood of housing price crises in China. To achieve this goal, a signal extraction approach is used to monitor the evolution of a number of economic indicators that tend to exhibit the abnormal behaviors. 13 economic variables were selected as the individual indicators, and constructed as the four comprehensive indicators. Our empirical work shows that the early warning system for urban housing price crises is suitable for China's four province-level municipalities. The in-sample forecasting results indicate the reliability of the early warning system for urban housing price crises. By studying the out-of-sample forecasting results, the likelihood of housing price crises for the four cities can be effectively predicted. We construct a novel weighted average comprehensive indicator, which performs better than the three others in terms of overall performance across all of the criteria considered in. It is shown that the extended system is more flexible in decision making than the traditional early warning system.

Description

© 2022 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. cc-by

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Citation

Xu, Y., Ma, Y., Zhu, Z., Li, J., & Lu, T.. 2022. Construct comprehensive indicators through a signal extraction approach for predicting housing price crises. PLoS ONE, 17(8 August). https://doi.org/10.1371/journal.pone.0272213

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