Open Access

ARTICLE

Early Warning of Commercial Housing Market Based on Bagging-GWO-SVM

Yonghui Duan¹, Keqing Zhao^1,*, Yibin Guo², Xiang Wang²
1 Department of Civil Engineering, Henan University of Technology, Zhengzhou, 450001, China
2 Department of Civil Engineering, Zhengzhou University of Aeronautics, Zhengzhou, 450015, China
* Corresponding Author: Keqing Zhao. Email:

Computer Systems Science and Engineering 2023, 45(2), 2207-2222. https://doi.org/10.32604/csse.2023.032297

Received 13 May 2022; Accepted 27 June 2022; Issue published 03 November 2022

Abstract

A number of risks exist in commercial housing, and it is critical for the government, the real estate industry, and consumers to establish an objective early warning indicator system for commercial housing risks and to conduct research regarding its measurement and early warning. In this paper, we examine the commodity housing market and construct a risk index for the commodity housing market at three levels: market level, the real estate industry and the national economy. Using the Bootstrap aggregating-grey wolf optimizer-support vector machine (Bagging-GWO-SVM) model after synthesizing the risk index by applying the CRITIC objective weighting method, the commercial housing market can be monitored for risks and early warnings. Based on the empirical study, the following conclusions have been drawn: (1) The commodity housing market risk index accurately reflect the actual risk situation in Tianjin; (2) Based on comparisons with other models, the Bagging-GWO-SVM model provides higher accuracy in early warning. A final set of suggestions is presented based on the empirical study.

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Keywords

Bagging; SVM; GWO; risk metrics; early warning

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