Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

• Published in: Socio-economic planning sciences 2020-12, Vol.72, p.100916, Article 100916
• Main Authors: Liu, Lianyi, Wu, Lifeng
• Format: Article
• Language: English
• Subjects: Algorithms; Computation; Data; Housing; Housing costs; Housing prices; Investors; Markets; MHES; Optimization; Optimization algorithms; Policy making; Predict; Prices; Real estate; Time series; Whales & whaling; WOA
• ISSN: 0038-0121; 1873-6041
• DOI: 10.1016/j.seps.2020.100916

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers. •A model combining modified Holt's exponential smoothing and whale optimization algorithm is proposed.•The proposed model can improve the accuracy of housing price prediction in China.•The proposed model has shorter computation time than that of other traditional models.