Author(s)

XinYue Dang

Affiliation(s)

School of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, Guangxi, China.

Corresponding Author

XinYue Dang

ABSTRACT

In recent years, China's residential commercial housing market has shown pronounced regional disparities, especially between large cities and smaller urban areas. This study utilizes cluster analysis to compare market conditions in 2020 and 2002. In 2020, regions were categorized into three tiers, with Beijing and Shanghai exhibiting the most significant differences. In contrast, the 2002 data divided regions into four tiers, with Guangdong Province standing alone in the first tier, indicating a more balanced market. From 2012 to 2020, Beijing's average housing prices increased substantially, whereas changes in the Guangxi Zhuang Autonomous Region were more moderate, reflecting significant regional economic disparities. Neural network models are used to make predictions on the data. This empirical analysis underscores the diversity and economic factors influencing China's residential commercial housing market. By comparing different regions and examining temporal sequences, the study provides theoretical insights into regional development imbalances, emphasizing the need for precise regional policies to promote coordinated market development.

KEYWORDS

Average sales price of residential commercial housing; Cluster analysis; Neural networks; Forecast

CITE THIS PAPER

XinYue Dang. Analysis and forecast of the average sales price of residential commercial housing. Trends in Social Sciences and Humanities Research. 2024, 2(4): 33-37. DOI: 10.61784/tsshr3008.

REFERENCES

[1] Bai Y. Research on Influencing Factors of Average Sales Price of Residential Commercial Housing in Major Cities of China. IOP Publishing Ltd.IOP Publishing Ltd, 2020:012020.

[2] Na C, Chen-Xu T, Wei L, et al. Analysis and Forecast of the Residential Commercial Housing Price in Zhoukou. Mathematics in Practice and Theory, 2019.

[3] Wanchuan W. Prediction Model and Application of BP Neural Network. Journal of Yangzhou Polytechnic Institute, 2013.

[4] G Peter, Zhang, et al. Neural network forecasting for seasonal and trend time series. Operations Research, 2005.

[5] Marzban C, Stumpf G J. A Neural Network for Damaging Wind Prediction. Weather & Forecasting, 1998, 13(1): 151-163.

[6] Chen Y, Yang B, Dong J. Time-series prediction using a local linear wavelet neural network. Neurocomputing, 2006, 69(4/6): 449-465.