Title: Real estate price prediction method for small and medium-sized cities based on data mining

Authors: Rucai Zhuo

Addresses: School of Public Management, Hunan Urban Construction Vocational and Technical College, Xiangtan, 411100, China

Abstract: In order to improve the retention rate of prediction results and reduce the error of data mining, the paper designs a real estate price prediction method for small and medium-sized cities based on data mining. Firstly, starting from the five levels of supply, demand, economy, politics, and others, establish an indicator set of real estate price influencing factors, and then denoise the data based on the independent components of the five types of data. Mine the data needed for price prediction through traversal data. Finally, calculate the general prediction error, and use the proximity technology to predict the real estate prices of small and medium-sized cities. Experiment shows that the retention rate of the prediction results of this method is 93.4%-95.7%, and the approximate root mean square error is 0.058-0.079, which shows that the mining effect of this method is better and the prediction results are more effective.

Keywords: real estate price; price forecast; influencing factors; data denoising; data mining; generalised error.

DOI: 10.1504/IJSD.2024.136623

International Journal of Sustainable Development, 2024 Vol.27 No.1/2, pp.201 - 215

Received: 20 Feb 2023
Accepted: 13 Sep 2023
Published online: 08 Feb 2024 *

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