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  4. Vol 4, Issue 1, 2023

House Price Forecasting in Ames Based on Bayesian Regularized BP Neural Network

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DOI: 10.23977/autml.2023.040103 | Downloads: 56 | Views: 715

Author(s)

Haiqing Bai 1, Xiaoyong Chen 2

Affiliation(s)

1 School of Computer Science & Engineering Artificial Intelligence, Wuhan Institute of Technology, Wuhan, 430205, China
2 School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, 210037, China

Corresponding Author

Haiqing Bai

ABSTRACT

Housing has always been an important issue related to the national economy and people's livelihood. House prices not only affect people's welfare, but also have a significant impact on the national economy and social stability. Therefore, the prediction of house prices is also a necessary means. This paper forecasts the housing price data of Ames City in the United States, and establishes a Bayesian regularized BP neural network model to solve the nonlinear mapping relationship between housing prices and indicators. Through curve fitting analysis of samples, the overall R value is 0.9667, the overall result is better, and the error histogram also conforms to normal distribution. The experimental results show that the BP neural network model based on Bayesian regularization is very effective in dealing with the problem of house price prediction, and can better analyze and predict the trend of house price. This study provides a basis for real estate developers to develop and position products, and also provides model support for buyers to better judge the real price of houses.

KEYWORDS

Bayesian regularization, BP neural network, house price prediction

CITE THIS PAPER

Haiqing Bai, Xiaoyong Chen, House Price Forecasting in Ames Based on Bayesian Regularized BP Neural Network. Automation and Machine Learning (2023) Vol. 4: 17-23. DOI: http://dx.doi.org/10.23977/autml.2023.040103.

REFERENCES

[1] Wu Xiuli, Zhang Feng. Application of time series analysis in house price prediction —Take the data of Guangzhou city as an example [J]. Science, Technology and Engineering, 2007,7 (21): 5631-5635. 
[2] Wang Dongxue, Guo Xiujuan. House-price prediction model based on the XGBoost algorithm [J]. North Building, 2021,6 (3): 79-82.
[3] Zhang Baichuan, Zhao Baiting. Lightweight convolutional neural network classification algorithm combined with batch normalization [J]. Journal of Harbin University of Commerce (Natural Science Edition), 2021,37 (3): 300-306.
[4] Wu Mingshan, Wang Bing, Qi Yaning, Zheng Piao. Research on cigarette sales portfolio forecasting model [J]. (Chinese Journal of Tobacco, 2019, 25(03): 84-91. doi:10.16472/j.chinatobacco.2019.039.)
[5] Sun T T, Shen Y, Zhao L. A House Price Forecasting Model Based on BP Neural Network[J]. Computer Knowledge and Technology, 2019.

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