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Academic Journal of Engineering and Technology Science, 2020, 3(7); doi: 10.25236/AJETS.2020.030702.

Prediction of California House Price Based on Multiple Linear Regression


Zixu Wu

Corresponding Author:
Zixu Wu

Guanghua Cambridge International School, 2788 Chuanzhou Road, Kangqiao Town, Pudong New Area, Shanghai, China


In recent years, the price of real estate continues to increase, which is related to the interests of the people and the society and has become a hotspot issue today. Therefore, it is very important to reasonably predict the price of real estate. This paper uses California house price data to solve the problem of how to predict the average annual sales price of California houses through multiple variables. The main distribution of housing prices is obtained through the data, and the influencing factors are analyzed by linear and lasso regression, including the floor area, the number of rooms, the proportion of low-income people, and educational resources in nearby areas. The research on the influencing factors of housing price is very important and basic for solving a series of problems in the current real estate market. Only when we have a full understanding of the micro mechanism of housing market price formation and the influencing factors of housing price, can we effectively and moderately regulate the price of commercial housing.


California House Price, Multiple Linear Regression, commercial housing

Cite This Paper

Zixu Wu. Prediction of California House Price Based on Multiple Linear Regression. Academic Journal of Engineering and Technology Science (2020) Vol. 3 Issue 7: 11-15. https://doi.org/10.25236/AJETS.2020.030702.


[1] Mishkin, F. S. (2001). The transmission mechanism and the role of asset prices in monetary policy (No. w8617). National bureau of economic research.
[2] Liu, J. G., Zhang, X. L., & Wu, W. P. (2006, May). Application of fuzzy neural network for real estate prediction. In International Symposium on Neural Networks (pp. 1187-1191). Springer, Berlin, Heidelberg.
[3] Cain, M., & Janssen, C. (1995). Real estate price prediction under asymmetric loss. Annals of the Institute of Statistical Mathematics, 47(3), 401-414.
[4] Li, D. Y., Xu, W., Zhao, H., & Chen, R. Q. (2009, July). A SVR based forecasting approach for real estate price prediction. In 2009 International Conference on Machine Learning and Cybernetics (Vol. 2, pp. 970-974). IEEE.
[5] Sarip, A. G., Hafez, M. B., & Daud, M. N. (2016). Application of fuzzy regression model for real estate price prediction. Malaysian Journal of Computer Science, 29(1), 15-27.
[6] Xiaolong, H., & Ming, Z. (2010, July). Applied research on real estate price prediction by the neural network. In 2010 The 2nd Conference on Environmental Science and Information Application Technology (Vol. 2, pp. 384-386). IEEE.