• Login
  • Register
  • Search

Second-Hand Housing Market Research Based on Random Forest Algorithm

Zhiyuan Bai


The research on second-hand houses mainly faces the following problem. what are the factors that affect the price of second-hand houses? Focusing on the problem of second-hand houses, based on data mining and data analysis, and with the help of software such as Stata, this paper establishes the random forest model, and explains the factors that affect the price of second-hand houses. Through the prediction of the random forest model, this paper concludes that the factors affecting the price of second-hand houses are ranked as follows: the number of primary and secondary schools > the age of the house > the building area > the number of supermarkets > the number of scenic spots > the number of subways > the degree of decoration > the number of bedrooms, and gives some suggestions to people buying second-hand hourses.


Second-Hand Housing; Data Analysis; Random Forest Model; Prediction

Full Text:


Included Database


Chen, L., Zhang, D., Pan, G., et al. (2015, September). Bike sharing station placement leveraging heterogeneous urban open data. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 571-575).

Wang, M., Yang, S., Sun, Y., & Gao, J. (2016, December). Predicting human mobility from region functions. In 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (pp. 540-547). IEEE.

Soltani, A., Pettit, CJ., Heydari, M., & Aghaei, F. (2021). Housing price variations using spatio-temporal data mining techniques. Journal of Housing and the Built Environment, 36(3), 1199-1227.

Gao, Y., & Zhang, R. (2014). Analysis of house price prediction based on genetic algorithm and BP neural network. Comput. Eng, 40(4), 187-191.

DOI: http://dx.doi.org/10.18686/fm.v8i2.6775