An empirical analysis of second-hand house transaction prices based on machine learning
DOI:
https://doi.org/10.54691/bcpbm.v24i.1525Keywords:
Decision tree model; SVM model; Random Forest model; Adaboost model.Abstract
The Beijing real estate market is one of the most developed and representative real estate markets in China. As of May 25, 2016, Beijing accounted for 88.2% of the market residential transactions, and the Beijing property market has fully entered the era of second-hand houses. Therefore, the price of second-hand houses in Beijing can be judged by studying the factors influencing the price of second-hand houses in Beijing, and the study has theoretical significance. Taking Beijing second-hand houses as the research object, the data source is mainly designed as a crawler to mine the data and obtain the transaction attribute information of chain house network, and the data sample size is 2000 items in total [1]. In this paper, we obtained the important variables affecting the house price through multiple model analysis and predicted the data of 100 unknown house prices.
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