An empirical analysis of second-hand house transaction prices based on machine learning

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.