基于Python的北京房价影响因素分析及购房建议  

Python-Based Analysis of Factors Affecting House Prices in Beijing and Suggestions for Buying a House

作　　者：史贺莹 刘芳远 

机构地区：[1]北方工业大学经济管理学院,北京

出　　处：《现代管理》2023年第12期1693-1698,共6页Modern Management

摘　　要：北京作为中国经济、政治和文化中心,房价一直处于较高水平,受到人们的广泛关注。本文基于Python爬虫功能,通过爬取链家网站的2023年北京房屋数据,对房价排名前十、后十的小区进行影响因素分析,并对房屋地理位置、房屋装缮、房屋面积等影响因素的影响度进行展开分析。研究发现,地理位置是影响北京市房价波动的主要因素,究其原因,不同地理位置的经济发展水平与对教育重视程度皆有差异。此外,房屋面积、格局、装修条件等内部因素对房价同样有一定影响。再结合分析的结果,提出购房建议,希望能够为购房者提供参考,以便能够更好的选房、购房。Beijing as China’s economic, political and cultural centre, housing prices have always been at a high level and have attracted widespread attention. Based on the Python crawler function, this paper analyzes the influencing factors of the top ten and bottom ten communities of house prices by crawling the 2023 Beijing housing data of the Lianjia website, and analyzes the influence of house geographical location, house decoration, housing area and other influencing factors. The study found that geographical location is the main factor affecting the fluctuation of housing prices in Beijing. The reason for this is that the level of economic development in different geographical locations and the degree of attention to education are different. In addition, internal factors such as housing area, pattern and decoration conditions also have a certain impact on house prices. Combined with the results of the analysis, we put forward suggestions for buying a house, hoping to provide reference for buyers so that they can better choose and buy a house.

关 键 词：PYTHON 房价影响因素 购房建议 

分 类 号：F29[经济管理—国民经济]