论文标题
住房市场预测问题使用不同的机器学习算法：案例研究
Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study
论文作者
论文摘要
对于社会经济发展和公民福祉，始终需要为住房价格开发准确的预测模型。在本文中，正在使用各种机器学习算法，例如XGBoost，Catboost，Random Forest，Lasso，投票回归者等，用于使用公共可用数据集预测住房价格。 2015年1月至2019年11月的62,723条记录的住房数据集可从佛罗里达Volusia County Property评估师网站获得。记录可公开可用，包括房地产或经济数据库，地图和其他相关信息。该数据库通常根据佛罗里达州法规每周更新。然后，开发了使用机器学习技术的住房价格预测模型，并比较其回归模型性能。最后，提出了改进的住房价格预测模型，以协助住房市场。特别是，考虑到住房价格预测，房屋卖家或买方或房地产经纪人可以洞悉做出更好的决定。经验结果表明，基于预测模型性能，确定系数（R2），均方根误差（MSE），平均绝对误差（MAE）和计算时间，XGBoost算法的性能优于其他模型，以预测住房价格。
Developing an accurate prediction model for housing prices is always needed for socio-economic development and well-being of citizens. In this paper, a diverse set of machine learning algorithms such as XGBoost, CatBoost, Random Forest, Lasso, Voting Regressor, and others, are being employed to predict the housing prices using public available datasets. The housing datasets of 62,723 records from January 2015 to November 2019 are obtained from Florida Volusia County Property Appraiser website. The records are publicly available and include the real estate or economic database, maps, and other associated information. The database is usually updated weekly according to the State of Florida regulations. Then, the housing price prediction models using machine learning techniques are developed and their regression model performances are compared. Finally, an improved housing price prediction model for assisting the housing market is proposed. Particularly, a house seller or buyer, or a real estate broker can get insight in making better-informed decisions considering the housing price prediction. The empirical results illustrate that based on prediction model performance, Coefficient of Determination (R2), Mean Square Error (MSE), Mean Absolute Error (MAE), and computational time, the XGBoost algorithm performs superior to the other models to predict the housing price.
