基于位置子市场划分的房价贝叶斯概率模型

Bayesian Probability Model for Real Estate Price Based on Location Submarket Segmentation

针对特征价格模型(HPM)在面对房价与特征复杂关系时容易出现预测精度和可解释性不足的情况,提出一种基于子市场效应的贝叶斯概率模型。在改进算法设计时,首先借鉴子市场聚类思想,引入一个潜在变量表示子市场,依据位置邻近性和可替代性建立子市场标准;其次,将子市场标准和特征价格模型作为贝叶斯网络的概率依赖确定各子市场效应的范围,完成子市场划分;最后,依据房屋所属子市场的概率预测房价,且分析子市场的关键影响因素,以提升预测精度和可解释性。将模型与5个现有模型从平均绝对百分比误差、平均绝对误差和均方根误差3个方面对比;根据杭州市2019年之前的房产数据,分别测试非子市场模型与子市场模型的算法性能。实验表明:该贝叶斯模型对房地产价格预测精度优于对比模型,且具有可解释性的优点。

A Bayesian probability model based on sub-market effects was proposed to address the situation that the hedonic price model(HPM)is prone to insufficient prediction accuracy and interpretability in the face of the complex relationship between house prices and characteristics.In improving the algorithm design,the idea of sub-market clustering was borrowed,a latent variable was introduced to represent sub-markets,and sub-market criteria were established based on location proximity and substitutability.Next,sub-market criteria and hedonic price models were used as probabilistic dependencies of Bayesian networks to determine the range of effects in each sub-market,completing the sub-market segmentation.Finally,house prices were predicted based on the probabilities of the submarkets to which the houses belonged,and the key influencing factors of the submarkets were analyzed to improve the prediction accuracy and interpretability.The model was compared with five existing models in terms of mean absolute percentage error,mean absolute error,and root mean square error.The performance of the algorithms of the non-submarket model and the submarket model were tested separately based on property data of Hangzhou City before 2019.The experiments show that the Bayesian model outperforms the comparison models in terms of accuracy in forecasting real estate prices and has the advantage of interpretability.