文章以2021年北京市Airbnb数据为基础,综合运用OLS回归和空间计量模型方法对北京市Airbnb房源价格影响因素进行研究。结果表明：①不同类型特征变量对房源价格的解释力从高到低依次为：房源特征、声誉特征、房东特征、邻里特征,房源物理属性是消费者支付意愿和Airbnb房价的决定因素;②房东特征是房源价值的重要组成部分,更多的身份验证信息和更短的回复时间有助于实现Airbnb房源溢价;③不同项目评分对价格的影响存在显著差异,其中性价比和干净整洁的评分影响最为显著;④交通可达性、旅游景点、周边酒店供应量对价格有显著积极影响;⑤空间计量模型的拟合优度优于传统OLS模型,Airbnb房价存在显著的空间溢出效应。
Based on the Beijing data of Airbnb in 2021, this study explores the influencing factors of housing price in Airbnb using the models of OLS and spatial econometric analysis. The results show that: 1) The explanatory power of influencing factors are sorted as follows from high to low: housing characteristics,reputation characteristics,landlord characteristics and neighborhood characteristics. The physical attributes of housing are the determinants of Airbnb housing price and consumers' willingness-to-pay. 2) Landlord characteristics are an important part of the property value. More verification information and shorter response time contribute to increase the housing price. 3) There are significant differences in the impact of different scores on the price, among which the ratings of value of money and cleanliness have the most significant impact. 4) Traffic accessibility,tourist attractions and hotel supply have significant positive impacts on the price. 5)The goodness of fit of spatial econometric model is better than that of OLS model,the housing price of Airbnb has significant spatial spillover effect.
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