Abstract:

Taking the southern Jiangsu area as a case, obtaining Airbnb website housing data, comprehensively using the Theil index, DBSCAN clustering algorithm and other analysis methods, discussing the spatial differentiation of sharing accommodation prices from a multi-scale perspective, using OLS regression model, The quantile regression model compares and analyzes the influencing factors of sharing accommodation prices from the perspective of combining the inside and outside of the housing. Results show that: (1) The price of sharing accommodations varies significantly within cities and districts and counties, and the difference between cities and districts and counties is relatively small; (2) There are significant differences in the spatial clustering centers of shared accommodation at different price levels. The dependence of high, middle and low-end shared accommodation on the water area gradually decreases. On the whole, the spatial clustering centers show a distribution of "more east and less west, more south and less north"; (3) The housing price of shared accommodation is the result of the co-coupling of internal attributes and external attributes. In comparison, housing prices are greatly affected by internal attributes such as the number of toilets in the housing, and less affected by external attributes. At the same time, different influencing factors have obvious differences and stratification in the direction and degree of influence of shared accommodation listings at the same price level.

Key words: sharing accommodation, housing price, quantile regression, southern Jiangsu

摘要：

以江苏省苏南地区为案例地，获取Airbnb网站房源数据，运用泰尔指数、DBSCAN聚类算法等分析方法，从多尺度视角对共享住宿价格的空间分异进行探讨，采用OLS回归模型和分位数回归模型从房源内部和房源外部相结合的角度对比分析共享住宿价格的影响因素。结果表明：（1）从苏南整体和各地市来看，共享住宿房源价格在城市内部和区县内部的差异较为明显，在城市之间和区县之间的差异较小；（2）不同价格水平共享住宿的空间聚类中心存在显著差异，高、中、低档次共享住宿对于水域的依赖逐渐降低，整体上，空间聚类中心呈现“东多西少，南多北少”分布；（3）共享住宿房源价格是内部属性和外部属性共同耦合驱动作用的结果，对比来看，房源价格受到内部属性如房源卫生间个数等指标的影响较大，受到外部属性的影响较小，同时不同影响因素对于同等价格水平共享住宿房源的作用方向和影响程度存在明显差异性和分层性。

关键词: 共享住宿, 房源价格, 分位数回归, 苏南地区