关键词: 共享住宿, 房源价格, 分位数回归, 苏南地区

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