Study On The Price Determinants Of Online Short-term Rental Properties

Since Airbnb officially entered the Chinese market,the platform has rapidly developed into a benchmark for the sharing economy in China.Furthermore,the online short-term rental market led by Airbnb has also significantly influenced people's choices for travelling accommodations.With the exponential growth in the number of the Airbnb listings in China,the intensity of competition in the market has been multiplying.This growth has created a competitive market environment in which landlords are being squeezed,requiring the use of sensible pricing and marketing strategies to ensure earnings.Especially,compared with traditional lodging sectors,the uniqueness of Airbnb's listings and the heterogeneity of the landlords make it more difficult for landlords to make optimal pricing decisions.Therefore,it is of great value to understand the factors that affect the price of Airbnb listings.Such understanding can help landlords to optimize prices,so that both the landlord and the tenant can benefit from this sharing economy model.This paper uses the effective sample data of 51,874 listings in 36 cities in China obtained from the Airbnb APP by using reptile technology.OLS regression and quantile regression models are used to analyze the influence of 27 subdivision variables in 9 categories(external factors,landlord characteristics,location characteristics,listing characteristics,room facilities,rental rules,trust,sociality,tenant characteristics)on listings prices,to determine the key factors that affect the price of Airbnb listings.The results of OLS and quantile regression showed that most of the variables(except real bed,phone and picture)passed the significance test,and the two types of non-economic factors,trust and sociality,had significant impact on Airbnb listings prices.In addition,for landlords operating houses at different price levels,there are significant differences in the influence of housing properties,sociability and trust on the housing price.Landlords who operate high-priced properties should provide more photos to help maintain the premium,be prepared for harsh comments,and work harder to improve the quality of service and better meet the expectations of tenants.Landlords who manages the low-price listings,seek to maintain high rental and review rates is very meaningful.Based on the regression results,this paper further uses the corresponding analysis method to analyze the key factors affecting the price of Airbnb listings in different cities.The results of the corresponding analysis show that landlords in different cities need to focus on different factors.For Airbnb hosts in first-tier cities and tourist cities,improvement of room facilities quality can effectively help to increase the rent price.In tourist cities,low prices are more susceptible to hotel supply.However,the influence of sociability and trust on Airbnb hosts in first-tier cities is more obvious.Both OLS and quantile regression analysis assume spatial homogeneity and ignore possible spatial dependencies and spatial heterogeneity.Therefore,this paper conducts a further spatial econometric analysis of the property attributes.Exploratory analysis of spatial data(such as Moran's I and Moran scatter plots)and various types of spatial econometric models(such as SEM,SAR,and SAC models)have shown that there is a spatial dependence on property price attributes.And A series of discussions on geographic weighted regression model also proved the existence of spatial heterogeneity of property attributes..The findings of this paper theoretically supplement the research on the pricing law of online short rental in the context of the sharing economy.Practically,this research can reduce the opacity of the pricing principle of Airbnb's “intelligent pricing” tool,and help landlords to better formulate prices and marketing strategies.