酒店网上预订价格离差实证研究——以北京、上海和广州三地为例

An Empirical Study on Price Dispersion in the Hotel Online Booking Market: Based on Beijing, Shanghai and Guangzhou

本文跟踪收集了2005年6月9日到2006年7月31日中国北京、上海和广州三地11个网站、23家不同星级酒店网上预订的相关数据,分别从房型、酒店星级、地域和时间四个维度进行了分析。结果表明:房型、酒店星级、地域和时间对网上酒店预订价格离差具有显著的影响,其中酒店星级的影响最大,而且房型、酒店星级和地域三个因素其影响并不随时间发生变化;北京、上海和广州三地在网上酒店预订价格离差上存在差异,上海的网上酒店预订价格离差最大,而北京与广州的差别不显著。

We collected data from 11 travel websites and 23 hotels of different star ranks in Beijing, Shanghai and Guangzhou from June 9, 2005 to July 31, 2006. Our empirical study tries to test price dispersion across four factors： Type, Rank, Location and Date. The results show that all the four factors have significant effects on price dispersion of hotel online reservation in which the hotel （star-） Rank has the most significant impact and the effects of Type, Rank and Location on price dispersion do not change along the factor of Date. Key findings include： the higher the hotel rank and room type, the larger the price dispersion; the three cities exhibit different price dispersion patterns and the higher the quality of the room, We also find that the magnitude of price dispersion is different across Beijing, Shanghai and Guangzhou. Shanghai data exhibit the largest price dispersion while the differences in Beijing and Guangzhou are not significant, The magnitude of price dispersion in Shanghai is the highest and Beijing is the lowest among all the three cities. The higher the quality of the room, the larger the price dispersion is affected by location. It is important to find out more about the factors that influence the price dispersion in the hotel online booking market. Brand loyalty, advertising, and distribution of hotels may all contribute to the persistent price dispersion in the hotel online booking markets. However, we have not assessed any embedded effect related to hotel name, such as trust, loyalty, and so on. As each hotel has different types of rooms available and charges different rates accordingly, are there any correlations among the rates？ Do the different distribution channels of hotels have a big impact on the price dispersion across cities and force hoteliers making different strategies？ Further studies should use data from a more comprehensive set of hotels to dig deeply into the pricing strategies.