Pricing determinants in the hotel industry: Quantile regression analysis

Due to the skewed distribution of hotel prices, quantile regression provides a more flexible and complete characterization of the determinants of the hotel prices at the higher and lower tail of the distribution. This study applies quantile regression approach to investigate the major determinants of hotel room pricing strategies. The ordinary least square regression is also used for comparative purposes. The data are drawn from 58 international tourist hotels in Taiwan and average room rate (ARR) is used as the proxy of hotel room price. The results of OLS and quantile regression share common characteristics but also have differences in some aspects. The OLS results reveal that number of rooms, hotel age, market conditions and number of housekeeping staff per room are the main attributes of hotel room rate. The quantile regression results further demonstrate that room number and the number of housekeeping staff per guest room do not significantly influence hotel price at the low price quantile. Hotel age and market conditions are only significant determinants in high-price category. Additionally, for the high-priced quantile hotels, the proportion of foreign individual travellers positively and significantly influences room price. The empirical results can help hoteliers in shaping investment and pricing strategies.