Data Mining For Hotel Occupancy Rate: An Independent Component Analysis Approach

ABSTRACT

The recent global financial crisis and the threat of a worldwide H1N1 influenza epidemic have greatly affected the tourism and hospitality industries around the world. Both hospitality practitioners and researchers are interested in finding analytical methods that enable forecasts to be made of hotel room demand under the uncertain conditions likely to affect the industry. In this article, a novel data mining technique called independent component analysis (ICA) is proposed to establish the major factors determining the hotel occupancy rate in Hong Kong. Then, extension of the model is suggested, incorporating these factors to decompose hotel occupancy rates and examine the effect of each factor on the hotel occupancy rate. Empirical findings show that outbreaks of infectious diseases, economic performance, and service price were the major determinants of the hotel occupancy rate in Hong Kong over the period studied.

KEYWORDS:

• Independent component analysis
• hotel occupancy rate
• infectious diseases
• financial crisis
• service price

The authors are grateful to the anonymous reviewers for offering valuable comments on an earlier version of this article. This study was partly supported by a research grant funded by The Hong Kong Polytechnic University.