Predicting Hotel Demand Using Destination Marketing Organization's Web Traffic Data

Yang Yang, Bing Pan, Haiyan Song

Research output: Journal article publicationJournal articleAcademic researchpeer-review

184 Citations (Scopus)


This study uses the web traffic volume data of a destination marketing organization (DMO) to predict hotel demand for the destination. The results show a significant improvement in the error reduction of ARMAX models, compared with their ARMA counterparts, for short-run forecasts of room nights sold by incorporating web traffic data as an explanatory variable.These empirical results demonstrate the significant value of website traffic data in predicting demand for hotel rooms at a destination, and potentially even local businesses' future revenue and performance. The implications for future research on using big data for forecasting hotel demand is also discussed.
Original languageEnglish
Pages (from-to)433-447
Number of pages15
JournalJournal of Travel Research
Issue number4
Publication statusPublished - 1 Jan 2014


  • big data
  • hotel occupancy
  • online data
  • time series
  • tourism demand forecasting
  • website traffic

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Transportation
  • Tourism, Leisure and Hospitality Management


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