Predicting hotel booking cancellations to decrease uncertainty and increase revenue


  • Nuno Antonio ISCTE-IUL
  • Ana de Almeida ISCTE-IUL and Centro de Informa?tica e Sistemas da Universidade de Coimbra
  • Luis Nunes ISCTE-IUL, Instituto de Telecomunicações and ISTAR


Data science, Hospitality industry, Machine learning, Predictive modeling, Revenue management


Booking cancellations have a substantial impact in demand-management decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of revenue management performance. To circumvent the problems caused by booking cancellations, hotels implement rigid cancellation policies and overbooking strategies, which can also have a negative influence on revenue and reputation.

Using data sets from four resort hotels and addressing booking cancellation prediction as a classification problem in the scope of data science, authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%.  This demonstrates that despite what was assumed by Morales and Wang (2010) it is possible to predict with high accuracy whether a booking will be canceled.

Results allow hotel managers to accurately predict net demand and build better forecasts, improve cancellation policies, define better overbooking tactics and thus use more assertive pricing and inventory allocation strategies.

Author Biographies

  • Nuno Antonio, ISCTE-IUL

    Has a Computer Science Engineering degree, a M.Sc in Hotel Administration and Management and he is currently a Computer Science PhD candidate. He is also CTO at Itbase/WareGuest, a software development company and invited lecturer at the School of Management, Hospitality and Tourism of the University of the Algarve, Portugal. His research interests include Corporate Performance Management, Decision Supports Systems and Machine Learning.

    Instituto Universitário de Lisboa (ISCTE-IUL), Av. das Forças Armadas, 1649-026 Lisboa,,+351 217903000, Department of Information Science and Technology.

  • Ana de Almeida, ISCTE-IUL and Centro de Informa?tica e Sistemas da Universidade de Coimbra

    Graduated in Mathematics with specialisation in Computer Science and got her Ph.D. in Applied Mathematics with specialisation in Complexity. Her research interests include evolutionary algorithmics, information sciences, applied mathematics models and modelling, combinatorial optimisation, and pattern recognition & feature extraction methods.

    Instituto Universitário de Lisboa (ISCTE-IUL), Av. das Forças Armadas, 1649-026 Lisboa,,+351 217650548, Assistant Professor, Department of Information Science and Technology.

  • Luis Nunes, ISCTE-IUL, Instituto de Telecomunicações and ISTAR

    Holds a PhD in  Computer Engeneering, a M.Sc in Electronics Engeneering and Computers, and he is graduated in Computer Science. He is a researcher at Instituto de Telecomunicações and ISTAR. Current research interests include Machine Learning applications, in particular to problems related to Intelligent Transport Systems, Intelligent Home Automation, Swarm controllers, and Decision Support Systems.
    Instituto Universitário de Lisboa (ISCTE-IUL), Av. das Forças Armadas, 1649-026 Lisboa,,+351 217650561, Assistant Professor, Department of Information Science and Technology.


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Tourism/Hospitality: Research Papers

How to Cite

Antonio, N., de Almeida, A., & Nunes, L. (2017). Predicting hotel booking cancellations to decrease uncertainty and increase revenue. Tourism & Management Studies, 13(2), 25-39.