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What is the optimal number of hotel rooms: Spain as a case study


  • Ben-David, Nissim
  • Teitler-Regev, Sharon
  • Tillman, Avi


The main contribution of the paper is that it proposes a well-defined model and an empirical estimation technique for determining an optimal targeted number of incoming tourists and for getting as close as possible to this target by controlling variables that are affected by tourism authorities policies. We assume that the planner wishes to minimize the gap between the actual and the optimal number of tourists in order to get as close as possible to the optimally desired number. The actual number of tourists from each country is affected by the cost of travel as well as by exogenous variables. We constructed a system of two simultaneous equations, where the number of tourists and the cost of travel are the endogenous variables. We estimated the system for incoming tourism to Spain from various countries and forecasted the actual number of incoming tourists. Using the forecasted equation we were able to extract the optimal number of rooms needed in order to get as close as we could to the desired number of tourists.

Suggested Citation

  • Ben-David, Nissim & Teitler-Regev, Sharon & Tillman, Avi, 2016. "What is the optimal number of hotel rooms: Spain as a case study," Tourism Management, Elsevier, vol. 57(C), pages 84-90.
  • Handle: RePEc:eee:touman:v:57:y:2016:i:c:p:84-90
    DOI: 10.1016/j.tourman.2016.05.016

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    References listed on IDEAS

    1. Jacint Balaguer & Manuel Cantavella-Jorda, 2002. "Tourism as a long-run economic growth factor: the Spanish case," Applied Economics, Taylor & Francis Journals, vol. 34(7), pages 877-884.
    2. Vasilios Patsouratis & Zoe Frangouli & George Anastasopoulos, 2005. "Competition in tourism among the Mediterranean countries," Applied Economics, Taylor & Francis Journals, vol. 37(16), pages 1865-1870.
    3. Zhenhua Chen & Kingsley E. Haynes, 2015. "Impact of high-speed rail on international tourism demand in China," Applied Economics Letters, Taylor & Francis Journals, vol. 22(1), pages 57-60, January.
    4. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    5. Po, Wan-Chen & Huang, Bwo-Nung, 2008. "Tourism development and economic growth–a nonlinear approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5535-5542.
    6. Rey, Belén & Myro, Rafael L. & Galera, Asun, 2011. "Effect of low-cost airlines on tourism in Spain. A dynamic panel data model," Journal of Air Transport Management, Elsevier, vol. 17(3), pages 163-167.
    7. Boopen Seetanah & Raja Sannassee & Sawkut Rojid, 2015. "The impact of relative prices on tourism demand for Mauritius: An empirical analysis," Development Southern Africa, Taylor & Francis Journals, vol. 32(3), pages 363-376, May.
    8. Yair Eilat & Liran Einav, 2004. "Determinants of international tourism: a three-dimensional panel data analysis," Applied Economics, Taylor & Francis Journals, vol. 36(12), pages 1315-1327.
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    Cited by:

    1. Suau-Sanchez, Pere & Voltes-Dorta, Augusto & Rodríguez-Déniz, Héctor, 2017. "An assessment of the potential for self-connectivity at European airports in holiday markets," Tourism Management, Elsevier, vol. 62(C), pages 54-64.

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    Hotel rooms; Policy; Tourism;


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