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Exploring the use of artificial intelligence in price maximisation in the tourism sector: its application in the case of Airbnb in the Valencian Community

Author

Listed:
  • Moreno-Izquierdo, Luis

    (University of Alicante)

  • Egorova, Galina

    (University of Alicante)

  • Peretó-Rovira, Alexandre

    (University of Alicante)

  • Más-Ferrando , Adrián

    (University of Alicante)

Abstract

The use of machine learning is becoming more and more frequent in companies’ search for competitiveness. Literature on the subject show us how in many cases artificial intelligence can help companies to improve their knowledge about users, optimize prices or guide buyers in their choices. To confirm that the application of artificial intelligence models allows companies to obtain specifically better price optimisation procedures than with other traditional models, we have studied more than 10,000 Airbnb properties in the three main cities in the Valencian Community (Valencia, Alicante and Castellón), noting that the estimation process using neural networks offers significantly more satisfactory results than the use of hedonic models.

Suggested Citation

  • Moreno-Izquierdo, Luis & Egorova, Galina & Peretó-Rovira, Alexandre & Más-Ferrando , Adrián, 2018. "Exploring the use of artificial intelligence in price maximisation in the tourism sector: its application in the case of Airbnb in the Valencian Community," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 42, pages 113-128.
    • Handle: RePEc:ris:invreg:0385
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    Cited by:

    1. Abrate, Graziano & Sainaghi, Ruggero & Mauri, Aurelio G., 2022. "Dynamic pricing in Airbnb: Individual versus professional hosts," Journal of Business Research, Elsevier, vol. 141(C), pages 191-199.
    2. Giulia Contu & Luca Frigau & Claudio Conversano, 2023. "Price indicators for Airbnb accommodations," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4779-4802, October.
    3. Jorge V Pérez-Rodríguez & Juan M Hernández & Julián Andrada-Félix, 2024. "Modelling prices and volatilities in the sharing economy," Tourism Economics, , vol. 30(5), pages 1189-1215, August.
    4. Karima Kourtit & Peter Nijkamp & João Romão, 2019. "Cultural Heritage Appraisal by Visitors to Global Cities: The Use of Social Media and Urban Analytics in Urban Buzz Research," Sustainability, MDPI, vol. 11(12), pages 1-21, June.
    5. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    6. Luis Moreno-Izquierdo & Adrián Más-Ferrando & Marta Suárez-Tostado & Ana B. Ramón-Rodríguez, 2022. "Reinvención del turismo en clave de inteligencia artificial. Buscando un modelo sostenible y competitivo para el siglo XXI," Fedea Economy Notes 2022-19, FEDEA.

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    More about this item

    Keywords

    Machine Learning; Airbnb; tourism; hedonic prices; Valencian Region;
    All these keywords.

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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