Exploring the use of artificial intelligence in price maximisation in the tourism sectorits application in the case of Airbnb in the Valencian Community

  1. Moreno Izquierdo, Luis 1
  2. Egorova, Galina 1
  3. Peretó Rovira, Alexandre 1
  4. Más Ferrando, Adrián 1
  1. 1 Universitat d'Alacant
    info

    Universitat d'Alacant

    Alicante, España

    ROR https://ror.org/05t8bcz72

Revista:
Investigaciones Regionales = Journal of Regional Research

ISSN: 1695-7253,  2340-2717

Año de publicación: 2018

Título del ejemplar: Tourism competitiveness in the digital economy

Número: 42

Páginas: 113-128

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Investigaciones Regionales = Journal of Regional Research

Resumen

El empleo del aprendizaje automático es cada vez más frecuente para explicar la competitividad de las empresas. La literatura nos muestra cómo la inteligencia artificial puede ayudar a empresas a mejorar su conocimiento de los usuarios, optimizar los precios o guiar a los compradores en su proceso de elección. Para confirmar que aplicando modelos de inteligencia artificial se permite obtener específicamente mejores procedimientos de optimización de precios respecto a otros modelos tradicionales, se estudian más 10.000 propiedades de Airbnb en las tres capitales de la Comunidad Valenciana (Valencia, Alicante y Castellón), observando que los resultados obtenidos con el modelo de redes neuronales artificiales son significativamente más satisfactorios que con el empleo de modelos hedónicos.

Referencias bibliográficas

  • Akin, M. (2015): «A novel approach to model selection in tourism demand modeling», Tourism Management, 48, 64-72.
  • Albrecht, J., and Hwa, R. (2007): «A re-examination of machine learning approaches for sentence-level MT evaluation», Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, 880-887.
  • Alspector, J., Koicz, A., and Karunanithi, N. (1997): «Feature-based and clique-based user models for movie selection: A comparative study», User Modeling and User-Adapted Interaction, 7(4), 279-304.
  • Bachis, E., and Piga, C. A. (2011): «Low-cost airlines and online price dispersion», International Journal of Industrial Organization, 29, 655-657.
  • Billsus, D., and Pazzani, M. J. (1999): «A hybrid user model for news story classification», UM99 User Modeling, Vienna, Springer, 99-108.
  • Chang, R. (2017): Using Machine Learning to Predict Value of Homes On Airbnb, Airbnb. URL: https://medium.com/airbnb-engineering/using-machine-learning-to-predict-valueof-homes-on-airbnb-9272d3d4739d.
  • Chen, K. Y., and Wang, C. H. (2007): «Support vector regression with genetic algorithms in forecasting tourism demand», Tourism Management, 28(1), 215-226.
  • Chen, Y., and Xie, K. (2017): «Consumer valuation of Airbnb listings: a hedonic pricing approach », International Journal of Contemporary Hospitality Management, 29(9), 2405-2424.
  • Claveria, O., and Torra, S. (2014): «Forecasting tourism demand to Catalonia: Neural networks vs. time series models», Economic Modelling, 36, 220-228.
  • Claveria, O., Monte, E., and Torra, S. (2015): «Tourism demand forecasting with neural network models: different ways of treating information», International Journal of Tourism Research, 17(5), 492-500.
  • Claveria, O., Monte, E., and Torra, S. 2016): «Combination forecasts of tourism demand with machine learning models», Applied Economics Letters, 23(6), 428-431.
  • Dogru, T., and Pekin, O. (2017): What do guests value most in Airbnb accommodations? An application of the hedonic pricing approach.
  • Ert, E., Fleischer, A., and Magen, N. (2016): «Trust and reputation in the sharing economy: The role of personal photos in Airbnb», Tourism Management, 55, 62-73.
  • Espinet, J. M., Saez, M., Coenders, G., and Fluvià, M. (2003): «Effect on prices of the attributes of holiday hotels: a hedonic prices approach», Tourism Economics, 9(2), 165-177.
  • Gibbs, C., Guttentag, D., Gretzel, U., Yao, L., and Morton, J. (2018): «Use of dynamic pricing strategies by Airbnb hosts», International Journal of Contemporary Hospitality Management, 30(1), 2-20.
  • Hamilton, J. M. (2007): «Coastal landscape and the hedonic price of accommodation», Ecological Economics, 62(3-4), 594-602.
  • Hornik, K., Stinchcombe, M., and White, H. (1989): «Multilayer feedforward networks are universal approximators», Neural networks, 2(5), 359-366.
  • Ifrach, B. (2015): How Airbnb uses machine learning to detect host preferences, Airbnb. URL: https://medium.com/airbnb-engineering/how-airbnb-uses-machine-learning-todetecthost-preferences-18ce07150fa3.
  • James, R. C., and Carol, E. B. (2000): «Artificial Neural Networks in Accounting and Finance: Modeling Issues», International Journal of Intelligent Systems in Accounting, Finance and Management, 9: 119-144.
  • Jennings, A., and Higuchi, H. (1993): «A user model neural network for a personal news service », User Modeling and User-Adapted Interaction, 3(1), 1-25.
  • Jordan, M. I., and Mitchell, T. M. (2015): «Machine learning: Trends, perspectives, and prospects », Science, 349(6245), 255-260.
  • Kutschinski, E., Uthmann, T., and Polani, D. (2003): «Learning competitive pricing strategies by multi-agent reinforcement learning», Journal of Economic Dynamics and Control, 27(11-12), 2207-2218.
  • Lang, K. (1995): «Newsweeder: Learning to filter netnews», Machine Learning Proceedings 1995, 331-339.
  • Laurent, P., Chollet, T., and Herzberg, E. (2015): Intelligent automation entering the business world.
  • Lawrence, R. D. (2003): «A machine-learning approach to optimal bid pricing», Computational modeling and problem solving in the networked world, Boston, MA, Springer, 97-118.
  • Limsombunchai, V., Gan, C., and Lee, M. (2004): «House Price Prediction: Hedonic Price Model Vs. Artificial Neural Network», American Journal of Applied Sciences, 1: 3, 193-201.
  • Lippmann, R. P. (1987): «An introduction to Computing with Neural Nets», IEEE ASSP Magazine, April, 4-22.
  • Litman, D. J., and Pan, S. (2000, July): «Predicting and adapting to poor speech recognition in a spoken dialogue system», AAAI/IAAI, 722-728.
  • Macskassy, S. A., Dayanik, A. A., and Hirsh, H. (1999): «Emailvalet: Learning user preferences for wireless email», Proceedings of Learning about Users Workshop, IJCAI’99.
  • Malighetti, P., Paleari, S., and Redondi, R. (2010): «Has Ryanair’s pricing changed over time? An empirical analysis of its 2006-2007 flights», Tourism Management, 31, 36-44.
  • Mantin, B., and Koo, B. (2010): «Weekend effect in airfare pricing», Journal of Air Transport Management, 16, 48-50.
  • McCulloch W. S., and Pitts W. A, (1943): «Logical calculus of ideas immanent in nervous activity », Bulletin of Mathematical Biophysics, 5, 115-133, https://www.cs.cmu.edu/~epxing/Class/10715/reading/McCulloch.and.Pitts.pdf.
  • Monty, B., and Skidmore, M. (2003): «Hedonic pricing and willingness to pay for bed and breakfast amenities in Southeast Wisconsin», Journal of Travel Research, 42(2), 195-199.
  • Moreno-Izquierdo, L., Ramón-Rodríguez, A., and Ribes, J. P. (2015): «The impact of the internet on the pricing strategies of the European low cost airlines», European Journal of Operational Research, 246(2), 651-660.
  • Otero, J. M., and Trujillo, F. (1991): «Red neuronal para la Predicción de la Tasa de Actividad Femenina». Comunicación presentada a la V Reunión ASEPELT-ESPAÑA, Gran Canaria, 20-21 de junio.
  • Otero, J. M., and Trujillo, F. (1993): «Predicción multivariante y mutiperiodo de una serie temporal económica mediante una red neuronal», Estadística Española, 35, 345-375.
  • Peterson, S., and Flanagan, A. (2009): «Neural network hedonic pricing models in mass real estate appraisal», Journal of Real Estate Research, 31(2), 147-164.
  • Piga, C., and Filippi, N. (2002): «Booking and Flying with Low-cost Airlines», International Journal of Tourism Research, 4(3), 237-249.
  • Portolan, A. (2013): «Impact of the attributes of private tourist accommodation facilities onto prices: A hedonic price approach», European Journal of Tourism Research, 6(1), 74.
  • Rigall-I-Torrent, R., and Fluvià, M. (2011): «Managing tourism products and destinations embedding public good components: A hedonic approach», Tourism Management, 32(2), 244-255.
  • Rystad, V., Aarseth, H. W., and Fardal, F. L. (2017): How two-sided platform startups can use machine learning to improve the value proposition (Master’s thesis, NTNU).
  • Selim, S. (2011): «Determinants of house prices in Turkey: Hedonic regression versus artificial neural network», Expert Systems with Applications, 36(2), 2843-2852.
  • Shaw, J. (1992): «Neural network resource guide», AI Expert, 8(2), 48-54.
  • Shawar, B. A., and Atwell, E. (2007): «Chatbots: are they really useful?», Ldv forum, 22(1), 29-49.
  • Teubner, T., Hawlitschek, F., and Dann, D. (2017): «Price Determinants On Airbnb: How Reputation Pays Off In The Sharing Economy», Journal of Self-Governance & Management Economics, 5(4).
  • Wang, D., and Nicolau, J. L. (2017): «Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb.com», International Journal of Hospitality Management, 62, 120-131.
  • Webb, G. I., Pazzani, M. J., and Billsus, D. (2001): «Machine learning for user modeling», User modeling and user-adapted interaction, 11(1-2), 19-29.
  • Wilson, I. D., Paris, S. D., Ware, J. A., and Jenkins, D. H. (2002): «Residential property price time series forecasting with neural networks», Knowledge-Based Systems, 15(5-6), 335-341.
  • Yang, Y., Tang, J., Luo, H., and Law, R. (2015): «Hotel location evaluation: a combination of machine learning tools and web GIS», International Journal of Hospitality Management, 47, 14-24.
  • Ye, Q., Zhang, Z., and Law, R. (2009): «Sentiment classification of online reviews to travel destinations by supervised machine learning approaches», Expert systems with applications, 36(3), 6527-6535.
  • Yu, G., and Schwartz, Z. (2006): «Forecasting short time-series tourism demand with artificial intelligence models», Journal of Travel Research, 45(2), 194-203.
  • Zukerman, I., and Albrecht, D. W. (2001): «Predictive statistical models for user modeling», User Modeling and User-Adapted Interaction, 11(1-2), 5-18.
Fuente de los datos: Dialnet