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Estimating demand in search markets: the case of online hotel bookings

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  • Sergei Koulayev

Abstract

In this paper, we emphasize that choice sets generated by a search process have two properties: first, they are limited; second, they are endogenous to preferences. Both factors lead to biased estimates in a static demand framework that takes choice sets as given. To correct for this bias, we estimate a structural model of search for differentiated products, using a unique dataset of consumer online search for hotels. Within a nested logit utility model, we show that the mean utility function and the search cost distribution of a representative consumer are non-parametrically identified, given our data. Using our model's estimates, we quantify both sources of bias: they lead to overestimation of price elasticity by a factor of five and four, respectively. The median search cost is about 38 dollars per 15 hotels; we also present some evidence on multi-modality of search cost distribution.

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  • Sergei Koulayev, 2009. "Estimating demand in search markets: the case of online hotel bookings," Working Papers 09-16, Federal Reserve Bank of Boston.
    • Handle: RePEc:fip:fedbwp:09-16
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    References listed on IDEAS

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    Cited by:

    1. Angela De Carlo & Angela Stefania Bergantino & Andrea Morone, 2013. "Experiments in transport related choices: the influence of risk and uncertainty in determining workers' behaviour with respect to parking alternatives," ERSA conference papers ersa13p407, European Regional Science Association.
    2. Navid Mojir & K. Sudhir, 2014. "Price Search Across Stores and Across Time," Cowles Foundation Discussion Papers 1942, Cowles Foundation for Research in Economics, Yale University, revised Mar 2016.
    3. Timothy Richards & Stephen Hamilton, 2015. "Attribute Search in Online Retail Grocery Markets," Working Papers 1505, California Polytechnic State University, Department of Economics.
    4. Navid Mojir & K. Sudhir, 2014. "Price Search Across Time and Across Stores," Cowles Foundation Discussion Papers 1942R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2019.
    5. Timothy J. Richards & Stephen F. Hamilton & Koichi Yonezawa, 2017. "Variety and the Cost of Search in Supermarket Retailing," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 50(3), pages 263-285, May.
    6. Richards, Timothy J. & Hamilton, Stephen F. & Empen, Janine, 2015. "Attribute Search in Online Retailing," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 202968, Agricultural and Applied Economics Association.
    7. Hema Yoganarasimhan, 2013. "The Value of Reputation in an Online Freelance Marketplace," Marketing Science, INFORMS, vol. 32(6), pages 860-891, November.
    8. Dr. Timothy J. Richards, 2015. "A Shameless Pitch for Quantitative Marketing," Agribusiness, John Wiley & Sons, Ltd., vol. 31(4), pages 564-567, October.
    9. Babur De los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2012. "Search with Learning," Working Papers 2012-03, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    10. Navid Mojir & K. Sudhir, 2014. "A Model of Multi-pass Search: Price Search across Stores and Time," Cowles Foundation Discussion Papers 1942R2, Cowles Foundation for Research in Economics, Yale University, revised Feb 2020.
    11. Richards, Timothy J. & Hamilton, Stephen F. & Allender, William, 2016. "Search and price dispersion in online grocery markets," International Journal of Industrial Organization, Elsevier, vol. 47(C), pages 255-281.

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    Keywords

    Consumer behavior; Consumers' preferences; Electronic commerce;
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