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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.

Suggested Citation

  • 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. 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.
    2. 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.
    3. Hema Yoganarasimhan, 2013. "The Value of Reputation in an Online Freelance Marketplace," Marketing Science, INFORMS, vol. 32(6), pages 860-891, November.
    4. 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.
    5. Dr. Timothy J. Richards, 2015. "A Shameless Pitch for Quantitative Marketing," Agribusiness, John Wiley & Sons, Ltd., vol. 31(4), pages 564-567, October.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Timothy Richards & Stephen Hamilton, 2015. "Attribute Search in Online Retail Grocery Markets," Working Papers 1505, California Polytechnic State University, Department of Economics.
    11. 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.

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    Keywords

    Consumer behavior; Consumers' preferences; Electronic commerce;
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