Where should a new restaurant be located? What type of restaurant would be best in a given location? What defines a geographical market? These are examples of questions about product design and product choice. While there is extensive literature on consumer response to prices, there is relatively little attention to firm choices about physical location and product characteristics. Recent trends in digitization have led to the creation of many large panel datasets of consumers, which in turn motivates the development of models that exploit the rich information in the data and provide precise answers to these questions.Answering many of these questions requires a model that incorporates individual-levelheterogeneity in preferences for product attributes and travel time, as these characteristics might vary substantially even within a city; and understanding individual heterogeneity in travel preferences is a key input for urban planning. To this end, we develop an empirical model of consumer choices over lunchtime restaurants, the travel-time factorization model (TTFM). We apply the model to a dataset derived from mobile phone locations. The personalized predictions of TTFM for individuals and restaurants are more accurate than existing methods, especially for high-activity individuals and restaurants.
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