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Nowadays, there is a wide range of domains in which there is a need to generate recommendations to groups of users instead of individuals. Several techniques for aggregating individual models or ratings have been developed; a disadvantage of these techniques is that they require a large amount of computations to estimate unknown ratings. In this article, we present an analysis of the impact of estimating ratings when an aggregation technique is used. For that propose, we describe a hybrid approach to generate group recommendations based on group modeling. We also present the results obtained when evaluating the approach and two well known aggregation techniques in the movie domain, and the variations of those results when the estimation process is not included.