Room demand estimation models are crucial in the performance of hotel revenue management systems. The advent of websites for online room booking has produced a decrease in the accuracy of prediction models due to the complex customers’ patterns. A reduction that has been particularly dramatic due to last-minute reservations. We propose the use of parsimonious models for improving room demand forecasting. The creation of the models is carried out by using a flexible methodology based on genetic algorithms whereby a wrapper-based scheme is optimized. The methodology includes not only an automated model parameter optimization but also the selection of most relevant inputs and the transformation of the skewed room demand distribution. The effectiveness of our proposal was evaluated using the historical room booking data from a hotel located at La Rioja region in northern Spain. The dataset also included sociological and meteorological information, and the list of local and regional festivities. Nine types of regression models were tuned using the optimization scheme proposed and grid search as the reference method. Models were compared showing that our proposal generated more parsimonious models, which in turn led to higher overall accuracy and better generalization performance. Finally, the applicability of the methodology was demonstrated through the creation of a six-month calendar with the estimated room demand.