Accurately representing the price-demand relationship is critical for the success of a price optimization system. This research first uses booking data from 28 U.S. hotels to investigate the validity of two key assumptions in hotel revenue management. The assumptions are: 1) customers who book later are willing to pay higher rates than customers who book earlier; and, 2) demand is stronger during the week than on the weekend. Empirical results based on an analysis of booking curves, average paid rates, and occupancy rates for group, restricted retail, unrestricted retail, and negotiated demand segments challenge the validity of these assumptions. The combination of lower utilization rates and greater product differentiation suggests that hotels should apply different approaches than simply matching competitor rates to avoid losing market share. On days when inventory is near capacity, traditional yield management tactics deliver tremendous value, but these should be augmented by incorporating price response of demand and competition effects. On days when demand is soft and occupancy is projected to be low, price and competition based strategies should dominate. The hotel price optimization problem with linear demand model is a quadratic programming problem with prices of products that utilize multiple staynight rooms as the decision variable. The optimal solution of the hotel price optimization problems has unique properties that enables us to develop an alternative optimization algorithm that does not require solving quadratic optimization problem. Using the well known least norm problem as a subroutine, the optimization problem can be solved as finding a minimum distance between a polyhedron defined by non-negative demand and capacity constraints. This algorithm is efficient when only a few of the staynights are highly constrained. In practice, the choice of a demand model is largely driven by the ease of estimation and model fit statistics such as R2 and mean absolute percentage error (MAPE). These metrics provide measures of statistical validity of the model, however, they do not measure how well the price optimization will perform which is the ultimate interest of the practitioners. In order to measure the impact of demand models on price optimization performance, we first investigate the goodness of fit of linear demand models with different driver variables using actual data from 23 U.S. hotels representing multiple brands and location types. We find that hotels within the same location types (such as urban, suburban, airport) share similar driver variables. Airport and suburban hotels have simpler model specifications with less drivers compared to the urban hotels. The airport hotel demand models are different from other location hotels in that the airport hotel demand level does not differ by day of week. We then measure the impact of demand model misrepresentation on the performance of price optimization through simulation experiments, which are performed for different levels of demand and forecast accuracy to represent various market environments that hotels operate in. We find that using models with missing driver variables can reduce the potential revenue by 13%∼53% and using the wrong functional form 5%∼43% under our simulation environment. The findings from our research imply that correctly representing the demand model in price optimization is crucial to its success. In order for hotels to realize the maximum potential revenue through pricing, efforts should be focused on identifying the major driver variables influencing demand including the ones that we found to be significant.