Estimating unconstrained hotel demand based on censored booking data

One of the most critical elements to the success of a hotel revenue management system is the ability to accurately forecast future unconstrained demand based on historical booking data. Unconstrained demand is only observable in the absence of any constraints such as rate controls, stay pattern controls, and capacity limitations. Most hotel demand data contained in historical booking records are censored by the presence of these constraints. This paper develops parametric regression models that consider not only the demand distribution, but also the conditions under which the data were collected. These models can be used to estimate the unconstrained hotel demand based on the censored booking data. Important conditions, such as the rate hurdles, capacity limitations and competitors' rates, can be included in the models explicitly as explanatory variables. The method of maximum likelihood is used to estimate the unknown parameters in the parametric demand distributions. Three numerical examples are given to demonstrate the modelling procedures.