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.