Casinos, revenue estimation, feasibility analysis
Original Research Article
In this study, multiple regression techniques were used to build two consumption models to better understand supply and demand influences on casino revenues. The initial model contained 15 independent variables and explained 97% of the variance in revenues. However, due to assumption violations, assessing the relative role of each independent variable proved to be problematic. Subsequently, a Reduced Variable Model was developed which explained 83% of the variance, and included six independent variables. As stand-alone prediction tools, the models do not yield useful revenue estimates, due to their large standard errors of the estimate, however, they do explain relative influences of both supply and demand measures on consumption (casino revenues). Several refinements were identified to improve the models' value as revenue prediction tools. The study's findings provide information which has led to a better understanding of casino revenues. The findings should also aid in identifying favorable regions nationally for new casino developments, as well as serve as a basis for subsequent gaming research.