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The purpose of this paper is to understand the potential of traditional and non-traditional statistical techniques to predict dynamic hotel room prices. Four forecast models were employed: the simple moving average, the autoregressive integrated moving average (ARIMA), the radial basis function (RBF), and the support vector machine (SVM). This research is based on an empirical study of data obtained from the company Smith Travel Research (STR). The economic predictors were obtained from other reliable sources such as the World Bank and the World Tourism Organization. This study agreed with existing literature on the ability of machine learning to predict hotel room prices precisely. Given the complexity of the hotel industry, the effect of external economic predictors was tested in the model. The challenge lay in dealing with the mixed frequencies observed in the collected data. This research is designed to add an innovative approach to the existing literature on machine learning in the hotel industry in the Middle East and North Africa (MENA) region. Some of the machine learning techniques used in this study constitute a contribution to the research conducted in this region. This creates a bridge between many academic disciplines such as computer science, economics, and marketing. Small hotel operators should benefit from this research when setting strategies as well as in using the model to set their relative room prices.

Forecasting Hotel Prices in Selected Middle East and North Africa Region (MENA) Cities with New Forecasting Tools