TITLE:
Forecasting Hotel Prices in Selected Middle East and North Africa Region (MENA) Cities with New Forecasting Tools
AUTHORS:
Mohammed Al Shehhi, Andreas Karathanasopoulos
KEYWORDS:
Hotel Forecasting, Analytics, Machine Learning
JOURNAL NAME:
Theoretical Economics Letters,
Vol.8 No.9,
June
15,
2018
ABSTRACT: 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.