REMODELING TOURISM DEMAND USING OPTIMAL PREDICTORS: THE CASE OF MUĞLA

Although there have been many studies that have approached the tourism phenomenon from an economic perspective, most of these studies ignore environmental, historical and infrastructural factors. The study aims to create the most optimal regression that can be created by including all the factors that play a role in the realization of tourism demand in the model. In the empirical process, algorithms of statistical package programs are used in accordance with the econometric methodology. While the sample is a panel dataset, the time period extends from 2014 to 2021. The horizontal section consists of the districts of Muğla. First, the variables most related to tourism are determined, then the existence of unobserved heterogeneity is investigated. The variables with the most significant coefficients are added to the model with the forward-stepwised method. The econometric findings of the study are not very surprising, but they also reject some hypotheses that can be considered hypothetically correct. As a matter of fact, it is seen that the highest coefficients after the lagged foreign visitor and time-specific dummy variables (COVID-19 restrictions and post-pandemic normalization) belong to the environmental variables. Having a great historical and cultural depth, it hosted the Carian Civilization, Lelegians, Persians, Hellenistic Period, Romans and Byzantines from 2000 BC to 1280 BC. It is a historically and culturally rich destination that came under Turkish sovereignty with the establishment of Menteşe Principality. However, historical elements do not have an important place among the demand factors of tourists. The reason may be the ineffective marketing of the existing historical assets. Empirical results show that number of visitors in the previous year, the blue flag beaches and forest density are the environmental factors that stand out in tourism demand elements. There are also findings that infrastructure variables are one of the factors affecting tourism demand.