Forecasting methodology for hospitality industry indicators as a prerequisite for planning of tourism entities

Abstract

The study substantiates the theoretical and methodological foundations and develops a practical toolkit for forecasting key hospitality industry indicators based on statistical analysis of inbound tourist flows. It is established that traditional models for forecasting hotel demand require significant adaptation due to high market dynamism and the impact of unpredictable factors, including the COVID-19 pandemic and military conflicts. A three-stage methodology for analyzing and evaluating hospitality industry indicators has been proposed and tested. Its originality lies in the integrated use of the widely available MS Excel for primary statistical data processing and the specialized Mathcad software for correlation and regression analysis. The advantages of the selected toolkit have been demonstrated in terms of clarity of calculation documentation, reproducibility of results, and accuracy of economic and mathematical models. Using statistical data from Germany for 1995–2022, the dynamics of inbound tourist flows were assessed, trend lines were constructed, and a forecast through 2030 was produced. Correlation and regression analysis revealed a significant relationship between the volume of incoming tourist flows and required room capacity. The consequences of the pandemic are long-lasting: according to forecasted growth rates, recovery to pre-crisis levels is not expected even by 2030. Formulas have been derived for calculating the number of accommodation establishments, rooms, and bed places depending on the projected tourist flow. The practical significance of the research lies in the possibility of direct application of the proposed methodology by hotel management for medium- and long-term planning and pricing strategy development..