The measurement of random situations is a relevant fact within the field of Econometrics. In the usual practice of the field of Econometrics assumptions are made about the statistical distributions of the data. In this paper, a concept from Physics is introduced, specifically the use of Entropy as an explanatory factor in the decision-making of tourist accommodation in apartments. The emergence of a concept called Sharing Economy has made the housing market to evolve. Assuming a direct competition against traditional accommodation such as hotels. For this, the Entropy time series have been calculated and defined for the periods between January 2005 and August 2018, to forecast and compare them with a time horizon of 12 months with the most usual predictive models. To carry out the forecast comparisons, we work with the new Matrix U1 Theil which allows quantifying the gain of the use of the Entropy models. The main locations in which the study has been applied are Spain, Catalonia and Barcelona. The theoretical implications and economic consequences are relevant for stakeholders in decisionmaking. The study details the relations of exchange in the decision making of accommodations in tourist apartments and details the relationship of exchange in situations of uncertainty with a high explanatory capacity of the models.

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A new Big Data cluster method was developed to forecast the hotel accommodation market. The simulation and training of time series data are from January 2008 to December 2019 for the Spanish case. Applying the Hierarchical and Sequential Clustering Analysis method represents an improvement in forecasting modelling of the Big Data literature. The model is presented to obtain better explanatory and forecasting capacity than models used by Google data sources. Furthermore, the model allows knowledge of the tourists’ search on the internet profiles before their hotel reservation. With the information obtained, stakeholders can make decisions efficiently. The Matrix U1 Theil was used to establish a dynamic forecasting comparison.

A new methodology is presented for measuring, classifying and predicting the cycles of uncertainty that occur in temporary decision-making in the tourist accommodation market (apartments and hotels). Special attention is paid to the role of entropy and cycles in the process under the Adaptive Markets Hypothesis. The work scheme analyses random cycles from time to time, and in the frequency domain, the linear and nonlinear causality relationships between variables are studied. The period analysed is from January 2005 to December 2018; the following empirical results stand out: (1) On longer scales, the periodicity of the uncertainty of decision-making is between 6 and 12 months, respectively, for all the nationalities described. (2) The elasticity of demand for tourist apartments is approximately 1% due to changes in demand for tourist hotels. (3) The elasticity of the uncertainty factor is highly correlated with the country of origin of tourists visiting Spain. For example, it has been empirically shown that increases of 1% in uncertainty cause increases in the demand for apartments of 2.12% (worldwide), 3.05% (UK), 1.91% (Germany), 1.78% (France), 7.21% (Ireland), 3.61% (The Netherlands) respectively. This modelling has an explanatory capacity of 99% in all the models analysed.

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