Aplicação e comparação dos métodos fuzzy topsis e fuzzy spotis na seleção dos parâmetros mais relevantes em avaliações imobiliárias

Abstract

In the real estate context, the connection between the characteristics of properties and their perception of value directly impacts the effectiveness of valuation models. However, given the significant diversity in real estate markets, the selection of parameters for valuation becomes a complex task. In many cases, this choice falls on the subjectivity of the evaluator. The lack of a clear method to justify the selection of these parameters raises questions about the validity and consistency of these decisions. In this context, this research aims to propose a model for selecting the best parameters to be used in real estate valuations respecting the individualities of the real estate markets. Initially, a literature search was conducted to identify and consolidate the most used parameters in real estate valuations. Subsequently, the Fuzzy TOPSIS and Fuzzy SPOTIS methods were used to order and select the main parameters in two analysis scenarios, the first focused on low-end properties and the second focused on high-end properties, these scenarios were evaluated considering the criteria of attractiveness of the buyer and sale liquidity associated with the property. Considering the opinion of the decision-maker. The application of the model indicated that for scenario 1 the parameters "Ease of financing", "Neighborhood", "Fixed Costs", "Number of bedrooms" and "Interest Rate" were the ones that reached the best placements and for scenario 2 the best parameters were: Neighborhood, Apparent Age (infrastructure), Property Area, Number of Bedrooms, Views of scenic landscapes, Number of bathrooms and Potential for development of the neighborhood. The two methods showed similar efficiency in the scenarios analyzed. Fuzzy SPOTIS stands out for usability due to its absence of order reversal and simpler axiomatics compared to Fuzzy TOPSIS. However, sensitivity analysis indicated that modeling with Fuzzy SPOTIS is more susceptible to changes in criterion weights, especially at extreme values, compared to Fuzzy TOPSIS. It is hoped that the outcomes and considerations resulting from this work will function as a stimulus for future investigations and practical approaches that favor decision-making processes in the field of civil construction.