Proposta de tratamento da variável localização em modelos inferenciais de avaliação imobiliária para municípios médios
Ferreira, Jose Fabricio
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Location is a central variable in mathematical models of real estate appraisal. The main goal of this dissertation is to propose a method for the treatment of this variable in a specific kind of appraisal, but very relevant, known as PGV (in Brazil, anything like an assessment roll). Besides being a important instrument of urban tax policies, PGVs also have their usefulness in urban planning for revealing the dynamics of real estate valuation. The study focuses in São Carlos (São Paulo State, Brazil), a city of nearly 210.000 inhabitants. It is a city of intermediary size, whose spatial patterns of real estate valuation are not quite simple, like in small towns, but factors whose effects have maximum expression in the metropolitan environment start to consolidate. The main of them is segregation, and the conditions of competition for the best location occur in the field of real estate market. This study aims to explore the amplitude of the city, with its variability of characteristics, incorporating in the object of study the locations of the urban core and expansion areas. It discusses the influence or correlation of factors such as demand for public services, infrastructures, attributes of location, neighbourhood aspects, urban amenities and aspects of illegality in the composition of the market value of these properties. Thus, diversity and variability of characteristics widen the study of the valuation pattern of the urban land. The innovative approach of this study is to link two fields of knowledge apparently distincts the inferential methodologies of mass evaluation and the studies of intra-urban location. The common potencial issue is tax application. It means, to the local governments, to consider the city as it shows itself, ensuring not only the success in evaluative formulations, but the respect for the principles of fairness that should characterize the tax policies.