Abstract
The financial transmission of the USA’s housing price bubble has highlighted the inadequacy of the valuation methods adopted by the credit institutions, due to their static nature and inability to understand complex socio-economic dynamics and their related effects on the real estate market. The present research deals with the current issue of using Automated Valuation Methods for expeditious assessments in order to monitor and forecast market evolutions in the short and medium term. The paper aims to propose an evaluative model for the corporate market segment, in order to support the investors’, the credit institutions’ and the public entities’ decision processes. The application of the proposed model to the corporate real estate segment market of the cities of Rome and Milan (Italy) outlines the potentialities of this approach in property big data management. The elaboration of input and output data in the GIS (Geographic Information System) environment allowed the development of an intuitive platform for the immediate representation of the results and their easy interpretation, even to non-expert users.
Highlights
In the last decade, the economic and financial crisis in Europe, triggered by the USA subprimes, has led to a relevant cogency of evaluation tools able to provide ‘slender’ and reliable mass appraisals [1,2,3]
In the real estate valuations sector, the role of mass appraisal techniques has become strategic: (i) to define the urban policies aimed at the enhancement of exiting property assets [30]; (ii) to develop technical and economic refunctionalization initiatives [31]; (iii) to evaluate the risk related to the provision of mortgage loans by the credit institutions [32]; and iv) to assess the urban planning choices carried out by Public Administration for territorial strategic programs definition [33,34]
The Evolutionary Polynomial Regression (EPR) technique was applied to the database by considering the following inputs: (i) the maximum number of terms is equal to 7, that is, the number of independent variables; (ii) Y is the dependent variable in the models A and B, and ln(Y) is dependent variable in model C of Table 4; (iii) the exponents of the dependent variables are positive in the models A and C of Table 4, and negative in model B of Table 4
Summary
The economic and financial crisis in Europe, triggered by the USA subprimes, has led to a relevant cogency of evaluation tools able to provide ‘slender’ and reliable mass appraisals [1,2,3]. The use of innovative statistical valuation methods has become necessary for the different market operators (buyers, sellers, institutions, real estate funds, insurance companies, banks, etc.) in order to determinate more appropriate and objective property values, and to effectively monitor the evolution of property values [19,20]. The model proposed constitutes an expeditious assessment tool that allows the Public Administration to identify the potential future value of public assets following enhancement processes. The proposed tool could be used for the representation of alternative scenarios related to different intended uses, in order to enhance abandoned or under-utilized property assets and, with reference to the current crisis trigged by Covid-19, to analyse the market trend of relevant and large assets.
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