The economic crisis of 2008 has highlighted the ineffectiveness of the banks in their disbursement of mortgages which caused the spread of Non-Performing Loans (NPLs) with underlying real estate. With the methods stated by the Basel III agreements, aimed at improving the capital requirements of banks and determining an adequate regulatory capital, the banks without the skills required have difficulties in applying the rigid weighting coefficients structures. The aim of the work is to identify a synthetic risk index through the participatory process, in order to support the restructuring debt operations to benefit smaller banks and small and medium-sized enterprises (SME), by analyzing the real estate credit risk. The proposed synthetic risk index aims at overcoming the complexity of Basel III methodologies through the implementation of three different multi-criteria techniques. In particular, the integration of objective financial variables with subjective expert judgments into a participatory process is not that common in the reference literature and brings its benefits for reaching more approved and shared results in the debt restructuring operations procedure. Moreover, the main findings derived by the application to a real case study have demonstrated how important it is for the credit manager to have an adequate synthetic index that could lead to the avoidance of risky scenarios where several modalities to repair the credit debt occur.

Purpose: This paper aims to test and compare two innovative methodologies (utility additive and evolutionary polynomial regression) for mass appraisal of residential properties. The aim is to deepen their characteristics, by exploring the potentialities and the operating limits.Design/methodology/approach: With reference to the same case studies, concerning samples of residential properties recently sold in three Italian cities, the two procedures are tested and the results are compared. The first method is the utility additive, which interprets the process of the property price formation as a multi-criteria selection of multi-objective typology, where the selection criteria are the property characteristics that are decisive in the real estate market; the second method is a hybrid data-driven technique, called evolutionary polynomial regression, that uses multi-objective genetic algorithms to search those models expressions that simultaneously maximize accuracy of data and parsimony of mathematical functions.Findings: The outputs obtained from the experimentation highlight the potentialities and the limits of the two methodologies, as well as the possibility of jointly applying them to interpret and predict the real estate phenomena in a more realistic representation.Originality value: In all countries, mass appraisal techniques have become strategic for the definition of management and enhancement policies of public and private property assets, in the case of investments of technical and economic refunctionalization (energy, environment, etc.), and for the alienation of buildings no longer suitable for public needs (military barracks, hospitals, areas in disuse, etc.). In this context, the use of mass appraisal techniques for residential properties assumes a leading role for sector operators (buyers, sellers, institutions, insurance companies, banks, real estate funds, etc.). Therefore, the results of the applications outline the potentialities of the two methodologies implemented and the opportunity of further insights of the topics that have been dealt with in this research.

In the paper an analysis of functional correlations of property prices with the main locational and socio-economic variables, which generally contribute to define the market value of properties, has been developed. Locational characteristics are represented by the surfaces of soil used for the main functions, borrowing the logic of the system of classification of CORINE Land Cover (European Commission). The analysis has been contextualized to the 258 municipalities of the Apulia region (Southern Italy), and has been referred to two different moments (years 2006 and 2011), and two different market segments (residential and retail). The functional relationships between property prices and explanatory variables considered, estimated through a software that implements a genetic algorithm, are particularly interesting. The methodology outlined constitutes a valuable reference for the definition of models aimed at supporting, in a more rational and convenient way, public planning decisions and private investment choices.

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.

In the decision-making process of urban regeneration projects, the lack of transparency is one of the most frequent causes of the deceleration and the stopping of initiatives. In the present work, with reference to a multi-criteria model recently developed for the municipality of Rome (Italy), a solution to this issue has been proposed. Borrowing the formal logic of fuzzy systems, the possibility to assign a different importance to the parameters which explain the objectives of the redevelopment, to compare the projects submitted by different operators, as well as to make it clear the phase in which the public administration evaluates the priorities of the projects, has been introduced. A measure of the coherence of the projects submitted by private operators with the importance of the objectives of redevelopment set by the public administration has been computed. The result is a flexible structure of the algorithm developed, able to adapt to the specificities of the case study and to the changes that, over time, could arise in the evaluation mechanism of the decision maker.