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 this research, the socio-economic determinants of housing market values have been examined, highlighting their respective contributions to the formation of the property prices and, in particular, verifying the property tax liability. The property tax is a factor that could determine, through modest fluctuations, the revival of the property demand, generating positive effects also on the construction sector, which has been currently affected by the negative real estate contingency. The functional correlations of housing prices with the main socio-economic variables considered (i.e., housing rents, household incomes, household consumptions, property taxes, population and mean population age) have been explained through an econometric analysis implemented with an innovative methodology that uses multi-objective genetic algorithms. The analysis is contextualized to the Apulia region in the South of Italy, and the population of the sample studied consists of 258 individuals, corresponding to the municipalities of the region. In particular, the data collected for each variable considered refer to two different moments, that is, the year 2011 and the year 2015, in order to take into account the fiscal tightening that has occurred in Italy in this period of time. The elaborations carried out have allowed us to enucleate interesting functional relationships between property prices and the explanatory variables considered.

The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016–2017. The ability to generate a “unique” functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies.

The cogency of evaluation models able to predict future trends and to monitor the consequences of scenarios different from those initially expected has been determining a growing scientific interest for the development of financial sustainability methods. With reference to quarterly time series collected for the metropolitan area of five Spanish cities, in this research an innovative methodology has been implemented, in order to make explicit, for each case study, the main functional relationships between the housing prices and the socio-economic factors. The models obtained are characterized by both high statistical performance and compliance with the expected market phenomena, highlighting the decisive role in the housing price formation of the factors that indirectly represent the population’s income capacity (market rents, unemployment level, mortgages). Then, an empirical procedure for the construction of the future property value trends has been developed. The results point out the forecasting and monitoring potentialities of the methodology used, as a fundamental decision support tool in the urban planning policies of the local administrations, interested in anticipating and checking future housing bubbles through appropriate economic policies, and for private operators, in the phases of selection of the most attractive territorial areas for new property realizations.

In the present research the financial convenience of refurbishment investments concerning residential properties located in the Italian territory was analyzed. The study aimed at determining the unit profit range for a potential investor deriving from the refurbishment of residential properties and contextualizing the analysis to the Italian provincial capitals. With reference to the three macro-areas that for geographic and socio-economic differences define the Italian territory, namely the North, Center, South and Islands, three financial convenience maps were elaborated with the aim of providing a useful support in the investment choices of private and public operators. In particular, the financial convenience maps could guide the private operators in their investment decisions through a higher awareness of the achievable earnings and could help to identify the optimal allocation of financial resources due to the increase in the market value of the refurbished property. For the public administrations, instead, the study will be a useful reference for the calibration of the fiscal policy decisions regarding the incentives for building refurbishment at the different territorial scales and of the tax revenues related to the increase in the market value of the refurbished properties.