Ricerc@Sapienza

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.

With reference to the current issue of energy efficiency of residential buildings, the aim of this research is to analyze the possible influence of the energy performance component on the property prices formation. The study sample consists of two hundred residential units recently sold and located in the city of Bari (Italy). The implemented methodology is represented by a data-driven technique that employs a genetic algorithm to identify the functional expressions.

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 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.

Although the presence of an urban green areas seems to be an important factor in housing decisions, there are few empirical research on the incidence of different types of urban green spaces on housing prices. With reference to a study sample of apartments located in the Flaminio district of the city of Rome (Italy), an innovative data-driven technique has been implemented.