Ricerc@Sapienza

Purpose
In the present paper, two innovative methodologies (Utility Additive and Evolutionary Polynomial Regression) for mass appraisal of residential properties have been tested and compared. The aim is to deepen their characteristics, by exploring the potentialities and the operating limits.
Design/metodology/approach

In the present paper, two innovative methodologies (Utility Additive and Evolutionary Polynomial Regression) for mass appraisal of residential properties have been tested and compared. The aim is to deepen their characteristics, by exploring the potentialities and the operating limits. 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 important role of the buildings energy issue, recognized by the current international policies, is confirmed by the strategies employed in different European countries to promote energy efficiency measures. With reference to two samples of residential properties recently sold in the two Italian cities of Bari (Southern Italy) and Bolzano (Northern Italy), located in different areas in terms of the socio-economic and climatic factors, in the present research an econometric analysis to verify the contribution of the energy performance on the residential selling prices is implemented.

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.

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.

Nowadays, as the role of energy retrofit on the existing building stock is recognized towards energy savings and emissions’ reductions, the actions to be undertaken towards this aim require complex decisions, in terms of the choice among active and passive strategies and among often conflicting objectives of the retrofit. Depending on the actor of the retrofit (e.g., private, public), the main objective could be minimizing the investment, minimizing the energy demand or cost, or minimizing emissions.