During the last years, a growing interest has pivoted around strategies and methodologies for energy efficiency in buildings. Nevertheless, the focus has always been on single properties, while the scientific research still lacks in solutions for building portfolios. Assets owners instead, would require reliable decisional tools to select the most effective retrofit solutions. This study intends to elaborate a model capable of identifying the optimal allocation of financial resources for energy enhancements in large building portfolios. The core idea is to assist and strongly orientate the decision-making process through a comprehensive new methodology. 

Some novelties characterize this research. First, the approach developed covers each aspect of energy retrofits, from preliminary analysis to construction and management. Second, the level of detail requested is not excessively burdensome, ensuring good reliability. Third, the approach is interdisciplinary, connecting statistical techniques (regression analysis), economic feasibility (life cycle costing, discounted cash flow analysis), optimization modelling (multi-attribute linear programming), and risk simulations (Monte Carlo simulation). 

The method developed has been implemented into a portfolio of 25 buildings in North Italy for testing and validation. It was possible to compare several design alternatives and reach for the best outcome. This demonstrated how the model could be successfully used in real applications. The most significant achievement in this study lies in its extreme flexibility, allowing confronting countless design scenarios until the optimal is attained. Another significant result is the synergic integration of traditional financial techniques with operational research. The last novelty is the employment of a two-dimensional Monte Carlo simulation to measure the risk, considering uncertainty as a structural part of the study.  This methodology helps in verifying what are the best options from both an energy and economic point of view, giving priorities, time-distribution of interventions and optimizing the cash flows. The research could, therefore, be useful for portfolio managers, asset holders, private investors or public administrations, who have to plan and handle a series of energy efficiency actions.