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Rita Fabbri, Laura Gabrielli and Aurora Greta Ruggeri
The purpose of this paper is to examine the cross-sectoral collaboration between conservation and economic appraisal, and to process a financial analysis for private…
The purpose of this paper is to examine the cross-sectoral collaboration between conservation and economic appraisal, and to process a financial analysis for private owners of a built heritage.
The methodology applied addresses the financial analysis of restoration through a discounted cash flow analysis, together with a life cycle costing. Costs and revenues are both analysed in this paper. Some energy-saving measures are applied to cut running costs and decrease the energy required by the building, using as reference the “Guidelines for improving energy efficiency in cultural heritage” drafted by MiBACT, which considers the respect of restoration principles. In order to increase revenues, part of the building is rented. The attractiveness of the investment opportunity is valued through the calculation of the net present value of cash flows, the payback period and the internal rate of return.
The paper offers a simple strategy for the planning of cost-revenues, preventively allowing verification if the conservation is economically feasible and if the owners can afford the operation. The strategic planning will give the owners the chance of maintaining the property of their building and achieve a proper restoration on it.
The novelty of the paper is the study of cooperation between conservation and economic valuation, but also the focus on a specific portion of twentieth-century heritage, the war-wounded houses, which represent a widespread patrimony, on which it is not clear how to operate yet.
Laura Gabrielli, Aurora Greta Ruggeri and Massimiliano Scarpa
This paper aims to develop a forecasting tool for the automatic assessment of both environmental and economic benefits resulting from low-carbon investments in the real…
This paper aims to develop a forecasting tool for the automatic assessment of both environmental and economic benefits resulting from low-carbon investments in the real estate sector, especially when applied in large building stocks. A set of four artificial neural networks (NNs) is created to provide a fast and reliable estimate of the energy consumption in buildings due to heating, hot water, cooling and electricity, depending on some specific buildings’ characteristics, such as geometry, orientation, climate or technologies.
The assessment of the building’s energy demand is performed comparing the as-is status (pre-retrofit) against the design option (post-retrofit). The authors associate with the retrofit investment the energy saved per year, and the net monetary saving obtained over the whole cost after a predetermined timeframe. The authors used a NN approach, which is able to forecast the buildings’ energy demand due to heating, hot water, cooling and electricity, both in the as-is and in the design stages. The design stage is the result of a multiple attribute optimization process.
The approach here developed offers the opportunity to manage energy retrofit interventions on wide property portfolios, where it is necessary to handle simultaneously a large number of buildings without it being technically feasible to achieve a very detailed level of analysis for every property of a large portfolio.
Among the major accomplishments of this research, there is the creation of a methodology that is not excessively data demanding: the collection of data for building energy simulations is, in fact, extremely time-consuming and expensive, and this NN model may help in overcoming this problem. Another important result achieved in this study is the flexibility of the model developed. The case study the authors analysed was referred to one specific stock, but the results obtained have a more widespread importance because it ends up being only a matter of input-data entering, while the model is perfectly exportable in other contexts.