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Valuation is the process of determining Market Value. Property valuation, as with the valuation of all assets, is an estimation of price in the market. It is value in exchange. The valuer role is to determine the appropriate approach, the method and use the right model to achieve this aim as best as possible. It is a combination of analysing the market and determining the critical variables for the valuation method/model. The method is separate from the valuation process which should be followed (according to the International Valuation Standards Council Valuation Standards) regardless the valuation method chosen. There are valuation approaches, valuation methods and, as a subset of the methods, techniques or models.

This practice briefing is an overview of the Valuation Methods and Models available to the valuer and comments on the appropriateness of valuation each in assessing Market Value for specific property types.

This briefing is a review of the valuation methods and models and models that can be applied to determine market value.

The role of the valuer in practice is to identify the method of valuation and then apply the correct mathematical model for the valuation task in hand.

This provides guidance on how valuations can be presented to the client in accordance with the International Valuation Standards.

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.

The historic assets are heterogeneous and different to each other, and for this reason, consolidated valuation methodologies do not exist in practice or in the literature. It is, therefore, necessary to dwell on the study of a particular historic building type or category. The assessment process of the valuers of Venetian Villas was explored, focusing on the study of the valuation function construction. The paper investigated if a possible value function, based on the partition of the characteristics, which significantly influence the value, exists and it is generalizable to the whole set of Venetian Villas. The purpose of this paper is to contribute knowledge on the economic valuation of Venetian Villas.

An application of Hedonic Pricing study to a database of 71 Venetian Villas has been tested. This statistical procedure allowed the authors to discover which results in a percentage of property values can be attributed to the historical characteristics of a building. Using a multiple linear regression and its variables an analysis of residuals and data relating to the variance has been performed.

This research identifies and proposes, therefore, a valuation approach that can be generalizable to the whole set of Venetian Villas (over 4,000 properties). The models show that the most important variables which influence the value of the villas are: age, internal and external area, maintenance conditions, and author. This model could be used for future valuations of the same type of asset.

The model enables valuers to address better to the property valuation of the Venetian Villas through the valuation functions, which suggest which are the main features which focus in the case of a Venetian Villa valuation and what impact they have on the value asset. The model can be specifically used for valuation reports in the enhancement project of such properties.

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.

Since the global financial economic crisis hit the world markets in 2007/2008, the role of property valuation has been under greater and greater scrutiny. The process of valuation and its quality assurance has been addressed by the higher prominence of the International Valuation Standards Council (IVSC). This is a significant initiative worldwide. However, there has been little written on the appropriate use of valuation approaches and methods in market valuations. There is now a hierarchy of valuation definitions. In order, there are valuation approaches, valuation methods and, as a subset of the methods, techniques or models. The purpose of this paper is to look at the importance of identifying the appropriate approach to be adopted in market valuations and the methods, techniques and models that should be applied to determine market value.

This practice briefing is an overview of the valuation approaches, methods and models available to the valuer and comments on the appropriateness of valuation each in assessing market value.

This paper reviews the IVSC-recognised approaches and prompts the valuer to be careful with the semantics involved so that they are better placed to provide an unambiguous service to their clients.

The role of the valuer in practice is to identify the appropriate approach for the valuation of the subject property, choose the right method and then apply the correct mathematical model for the valuation task in hand.

This provides guidance on how valuations can be presented to the client in accordance with the International Valuation Standards.

This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data.

This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals.

Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level.

Data are measured as the average price in squared meters, and the resulting index is not quality controlled.

The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices.

This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.

Valuation is the process of estimating price. The methods used to determine value attempt to model the thought processes of the market and thus estimate price by reference to observed historic data. This information is utilised in the discounted cash flow (DCF) valuation model to determine the single point valuation figure. However, the valuation will be affected by uncertainties: uncertainty in the comparable data available; uncertainty in the current and future market conditions and uncertainty in the specific inputs for the subject property. These input uncertainties will translate into an uncertainty with the output figure, the estimate of price. This paper discusses ways in which uncertainty can be incorporated into the DCF model.

This paper looks at the way in which uncertainty can be incorporated into the explicit DCF model. This is done by recognising that the input variables are uncertain and will have a probability distribution pertaining to each of them. Thus by utilising a probability‐based valuation model (using Crystal Ball) it is possible to incorporate uncertainty into the analysis and address the shortcomings of the current model.

The outcome of introducing uncertainty in the inputs is to produce a range of different answers. The central tendency of this distribution is very close to the single point estimate of the static model, yet the user of the technique now benefits from an understanding of the upside and downside risk pertaining to this single point estimate.

This study contributes significantly to the practical application of probability‐based models to valuation. In particular, the findings from the study will be useful for clients to understand better the context in which a valuation figure is provided to them.