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.
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