The uncertainty of valuation

Published date01 December 2004
Date01 December 2004
AuthorNick French,Laura Gabrielli
Subject MatterProperty management & built environment
The uncertainty of valuation
Nick French
The Department of Real Estate and Planning,
The University of Reading Business School, Reading, UK, and
Laura Gabrielli
Urban Planning Department, IUAV Venice University of Architecture,
Venice, Italy
Keywords Uncertainty management, Market value, Asset valuation, Property,
United Kingdom
Abstract Valuation is often said to be “an art not a science” but this relates to the techniques
employed to calculate value not to the underlying concept itself. Valuation is the process of
estimating price in the market place. Yet, such an estimation will be affected by uncertainties.
These input uncertainties will translate into an uncertainty with the output figure, the valuation.
The degree of the uncertainties will vary according to the level of market activity; the more active a
market, the more credence will be given to the input information. In the UK at the moment the
Royal Institution of Chartered Surveyors (RICS) is considering ways in which the uncertainty of
the valuation can be conveyed to the use of the valuation, but as yet no definitive view has been
taken apart from a single Guidance Note (GN5). One of the major problems is that valuation
models (in the UK) are based on comparable information and rely on single inputs. They are not
probability-based, yet uncertainty is probability driven. This paper discusses the issues underlying
uncertainty in valuations and suggests a probability-based model (using Crystal Ball) to address
the shortcomings of the current model.
In those situations where a single value can be misleading it has been suggested that a range
of values might be more meaningful (Brown, 1991, p. 63).
The thesis of this paper is that uncertainty is a real and universal phenomenon in
valuation. The sources of uncertainty are rational and can be identified. They can be
described in a practical manner and, above all, the process of identification and
description of uncertainty will greatly assist many clients and will improve the content
and the credibility of the valuer’s work.
The paper concentrates on the practical and the impact of uncertainty in
property valuation. Uncertainty impacts on the process in two ways: first, the cash
flows from investment are, to varying degrees, uncertain and second, the resultant
valuation figure is therefore open to uncertainty. The paper looks at how
uncertainty can be accounted for in the valuation and how it can be reported to the
client in an effective and meaningful way. This requires a standardised approach
and we suggest that the use of a generic forecasting software package, in this case
Crystal Ball [1], allows the valuer to work with familiar pricing models set up in
Excel or Lotus 123 and to work with a predetermined set of probability
The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at
Received June 2003
Accepted July 2004
Journal of Property Investment &
Vol. 22 No. 6, 2004
pp. 484-500
qEmerald Group Publishing Limited
DOI 10.1108/14635780410569470
Literature review – risk and uncertainty
Before we can consider uncertainty within the valuation process it is important to
define what it is that we mean by uncertainty. Both within the academic literatureand,
more so, the property profession, the terms risk and uncertainty are often used
interchangeably. Risk is seen as a euphemism for uncertainty. However, this colloquial
use of the words is unhelpful in identifying the principal issues involved. It is
important to define these words more precisely.
Definitions and discussion about risk and uncertainty are the cornerstone of a
number of papers and books (see for example Byrne, 1995, Hargitay and Yu, 1993;
Pellat, 1972; Pyrrh, 1973; Robinson, 1989; Sykes, 1983; Whipple, 1988; Wooford, 1978).
The definitions that we are adopting follow the work of Byrne and Cadman (1984):
.Uncertainty. This is anything that is not known about the outcome of a venture
at the time when the decision is made.
.Risk. This is the measurement of a loss identified as a possible outcome of the
It is generally agreed that uncertainty is due to the lack of knowledge and poor or
imperfect information about all the inputs that can be used in the analysis. In the
context of valuation this refers to the input variables; the comparable information. If
we are unable to confirm the veracity of the inputs then the resulting outcomes
(valuation) are partially uncertain. However, if we are able to assign a probability to the
input variables it will allow us to determine the range of possible outcomes. The ou tput
is therefore a measure of risk (Byrne, 1995).
The outcome of a valuation is only certain if we can accurately predict the future.
Given that is not possible, there will always be an element of risk that the “actual”
value differs from the predicted estimate. With a single point valuation, a single figure
is produced with no understanding of the uncertainty pertaining to the input variables
and thus no measure of the resulting risk. An improvement on this method would be to
undertake the same valuation a limited number of times, allowing the user to change
the input variables and recalculate each time to derive a number of possible outcomes
or values. This analysis is a simple sensitivity or scenario analysis, but is restricted to
(maybe) only three or four scenarios based on a subjective assessment of how the input
variables should be changed. A more robust model would allow the use r to simulate a
much larger range of possible outcomes.
Literature review – simulation
Probability theory is a way of measuring uncertainty (Byrne and Cadman , 1996). It
allows the user to identify a range of outcomes for the most important variables and to
assign probabilities to these variables. Simulation is a further development of
probability analysis and Monte Carlo simulation has been an important component of
quantitative risk since 1960s (see Hertz, 1964). The underlying premise of Monte Carlo
simulation is to carry out the process, in this case valuation, a large number of times.
Instead of using a single point estimate for each input variable the user ascribes a
probability distribution to each input and the Monte Carlo technique selects random
numbers for each variable and produces an answer (valuation) on that basis before
selecting another random input (from within the set range) and repeating the exercise.
The uncertainty
of valuation

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