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Measuring how well the options perform on each attribute 37
This procedure for obtaining values can be repeated for the other less
easily quantified attributes. The values allocated by the owner for the
attributes ‘comfort’, ‘visibility’ and ‘car-parking facilities’ are shown in
Table 3.2 (see page 39).
Value functions
Let us now consider the benefit attributes which can be represented
by easily quantified variables. First, we need to measure the owner’s
relative strength of preference for offices of different sizes. The floor area
of the offices is shown below.
Floor area (ft2 Addison Square (A)
)
1000
Bilton Village (B) 550
Carlisle Walk (C) 400
Denver Street (D) 800
Elton Street (E) 1500
Filton Village (F) 400
Gorton Square (G) 700
Now it may be that an increase in area from 500 ft 2 to 1000 ft 2 is
very attractive to the owner, because this would considerably improve
working conditions. However, the improvements to be gained from
an increase from 1000 ft 2 to 1500 ft 2 might be marginal and make this
increase less attractive. Because of this, we need to translate the floor
areas into values. This can be achieved as follows.
The owner judges that the larger the office, the more attractive it is.
The largest office, Elton Street, has an area of 1500 ft 2 so we can give
1500 ft 2 a value of 100. In mathematical notation we can say that:
v(1500) = 100
where v(1500) means ‘the value of 1500 ft 2 ’. Similarly, the smallest offices
(Carlisle Walk and Filton Village) both have areas of 400 ft 2 so we can
attach a value of 0 to this area, i.e. v(400) = 0.
We now need to find the value of the office areas which fall between
the most-preferred and least-preferred areas. We could ask the owner
to directly rate the areas of the offices under consideration using the
methods of the previous section. However, because areas involving
rather awkward numbers are involved, it may be easier to derive a value