Discussion of the Use of Multi-Attribute Utility Theory in the Domain of Housing Preferences

The goal of this chapter was to explain and to explore the use of Multi-Attribute Utility in the domain of housing preferences research. In the previous sections, two examples of Multi-Attribute Utility theory in the domain of housing were described.

The first example showed a hypothetical decision-making situation. This is the case when a number of options are available and the decision-maker has to choose between them. The best choice would be the alternative with the highest multi-attribute utility. The second example showed the attractiveness and the importance of eight dwelling characteristics according to about 2,000 respondents. The method allows the calculation of the multi-attribute utility for each possible combination of dwelling characteristics. Also, the impact of varying attribute levels can be exam-ined by replacing one (or more) attribute levels with another and calculating the difference in utility.

Note that the current study has a number of limitations. Firstly, the current appli-cation of Multi-Attribute Utility theory was somewhat loosely applied. Formally, when using the direct rating technique, the worst and best level of each attribute should be used as endpoints or anchors (Von Winterfeldt and Edwards 1986, p. 218, p. 227). These anchors should be assigned a value of 0 and 100, respectively, and the remaining stimuli should be compared to these endpoints. For example, a dwell-ing with one room could have been referred to as a “bad” endpoint (value 0) and a dwelling with 6 rooms as a “good” endpoint (100). The attribute levels 2, 3, 4 and 5 rooms could then have been judged relative to these anchors. However, it was considered to be too difficult for respondents to answer these types of questions dur-ing a telephone interview. Furthermore, not all of the attributes, such as residential environment and architectural style, have clear ‘good’ and ‘bad’ endpoints. For these reasons, the use of simple rating scales was chosen to elicit the values. A benefit of the current approach is that the resulting single- and multi-attribute utilities are mea-sured using some type of global scale and not on a local scale (Monat 2009). The latter means that the poorest choice among the local options – in the above described example this concerns a range from 1 to 6 rooms – would obtain a score of 0, and the best 100. In the case of a global scale, the poorest choice among the entire uni-verse of choices can get a score of 0, and the best 100. The drawback of a local scale is that it may over-emphasize the importance of small differences in values and may consequently lead to wrong decisions (Monat 2009).

Secondly, the decision problem was simplified. A number of attributes that probably influence the decision for a particular place of residence were not included, such as, for example, distance to work and school, distance to services and public transport and distance to the dwellings of relatives and friends.

Furthermore, the attribute levels were also limited. For example, detached houses were not included in the research. The third potential limitation is that the multi-attribute utilities were calculated using the linear additive preference function. An important limitation of this approach is that it allows small advantages on some

attributes to compensate for a large disadvantage on another. Thus, the possibility that thresholds of unacceptable performance exist for some criteria is neglected.

Note, however, that Burnett (2008) showed in a study into shopping travel behav-iors that compensatory rules (utility maximization) were quite important in a num-ber of situations, especially for higher order goods (high involvement goods).

Finally, the application of Multi-Attribute Utility theory described in this chapter did not include trade-offs between attributes. Payne et al. (1999) propose that it is in making trade-offs that one’s values are most often revealed to oneself and to outside observers. Timmermans et al. (1994) argue that when trade-offs are not included, the measurement task might not reflect the mechanisms underlying the actual decision-making and choice processes and may not be realistic to respon-dents. Multi-Attribute Utility theory in its purest form does indeed include trade-offs between attributes. Keeney and Raiffa (1976, p. 82 and following) describe methods to compose preference structures and value functions in the case of two or three attributes. The value functions are based on trade-offs. For example, suppose that the attribute size of the backyard has the value of 30 for a size of 5 m and the attribute number of rooms has the value of 80 for five rooms. With what amount should the size of the backyard increase in order to make up for the loss of one room? These kinds of questions are repeated for different levels and values of the attributes. However, such questions may not be easy to answer and the number of questions may become quite large if there are more than two or three attributes.

Besides, respondents might not like having to make trade-offs because it is emo-tionally and cognitively burdening (Payne et al. 1999). Forcing respondents to make trade-offs might lead to behaviors such as selecting the status quo option, unwillingness to trade-off at all and delaying choice. For these reasons, trade-offs were not included in the study described in this chapter.

There are also some limitations that apply to Multi-Attribute Utility theory in general that have to be mentioned. Firstly, the theory presumes that rational deci-sion-making takes place, i.e., more utility is preferred to less utility. The theory supposes that human values may only influence consumer choices by affecting what product attributes consumers prefer and that it is the calculated evaluation of product attributes that in turn determines product choice (Allen 2002). However, consumers also make emotionally laden, intuitive and holistic judgments about products. Allen (2002) suggests that human values shape consumers’ product choices in two ways.

Firstly, human values may influence the importance of the products’ attributes, which in turn influences product preference. Secondly, human values may influence product preference directly by making an affective, intuitive and holistic judgment.

Secondly, the assumption that the importance of the attribute is independent of the level of the attribute may not hold as the importance of the attributes may be dependent upon the range of the scale over which the value function is defined. The weight could change when the range of the scale changes. For example, a respon-dent may be indifferent about the size of the backyard when the range is limited to between 5 and 15 m, because he does not consider a backyard interesting if it does not have a length of at least 25 m. Anything less is deemed unimportant. However, when the range stretches from 5 to 500 m, then backyard size does indeed become

important to him. The solution is to choose a range of attribute levels that is wide enough to appeal to most respondents.

Related to the previously mentioned limitation is the third issue, which is that respondents may not be able to provide evaluations for a distinct attribute level with-out taking related attributes into account. The importance or value of a particular attribute may be dependent upon the level of other attributes. For example, the size of the living room may not be important to some respondents. However, it may become important when the number of rooms in the dwelling is very small. In our study, it seemed that the lower level of the attribute of purchase costs of € 140,000 was influenced by assumptions about the size or state of maintenance of such a rela-tively cheap dwelling. Timmermans et al. (1994) and Molin et al. (1996) wonder whether respondents are capable of expressing their evaluation of separate housing attributes, not knowing what to assume about the values of the remaining attributes influencing their preferences. As our respondents did not mention having problems providing values for individual attribute levels and the results showed face-validity for all attributes, except purchase costs, the problem may be manageable.

Fourthly, the multi-attribute approach does not allow the testing of the appropri-ateness of the chosen preference function (for example, additive or multiplicative) to combine the single-attribute utilities into an overall utility, unless some external criterion is available, such as an overall evaluation or overt behavior (Veldhuisen and Timmermans 1984).

Finally, the preferences obtained using Multi-Attribute Utility theory may not represent real housing choices. This is because housing choices may frequently reflect the dominance of constraints rather than preferences (Maclennan 1977).

Note, however, that this drawback is not limited to Multi-Attribute Utility theory but applies to all methods based on stated preferences. Furthermore, it does not mean that housing preferences should not be examined. Housing preferences may be especially valuable in a time when the housing market becomes more and more demand-oriented, as we see nowadays.

In conclusion, despite the limitations, Multi-Attribute Utility theory may have additional value in the field of housing preferences research. It provides the possibil-ity of examining the importance and attractiveness of separate dwelling characteris-tics, to calculate single-attribute utilities, to calculate overall utilities for combinations of attribute levels, to distinguish consumer groups with different preferences, and to choose amongst alternatives when different alternatives are available.

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6.1 Introduction