Now that we have introduced the nine methods and analytical techniques that form the core of this book, we describe three dimensions that can help to detect and explain differences and similarities between the selected methods. Note that the potential limitations that are related to the three dimensions are described in Chap. 11 of this book. The three dimensions apply to: (1) the origin of the data (stated or revealed), (2) freedom of attribute choice, and (3) compositional or decompositional approach.
An overview is provided in Table 1.3.
Table 1.2 Type of outcome of the nine selected methods and analytical techniques for measuring housing preferences
Type of outcome Traditional Housing Demand
Research method
A quantitative description of housing preferences and of the willingness to move
Decision Plan Nets method The substitution interval that defines a ranked set of houses that the consumer would consider acceptable Meaning Structure method An overview of the preferred attribute level per housing
attribute and the meanings of these housing attribute levels
Multi-Attribute Utility method A multi-attribute utility (strength of preference) for every alternative
Conjoint Analysis method A utility function that describes to what extent each attribute level contributes to the overall utility of a residential alternative
Residential Images method A ranking of new alternatives
Lifestyle method An assignment into a particular lifestyle group Neoclassical economic analysis Monetary estimates of the willingness to pay for and
equilibrium price of alternatives
Longitudinal analysis An indication of the stability of one or more variables or the relationship between two or more variables over time
1.6.1 First Dimension: Stated or Revealed Preferences
The first dimension relates to the origin of the data: does it concern choices that have actually been made in the “real world” (revealed preferences) or stated choices and preferences in response to survey questions (stated preferences)? The latter type of analysis combines observations of elicited preferences and hypothetical choices with assumptions about the underlying processes of preference formation to yield predictions. The first seven methods in Table 1.3 yield stated preferences.
In contrast, the revealed approach is based on observed actual housing choices in real markets and it is assumed to reflect people’s preferences (Timmermans et al.
1994). The technique combines observations of realized choices with assumptions about underlying decision processes to yield predictions (Manski 1999). For instance, in hedonic price models, the price of the dwelling is regressed on the observed hous-ing attributes. This provides an indication of the “worth” (the preference) of the various housing attributes. Advocates of this approach argue that it is only in the act of choice that people can reveal their preferences.
The Neoclassical Economic Analysis and the Longitudinal Analysis can be per-formed independent of the origin of the data. They can be perper-formed on either revealed or stated preferences, or on a combination of the two approaches. An excellent exam-ple of the latter is provided in Chap. 10 in which data on stated preferences (intentions to move) are linked to data on actual moving behavior obtained from a register.
1.6.2 Second Dimension: Freedom of Attribute Choice
The second dimension on which the methods and techniques might differ is freedom of attribute choice for the respondent. A method that allows freedom of attribute choice can be applied (but not necessarily so) in such a way that respondents
Table 1.3 Overview of methods and analytical techniques with regard to the three dimensions
Applies to Origin Design Traditional Housing Demand
Research method
Stated No Compositional
Decision Plan Nets method Stated Yes Compositional
Meaning Structure method Stated Yes Compositional
Multi-attribute Utility method Stated Yes Compositional
Conjoint Analysis method Stated No Decompositional
Residential Images method Stated No Decompositional
Lifestyle method Stated No NA
Neoclassical economic analysis Both No NA
Longitudinal analysis Both No NA
NA not applicable
can choose their own salient attributes. Respondents can be left entirely free in their choice of attributes but they can also be provided with a list of preselected attributes to choose from. Usually, they can also add additional attributes to the preselected list, if that is deemed necessary. The Decision Plan Nets method, the Meaning Structure method, and the Multi-Attribute Utility method are approaches that allow freedom of attribute choice. With these methods, respondents can first be asked which dwell-ing attributes are important to them. Based on these attributes, further enquiries can be made into the trade-off between attributes (Decision Plan Nets method), the underlying motives (Meaning Structure method) and the evaluation and importance of attribute levels (Multi-Attribute Utility method). Note that freedom of attribute choice comes at a cost. Such data are usually collected by face-to-face or telephone interviews, which are relatively time-consuming and costly. The other methods, and analytical techniques, usually apply a preselected set of attributes and attribute levels.
In such designs, there is no freedom of attribute choice for the respondents.
1.6.3 Third Dimension: Compositional Versus Decompositional Methods
The third dimension relates to whether the measurement method is attribute-based (compositional) or alternative-based (decompositional). A decompositional method starts with evaluating alternatives and decomposes these into separate attributes. To estimate the contributions of the attributes and attribute levels, statistical methods are applied. Parameters for the attributes are derived from the decision-maker’s holistic evaluative responses to profile descriptions designed by the researcher. The Conjoint Analysis method and the Residential Images method are examples of the decompositional approach.
The compositional approach starts with single attributes and combines these into alternatives. Housing preferences are explored by recording separately and explic-itly how people evaluate housing attributes. The importance of each attribute can be weighted and combined with the values, using some algebraic rule, to arrive at an overall evaluation. Note, however, that not all methods explicitly calculate an over-all evaluation for each possible alternative. Methods that are based on the compo-sitional approach are the Traditional Housing Demand Research method, the Decision Plan Nets method, the Meaning Structure method, and the Multi-Attribute Utility method.
The Neoclassical economic analysis is generally based on predicting some overall dependent variable, such as house price or the probability of moving, from several predictors, which usually refer to the characteristics of the dwelling, the dwelling environment, and the inhabitants. For example, the Hedonic Price Analysis (Neoclassical Economic Analysis) is used to determine the “worth” of certain dwelling characteristics (attributes) by statistically inferring it from the house price. Such a procedure could be termed a decompositional approach. However, this is less clear for the Longitudinal Analysis. Therefore, this dimension is deemed not to be appli-cable for these analytical techniques.
1.6.4 Compensatory Versus Non-compensatory Methods
There exists a discerning dimension that we did not include in Table 1.3. This concerns the difference between compensatory and non-compensatory methods.
Compensatory decision-making implies that a low value on one attribute can be compensated for by a high value on one or more other attributes. Thus, the specific alternative may still obtain a high overall evaluation score despite a low value on one or more attributes. In contrast, a non-compensatory decision method implies that a highly valued attribute cannot make up for a weakly valued one. The valua-tion of an attribute above or below a certain preferred threshold must therefore lead to the rejection of an alternative. Consumers can use cut-offs to qualify products, such as setting a limit on the minimum number of rooms in a dwelling. They may no longer consider a specific housing alternative as appropriate if it does not meet the specific criterion. This may be in accordance with the way in which people decide in reality, for example, a dwelling without a garden may not be acceptable to a family with young children, irrespective of the size of the living room or the number of bedrooms.
Our reason for not including this dimension in Table 1.3 is that we believe that almost all methods can be compensatory or non-compensatory depending on the way in which the questions are framed or the analysis is performed. For example, in the Multi-Attribute Utility method a linear additive function can be used to describe compensatory decision strategies. This means that evaluations for separate attribute levels are simply added to obtain an overall utility for a particular dwell-ing. A low evaluation for a particular attribute level can be compensated by high evaluations on other attributes. However, a multiplicative function, which may approximate non-compensatory preference structures, can also be applied.
This means that low evaluations can hardly be compensated for. Furthermore, for the less statistically sophisticated methods, whether or not some method is compen-satory might be dependent upon whether the trade-off of preferences is questioned.
If respondents are allowed to reject an alternative based on its level of functioning on one or more attributes, the method used is non-compensatory. If they were not allowed to reject alternatives, the method used is compensatory.
1.6.5 Combinations of Methods and Techniques
Up to now, we have presented the methods and techniques separately. However, the methods can be seen as complementary. For example, the methods that allow free-dom of attribute choice are relatively time-consuming and costly. They can be deployed in a relatively small sample of respondents to obtain insight into the salient attributes (levels) for the particular study. These salient attributes (levels) can subsequently be used in a method that does not allow freedom of attribute choice and that can be used in larger samples because it is relatively cheap and quick.
Furthermore, the combination of compositional and decompositional methods in one measurement task is possible. For example, the task can be split up into two parts. In the first part, respondents are asked to evaluate the attributes and the attribute levels separately. In the next part, a conjoint analysis task is performed.
The underlying goal here is to let the respondent grow accustomed to the attribute levels in order to make the conjoint analysis task easier. For an example, see Vriens (1997).
Another combination of methods is described in Chap. 10. Here, stated prefer-ences (who want to move?) and revealed preferprefer-ences (who have moved?) are com-bined to explore the factors that can predict an actual move in respondents that have previously indicated that they have the intention to move. Earnhart (2002) also describes a combined study of stated and revealed preference data. In this study, more insight was obtained into the factors driving housing decisions.
Lindberg et al. (1988) described a study in which the methods of Laddering (Meaning Structure method) and Multi-Attribute Utility were combined in order to predict both preference ratings and choices with regard to housing. Boumeester et al. (2008) published a report in which the methods of Decision Plan Nets, Meaning Structure method, and Conjoint Analysis method were combined in order to reveal respondents’ preferences with regard to housing as well as to obtain insight into the flexibility of their preferences and in their underlying motives.