Housing in a Water-Rich Environment Conjoint Analysis Approach (2008)

evaluating the attributes of quality, price, parking, mooring, and outdoor space.

The evaluation scores were positive (+), negative (−), and neutral (0). Finally, the images had to be ranked from 1 to 9 according to preference. This approach dif-fers from the elimination-by-aspects, where the selection itself is part of the observation.

Next, respondents had to answer whether they were willing and able to move to

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an alternative within 2 years. The reason for this period is that the dwellings were not yet constructed. An alternative could have been “do you accept this?”

The answers were “yes,” “no,” “maybe, because ….”

Finally, people were asked to explain the reasoning behind their decision,

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including the qualities of the offer, price, intervening opportunities, and personal circumstances.

Table 7.2 shows the mean and median scores for the nine types. Each type is characterized firstly by the type and secondly by the construction technology/

design such as dykes, wharfs, poles and floating. The results are rather surprising and in contrast to other findings. The highest ranking has the apartment instead of the usually more highly valued detached house. It turns out that this ranking is not an effect of the specific segment of housing searchers, but an effect of the price levels. The price levels for the detached and semidetached alternatives are too high.

Since budget restrictions are important, respondents have valued the lower-priced apartments more highly. As we said earlier, a design must be a real option. The construction technique is important as well, but is also linked to the type.

Respondents do not value apartments on poles, whereas both this technique and floating score pretty well for (semi)detached alternatives.

The respondents also had generic preferences with respect to the neighborhood.

Access to the shops was vital to add value to projects. Other amenities like leisure, health care, restaurants/pubs, and culture were less important. The preferences for a type of neighborhood suggested that a homogeneous composition is not popular.

However, even though a neighborhood collective was not valued highly, a pleasant chat in the street was valued highest. This is the same result as for the elderly neigh-borhoods we discussed previously.

7.3.3 Housing in a Water-Rich Environment Conjoint Analysis Approach (2008)

The SEV commissioned a conjoint analysis to detect the preference structure. The SEV wanted the inclusion of images. Based on the assumption from other research (see Kauko et al. 2009) an exhaustive set of attributes was selected. The design

Table 7.2Average ranking scores (mean, median) and confidence levels ApartmentApartmentDetachedDetachedDetachedDetachedSemidetachedSemidetached DykeWharfPolesWharfFloatingDykeFloatingPolesBoat N12412412512412512412512460 Mean6.86.66.14.95.75.35.54.85.4 Median8.08.07.06.05.55.55.55.35.0 Confidence (95%)0.30.40.40.40.40.40.40.30.6 Upper limit7.17.06.55.36.15.75.95.16.0 Lower limit6.56.25.74.55.34.95.14.54.8 Source: Boogaard and Sievers (2009), OTB adaptation

was based on earlier research that focused on the preference functions for the entire Dutch populations (Boumeester et al. 2008). Table 7.3 shows the attributes and their levels (Singelenberg 2008).

We visualized most attribute levels as Table 7.4 shows. In the measurement task, the verbal explanations were included. All levels of attributes were com-bined, which results in a so-called full factorial design of all the possible alterna-tives. Each respondent evaluated a set of 13 pairs of alternaalterna-tives. Most are verbally expressed, but the respondent can also look at a visual version (I:

Information). The respondent had three tasks: (1) assigning a report mark to each profile, which is split up into the dwelling and the neighborhood; (2) selecting profile 1 or 2; and (3) an extended choice that includes an “opt-out” (none). The last task is important since it allows the respondent to say “no move at all.” The opt-out is of course important since expressing one’s appreciation is very differ-ent to moving.

We summarize the results of the opt-out model. Our first observation was that the model confirmed the importance of the ‘opt-out’ in marketing research. For developers and municipalities the turnover – construction volume times the average house price – counts in decision-making. The “opt-out” model showed that the mean probability to move was only 29%. Secondly, we concluded that our consum-ers considered the differences between traditional housing nearby water or new designs nearby and on water as irrelevant. This implied a potential market for the new designs that might compete with traditional designs. The developer’s best offer would be a detached house, 180 m² or more, with water 200 m wide, a regulation that allows all kinds of boats (rowing, sailing, electric, and combustion motor) and an open and direct connection to waterways. Thirdly, an important characteristic of the conjoint model is that it allows for interpolation between the three price levels in relationship to other attributes (Brouwer et al. 2007). This allows suppliers to evaluate the market value of various designs and prices. The picture-enhanced con-joint models have advantages over the simple residential images method. The main advantage for developers is that all kinds of alternatives can be evaluated, the price elasticity can be measured and finally the price per unit quality can be derived. This provides vital information for financial risk assessment and especially for new products.

Table 7.3 Selected attributes

Profile defined by attributes (attribute levels)

House Neighborhood

Layers/type (3) Living environment (3)

Type (3) Width of water (3)

Surface (3) View on water (3)

H₂O technology (3) H₂O fluctuation (2) Street type (3) Boat traffic (3)

Price and tenure (3 * 2) Connection waterways (3) Source: Singelenberg (2008)

Table 7.4A selection of some visual attribute levels House H2O technology (3) Street type (3) Living environment (3) H2O fluctuation (2) Connection waterways (3) Source: Singelenberg (2008)

7.3.4 Conclusion

The examples illustrate two types of applications. We can define them as a profes-sional’s selection of images out of a large set of images and a selection based on a statistical design. Both differ with respect to their goals. If a professional is only interested to select the best design out of a set he or she is able to construct, the first application is justified. If the goal is to unravel the preference function, a sta-tistical sound design is necessary. The quality and the right use of the image are vital in both applications. In the next section, we will discuss the second application in more detail.