Conjoint Analysis
6.3 Constructing the Conjoint Experiment
6.4.1 The Experiment
The first step in building a stated preference model is to identify the attributes of interest. In the present study, attributes were identified on the basis of previous research efforts (e.g., Timmermans 1989; Louviere and Timmermans 1990) and discussions with experts, more specifically with representatives of the commissioning parties. The following attributes were selected to vary the costs and functionality of semi-detached houses: Tenure, monthly costs, number of bedrooms, size of living room, and depth of backyard. However, depth of backyard can also be used to vary the housing density. Likewise, more centralized parking and realizing more high-rise buildings in the housing district can also be used for this purpose. The attribute size of green space was included to examine whether creating a large central park could compensate for increasing the density elsewhere in the housing district. Finally, shopping centre was included to examine to what extent tenants were willing to use shopping facilities in neighboring districts. All attributes were varied in three levels, which allows the continuous attributes to test whether utility is linearly related to changing attribute values. Note that the monthly costs presented applied for both rental and owner-occupied; in the questionnaire it was explained which costs are covered. The list of selected attributes and their levels is portrayed in Table 6.5.
In the present study, it was assumed that all interaction effects were equal to zero. Hence, the smallest orthogonal fractional-factorial design to construct the residential profiles was selected, involving the construction of 27 profiles. These profiles were then randomly placed in choice sets of three profiles each. The option
“do not move” was added to each choice set as a base alternative. In order to avoid order effects, the placement of profiles in choice sets was randomized nine times.
Respondents were requested to choose the housing alternative in each choice set that they were most likely to move into. If none of the housing alternatives in the choice set were acceptable, they could choose the option “do not move”, which served as a base option in each choice set. Respondents were requested to complete nine choice sets, which included all 27 profiles, presented in one of the nine differ-ent random orders.
6.4.1.1 Sample
Stated choice data were collected in early 1996. Respondents were selected from a previous survey, which was primarily intended to collect data on the housing situation and housing needs in the region. In this data file, those households were selected which indicated that they (i) were willing to move house within 5 years, (ii) preferred new housing or had no preference regarding housing age, (iii) were looking for housing in Eindhoven, (iv) were willing to spend at least Nlg. 700 a month on housing, (v) preferred a semi-detached house, and (vi) agreed to participate in a choice experiment. A questionnaire containing the conjoint choice experiment was sent by mail to the selected households. A total of 154 respondents were contacted by mail, and 99 of them returned the form resulting in a response rate of 64%.
The household type of the response group was distributed as follows: 12.1%
single. 47.3% couples and 40.7% couples with children. On average, the house-holds consisted of 2.7 persons. The person who earned the most in the household had an average age of 36 years. The mean average net income was about 3,700 Dutch guilders.
As housing choice is often the result of a multi-person decision-making process, multi-person households were asked to complete the questionnaire together with all the household members who were involved in the housing choice. Previous research found evidence for the hypothesis that models based on group tasks better predict group housing choices than models based on tasks completed by individual group members (Molin et al. 1999). In 62% of the multi-person households, at least two
Table 6.5 Selected attributes and their levels
Tenure Size of living room Buildings in neighborhood
Rental 30 m2 Mainly high-rise
Owner-occupied 40 m2 Mixed low-rise and high-rise
50 m2 Mainly low-rise
Monthly costs Depth of backyard Green space
Nlg. 900 10 m Large central park
Nlg. 1,200 15 m A few fairly large public gardens
Nlg. 1,400 20 m More small public gardens
Number of bedrooms Car park Shopping centre
2 Central in neighborhood Outside district
3 In the street Central (one big)
4 On private property Neighborhood (a few small)
persons, mostly husband and wife, completed the questionnaire together.
Additionally, in about a quarter of the households with children age 14 or older, at least one child participated.
6.4.1.2 Model Estimation
The observed responses in the choice experiment were aggregated into frequencies that indicated how often the various alternatives included in any particular choice set were chosen. This set of frequencies served as the dependent variable in the MNL model. A main-effects-only model was assumed, so the problem was to find the part-worth utilities that, given these assumptions, best reproduced the observed choice probabilities.
To that effect, the econometric software package Nlogit was used to estimate a multinomial logit model. Model estimation is based on the principles of maximum likelihood estimation, which involves maximizing the log-likelihood function (Ben-Akiva and Lerman 1985). The commonly used goodness-of-fit model mea-sure, which indicates how well the estimated model is able to reproduce the observed choices in the experiment, is based on a comparison of the log-likelihood of the estimated model (LB) with the log-likelihood of the null model (L0), the model in which all parameters are assumed to be zero. In the present study, LB is equal to −329.03 and L0 is equal to −553.30. By these log-likelihoods, McFadden’s Rho Square (the likelihood ratio index = 1 − LB/L0) is equal to 0.41. Similar to explained variance in regression analysis, higher McFadden’s Rho values indicate higher model fit, but this measure typically has lower values than the R2 (though this depends on the level of aggregation). Taking this into account, it can be con-cluded that the estimated model reproduces the observed choices well.
6.4.1.3 Part-Worth Utilities
The attribute levels were effects-coded. As explained before, two indicator vari-ables are estimated for each three-level effects-coded attribute, the values of which are equal to the part-worth utility of the first two levels of each attribute.
Consequently, only two t-values are presented in Table 6.6.
Table 6.6 shows that the utility constant is equal to −0.87, expressing the mean utility derived from all residential profiles included in the choice experiment. As the ‘do not move’ alternative was, by definition, given a utility of zero, the negative constant indicates that on average the residential profiles included are considered less attractive than the current residence. To illustrate this further, this means that if the potential tenants have a choice between the ‘average profile’ and ‘not moving house’, then according to the estimated model 70.5%1 of the tenants will choose
‘not moving house’. This confirms the earlier discussed belief that the target group
1 The probability of choosing the average alternative (utility = −0.87) above the do not move option (utility = 0), is predicted by the MNL model as: p = e −0.87/(e −0.87 + e0) = 0.295.
is quite satisfied with their current residence. Hence, quality housing needs to be provided in Meerhoven in order to tempt the potential tenants to move house.
As explained before, a part-worth utility indicates the contribution of an attribute level to the overall utility of a residential alternative. Let us now summarize the most important results based on the part-worth utilities:
1. Owner-occupied houses are preferred to rental houses, as may be expected in the segment of residents preferring the semi-detached housing type.
Table 6.6 Part-worth utilities and t-values
Part-worth
Central in neighborhood −0.46 −5.180 *
On the street 0.35 3.912 *
On private property 0.11
Buildings in neighborhood 10.2%
Mainly low-rise 0.30 3.997 *
Mixed low-rise and high-rise 0.13 1.665
Mainly high-rise −0.43
Green space 1.8%
Large central park 0.06 0.765
A few fairly large public gardens −0.07 −0.914 More small public gardens 0.01
Shopping centre 7.4%
Outside district −0.27 −3.348 *
Central (one big) 0.26 3.278 *
Neighborhood (a few small) 0.01
* Absolute t-values >1.96 indicate statistical significance at 0.05 level and are marked by *
2. Residential utility decreases with increasing monthly costs from 900 to 1,500 Dutch guilders. As the second estimated coefficient is not statically significant, it can be concluded that this relationship is linear. Hence, an increase in the monthly cost has the same effect on utility across the whole utility cost range.
3. Utility increases with increasing number of bedrooms. However, this relation-ship is not linear. The increase in utility between two and three bedrooms is much higher (+0.90 utility points) than the utility increase from three to four bedrooms (+0.15 utility points).
4. Utility linearly increases with the increasing size of the living room. This indi-cates that within the varied range of 30–50 m2, each m2 increase of the living room increases utility to the same extent.
5. With respect to the depth of the backyard, the utility distribution clearly shows an optimal value: 15 m is considered the most attractive depth (+0.30 utility points). A depth of 20 m comes second in attractiveness (+0.07 utility points), while a depth of only 10 m is the least preferred (−0.37 utility points).
6. Car parking on the street is clearly preferred to parking at a central place in the neighborhood. Surprisingly, parking on the street is also preferred to parking on private property.
7. A neighborhood with mainly low-rise buildings is preferred, although the utility difference between a mixed low- and high-rise neighborhood is not very large (0.17 utility points difference), suggesting that some high-rise buildings in the neighborhood are tolerated. On the other hand, a neighborhood with mainly high-rise buildings is clearly disliked.
8. None of the part-worth utility levels of concentration of green space are statisti-cally significant. This indicates that tenants in this segment are indifferent with respect to the way green space is spread across the neighborhood. An alternative explanation is that tenants have a clear preference for one of the levels, but that the preference is equally spread across the three levels, which therefore cancels out at the average level. The estimated MNL model is not able to distinguish between these two explanations.
9. Using shopping centers outside the district is clearly disliked above using shop-ping centers within the district. Furthermore, using a central district shopshop-ping center is preferred above several smaller neighborhood shopping centers.
6.4.1.4 Attribute Importance
To quantify the total impact an attribute has on residential utility the attribute’s rela-tive importance can be calculated by considering the attribute’s utility ranges. An attribute’s utility range is the difference between the highest and the lowest esti-mated part-worth utility of its levels. The utility ranges are summed across all attributes and the perceptual contribution of each attribute to this sum is calculated.
However, it should be noted that attribute importance is conditional on the selected attribute levels. For example, if a smaller range of price levels was selected, say Nlg. 900–1,300, the utility range in part-worth utilities would probably have been lower and therefore also its estimated importance.
The resulting relative importances of the attributes are presented in the last col-umn of Table 6.6. This table shows that monthly costs is the most important attri-bute, closely followed by tenure. This is followed by the housing attribute number of bedrooms. Next in importance are the attributes related to housing density: car park, type of buildings in the neighborhood, and depth of backyard. The attributes shopping centre, size of living room and green space have the least impact on hous-ing choice. It is remarkable that size of livhous-ing room has such a low impact. Probably the lowest level of 30 m2 is already considered sufficiently large.
6.4.1.5 Willingness to Pay
Because monthly costs was included as an attribute, the willingness to pay for improvements in housing quality can be calculated. The idea underlying this cal-culation is to find out by how much monthly costs can be increased to compensate for a utility increase due to an improvement in another attribute in order to keep the overall utility at the same level. For this calculation, the utility change related to the varied costs range is considered first. Table 6.6 indicates that an increase in costs from Nlg. 900 to Nlg. 1,500 decreases utility from 0.70 to −0.73, thus by 1.43 utility points. Hence, each utility point is worth Nlg. 600/1.43 utility points = Nlg.
418. Willingness to pay for a quality improvement can now be calculated by mul-tiplying this amount by the utility increase due to a change in attribute values. For example, an increase from 2 to 3 bedrooms increases the utility by 0.90 utility points, which results in an estimated willingness to pay off 0.90 * 418 = 377 Dutch guilders more per month. Likewise, it can be calculated that potential tenants are only willing to pay an increase of Nlg. 63 per month for a further increase from 3 to 4 bedrooms.
6.4.1.6 Prediction of Choice Probabilities
As argued before, an important advantage of choice-based conjoint models above rating-based models is that choice probabilities for new housing alternatives can be predicted directly based on the estimated model, thus without making any untest-able assumptions. To illustrate this, latent choices for residences in Meerhoven are predicted under varying density scenarios. Under the assumption that no other resi-dences are available at the same time, the percentage of households that will choose for any of two specified houses or none of either type is predicted. It should be noted that all scenarios discussed here are formulated for illustrative purposes only and thus do not reflect any intended policy of the parties involved.
First, the choice probabilities are predicted for a base scenario including two houses that reflect typical semi-detached houses found in the Eindhoven region in the study period. The first house is relatively small and cheap, whereas the second house is relatively large and expensive. The values of all other attributes are kept the same for both houses, with the exception of the type of environment: House 1 is located in a mixed low- and high-rise neighborhood, whereas house 2 is located
in a mainly low-rise environment. The attribute values of both houses can be found in the top part of Table 6.7.
To examine the effects that increasing the housing density has on choice probability, the values of three attributes related to housing density are changed.
A first change involves increasing the number of high-rise buildings in the neighbor-hood by one level. The first house is then assumed to be located in a mainly high-rise environment and the second house in a mixed low- and high high-rise environment.
The second change involves decreasing the depth of the backyard by 5 m, arriving at a backyard depth of 10 m. The final change involves that parking is centralized, hence, parking in the street is no long allowed. In a final step, the possibility that the disutility due to increased housing density can be compensated by improving other housing attributes – increasing the living room by 10 m2, realizing a central shopping center in the new housing district and decreasing the monthly costs by Nlg. 300 – is examined. Note that all changes are cumulative, which, for example, means that the second change assumes that the first change has already taken place.
Table 6.7 shows that under the base scenario, the smaller and cheaper house 1, is somewhat more popular than the larger and more expensive house 2. Using this
Table 6.7 Choice prediction for two residences under varying housing density scenarios
Changes House 1 House 2 Nlg. 1,200 per month Nlg. 1,500 per month 15 m backyard 15 m backyard Car park in street Car park in street Mixed low- & high-rise Mainly low-rise Large public gardens Large public gardens Shopping outside district Shopping outside district
Base 30.6% 25.1% 44.3% 100%
Increasing densitya
More high-rise 21.1% 25.5% 53.4% 100%
5 m smaller backyard
14.0% 16.9% 69.2% 100%
Central car park 7.5% 9.1% 83.4% 100%
Improving qualitya
aAll changes are cumulative: for example, 5 m smaller backyard assumes more high-rise has already been applied
scenario, it is predicted that almost half of the potential tenants would not move to any of these two houses. The table further shows that the propensity not to move house rapidly increases with increasing density.
Table 6.7 shows that in the densest housing environment, 83.4% of the potential tenants is not inclined to move house, illustrating that potential tenants are quite sensitive to housing density. The question of whether the disutility due to the increased housing density can be compensated for is answered by considering the lowest three rows of Table 6.7. These results indicate that the choice probabilities do increase due to the proposed improvements, but that these improvements are not sufficient to fully compensate for the housing density effect.
It should be noted that the predicted choice probabilities should be interpreted with care. As already discussed, the predictions are based on the assumption that no other housing alternatives than those included in the scenarios are available for the prospective tenants. This is of course a very stringent assumption as hous-ing alternatives are constantly behous-ing added to and removed from the houshous-ing market and at any moment in time it is likely that more than two alternatives will be available for most prospective occupants. A full simulation of the housing choice would require taking the dynamic aspect of the housing market is into account. Hence, the predictions should not be interpreted as absolute predictions of market shares of the residential alternative, but more valued for providing the possibility to compare the relative impact that possible scenarios have on changes in housing choice.