The Decision Plan Nets Method
3.5 Preference Modeling and Knowledge-Based Simulation Models
Figure 3.1 showed that the DPN method is not rooted in algebraic mathematics, like the conjoint model or hedonic price model, but in “Set theory” (Timmermans et al.
1994, Van Zwetselaar and Goetgeluk 1994; Witlox 1995; Wets 1998). This theory states that an object, such as a real-world vacancy (supply), can be a member of a set of objects, such as a set of imaginary houses that the housing consumer would consider acceptable as well as the rules that determine when they consider them.
The set of imaginary houses and rules is the DPN (demand). If we look again at the DPN above in Fig. 3.1, we see that it is a set of objects in which each object is an exhaustive set of independent choice rules that lead to an event to accept. For instance, the independent choices rules, ranked by utility, to accept a dwelling of Fig. 3.1 are:
IF Offer is
element of Set {{Garden = Yes} AND {Rooms > 5} AND {Garage = Yes}}
Then Accept OR
element of Set {{Garden = Yes} AND {Rooms > 4 AND Attic = Yes}
AND {Garage = Yes}}
Then Accept OR
element of Set {{Garden = Yes} AND {Rooms > 5}}
Then Accept End If
In research, we use a representative survey and have a large set of choice rules.
If the choice rules have predictive power, which can be tested in, for instance, a longitudinal survey (see below), we may use the DPNs in a decision support sys-tem (DSS). The DPN is a substitute for a respondent. The DSS accepts all offers, which are defined as a set of attributes and their levels. A DPN (respondent) evalu-ates an offer, which must result in a reject or accept. The final result is a sum of rejects and accepts that can be linked directly to the set of attributes and their levels of each offer.
The use of this property of the DSS is twofold. The DSS is an interface that allows researchers, policy-makers, real estate agents, project developers, or housing corporations to offer real supply to the DPNs. It allows them to change attribute
levels like price for sensitivity analysis. Van Zwetselaar and Goetgeluk (1994) developed such a DSS “housing supply machine.”
A necessary condition to use the DSS is the predictive value of the DPN.
Goetgeluk’s study “Trading off housing preferences; housing market research with Decision Plan Nets” analyzed the DPNs predictive value in a longitudinal perspective (1993–1994). The respondents originated from a representative (longitudinal) sample of active searchers in two Dutch housing market regions, Utrecht and Arnhem. These regions were chosen based on an earlier analysis that had shown that demand exceeded supply far more in the Utrecht region than in the Arnhem region. A housing market region is the aggregate search area of housing searchers and is defined as the area in which a person moves without loss of accessibility (time or social distance) to work, family, and friends. The selection of a housing market region therefore implies that the motives “improve housing” and “household formation” overrule “study/
work” as was indicated in Sect. 3.2. The analysis consisted of three stages.
The first stage of the analysis took a closer look at the preference structure of various housing consumers. In Sect. 3.2 it was argued that valid measurements of stated preferences can only be done within the framework of a meaningful relation-ship between individual housing choice and the housing market as a system in which supply and demand intersect. Indeed, it turned out that if the urgency of the move, individual resources and the search area were taken into account, the housing consumers had different sets of preference. The differences lie not so much in the type of preferred attributes, but in the number and the rigidity. Housing consumers whose move is not too urgent and who have ample resources provide more RIPs and relatively few TOPs. This was predicted.
In the second stage, it was tested if the stratification based on stage 1 resulted in differences between the various respondents in the propensity to move within 1 year. It was observed that the propensity to move house differed considerably across various categories of housing consumers, in accordance with expectations of the conceptual framework of Sect. 3.2. Especially the urgency of the move proved to be significantly important: the more urgent the move, the higher the propensity.
On an average, almost half of the actively searching housing consumers moved within a year. This share was predicted.
The third stage examined the proportion of movers who had accepted a house that matches one of the alternatives specified in the DPN. This is expressed as the success rate. The success rate is therefore dependent on the propensity to move.
A calculation was also made of the extent to which the preferences of housing consumers still searching in 1994 differed from the preferences they expressed in 1993. The success rate was 0.25. It was shown that the shift in preferences of those still looking for a house was many times greater than the difference between the choice of house and the original preferences stated by people who had already moved. This suggests that people looking for houses adapt their preferences con-stantly, especially if the urgency to move is less high! This implies that besides our stratification variables “urgency to move” in combination with “would you accept a preferred offer immediately,” we have to add the search period as well. Luckily enough one attribute is responsible for the major deviance in time, conditional on the urgency to move: the substitution of price. If we were to take price developments
into account, though, the success rate would be greatly increased. It turned out that substitution of the other preferences like housing type, volume, and size of the neighborhood were fairly stable.
Based on these outcomes, Goetgeluk (1997) and Goetgeluk and Hooimeijer (2002) assumed that the predictive value was sufficient enough to use the DPNs in a knowledge-based DSS. Table 3.3 shows the segmentation of demand according to the framework described in Sect. 3.2. Two kinds of simulations with the DSS are presented here.
3.5.1 Unravel the Way One Attribute of a Vacancy is Valued, Controlling for All Satisfied Preferences
In the experiment, we increased the price of a vacancy (ownership). The minimum level started at 0–75,000 Dutch Florins and ended with 526,000 Dutch Florins or more. We increased the level by 50,000 Dutch Florins. We expected a negative nonlinear (S-curved) relationship. We assumed threshold levels: at a certain price level the acceptance rate will remain constant.
Figure 3.2 shows that the relation is as expected. Threshold levels exist and they are segment-specific. The highest acceptance rates were movers with a high income. In all cases, these were couples with children and childless couples younger than 45 years (9, 10, 12, 13). Only the singles deviated from this general view of the high incomes. A small group is formed by the young singles (8) and starters who planned to cohabit (1B). The lowest rates include all the other low-income groups plus the older singles with a high low-income (11).
Figure 3.2 shows the acceptance rate as a function of only one dynamic attribute level. Here we state that we could offer the DSS a full fractional design, like in conjoint models, to create “rejects” and “accepts.”
Table 3.3 Description of segments (Goetgeluk 1997) Code segment Description
1A Starter, single
1B Starter, couple
2 Mover, low income, <45 age, single
3 Mover, low income, <45 age, couple/single parent 4–7 Mover, low income, couple with children 5 Mover, low income, ³45 age, single
6 Mover, low income, ³45 age, couple/single parent 8 Mover, high income, <45 age, single
9 Mover, high income, <45 age, couple/single parent 10–13 Mover, high income, couple with children 11 Mover, high income, ³45 age, single
12 Mover, high income, ³45 age, couple/single parent
3.5.2 The Role of Location
How important is location in the residential choice? The statement “location, loca-tion, location” postulates that only location counts. We have seen that location is a latent variable that can be deconstructed in functional, esthetical and social attri-butes. We have also shown that not all location attributes are valued equally impor-tant. So, how important is location?
We tested the importance of the attribute municipality. We offered our DPNs 22 real vacancies. They originated from estate agents’ announcements in a special local paper. We selected them randomly. We offered them to all respondents irre-spective of their segmentation. Here we present the results of the Arnhem housing market region. We are interested in the acceptance rate. The application is of course very applied. Any real estate agent can use the DSS.
How important is the preferred municipality? As indicated earlier, we know that trading off the location at a low spatial scale in the search region – the housing mar-ket region – is at stake. In this scenario, we assumed that the acceptance rate drops if the municipality is out of reach. To test this we defined two “supply” simulations.
In the first, our respondents – the DPN – evaluated each offer. In the second simula-tion, we offered the same vacancies, but the DPNs assume that the preference for the municipality is always satisfied. Hence, the difference in the acceptance rate is to be accounted for by a spatial mismatch: the preferred set of municipalities is either an RIP or a TOP. Our developed DSS housing supply machine (Van Zwetselaar and Goetgeluk 1994) is able to perform such simulations.
80
60
40
20
0
75-6 lA Segments housing consumers
8 9 10-13
2 3 4-7
11 12
5 1B
76-125 126-175 176-225 226-275 276-325 326-375 376-425 426-475 476-525 526+
Price (x1.000)
%
Fig. 3.2 The rate of acceptance for price ownership for various segments of housing consumers (Goetgeluk 1997)
In Fig. 3.3 both rates are depicted: black (location not realizable), dark gray (location realizable). The conclusion is that in many instances people have fairly strong location preferences for municipalities. However, at some points the differ-ence between the bars is less. The scenario outcome is in concordance with other empirical data that show that most people move within the boundary of their municipality or define a limited number of preferred municipalities. The DSS shows immediately which part of the supply is relatively indifferent to location and which is not. For real estate agents the DSS is therefore a useful tool to optimize the portfolio.
3.5.3 The Role of the Search Area
In this scenario, we tested how important the housing market region structures the demand. We use the same 22 offers as before. We also forced the DPNs to accept that their municipal preferences would be satisfied. This implies that we assume that all offers are located in the preferred set of municipalities. This means that we measure to what extent our two groups evaluate offers by dwelling-type char-acteristics. We assume that the respondents of Utrecht will have a higher accep-tance rate since Utrecht housing seekers are accustomed to less quality for a higher price. In contrast to the other scenario, the DSS offers these vacancies first to housing seekers in Arnhem (dark gray bar) and later to housing seekers in Utrecht (light gray).
Figure 3.3 shows that the impact of the housing market region is huge. In nearly all cases, the acceptance rate for the Utrecht seeker is higher than for the Arnhem seekers. Two conclusions can be drawn. We start with an “applied” one and end with a “scientific” one.
Firstly, using this information, real estate agents could expand their market.
Why? We have witnessed housing supply–driven migration from the Utrecht region toward the Arnhem region. Commuting from Arnhem to work in Utrecht – in gen-eral the Randstad-Holland – is possible. The cost of an increased commute is less than the benefits of cheaper or better housing. Moving is good value for money.
Therefore local real estate agents could extend their market area toward housing market regions within commuting distance.
Secondly, the difference in acceptance rates and the notion that housing market regions are dynamic implies that any research design for housing choice must define the functional housing market as a starting point since the search area particularly influences the attribute levels. This means that regional hous-ing market simulations should at least be tested regularly to check that the regions are still valid. A better solution is an endogenous housing search area within these models. How the functional housing market can be calculated in a flexible way is demonstrated elsewhere (Goetgeluk 1997; Goetgeluk and de Jong 2005).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
0 5 10 15 20 25%
Location (municipality) correct (response Arnham) Location (municipality) correct in Arnhem(response Arnhem) Location (municipality) not correct in Arnhem(response Arnhem)
ACCEPTANCE RATE
OFFERED DWELLING TYPE
Fig. 3.3 The rate of acceptance of Utrecht and Arnhem respondents for real Arnhem vacancies (Goetgeluk 1997)