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Oonderzoek uitgevoerd in opdracht van de Provincie Zeeland.

This paper introduces anchor levels as a new tool for multiattribute utility theory. Anchor levels are attribute levels whose values are not affected by other attributes. They allow for new interpretations and generalizations of known representations and utility measurement techniques. Generalizations of earlier techniques can be obtained because cases with complex interactions between attributes can now be handled. Anchor levels serve not only to enhance the generality, but also the tractability, of utility measurements, because stimuli can better be targeted toward the perception and real situation of clients. In an application, anchor levels were applied to the measurement of quality of life during radiotherapy treatment, where there are complex interactions with what happens before and after. Using anchor levels, the measurements could be related exactly to the situation of the clients, thus simplifying the clients' cognitive burden.

Deze studie onderzocht de bewonerservaringen van 21 sociale huurders in seriematig gebouwde passiefwoningen in Nederland. Gegevens werden verzameld over de bewoners, hun motivatie om te kiezen voor hun woning, hun algemene tevredenheid met de woning en hun tevredenheid met het comfort, de klimaatregeling, het ventilatie-onderhoud en de informatieverstrekking. Deze gegevens werden geanalyseerd rekening houdend met het door de bewoners genoteerde gedrag in winter- en zomersituaties. De bewoners bleken over het algemeen tevreden te zijn, maar klaagden wel met name over koude slaapkamers. Tevens was er relatief lage tevredenheid over het ventilatiesysteem en de informatieverstrekking. De studie vond dat geluid en tocht ontwerpmatig meer aandacht verdienen en dat de verstrekking van informatie kan worden verbeterd, bijvoorbeeld over het gebruik van het ventilatiesysteem, het openen van luiken en de werking van PV panelen. Betere bewaking van prestatiecriteria werd aanbevolen voor toekomstige projecten.

People’s residential preferences are generally predicted on the basis of socio-demographic characteristics. Recently, however, it is argued that these variables no longer suffice and that they should be supplemented with lifestyle variables. The cur-rent study explores this assumption for a number of residential preferences. A life-style typology was developed that divides people into those that are more individualistic orientated with regard to housing and those that are more collectivistic orientated on the basis of 29 values (e.g., privacy). The lifestyle groups differ statistically significantly with regard to the current housing situation and residential preferences. However, after correction for socio-demographic variables many of these differences disappear, indicating that they are not due to underlying value orientations. In conclusion, values may have some additional worth for explaining and predicting housing preferences, especially in cases where socio-demographic variables alone fall short. However, their impact on housing preferences seems to be rather limited.

Residential satisfaction is an important topic in the domain of housing. It refers to the individuals’ appraisal of the conditions of their residential environment, in relation to their needs, expectations and achievements. In the current study respondents were asked to indicate their residential satisfaction on a scale ranging from 1 (very dissatisfied) to 10 (very satisfied). In general, the respondents are quite satisfied (mean satisfaction = 8.2; n = 1557). With the use of regression analyses, the impact of socio-demographic characteristics and objective housing quality on residential satisfaction is explored. A statistically significant impact is observed for tenure, age, number of persons in household, dwelling type, liveliness of neighborhood, size of outdoors space and dwelling ‘value’ (R2 = 18%). The study also explored the impact of preference on satisfaction. Surprisingly, the results showed that respondents who lived in accordance to their preferences were as satisfied as those who did not.

This paper describes the development of a house price index that has been introduced in May 2005 in The Netherlands. This monthly index, called Woningwaarde Index Kadaster (House Price Index Kadaster), is designed to detect changes in the price of the overall stock of owner-occupied homes. Fifty-five indices are calculated: one overall index, four regional indices, 12 provincial indices and 38 indices based on combinations of region/province and dwelling type. We used Case and Shiller’s geometric Weighted Repeat Sales Model to calculate monthly house price indices. We used recorded data on the sales of over 500,000 owner-occupied homes in The Netherlands, all representing repeat sales between January 1993 and December 2006. The accuracy of the index was determined using the 95% confidence interval. We observed that accuracy might become a problem in smaller sub samples. Revision volatility was explored by comparing the index values computed from all available data until December 2005 with the index values computed from the data available until December 2006. Our analysis showed that revision volatility does not seem to be a major problem to the index. We also explored heteroskedasticity in the Repeat Sales method but did not find conclusive evidence for the proposed heteroskedasticity. Given our target (a geometric mean index value) and the characteristics of the dataset (very large but without property characteristics) the Repeat Sales Method seems to be adequate for calculating a house price index for The Netherlands.

The demographic, socio-economic and socio-cultural shifts that have taken place in Western economies in recent decades have generated a broader variety in housing behavior. For this reason, some researchers argue that socio-demographic characteristics alone no longer suffice to predict the housing demand. They should be supplemented with ‘lifestyle’ variables. However, the worth of lifestyle for the prediction of the housing demand is a highly debated topic. The purpose of the current study, therefore, is to explore the worth of lifestyle (operationalized as values) in the prediction of residential preferences and choices. Data were collected through telephone interviews in January and February 2010. Respondents were asked to indicate the importance of 29 values as a guiding principle in housing. Furthermore, respondents who had indicated that they were willing to move (n = 930) were asked about their preferences for a number of dwelling characteristics, such as tenure and dwelling type. Respondents who had indicated that they were not willing to move (n = 667) provided their residential choices. Hierarchical logistic regression analyses were used to explore the relationship between values and residential preferences and choices, after correction for socio-demographic characteristics. The results showed that values might have some additional worth for predicting residential preferences and choices, but the relationship seems to be rather limited.

Introduction: People’s preferences for residential environments have long been predicted on the basis of sociodemographic characteristics alone. Recently, however, some researchers argue that these variables no longer suffice to explain and predict preferences and that they should be supplemented with lifestyle variables. The current study explores this assumption for a number of housing preferences. For this purpose, a lifestyle typology has been developed that is based on universal requirements of human nature and interests (individualistic value-orientation versus collectivistic value-orientation). Methods: Data were collected though telephone interviews in January and February 2010. Respondents were asked to indicate the importance of 29 values, such as pleasure, as a guiding principle in housing. Together, these values form two domains: an individualistic oriented value system and a collectivistic oriented value system. These were used to distinguish four lifestyle categories: 1) low in both individualistic and collectivistic value-orientation (n = 593, 38%), 2) mostly individualistic value-orientation (n = 262, 174%, 3) mostly collectivistic value-orientation (n = 174, 11%), and, 4) high in both individualistic and collectivistic valueorientation (n = 524, 34%). Results: The four lifestyle groups differ statistically significantly with regard to age, income, education, gender, having paid work and household type. Current housing characteristics (in respondents who are not willing to move) and preferred housing characteristics (in respondents that are willing to move) are compared between the four groups. A number of statistically significant differences in housing preferences between the four groups are observed. However, after correction for socio-demographic variables many of these differences disappear, indicating that they are a result of differences in socio-demographic variables and not of differences in actual housing preferences between the four groups. Conclusion: Values may have some additional worth for explaining and predicting housing preferences, especially in cases where socio-demographic variables alone fall short. However, their impact on housing preferences seems to be rather limited.