The Multi-attribute Utility Method

  • Sylvia J. T. Jansen


In this chapter the methodology and techniques behind Multi-Attribute Utility Theory are introduced. The basic assumption underlying this theory is that a decision-maker chooses the alternative (for example, a particular dwelling) that yields the greatest multi-attribute utility from a number of possible alternatives. An alternative is seen as a bundle of attributes, such as dwelling type and number of rooms. The decision-maker is assumed to evaluate every alternative on each of its salient attributes. Furthermore, the importance of each attribute is determined. Finally, the attribute values are combined with the importance weights and aggregated into a multi-attribute utility for each alternative. The alternative with the highest multi-attribute utility is expected to be preferred. In terms of the main dimensions for distinguishing between methods and techniques for measuring housing preference and choice the multi-Attribute utility method can be characterized as measuring stated preferences and providing an outcome in the form of utilities. The approach is attribute-based (compositional) and mathematical. Often, the simple-additive combination rule is applied (compensatory rule), but non-compensatory rules (such as multiplicative rules) are also possible.


Attribute Level Residential Environment Importance Score Salient Attribute Natural Scale 
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  1. Allen, M. (2002). Human values and product symbolism: Do consumers form product preference by comparing the human values symbolized by a product to the human values that they endorse? Journal of Applied Social Psychology, 32, 2475–2501.CrossRefGoogle Scholar
  2. Barreiro-Hurlé, J., & Gómez-Limón, J. A. (2008). Reconsidering heterogeneity and aggregation issues in environmental valuation: A multi-attribute approach. Environmental and Resource Economics, 40, 551–570.CrossRefGoogle Scholar
  3. Bettman, J. R., Luce, M. F., & Payne, J. W. (2006). Constructive consumer choice processes. In S. Lichtenstein & P. Slovic (Eds.), The construction of preference. New York: Cambridge University Press.Google Scholar
  4. Boumeester, H. J. F. M., Hoekstra, J. S. C. M., Meesters, J., & Coolen, H. C. C. H. (2005). Woonwensen nader in kaart: de woonbeleving van bewoners. Voorburg: NVB Vereniging voor ontwikkelaars en bouwondernemers.Google Scholar
  5. Boumeester, H. J. F. M., Coolen, H. C. C. H., Dol, C. P., Goetgeluk, R. W., Jansen, S. J. T., Mariën, A. A. A., & Molin, E. (2008a). Module Consumentengedrag WoON 2006, Hoofdrapport. Delft: Onderzoeksinstituut OTB.Google Scholar
  6. Boumeester, H. J. F. M., Mariën, A. A. A., Rietdijk, N., & Nuss, F. A. H. (2008b). Huizenkopers in Profiel. Onderzoek naar wensen van potentiële huizenkopers. Voorburg: NVB Vereniging voor ontwikkelaars en bouwondernemers.Google Scholar
  7. Breij, I., de Hoog, R., & Zandvliet, L. (1989). Computer ondersteund onderzoek naar woonvoorkeuren. In S. Musterd (Ed.), Methoden voor woning-en woonmilieubehoefte onderzoek. Amsterdam: SISWO.Google Scholar
  8. Burnett, P. (2008). Variable decision strategies, rational choice, and situation-related travel demand. Environment and Planning A, 40, 2259–2281.CrossRefGoogle Scholar
  9. Canbolat, Y. B., Chelst, K., & Garg, N. (2007). Combining decision tree and MAUT for selecting a country for a global manufacturing facility. Omega, 35, 312–325.CrossRefGoogle Scholar
  10. Edwards, W., & Newman, J. R. (1982). Multiattribute evaluation. Beverly Hills: Sage.Google Scholar
  11. Floor, H., & van Kempen, R. (1994). Wonen op maat. In I. Smid & H. Priemus (Eds.), Bewonerspreferenties: Richtsnoer voor Investeringen in Nieuwbouw en de Woningvoorraad (pp. 13–32). Delft: Delftse Universitaire Pers.Google Scholar
  12. Goetgeluk, R. (1997). Bomen over wonen, woningmarktonderzoek met beslissingsbomen (Dissertation, University of Utrecht, Utrecht: Utrecht Geographical Studies), p. 235.Google Scholar
  13. Heins, S. (2002). Rural residential environments in city and countryside: Countryside images, demand for and supply of rural residential environments. (Dissertation, University of Utrecht, Delft: Uitgeverij Eburon).Google Scholar
  14. Jansen, S., Boumeester, H., Coolen, H., Goetgeluk, R., & Molin, E. (2009). The impact of including images in a conjoint measurement task: Results of two small-scale studies. Housing and the Built Environment, 24(3), 271–297.CrossRefGoogle Scholar
  15. Jia, J., Fischer, G. W., & Dyer, J. S. (1998). Attribute weighting methods and decision quality in the presence of response error: A simulation study. Journal of Behavioral Decision Making, 11, 85–105.CrossRefGoogle Scholar
  16. Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. New York: Wiley.Google Scholar
  17. Latinopoulos, D. (2008). Estimating the potential impacts of irrigation water pricing using multicriteria decision making modelling. An application to Northern Greece. Water Resource Management, 22, 1761–1782.CrossRefGoogle Scholar
  18. Lindberg, E., Garling, T., & Montgomery, H. (1989). Belief-value structures as determinants of consumer-behavior – A study of housing preferences and choices. Journal of Consumer Policy, 12, 119–137.CrossRefGoogle Scholar
  19. Linkov, I., Satterstrom, F. K., Kiker, G., Batchelor, C., Bridges, T., & Ferguson, E. (2006). From comparative risk assessment to multi-criteria decision analysis and adaptive management: Recent developments and applications. Environment International, 32, 1072–1093.CrossRefGoogle Scholar
  20. Maclennan, D. (1977). Information, space and measurement of housing preferences and demand. Scottish Journal of Political Economy, 24, 97–115.CrossRefGoogle Scholar
  21. Meyer, V., Scheuer, S., & Haase, D. (2009). A multicriteria approach for flood risk mapping exemplified at the Mulde river, Germany. National Hazards, 48, 17–39.CrossRefGoogle Scholar
  22. Molin, E., Oppewal, H., & Timmermans, H. (1996). Predicting consumer response to new housing: A stated choice experiment. Journal of Housing and the Built Environment, 11(3), 197–311.Google Scholar
  23. Monat, J. P. (2009). The benefits of global scaling in multi-criteria decision analysis. Judgment and Decision Making, 4(6), 492–508.Google Scholar
  24. Park, W. C., Hughes, R. W., Thukral, V., & Friedmann, R. (1981). Consumers’ decision plans and subsequent choice behavior. Journal of Marketing, 45(Spring), 33–47.CrossRefGoogle Scholar
  25. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. Cambridge: Cambridge University Press.Google Scholar
  26. Payne, J. W., Bettman, J. R., & Schkade, D. A. (1999). Measuring constructed preferences: Towards a building code. Journal of Risk and Uncertainty, 19, 243–270.CrossRefGoogle Scholar
  27. Raju, K. S., & Vasan, A. (2007). Multi attribute utility theory for irrigation system evaluation. Water Resource Management, 21, 717–728.CrossRefGoogle Scholar
  28. Timmermans, H., Molin, E., & van Noortwijk, L. (1994). Housing choice processes: Stated versus revealed modelling approaches. Journal of Housing and the Built Environment, 9, 215–227.CrossRefGoogle Scholar
  29. Veldhuisen, K. J., & Timmermans, H. J. P. (1984). Specification of individual residential utility functions: A comparative analysis of three measurement procedures. Environment and Planning A, 16, 1573–1582.CrossRefGoogle Scholar
  30. von Winterfeldt, D., & Edwards, W. (1986). Decision analysis and behavioral research. Cambridge: Cambridge University Press.Google Scholar
  31. Vreeker, R. (2006). Evaluating effects of multiple land-use projects: A comparison of methods. Journal of Housing and the Built Environment, 21, 33–50.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.OTB Research Institute for the Built EnvironmentDelft University of TechnologyDelftThe Netherlands

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