Advertisement

Quality & Quantity

, Volume 45, Issue 4, pp 969–983 | Cite as

A MCGP decision aid for homebuyers to make the best choice

  • Ching-Ter Chang
  • Cheng-Yuan Ku
  • Hui-Ping Ho
  • Chechen Liao
Research Note

Abstract

This study adopts a new approach, the multi-choice goal programming (MCGP), to evaluate houses in order to help homebuyers to find better house based on the residential preferences. According to the function of MCGP, homebuyers can set multiple housing goals with multiple aspiration levels. This increases the flexibility to find a suitable house. Compared with other classical methods such as checklist and analytic hierarchy process, MCGP is more efficient, especially while considering a lot of housing criteria and house alternatives. In order to demonstrate the usefulness of MCGP decision aid for housing selection, a real case study is then provided. Furthermore, ten volunteers are invited to participate in the empirical experiment. The results also validate the effectiveness and efficiency of MCGP decision aid.

Keywords

Multi-choice goal programming Housing decision Optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Asberg P.: Housing decisions of young Swedish adults. J. Hous. Econ. 8, 116–143 (1999)CrossRefGoogle Scholar
  2. Arimah B.C.: An empirical analysis of the demand for housing attributes in a third world city. Land Econ. 68(4), 366–379 (1992)CrossRefGoogle Scholar
  3. Chang C.-T.: Multi-choice goal programming. Omega 35(4), 389–396 (2007)CrossRefGoogle Scholar
  4. Charnes A., Cooper W.W.: Management Model and Industrial Application of Linear Programming, vol. 1. Wiley, New York (1961)Google Scholar
  5. Chau C.K., Yung H.K., Leung T.M., Law M.Y.: Evaluation of relative importance of environmental issues associated with a residential estate in Hong Kong. Landsc. Urban Plan. 77(1-2), 67–79 (2006)CrossRefGoogle Scholar
  6. Charnes, A., Cooper, W. W.: 1961, Management Model and Industrial Application of Linear Programming, vol. 1, Wiley, New York (1961)Google Scholar
  7. Earnhart D.: Combining revealed and stated preference methods to value environmental amenities at residential locations. Land Econ. 77, 12–29 (2001)CrossRefGoogle Scholar
  8. Freiden J.B., Bible D.S.: The home purchase process: measurement of evaluative criteria through pairwise measures. Acad. Mark. Sci. J. 10, 359–376 (1982)CrossRefGoogle Scholar
  9. Greening L.A., Bernow S.: Design of coordinated energy and environmental policies: use of multi-criteria decision-making. Energy Policy 32, 721–735 (2004)CrossRefGoogle Scholar
  10. Ioannides Y.M.: Residential neighborhood effects. Reg. Sci. Urban Econ. 32, 145–165 (2002)CrossRefGoogle Scholar
  11. Johnson M.P.: Spatial decision support for assisted housing mobility counseling. Decis. Support Syst. 41(1), 296–312 (2005)CrossRefGoogle Scholar
  12. Juan Y.-K., Shih S.-G., Perng Y.-H.: Decision support for housing customization: A hybrid approach using case-based reasoning and genetic algorithm. Expet. Syst. Appl. 31(1), 83–93 (2006)CrossRefGoogle Scholar
  13. Kaufman P.C., Corrigan A.: Understanding Buying & Selling a House. Longmeadow Press, Stamford (1987)Google Scholar
  14. Kim J.H, Pagliara F., Preston J.: The intention to move and residential location choice behaviour. Urban Stud. 42(9), 1621–1636 (2005)CrossRefGoogle Scholar
  15. Kim S.-S., Yang I.-H., Yeo M.-S., Kim K.-W.: Development of a housing performance evaluation model for multi-family residential buildings in Korea. Build. Environ. 40, 1103–1116 (2005)CrossRefGoogle Scholar
  16. Levy D., Lee C.K.-C.: The influence of family members on housing purchase and decisions. J. Property Invest. Finance 22, 320–338 (2004)CrossRefGoogle Scholar
  17. Lindberg E., Garling T., Montgomery H.: Belief-value structures as determinants of consumer behaviour: a study of housing preferences and choice. J. Consum. Pol. 12, 119–137 (1989)CrossRefGoogle Scholar
  18. Molin E.J.E., Oppewal H., Timmermans H.J.P.: Analyzing heterogeneity in conjoint estimates of residential preferences. J. Hous. Built Environ. 16, 267–284 (2001)CrossRefGoogle Scholar
  19. Natividade-Jesus E., Coutinho-Rodrigues J., Antunes C.H.: A multicriteria decision support system for housing evaluation. Decis. Support Syst. 43(3), 779–790 (2007)CrossRefGoogle Scholar
  20. Ortúzar J.d.D., Rodríguez G.: Valuing reductions in environmental pollution in a residential location context. Transp. Res. Part D Transp. Environ. 7, 407–427 (2002)CrossRefGoogle Scholar
  21. Rapaport C.: Housing demand and community choice: an empirical analysis. J. Urban Econ. 42, 243–260 (1997)CrossRefGoogle Scholar
  22. Saaty T.L.: The analytical hierarchy process: planning, priority setting, resource allocation. McGraw-Hill, New York (1980)Google Scholar
  23. Schrage, L.: LINGO Release 6.0. LINGO System, Inc. (1999)Google Scholar
  24. Tamiz M., Jones D.F., Romero C.: Goal programming for decision making: an overview of the current state-of-the-art. Eur. J. Operat. Res. 111, 569–581 (1998)CrossRefGoogle Scholar
  25. VanderHart P.G.: The housing decisions of older households: a dynamic analysis. J. Hous. Econ. 7, 21–48 (1998)CrossRefGoogle Scholar
  26. Vogt C.A., Marans R.W.: Natural resources and open space in the residential decision process: a study of recent movers to fringe counties in southeast Michigan. Landsc. Urban Plan. 69, 255–269 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Ching-Ter Chang
    • 1
  • Cheng-Yuan Ku
    • 2
  • Hui-Ping Ho
    • 2
    • 3
  • Chechen Liao
    • 2
  1. 1.Department of Information ManagementChang Gung UniversityKwei-Shan, Tao-YuanTaiwan, R.O.C.
  2. 2.Department of Information ManagementNational Chung Cheng UniversityMin-Hsiung, Chia-YiTaiwan, R.O.C.
  3. 3.Department of International Business AdministrationChienkuo Technology UniversityChanghuaTaiwan, R.O.C.

Personalised recommendations