AN EVALUATION OF HIGHER EDUCATION STUDENTS’ APARTMENT PREFERENCES: A REAL WORLD STUDY IN A NEWLY URBANIZED CITY

AN EVALUATION OF HIGHER EDUCATION STUDENTS’ APARTMENT PREFERENCES: A REAL WORLD STUDY IN A NEWLY URBANIZED CITY

This paper presents an evaluation of higher education students’ apartment preferences with respect to multiple criteria. In order to find out the importance rankings of these criteria and determine the optimum apartment option conjoint analysis method is used. Location, renter, room, price, floor, age were identified as attributes. 343 students participated in the study by rating sixteen apartment profiles with different combinations of above mentioned six attributes. According to the results, number of rooms, price and location attributes have the greatest influence on students’ decisions and these are followed by age, floor and renter attributes. Additionally the optimum apartment option is a central, 0-5 years aged, 3 bedroom and 1 living room apartment, rented by the householder with a price of 300-400 Turkish Liras. As an evaluation of a real world decision problem, the outcomes will help real estate agencies, householders and constructing firms in probable future decisions. Researchers who will perform a study in this field will be able take advantage of the results as well. disadvantages. While some of the students prefer to study in their hometowns, others leave their family homes for HE

___

  • Abdul Hamid b. Hj. M. I., Norhaya bt. K., Seah L. H., (2008) ‘Buyer's conjoint
  • preference for the attributes of condominium properties’, International Real Estate Research Symposium. Putra World Trade Center, Kuala Lumpur, 28-30 April.
  • http://eprints.utm.my/5220/1/Conjoint_for_Condo.pdf. Accessed 12 June 2014.
  • Acosta, L.A., Enano Jr., N.H., Magcale-Macandog, D.B., Engay, K.G., Herrer,
  • M.N.Q., Nicopior, O.B..S., Sumilang, M.I.V., Eugenioe, J.M.A., Lucht, W., (2013). How
  • sustainable is bioenergy production in the Philippines? A conjoint analysis of knowledge and
  • opinions of people with different typologies. Applied Energy, 102, 241–253. Adhikari, A., Basu, A., Raj, S.p., (2013). Pricing of experience products under
  • consumer heterogeneity. International Journal of Hospitality Management, 33, 6-18.
  • Altun, A. & Gök, B., (2010). Determining in-service training programs’ characteristics
  • given to teachers by conjoint analysis. Procedia - Social and Behavioral Sciences, 2(2), 1709- 1714. Arlı, E., (2013). Barınma yerinin üniversite öğrencilerinin kişisel ve sosyal gelişim ve
  • akademik başarı üzerindeki etkilerinin odak grup görüşmesi ile incelenmesi. Yükseköğretim
  • ve Bilim Dergisi, 3 (2), 173-178.
  • Borgers, A., Snellen, D., Poelman, J., Timmermans, H. (2008). Preferences for car
  • restrained residential areas. Journal of Urban Design, 13, 257–267. Borgers, A., Vosters, C., (2011). Assessing preferences for mega shopping
  • centres: A conjoint measurement approach. Journal of Retailing and Consumer
  • Services, 18(4), 322-332.
  • Chakrabarty, B.K., (2001). Concepts, principles, techniques and education. Cities, 18(5), 331–345.
  • De Jong, P., Rouwendal, J., Van Hattum, P., Brouwer, A., (2012). Housing
  • preferences of an ageing population: Investigation in the diversity among Dutch older adults.
  • http://arno.uvt.nl/show.cgi?fid=123055. Accessed 10 May 2014. Demir, A., Pala, A., Baytekin, H., (2006). Ziraat fakülteleri öğrencilerinin sosyal
  • yapıları, eğilimleri ve sorunları üzerinde bir araştırma. Tekirdağ Ziraat Fakültesi Dergisi, 3 (3), pp. 259-267.
  • Earnhart, D., (2002). Combining revealed and stated data to examine housing decisions
  • Eğitim Fakültesi Dergisi, 5 (2), pp. 225-234. Fisher K., Orkin, F., Frazser, C., (2010). Utilizing conjoint analysis to explicate
  • health care decision making by emergency department nurses: A feasibility study.
  • Applied Nursing Research, 23 (1), pp. 30-35. Green, P.E., Rao, V.R., (1971). Conjoint measurement for quantifying judgmental
  • data, Journal of Marketing Research, 8, pp. 355-363. Gustafsson, A., Herrmann, A., Huber, F., (2007). ‘Conjoint Analysis as an Instrument
  • of Market Research Practice’ In: Gustafsson et al. eds. Conjoint Measurement Methods and
  • Applications, New York: Sage. Hassanain, M. A., (2008). On the performance evaluation of sustainable student
  • housing facilities. Journal of Facilities Management, 6 (3), pp. 212-225.
  • Jansen, S.J.T., Coolen, Henny C.C.H., Goetgeluk, R.W., (2011). ‘Introduction’. In:
  • Jansen et al. eds. The Measurement and Analysis of Housing Preference and Choice, New York: Springer.
  • Katoshevski, R., Timmermans, H., (2001). Using conjoint analysis to formulate user
  • centred guidelines for urban design: The example of new residential development in Israel.
  • Journal of Urban Design, 6, pp. 37–53. Koç, M., Polat, Ü., (2006). Üniversite Öğrencilerinin Ruh Sağlığı, Uluslararası İnsan
  • Bilimleri Dergisi, 3 (2), pp. 1-22.
  • Kuzmanovic, M., Savic, G., Gusavac, B.A., Nikolic, D.M., Panic, B., (2012). A
  • conjoint-based approach to student evaluations of teaching performance. Expert Systems with
  • Applications, 40, pp. 4083–4089. Kuzmanovic, M., Martic, M., (2012). An approach to competitive product line design
  • using conjoint data. Expert Systems with Applications, 39 (8), pp. 7262–7269. Kuzmanovic, M., Vujosevic, M., Martic, M., (2012). Using conjoint analysis to elicit
  • patients’ preferences for public primary care service in Serbia. HealthMED, 6 (2), pp. 496– 504. Lee, J. K., Lee, J. H., Sohn, S. Y., (2009). Designing a business model for the content
  • service of portable multimedia players, Expert Systems with Applications, 36, pp. 6735–6739. Liu, J., Deng, W., Zhang, B., (2011). Conjoint analysis based transit service quality
  • research. Journal of Transportation Systems Engineering and Information Technology, 11 (4), pp. 97-102. Louviere, J. J., and Henley, D. H., (1977). An empirical analysis of student apartment
  • selection decisions. Geographical Analysis, 9, 130–141. Luce, R. D. & Tukey, J.W., (1964). Simultaneous conjoint measurement: A new scale
  • type of fundamental measurement. Journal of Mathematical Psychology, 1(1), 1–27. Michalek, J., Feinberg, F.M., Papalambros, P.Y., (2005). Linking marketing and
  • engineering product design decisions via analytic target cascading. Journal of Product
  • Innovation Management, 22 (1), pp. 42–62.
  • Molin, E.J.E, (2011), ‘Conjoint Analysis’. In: Jansen et al. eds. The Measurementand
  • Analysis of Housing Preference and Choice, New York: Springer.
  • Molin, E. J. E. and Oppewal, H., Timmermans, H. J. P., (2000). A comparison of full
  • profile and hierarchical information conjoint methods in modeling group preferences.
  • Marketing Letters, 11, pp. 165–172. Moore, W.L., (2004). A cross-validity comparison of rating-based and choice-based
  • conjoint analysis models. International Journal of Research in Marketing, 21, pp.299–312.
  • Natter M., Feurstein, M., (2002). Real world performance of choice
  • based conjoint models. European Journal of Operational Research, 137 (2), pp.448-458.
  • North, E.J. & de Vos, R.B., (2002). The use of conjoint analysis to determine
  • consumer buying preferences: A literature review, Journal of Family Ecology and Consumer
  • Sciences, 30, pp. 32–39.
  • Opoku, R. A. and Abdul-Muhmin, A. G., (2010). Housing preferences and attribute
  • importance among low-income consumers in Saudi Arabia. Habitat International, 34, 219- 227. Oppewal, H., Louviere, J.J., Timmermans, H.J.P., (2000).
  • Modifying conjoint methods to model managers' reactions to business environmental
  • trends: An application to modeling retailer reactions to sales trends. Journal of Business
  • Research, 50 (3), pp.245-257. Orzechowski, M. A., Arentze, T. A., Borgers, A. W. J., Timmermans, H. J. P.,
  • (2005). Alternate methods of conjoint analysis for estimating housing preference
  • functions: Effects of presentation style. Journal of Housing and the Built Environment,
  • 20, pp.349–362. Rao, V.R. & Sattler, H., (2011). ‘Measurement of price effects with conjoint analysis:
  • Separating informational and allocative effects of price’. In: Jansen et al. eds. The
  • Measurementand Analysis of Housing Preference and Choice, New York: Springer. Sattler, H., Hensel-Börner, S., (2007). ‘A comparison of conjoint measurement with self-explicated approaches’ In: Gustafsson et al. eds. Conjoint Measurement Methods and
  • Applications, New York: Sage. Sohn, S. Y. & Ju, Y. H., (2010). Conjoint analysis for recruiting high quality students
  • for college education. Expert Systems with Applications, 37(5), 3777-3783. Tayyaran, M.R., Kahn, A.M., Anderson, D.A., (2003). Impact of telecommuting and
  • intelligent transportation systems on residential location choice. Transportation Planning and
  • Technology, 26 (2), pp. 171–193. Theysohn, S., Klein, K., Völckner, F., Spann, M., (2013). Dual effect-based market
  • segmentation and price optimization. Journal of Business Research, 66 (4), pp.480-488. Timmermans, H., Molin, E. and Noortwijk, L.V., (1994). Housing choice processes:
  • Stated versus revealed modeling approaches, Netherlands Journal of Housing and the Built
  • Environment, 9, pp. 215-227. Tu, Y. & Goldfinch, J., (1996). A two-stage housing choice forecasting model. Urban
  • Studies, 33, pp.517–537. Turkish Statistical Institute (2012). Turkish Statistical Institute. Available at:
  • http://www.tuik.gov.tr/[Accessed 25 June 2014]. Venkatesh V., Chan, F.K.Y., Thong, J.Y.L., (2012). Designing e-government services:
  • Key service attributes and citizens’ preference structures, Journal of Operations Management,
  • 30, pp.116–133 Vetschera, R., Weitzl, W., Wolfsteiner, E., (2014). Implausible alternatives in eliciting
  • multi-attribute value functions. European Journal of Operational Research, 234, pp.221–230. Wang, D. & Li, S.M., (2004). Housing preferences in a transitional housing system:
  • The case of Beijing, China, Environment and Planning A, 36, pp.69–87. Ween, B., Kristoffersen, D.T., Hamilton, G.A., Olsen D.R., (2005). Image quality
  • preferences among radiographers and radiologists. A conjoint analysis. Radiography, 11 (3), pp.191-197. Wittink, D.R., Huber, J.C., Zandan, P., Johnson, R.M., (1992). The Number of Levels
  • Effect in Conjoint: Where Does It Come From, and Can It Be Eliminated?. Available at:
  • http://www.sawtoothsoftware.com/support/technical-papers/general-conjoint-analysis/the
  • number-of-levels-effect-in-conjoint-where-does-it-come-from-and-can-it-be-eliminated-1992
  • [Accessed: 5 May 2014]. Wu, W. Y., Liao, Y. K., Chatwuthikrai, A., (2014). Applying conjoint analysis to
  • evaluate consumer preferences toward subcompact cars. Expert Systems with Applications,
  • 41, pp.2782–2792. Yano, C., Dobson, G., (1998). Profit-optimizing product line design, selection and
  • pricing with manufacturing cost consideration. In: Ho et al. eds. Product Variety
  • Management: Research Advances, Boston: Kluwer Academic Publishers. Zafer Development Agency (2011). Zafer Development Agency. Available at:
  • http://www.zafer.org.tr/[Accessed 16 June 2014]. Zardari, N.H. & Cordery, I., (2007). ‘Modelling water allocation decisions: a conjoint
  • analysis approach’, International Congress on Modelling and Simulation, Christchurch, New
  • Zealand, 10-13 December.