Typological Classification of the Mortgage Borrowers With the use of the Latent Class Models
DOI:
https://doi.org/10.18778/0208-6018.323.14Keywords:
mortgage borrowers, typological classification, latent class modelsAbstract
The holders of the mortgage loans constitute more than 6 percent of the individual customers of banks. In a wide-spread opinion, this group is regarded as homogeneous; however, the sociodemographic features not only do not explain, but actually conceal the diversified circumstances of the decisions made by the mortgage borrowers on the financial market. The diversifying factors are as follows: the psychographic profile, the attitude towards taking risks, the knowledge about the finances, caution, the inclination to get indebted and make savings. The objective of the research was to isolate homogeneous segments of the mortgage loan holders in terms of the circumstances of making consumer decisions on the financial market. Five homogeneous groups of mortgage borrowers were selected in terms of circumstances and motives behind the decisions on the financial market. This segmentation was conducted using latent class models (LCA). Latent class models enabled us to identify the feature subtypes connected with each other which are not recorded in a traditional approach. The research was conducted using a CAPI method on a nation-wide representative sample of mortgage loan holders of N=900, out of which N=800 were borrowers in Swiss francs, and N=100 were borrowers in Polish zlotys. The research was conducted by TNS Polska in March 2014 and second wave in March 2015 r.
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References
Akaike H. (1987), Factor analysis and AIC, “Psychometrika”, vol. 52.
Google Scholar
Antonides G., van Raaij W.F. (1998), Consumer Behaviour. A European Perspective, John Wiley & Sons, New York.
Google Scholar
Bańbuła P. (2006), Oszczędności i wybór międzyokresowy: podejście behawioralne, „Materiały i Studia” 2006, nr 208, Narodowy Bank Polski, Warszawa 2006.
Google Scholar
Bartholomew D.J., Knott M. (2002), Latent Variable Models and Factor Analysis, Arnold.
Google Scholar
Bollen K. (1989), Structural equations with latent variables, New York, Wiley.
Google Scholar
Browning M., Lusardi A. (1996), Household saving: micro teories and micro facts, „Journal of Economic Literatur”, vol. 34.
Google Scholar
Dempster A.P., Laird N.M., Rubin D.B. (1997), Maximum Likelihood from incomplete data via EM algorithm, „Journal of the Royal Statistical Society” (series B), vol. 39, no. 1.
Google Scholar
Formann A.K. (2003), Latent class model diagnostics-A review and some proposals. Computational, Statistics & Data Analysis, vol. 41.
Google Scholar
Hagenaars J.A., McCutcheon A.L. (2002), Applied Latent Class Analysis, Cambridge University Press.
Google Scholar
Kamakura Du R. W.A. (2006), Household Lifecycles and Life Styles in America, „Journal of Marketing Research”, vol. 43.
Google Scholar
Kaplan D. (2003), Latent Class Models, Forthcoming. “Handbook for Quantitative Methodology”, Sage.
Google Scholar
Keel P., Fichter M., Quadflieg, N., Bulik C., Baxter M., Thornton L. (2004). Application of a latent class analysis to empirically define eating disorder phenotypes, “Psychiatry”, vol. 61.
Google Scholar
Keynes J.M. (1936), The general teory of employment, interest and money, MacMillan, London.
Google Scholar
Langeheine R. van de Pol F. (2002), Latent Markov Chains, “Applied Latent Class Analysis”, red. J.A. Hagenaars, A.L. McCutcheon, Cambridge University Press, New York.
Google Scholar
Lubke G.H., Muthén B. (2005), Investigating population heterogeneity with factor mixture models. “Psychological Methods”, vol. 10.
Google Scholar
Magdison J., Vermunt J.K., Tran B. (2007), Using a Mixture Latent Markov Model to Analyze Longitudinal U.S. Employment Data Involving Measurement Error, w: New Trends in Psychometrics, red. Shigemasu K., Okada A., Imaizum T., Hoshino T., „Frontiers Science Series” 2007, no. 55, Universal Academy Press Inc.
Google Scholar
Paas L.J., Vermunt J.K., Bijmolt T.H.A. (2007), Discrete time discrete state latent Markov modeling for assessing and predicting household acquisition of financial products, „Journal of the Royal Statistical Society” (series A), vol. 170, no. 4.
Google Scholar
Shefrin H.M., Thaler R.H. (1988), The behavioral life‑cycle hypothesis, „Economic Inquiry”, vol. 26 (4), Oxford University Press.
Google Scholar
Singh A. (2010), Market segmentation in FMCG: time to drive new basis for market segmentation, “International Journal of Research in Commerce & Management”, vol. 1, no. 8.
Google Scholar
Smith W. (1956), Product differentiation and market segmentation as alternative marketing strategies, “Journal of Marketing”, vol. 21.
Google Scholar
Thaler R.H., Shefrin H. M. (1981), An Economic Theory of Self‑Control, „Journal of Political Economy”, vol. 89(2), University of Chicago Press.
Google Scholar
Tofighi D., Enders C.K. (2007), Identifying the correct number of classes in a growth mixture model, [w:] G.R. Hancock (ed.), Mixture models in latent variable research, Greenwich.
Google Scholar
Vermunt J., Magidson J. (2003), Encyclopedia of Social Science Research Methods, Sage Publications: Vermunt
Google Scholar
Vermunt J.K., Magidson J. (2005), Technical Guide for Latent GOLD 4.0: Basic and Advanced, Statistical Innovations, Belmont.
Google Scholar
Vermunt J.K., Magidson J. (2008), LG‑Syntax User’s Guide: Manual for Latent GOLD 4.5 Syntax Module, Statistical Innovations, Belmont.
Google Scholar
Webley P., Nyhus E.K. (2001), Representations of Saving and Saving Behaviour, w: Everyday Representations of the Economy, red. Ch. Roland‑Levy, E. Kirchler, E. Penz, C. Gray, WUV Universitatsverlag, Wien.
Google Scholar
Yang C. (2006), Evaluating latent class analyses in qualitative phenotype identification, “Computational Statistics & Data Analysis”, vol. 50.
Google Scholar