Typological Classification of the Mortgage Borrowers With the use of the Latent Class Models

Authors

  • Marcin Idzik Warsaw University of Life Sciences, Faculty Of Economic Sciences
  • Jacek Gieorgica The Polish Bank Association (ZBP)

DOI:

https://doi.org/10.18778/0208-6018.323.14

Keywords:

mortgage borrowers, typological classification, latent class models

Abstract

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.

Downloads

Download data is not yet available.

References

Akaike H. (1987), Factor analysis and AIC, “Psychometrika”, vol. 52.

Antonides G., van Raaij W.F. (1998), Consumer Behaviour. A European Perspective, John Wiley & Sons, New York.

Bańbuła P. (2006), Oszczędności i wybór międzyokresowy: podejście behawioralne, „Materiały i Studia” 2006, nr 208, Narodowy Bank Polski, Warszawa 2006.

Bartholomew D.J., Knott M. (2002), Latent Variable Models and Factor Analysis, Arnold.

Bollen K. (1989), Structural equations with latent variables, New York, Wiley.

Browning M., Lusardi A. (1996), Household saving: micro teories and micro facts, „Journal of Economic Literatur”, vol. 34.

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.

Formann A.K. (2003), Latent class model diagnostics-A review and some proposals. Computational, Statistics & Data Analysis, vol. 41.

Hagenaars J.A., McCutcheon A.L. (2002), Applied Latent Class Analysis, Cambridge University Press.

Kamakura Du R. W.A. (2006), Household Lifecycles and Life Styles in America, „Journal of Mar­keting Research”, vol. 43.

Kaplan D. (2003), Latent Class Models, Forthcoming. “Handbook for Quantitative Methodology”, Sage.

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.

Keynes J.M. (1936), The general teory of employment, interest and money, MacMillan, London.

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.

Lubke G.H., Muthén B. (2005), Investigating population heterogeneity with factor mixture models. “Psychological Methods”, vol. 10.

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.

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.

Shefrin H.M., Thaler R.H. (1988), The behavioral life‑cycle hypothesis, „Economic Inquiry”, vol. 26 (4), Oxford University Press.

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.

Smith W. (1956), Product differentiation and market segmentation as alternative marketing strate­gies, “Journal of Marketing”, vol. 21.

Thaler R.H., Shefrin H. M. (1981), An Economic Theory of Self‑Control, „Journal of Political Econ­omy”, vol. 89(2), University of Chicago Press.

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.

Vermunt J., Magidson J. (2003), Encyclopedia of Social Science Research Methods, Sage Publica­tions: Vermunt

Vermunt J.K., Magidson J. (2005), Technical Guide for Latent GOLD 4.0: Basic and Advanced, Statistical Innovations, Belmont.

Vermunt J.K., Magidson J. (2008), LG‑Syntax User’s Guide: Manual for Latent GOLD 4.5 Syntax Module, Statistical Innovations, Belmont.

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.

Yang C. (2006), Evaluating latent class analyses in qualitative phenotype identification, “Computa­tional Statistics & Data Analysis”, vol. 50.

Downloads

Additional Files

Published

2016-12-08

Issue

Section

Finance

How to Cite

Idzik, Marcin, and Jacek Gieorgica. 2016. “Typological Classification of the Mortgage Borrowers With the Use of the Latent Class Models”. Acta Universitatis Lodziensis. Folia Oeconomica 4 (323): [203]-220. https://doi.org/10.18778/0208-6018.323.14.