A comparison of the predictive powers of tenure choices between property ownership and renting
Abstract
This paper compares the predictive powers of hierarchical generalized linear modeling (HGLM), logistic regression, and discriminant analysis with regard to tenure choices between buying property and renting property by sampling the residents of the Greater Taipei area. The results imply that the hit rate and other indicators included in HGLM have better predictive power with regard to tenure choices than the binary logistic regression model and the discriminant analysis model. That is, using HGLM to process nested data can increase prediction accuracy regarding household tenure choices. Furthermore, cross-validation is performed to analyze hit rate stability. The hit rate sequencing from this cross-validation is found to be consistent with the HGLM results, implying that the comparison of the three models in terms of hit rate performance prediction in this study is stable and reliable.
Keyword : tenure choice, hit rate, hierarchical generalized linear modeling, logistic regression model, discriminant analysis, cross-validation
How to Cite
Lee, C.-C., Liang, C.-M., & Liu, Y.-T. (2019). A comparison of the predictive powers of tenure choices between property ownership and renting. International Journal of Strategic Property Management, 23(2), 130-141. https://doi.org/10.3846/ijspm.2019.7064
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Vera-Toscano, E., & Ateca-Amestoy, V. (2008). The relevance of social interactions on housing satisfaction. Social Indicators Research, 86(2), 257-274. https://doi.org/10.1007/s11205-007-9107-5
Wagner, K. (2014). Intergenerational transmission: how strong is the effect of parental homeownership? Results of a survey on households in Austria. Monetary Policy and the Economy Q, 2, 49-64.
Wen, F. H. (2006). Principles, methods and applications of hierar-chical linear modeling. Taipei: Yeh Yeh Book Gallery.
Cardozo, R. N. (1965). An experimental study of consumer effort, expectation, and satisfaction. Journal of Marketing Research, 2(3), 244-249. https://doi.org/10.2307/3150182
Carter, S. (2011). Housing tenure choice and the dual income household. Journal of Housing Economics, 20(3), 159-170. https://doi.org/10.1016/j.jhe.2011.06.002
Chambers, M., Garriga, C., & Schlagenhauf, D. (2009). The loan structure and housing tenure decisions in an equilibrium model of mortgage choice. Review of Economic Dynamics, 12(3), 444-468. https://doi.org/10.1016/j.red.2009.01.003
Chen, G. (2016). The heterogeneity of housing-tenure choice in urban China: a case study based in Guangzhou. Urban Studies, 53(5), 957-977. https://doi.org/10.1177/0042098015571822
Espahibodi, P. (1991). Identification of problem banks and binary choice models. Journal of Banking and Finance, 15(1), 53-71. https://doi.org/10.1016/0378-4266(91)90037-M
Feng, Y., & Jones, K. (2015). Comparing methods: using multilevel modelling and artificial neural networks in the prediction of house prices based on property, location and neighbourhood characteristics. School of Geographical Sciences, University of Bristol.
Henley, A. (1998). Residential mobility, housing equity and the labour market. The Economic Journal, 108(447), 414-427. https://doi.org/10.1111/1468-0297.00295
Huang, Y., & Clark, W. A. V. (2002). Housing tenure choice in transitional urban China: a multilevel analysis. Urban Studies, 39(1), 7-32. https://doi.org/10.1080/00420980220099041
Kim, K., & Jeon, J. S. (2012). Why do households rent while owning houses? Housing sub-tenure. Habitat International, 36(1), 101-107. https://doi.org/10.1016/j.habitatint.2011.06.005
Kontrimas, V., & Verikas, A. (2011). The mass appraisal of the real estate by computational intelligence. Applied Soft Computing, 11(1), 443-448. https://doi.org/10.1016/j.asoc.2009.12.003
Lang, B. J., & Hurst, E. H. (2013). The effect of down payment assistance on mortgage choice. The Journal of Real Estate Finance and Economics, 49(3), 329-351. https://doi.org/10.1007/s11146-013-9432-1
Lee, C. C., Ho, Y. M., & Chiu, H. Y. (2016). Role of personal conditions, housing properties, private loans, and housing tenure choice. Habitat International, 53, 301-311. https://doi.org/10.1016/j.habitatint.2015.11.016
Lee, C. C., Liang, C. M., Chen, J. Z., & Tung, C. H. (2018). Effects of the housing price to income ratio on tenure choice in Taiwan: Forecasting performance of the hierarchical generalized linear model and traditional binary logistic regression model. Journal of Housing and the Built Environment, 33(4), 657-694. https://doi.org/10.1007/s10901-017-9572-3
Lewis, C. D. (1982). Industrial and business forecasting methods. London: Butterworths Scientific.
Mossman, D. (1994). Assessing predictions of violence: being accurate about accuracy. Journal of Consulting and Clinical Psychology, 62(4), 783-792. https://doi.org/10.1037/0022-006X.62.4.783
Quinsey, V. L., Harris, G. T., Rice, M. E., & Cormier, C. A. (1998). Violent offenders: appraising and managing risk. Washington: American Psychological Association. https://doi.org/10.1037/10304-000
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: applications and data analysis methods (2nd ed.). Thousand Oaks: CA: Sage.
Rice, M. E., & Harris, G. T. (1995). Violent recidivism: assessing predictive validity. Journal of Consulting and Clinical Psychology, 63(5), 737-748. https://doi.org/10.1037/0022-006X.63.5.737
Spalkova, D., & Spalek, J. (2012). Factors of the tenure choice: the case of the Czech Republic. Masaryk University.
Steenackers, A., & Goovaerts, M. J. (1989). A credit scoring model for personal loans. Insurance: Mathematics and Economics, 8(1), 31-34. https://doi.org/10.1016/0167-6687(89)90044-9
Subhan, S., & Ahman, E. (2012). The economic and demographic effects on housing tenure choice in Pakistan. American International Journal of Contemporary Research, 2(7), 15-24.
Tao, L., Hui, E. C. M., Wong, F. K. W., & Chen, T. (2015). Housing choices of migrant workers in China: beyond the Hukou perspective. Habitat International, 49, 474-483. https://doi.org/10.1016/j.habitatint.2015.06.018
Tung, C. H., Lee, C. C., Chen, C. L., & Wu, Y. L. (2016). Application of support vector machines for the prediction of the residence price in Taipei city. Journal of Housing Studies, 25(2), 31-51.
Uno, H., Cai, T., Pencina, M. J., Agostino, R. B., & Wei, L. J. (2011). On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine, 30(10), 1105-1117. https://doi.org/10.1002/sim.4154
Vera-Toscano, E., & Ateca-Amestoy, V. (2008). The relevance of social interactions on housing satisfaction. Social Indicators Research, 86(2), 257-274. https://doi.org/10.1007/s11205-007-9107-5
Wagner, K. (2014). Intergenerational transmission: how strong is the effect of parental homeownership? Results of a survey on households in Austria. Monetary Policy and the Economy Q, 2, 49-64.
Wen, F. H. (2006). Principles, methods and applications of hierar-chical linear modeling. Taipei: Yeh Yeh Book Gallery.