Combining structural models and accounting-based models for measuring credit risk in real estate companies

Purpose - The purpose of this paper is to, first, analyse to what extent the default probability based on structural models provides additional information and that accounting ratios do not contemplate. Second, to design hybrid models by including the default probability from structural models as explanatory variable, in addition to accounting ratios, in order to evaluate the differences in the accuracy of default predictions using an accounting-based model and a hybrid model. Design/methodology/approach - The authors calculated the scores from the accounting models annually during the period from 2003 to 2007 and estimated several structural models. Findings - The results show that the market information obtained from the structural models includes additional information not reflected in the accounting information. Also, it can be concluded that including default probability from structural models as an explanatory variable allows the out-sample predictive capacity of accounting-based models to be improved. Practical implications - The study highlights the importance of combining a structural model with an accounting model rather than expending energy on determining which of the two provides a greater predictive capacity. In fact, recent literature demonstrates no superiority of one approach over the other because both approaches capture different aspects related to the risk of bankruptcy in companies and they should be combined to improve credit risk management. Originality/value - This study expands on the existing literature on the probability of business failure in the real estate sector. The authors present a comparative analysis of the accuracy of default predictions using accounting-based models and hybrid models which will consider the default probability implicit in market information.