IDEAS home Printed from https://ideas.repec.org/p/arz/wpaper/eres2019_86.html
   My bibliography  Save this paper

Relevance of loan characteristics in probability of default prediction for commercial mortgage loans

Author

Listed:
  • Nicole Lux

Abstract

The current papers examines the sensitivity of loan characteristics on mortgage default probability for UK commercial mortgages. Commercial real estate (CRE) mortgages are major asset holdings for commercial banks, life insurance companies and thrift institutions. The slumping market for real estate threatened to drag down regional banks and other smaller financial institutions in the 08/09 financial crisis and led to the collapse of some financial institutions. Despite the prominence of CRE mortgages, modeling and analysing credit risk of CRE mortgages has been lagging behind those of non-CRE commercial loans. Modelling the probability of default for commercial real estate mortgages is more complicated than that for non-commercial real estate loans. Many distressed loans passed traditional underwriting standards suggesting that, in addition to LTV and DSCR ratios, other characteristics should be taken into consideration such as the inclusion property characteristics. The accuracy of default prediction is tested comparing two traditional statistical methods a) logistic regression (logit) and b) multiple discriminant analysis (MDA) using a unique dataset of defaulted commercial loan portfolios from 60 financial institutions lending in the UK between 2005 – 2017. Overall, both models show that the inclusion of property characteristics such as geography and asset type have been significant factors in determining default probability and improve model accuracy, while LTV shows no clear significance.

Suggested Citation

  • Nicole Lux, 2019. "Relevance of loan characteristics in probability of default prediction for commercial mortgage loans," ERES eres2019_86, European Real Estate Society (ERES).
  • Handle: RePEc:arz:wpaper:eres2019_86
    as

    Download full text from publisher

    File URL: https://eres.architexturez.net/doc/oai-eres-id-eres2019-86
    Download Restriction: no

    More about this item

    Keywords

    commercial mortgage risk; Credit risk modelling; linear discriminant analysis; Logistic Regression; Probability of default (PD);

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arz:wpaper:eres2019_86. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Architexturez Imprints). General contact details of provider: http://edirc.repec.org/data/eressea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.