Introduction

Commercial real estate (CRE) is a complex asset class with a relatively long investment horizon which makes it hard to assess the long term risk of any given deal. Furthermore, as an asset class that tends to constitute a significant portion of the balance sheet, having a rigorous and comprehensive risk management platform should be a key objective of any financial institution involved in CRE lending or investment.

In this note we present results from a recent study of the predictive ability of the Specialized Finance System (SFS). The SFS is a comprehensive risk management platform for CRE that provides in-depth analysis to address the needs of financial institutions lending to or investing in CRE. The results show that the SFS provides very good discriminatory power that can help an institution improve the management of their CRE assets.

Data

The validation study is based on three snapshots (in time) of a well-diversified income producing CRE portfolio plus information about which deals had defaulted and the time of their default1.

The portfolio snapshots consisted of complete deal information, including all financing and property variables (incl. rent-roll) for each deal within the portfolio at a given point in time;

  1. September 2007
  2. September 2008
  3. September 2009

The snapshots thus allowed us to evaluate how the SFS performed in the environment leading up to the onset of the recent financial crisis (i.e., the default of Lehman Brothers in September 2008) as well as the first couple of years into the crisis.

Methodology and Results

We conducted a discriminatory power test by taking all deals in a given portfolio snapshot, e.g., September 2007, and grading them using the SFS with neutral macro-economic outlooks. We recorded the results and then rank-ordered the deals according to their PD within a given time-frame, e.g., looking 1 year into the future, 2 years into the future, etc. By then comparing this rank-ordering of deals with the deals that actually defaulted during the given time-frame – the cumulative percentage of defaulted deals - we calculated the ROC2, which is the standard metric for calculating the power of a model. A ROC of 50% indicates that the model is purely random, whereas a ROC of 100% indicates a model that is perfect in estimating which deals will default.

The result from this standard test shows that the SFS has a degree of discriminatory power that is unusually high for a commercial real estate risk model, as shown in Figure 1.

Figure 1 - ROC by horizon (SFS PD)
1 year 2 years 3 years 4 years 5 years
Sep 2007 90.3% 81.8% 76.8% 77.0% 77.0%
Sep 2008 86.7% 83.9% 80.1% 80.1% ---
Sep 2009 86.4% 80.4% 81.6% --- ---

Summary

Risk Integrated’s Specialized Finance System captures the complexity of CRE assets by taking the whole deal structure into account thereby capturing all the subtleties in any given deal. Although complex, this approach is able to produce very good results in terms of estimating which deals are at higher risk of defaulting over a relatively long time-horizon.

By providing a detailed and comprehensive analysis of CRE deal, the SFS is not only able to predict defaults with a high degree of accuracy, but also provides the necessary insights to effectively structure or restructure those deals to avoid default.

Dr. Peter Andresén
Senior Risk Methodologist


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  1. The recorded defaults stretched from 2007 through 2012
  2. Receiver Operating Characteristic