Explaining an NPL portfolio valuation process

Disclaimer: This series of posts (1,2) is a synthesis experience and process description. The process is also described in more details in public documentation (Blackrock report for Greek banks, published by Bank of Greece and EIB AQR process report, published by the European Central Bank).

The different models

We see widely different views about the real value of a loan portfolio as if every party has its own way of evaluating it. Why there isn’t one accepted way of doing it? Is it just a quantitative model that does all the work?

Predicting how many of the loans will default, is a key for valuation and it is not an easy exercise but certainly an important one as this is a major part of valuation, when you try to assess NPLs. Many methods exist to assess credit losses (i.e. Roll Rate Models, Vintage Loss Models, Expected Losses Models), some of which more theoretical and others more practical. I am going to describe the method that I have experienced in and was used by Blackrock Solutions when they were implementing Asset quality Assessment of the Greek banks. That specific assignment was not a valuation, but just a Credit losses forecast over the lifetime of the portfolio, however, I am going to use the same mindset for a valuation exercise.

The process

Let’s start with an overview of the process, to better understand, what a proper exercise entails;

1 .First, we assess the bank’s underwriting process. Are they legit or should we walk away before entering the data room?

2. We choose the a loan sample that can be representative of the portfolio. If we cannot review all the loans, we should choose a representative sample (in fact not something easy) that can tell us something about the whole portfolio.

3. After that we perform the loan file review / re-underwriting to the sample. Understand the borrowers, ratings and ant country specific procedures necessary to make a reliable prediction.

4. Usually, along we a specialised partner, we make independent collateral valuations. Is the collateral worth as much as the bank says?

5. We must then take some macroeconomic assumptions. Here we use reliable sources such as IMF, Central banks, EIB etc.

6. Entering the heart of the valuation, we make credit default projection. Put everything into the model. Always remember, in every model the outcome will be good, only if the inputs are good.

7. Being able to estimate the annual cash flows. We perform the valuation.

We have signed an NDA, so after entering the data room you get to know the current performance of the portfolio, by electronic means (i.e. data tapes) and also by physical means (i.e. credit files). Therefore, assuming, you have evaluated the underwriting process / you understand the underwriting risk, you have a picture of the defaulted loans, the loans with arrears, and other financial and qualitative information for the borrowers. However, this is now (day 0) and you need to have a projection of the same data in the future.

In the valuation, you care for the future FCF. The real value you are giving is that you can predict how much money the owner will get in every period, taking into account, defaults, principal payments and prepayments, etc. So, if it is was just a loan, we would expect something like the following.

Of course in a portfolio, it will be something complex; however, depending upon if we are looking 20 large corporate loans or 1000 consumer loans, we can have a sheet with multiple control accounts for each one with a Monte Carlo simulation predicting average credit losses for the total portfolio. Needless to say that the difficult part is to calculate the impairments / Credit losses each year and our focus would be there.

In order to get the credit losses, the procedure is split into 2 parts;

1. A manual re-underwriting of a Sample of Loans in order to understand the bank’s ratings and to get historic data and country averages that would be necessary for the projections

2. Adjust our model to our findings; that is to make reliable predictions of the credit loss forecasts for the entire portfolio.

Loan review / Re-underwriting of the Sample of Loans

The number of loans for a reliable sample can be around 1/3 of the total portfolio. For these loans we get the credit files from the bank and as first assignment we evaluate the business, the fundamentals and the financial projections taking into consideration macroeconomic assumptions to understand what is the FCF for debt service. We need FCF to understand the sustainability of the current debt and estimate sustainable debt capacity with respect to company’s FCF.

However, because also defaults exist, we must also look the possibility of recovery, by examining the capital structure and key risks inherent to each borrower. Then perform a liquidity analysis based on collateral and exposures and at some cases understand any restructuring that has taken place.

So in this section we get information about the loan portfolio that we will be using later in our projections.

For more a more detailed description you can see this post.

Using the model, make the credit prediction

The Expected Credit losses method uses three parameters and each of the parameters is modeled separately with different inputs (economic indicators, portfolio inputs). For each individual account, the expected loss during the course of next 12 months can be formulated as:

Credit Loss Projections = Exposure at Default * Probability of Default * Losses Given Default

Or

CLP = EAD * PD * LGD

This modeling methodology is in line with Basel framework but, also, has following advantages over traditional methods for loss forecasting.

Here we see the value of Loan assessment exercise we have done in the previous step, because with these inputs we have increased the model’s predictive power, as we managed to:

1. Collect data — Got a history of ratings, found the amount of data which were missing and avoided sample biases

2. Analyse Data — Reconsolidated them in a common format that could be used in our models (floating rate, amortization vs bullets, default rating)

3. Get historical performance — Constructed statistical relationships between performance and macroeconomic factors that allowed for a better predictive power to the model.

4. Make collateral analysis — Categorized them and made forward looking value curves using 3rd party data to get a better picture of the future liquidation value (e.g. Real estate value increase/decrease relative to GDP growth)

5. Understand Loan and country specific factors — Got findings that could be used in the model such as recovery timeline, treatment of modified loans, unsecured recovery assumptions

For more a more detailed description of the credit losses forecast method you can see this post.

Conclusion

Of course, like every valuation, it is harder than it sounds. This method is very time consuming and experience plays a crucial part. The process described need cooperation of the bank, therefore is not always used and it is usually for the final stage of the negotiations.