Vintage Analysis

--Kelly Kent

In a recent presentation conducted by The Tower Group, “2010 Top 10 Business Drivers, Strategic Responses, and IT Initiatives in Bank Cards,” the conversation covered many of the challenges facing the credit card business in 2010.  When discussing the shift from “what it was," to “what it is now” for many issues in the card industry, one specific point caught my attention – the perception of unused credit lines – and the change in approach from lenders encouraging balance load-up to the perception that unused credit lines now represent unknown vulnerability to lenders.

Using market intelligence assets at Experian, I thought I would take a closer look at some of the corresponding data credit score profile trends to see what color I could add to this insight. Here is what I found:

• Total unused bankcard limits have decreased by $750 billion from Q3 2008 to Q3 2009
• By risk segment, the largest decline in unused limits has been within the VantageScore® A consumer – the super prime consumer – where unused limits have dropped by $420 billion
• More than 82 percent of unused limits reside with VantageScore A and B consumers – the super-prime and prime consumer segments

So what does this mean to risk management today? If you subscribe to the approach that unused limits now represent unknown vulnerability, then this exposure does not reside with traditional “risky” consumers, rather it resides with consumers usually considered to be the least risky. 

So this is good news, right? Well, maybe not.

Vintage analysis of recent credit trends shows that vulnerability within the top score tiers might represent more risk than one would suspect. Delinquency trends for VantageScore A and B consumers within recent vintages (2006 through 2008) show deteriorating rates of delinquency from each year’s vintage to the next. Despite a shift in loan origination volumes towards this group, the performance of recent prime and super-prime originations shows deterioration and underperformance against historical patterns.

If The Tower Group’s read on the market is correct, and unused credit now represents vulnerability and not opportunity, it would be wise for lenders to reconsider where and how yesterday’s opportunity has become today’s risk.

--by Jeff Bernstein

In my last blog, I discussed the basic concept of a maturation curve, as illustrated below:

Exhibit 1

In Exhibit 1, we examine different vintages beginning with those loans originated by year during Q2 2002 through Q2 2008. The purpose of the vintage analysis is to identify those vintages that have a steeper slope towards delinquency, which is also known as delinquency maturation curve.

The X-axis represents a timeline in months, from month of origination.  Furthermore, the Y-axis represents the 90+ delinquency rate expressed as a percentage of balances in the portfolio.  Those vintage analyses that have a steeper slope have reached a normalized level of delinquency sooner, and could in fact, have a trend line suggesting that they overshoot the expected delinquency rate for the portfolio based upon credit quality standards.

So how can you use a maturation curve as a useful portfolio management tool?

As a consultant, I spend a lot of time with clients trying to understand issues, such as why their charge-offs are higher than plan (budget).  I also investigate whether the reason for the excess credit costs are related to collections effectiveness, collections strategy, collections efficiency, credit quality or a poorly conceived budget.

I recall one such engagement, where different functional teams within the client’s organization were pointing fingers at each other because their budget evaporated. One look at their maturation curves and I had the answers I needed. I noticed that two vintages per year had maturation curves that were pointed due north, with a much steeper curve than all other months of the year. Why would only two months or vintages of originations each year be so different than all other vintage analyses in terms of performance?

I went back to my career experiences in banking, where I worked for a large regional bank that ran marketing solicitations several times yearly. Each of these programs was targeted to prospects that, in most instances, were out-of-market, or in other words, outside of the bank’s branch footprint.

Bingo! I got it! The client was soliciting new customers out of his
market, and was likely getting adverse selection. While he targeted the “right” customers – those with credit scores and credit attributes within an acceptable range, the best of that targeted group was not interested in accepting their offer, because they did not do business with my client, and would prefer to do business with an in-market player.

Meanwhile, the lower grade prospects were accepting the offers, because it was a better deal than they could get in-market. The result was adverse selection...and what I was staring at was the "smoking gun" I’d been looking for with these two-a-year vintages (vintage analysis) that reached the moon in terms of delinquency.

That’s the value of building a maturation curve analysis – to identify
specific vintages that have characteristics that are more adverse than others.  I also use the information to target those adverse populations and track the performance of specific treatment strategies aimed at containing losses on those segments. You might use this to identify which originations vintages of your home equity portfolio are most likely to migrate to higher levels of delinquency; then use credit bureau attributes to identify specific borrowers for an early lifecycle treatment strategy.

As that beer commercial says – “brilliant!”

 

--by Jeff Bernstein

In the current economic environment, many lenders and issuers across the globe are struggling to manage the volume of caseloads coming into collections. The challenge is that as these new collection cases come into collections in early phases of delinquency, the borrower is already in distress, and the opportunity to have a good outcome is diminished.

One of the real “hot” items on the list of emerging best practices and innovating changes in collections is the concept of early lifecycle treatment strategy. Essentially, what we are referring to is the treatment of current and non-delinquent borrowers who are exhibiting higher risk characteristics.  There are also those who are at-risk of future default at higher levels than average. The challenge is how to identify these customers for early intervention and triage in the collections strategy process.

One often-overlooked tool is the use of maturation curves to identify vintages within a portfolio that is performing worse than average. A maturation curve identifies how long from origination until a vintage or segment of the portfolio reaches a normalized rate of delinquency.

Let’s assume that you are launching a new credit product into the marketplace. You begin to book new loans under the program in the current month. Beyond that month, you monitor all new loans that were originated/booked during that initial time frame which we can identify as a “vintage” of the portfolio. Each month’s originations are a separate vintage or vintage analysis, and we can track the performance of each vintage over time.

How many months will it take before the “portfolio” of loans booked in that initial month reach a normal level of delinquency based on these criteria: the credit quality of the portfolio and its borrowers, typical collections servicing, delinquency reporting standards, and factor of time?  The answer would certainly depend upon the aforementioned factors, and could be graphed as follows:

Exhibit 1

  Comments (0)

-- by Kelly Kent

Source: Experian-Oliver Wyman Market Intelligence Reports

Analyzing recent trends from vintages published in the Experian-Oliver Wyman Market Intelligence Reports, there are numerous insights that can be gleaned from just a cursory review of the results.

Mortgage trends

As noted in an earlier posting, recent mortgage vintage analysis' show a broad range of behaviors between more recent vintages and older, more established vintages that were originated before the significant run-up of housing prices seen in the middle of the decade. The 30+ delinquency levels for mortgage vintages in 2005, 2006, and 2007 approach and in two cases exceed 10 percent of trades in the last 12 months of performance, and have spiked from historical trends, beginning almost immediately after origination. On the other end of the spectrum, the vintages from 2003 and 2002 have barely approached or exceeded 5 percent for the last 6 or 7 years.

Band card trends

As one would expect, the 30+ delinquency trends demonstrated within bankcard vintage analysis are vastly different from the trends of mortgage vintages. Firstly, card delinquencies show a clear seasonal trend, with a more consistent yearly pattern evident in all vintages, resulting from the revolving structure of the product. The most interesting trends within the card vintages do show that the more recent vintages, 2005 to 2008, display higher 30+ delinquency levels, especially the Q2 2007 vintage, which is far and away the underperformer of the group.

Within each vintage pool, an analysis can extend into the risk distribution and details of the portfolio and further segment the pool by credit score, specifically VantageScore.  In other words, the loans in this pool are only for the most creditworthy customers at the time of origination. The noticeable trend is that while these consumers were largely resistant to deteriorating economic conditions, each vintage segment has seen a spike in the most recent 9-12 months.

Given that these consumers tend to have the highest limits and lowest utilization of any VantageScore band, this trend encourages further account management consideration and raises flags about overall bankcard performance in coming months.

Even a basic review of vintage analysis pools and the subsequent analysis opportunities that result from this data can be extremely useful. This vintage analysis can add a new perspective to risk management, supplementing more established analysis techniques, and further enhancing the ability to see the risk within the risk.

-- By Kelly Kent

Source: Experian-Oliver Wyman Market Intelligence Reports

In the most recent release of the Experian-Oliver Wyman Market Intelligence Reports, each product report contains a series of vintage data reports that shed light on the delinquency, charge-off, and prepayment trends discussed earlier in this series.

These examples of vintage pool curves are taken from the Q2 2009 release and pertain to the mortgage product.

Vintage performance - delinquency
The performance metrics of each vintage are the essence of the benchmarking process. Having properly weighed and balanced each vintage pool, a comparison can be made to the performances of each pool. In the chart shown here, “30+ delinquency rates as % of
trades,” each vintage pool is tracked based on the months on book since its origination. For instance, the longest trend line belongs to the oldest vintage, Q2 2002, and reflects the 30+ delinquency rates over the past 84 months. Conversely, the newest vintage, Q2 2008, is the shortest trend line and reflects only the performance for the past 12 months for those trades. In this chart, it can be easily observed that the delinquency levels for the vintages from 2005, 2006, and 2007 deviate significantly from the older vintages and have spiked for the past 12 to 18 months while older vintages have behaved more consistently.

Distribution of trades
As mentioned earlier, vintage pools are defined by the score at origination for each of the loans within the pool. This is significant in that the distribution of loans will impact the ability to correctly benchmark against each pool. For instance, the chart shown here displays the distributions in each vintage pool, by VantageScore band. 

Despite the clear advantages of using vintage analysis, a benchmarking exercise will require significant weighing and balancing to ensure that the risk profiles of the portfolios are comparable.

Vintage performance - prepayment
Less prominent to delinquency trends are the prepayment trends of each pool. From the moment of origination, each pool begins to change its composition as a result of prepayments/closures which need to be considered in any analysis in order to understand the changing composition of each pool. It is vital that a user understand the shifting risk profile of each vintage, over time. The risk profile, by VantageScore for instance, may skew away from the higher quality consumers over time as prepayment removes them from the pool, leaving only the lowest-scoring consumers in the pool.

These are just three examples of the data required in order to perform vintage analysis. For the sake of brevity, other aspects of these analyses, such as geographic footprint, have been excluded.  These would also add significant insight to the analysis results.


 

-- By Kelly Kent

Vintage analysis, specifically vintage pools, present numerous useful opportunities for any firm seeking to further understand the risks within specific portfolios. While most lenders have relatively strong reporting and metrics at hand  for their own loan portfolio monitoring...these to understand the specific performance characteristics of their own portfolios -- the ability to observe trends and benchmark against similar industry characteristics can enhance their insights significantly.

Assuming that a lender possesses the vintage data and vintage analysis capability necessary to perform benchmarking on its portfolio, the next step is defining the specific metrics upon which any comparisons will be made. As mentioned in a previous posting, three aspects of vintage performance are often used to define these points of comparison:

1. Vintage delinquency including charge-off curves, which allows for an understanding of the repayment trends within each pool. Specifically, standard delinquency measures (such as 30+ Days Past Due (DPD), 60+ DPD, 90+ DPD, and charge-off rates) provide measures of early and late stage delinquencies in each pool.

2. Payoff trends, which reflect the pace at which pools are being repaid. While planning for losses through delinquency benchmarking is a critical aspect of this process, so, too, is the ability to understand pre-repayment tendencies and trends. Pre-payment can significantly impact cash-flow modeling and can add insight to interest income estimates and loan duration calculations.

As part of the Experian-Oliver Wyman Market Intelligence Reports, these metrics are delivered each quarter, and provide a consistent, static pool base upon which vintage benchmarks can be conducted.

Clearly, this is a rather simplified perspective on what can be a very detailed analysis exercise. A properly conducted vintage analysis needs to consider aspects such as: lender portfolio mix at origination; lender portfolio footprint at origination; lender payoff trends and differences from benchmarked industry data in order to properly balance the benchmarked data against the lender portfolio.
 

-- by Kelly Kent

The title of this edition, ‘The risk within the risk’ is a testament to the amount of information that can be gleaned from an assessment of the performances of vintage pools.

Vintage pools offer numerous perspectives of risk. They allow for a deep appreciation of the effects of loan maturation, and can also point toward the impact of external factors, such as changes in real estate prices, origination standards, and other macroeconomic factors, by highlighting measurable differences in vintage to vintage performance.

What is a vintage pool?

By the Experian definition, vintage pools are created by taking a sample of all consumers who originated loans in a specific period, perhaps a certain quarter, and tracking the performance of the same consumers and loans through the life of each loan.

Vintage pools can be analyzed for various characteristics, but three of the most relevant are:

* Vintage delinquency, which allows for an understanding of the repayment trends within each pool;

* Payoff trends, which reflect the pace at which pools are being repaid; and

* Charge-off curves, which provide insights into the charge-off rates of each pool.

The credit grade of each borrower within a vintage pool is extremely important in understanding the vintage characteristics over time, and credit scores are based on the status of the borrower just before the new loan was originated. This process ensures that the new loan origination and the performance of the specific loan do not influence the borrower’s credit score. By using this method of pooling and scoring, each vintage segment contains the same group of loans over time – allowing for a valid comparison of vintage pools and the characteristics found within.

Once vintage pools have been defined and created, the possibilities for this data are numerous...