Market Intelligence Solutions

--by Kelly Kent

For the past couple of years, the deterioration of the real estate market and the economy as a whole has been widely reported as a national and international crisis. Events such as 401k plans have fallen, bankruptcy scores are high and homeowners have simply abandoned their now under-valued properties, and the federal government has raced to save the banking and automotive sectors. While the perspective of most is that this is a national decline, this is clearly a situation where the real story is in the details.

A closer look reveals that while there are places that have experienced serious real estate and employment issues (California, Florida, Michigan) there are also areas (Texas) that did not experience the same deterioration in a similar manner.

Flash forward to November, 2009 – with signs of recovery seemingly beginning to appear on the horizon – there again seems to be a great deal of variability between areas that seem poised for recovery and those that are continuing down the slope of decline.

Interestingly though, this time the list of usual suspects is changing.

In a recent article posted to CNN.com, Julianne Pepitone observes that many cities that were tops in foreclosure a year ago have since shown stabilization, while at the same time, other cities have regressed. A related article outlines a growing list of cities that, not long ago, considered themselves immune from the problems being experienced in other parts of the country. Previous economic success stories are now being identified as economic laggards and experiencing the same pains, but only a year or two later.

So – is there a lesson to be taken from this?

From a business intelligence perspective, the lesson is generalized reporting information and forecasting capabilities are not going to be successful in managing risk. Risk management solutions and forecasting techniques will need to be developed around specific macro- and micro-economic changes. They will also need to incorporate a number of economic scenarios to properly reflect the range of possible future outcomes. Moving forward, it will be vital to understand the differences in unemployment between Dallas and Houston and between regions that rely on automotive manufacturing and those with hi-tech jobs.

These differences will directly impact the performance of lenders’ specific footprints, as this year’s “Best Place to Live” according to Money.CNN.com can quickly become next year’s foreclosure capital.

*ihttp://money.cnn.com/2009/10/28/real_estate/foreclosures_worst_cities/index.htm?postversion=2009102811

*iihttp://money.cnn.com/galleries/2009/real_estate/0910/gallery.foreclosures_worst_cities/2.html

--by Tracy Bremmer

There has been a lot of hype these days about people strategically defaulting on their mortgage loans. In other words, a consumer is underwater on their house and so he/she makes a strategic decision to walk away from it. In these instances, the consumer is current on all of their non-mortgage accounts, but because the value of their home is less than what they owe, they make the decision to default on their mortgage loan.

Experian and Oliver Wyman teamed up to really dig into this population and determine these issues:

• Does this population really exist?
• If so, what are the characteristics of this population, such as assessing credit risk or bankruptcy scores?
• How should loan modification strategies be differentiated based on this population?

This blog will be one of a three-part series that addresses these questions. Let’s begin with the first question.

1.  Does this population really exist?
The quick answer is yes – this population does indeed exist. In fact, in 2008 strategic defaulters represented 18 percent of all mortgage defaults, up 500 percent from 2004. When we conducted our study we found there were varying populations that also existed when it came to mortgage defaults. In fact, we classified mortgage defaulters into five categories: strategic defaulter, cash flow manager, distressed defaulter, no non-real estate trades, and pay-downs.

We defined these populations as follows:

• Strategic defaulter - Borrowers who are delinquent on their mortgages, even when they can afford the payment, because their loan balance exceeds the value of their home,
• Cash flow manager - Borrowers facing delinquency issues with their mortgage because of temporary distress, but continue to make payments on all credit obligations,
• Distressed defaulter - Borrowers facing potential affordability issues that go delinquent on their mortgage along with other credit obligations,
• No non-real estate trades – Borrowers who are delinquent on their mortgage, however they do not have any other non-mortgage trades to evaluate if they have strategically defaulted or are in distress,
• Pay-downs – Borrowers who pay down their mortgage loan.

In my next blog, I will address the characteristic differences in behavior between these populations. Specifically, I will evaluate what characteristics make strategic defaulters stand out from the rest and what is unique about the cash flow managers.

Source: Experian-Oliver Wyman Market Intelligence Reports; Understanding Strategic Default in Mortgage topical study / webinar. August 2009.

-- 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

In a recent article, www.CNNMoney.com reported that Federal Reserve Chairman, Ben Bernanke, said that the pace of recovery in 2010 would be moderate and added that the unemployment rate would come down quite slowly, due to headwinds on ongoing credit problems and the effort by families to reduce household debt.’

While some media outlets promote an optimistic economic viewpoint, clearly there are signs that significant challenges lie ahead for lenders. As Bernanke forecasts, many issues that have plagued credit markets will sustain themselves in the coming years. Therefore lenders need to be equipped to monitor these continued credit problems if they wish to survive this protracted time of distress.

While banks and financial institutions are implementing increasingly sophisticated and thorough processes to monitor fluctuations in credit trends, they have little intelligence to compare their credit performance to that of their peers.  Lenders frequently cite that they are concerned about their lack of awareness or intelligence regarding the credit performance and status of their peers.  Marketing intelligence solutions are important for management of risk, loan portfolio monitoring and related decisioning strategies.

Currently, many vendors offer data on industry-wide trends, but few vendors provide the information needed to allow a lender to understand its position relative to a well-defined group of firms that it considers its peers. As a result, too many lenders are performing benchmarking using data sources that are biased, incomplete, inaccurate, or that lack the detail necessary to derive meaningful conclusions.

If you were going to measure yourself personally against a group to understand your comparative performance, why would you perform that comparison against people who had little or nothing in common with you? Does an elite runner measure himself against a weekend warrior to gauge his performance? No; he segments the runners by gender, age, and performance class to understand exactly how he stacks up.

Today’s lending environment is not forgiving enough for lenders to make broad industry comparisons if they want to ensure long-term success. Lenders cannot presume they are leading the pack, when, in fact, the race is closer than ever.

-- 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.