What a Subprime Mess We've MadeBy Lawrence Walsh Print
Could business intelligence have prevented the mortgage meltdown? Doubtful.A day doesn't go by without a headline about the subprime mortgage crisis. At the time of this writing, economists and market analysts were debating whether the housing meltdown would push the U.S. economy into a recession. When all is said and done, banks and investors stand to lose $300 billion to $400 billionor roughly one-third of the entire subprime market valuation.
You would think banks and financial institutions, the vanguards of business intelligence, would have performed exhaustive analysis and market modeling before signing off on so many bad notes. Perhaps more so than any other industry, financial institutions crunch and analyze vast amounts of data to decide where best to park investors' money.
As it turns out, the subprime crisis is the perfect storm of good old fashioned greed, data and intelligence blindness, and blissful ignorance.
Subprime mortgages opened home ownership to millions of people who had previously been locked out of the housing market. By extending credit to riskier borrowers, home ownership climbed from 64 percent at the beginning of the decade to 70 percent in 2006. At the same time, subprime loans created new products for long-term securities investments, namely collateralized debt obligations (CDOs). With 30-year terms and high-interest returns, CDOs provided the perfect alternative to bonds. The insatiable appetite for long-term investment products prompted mortgage and investment houses to create more CDOs with riskier profiles.
"People couldn't have avoided the [crisis] because it started with demand and the lack of supply [for CDOs]," explains John Garvey, a PricewaterhouseCoopers partner who studies the financial services market. "When you need more and more, you can't get the quality of supply to meet the demand."
Surprisingly, the market modeling of CDOs and its cousin, CDO-squared, was performed under the optimistic assumption that housing prices would continue to increase. But when the Center for Responsible Lending did analysis based on flat housing prices, the subprime market model fell apart. While acknowledging the riskiness of subprime mortgages, federal regulators, including former Federal Reserve chairman Alan Greenspan, thought the need to open home ownership to more Americans was worth the chance. Of course, we all now know the price of taking that risk.
Investors easily get caught up in the exuberance of bullish opportunities like the subprime market, but really, only a pitiful lack of intelligence was available. Mortgages aren't written by investment banks or investment brokerages. Few investors knew how risky subprime mortgages were or how loan originators were signing off on notes with low initial teaser rates, no income-verification and no down-payment requirements. The disaggregation of information and stakeholders was so great that the warning signs were diluted.
It also turns out that our assumptions about the sophistication of financial institutions' business intelligence capabilities are greatly exaggerated. As Garvey explains, financial institutions' data collection and analytics are fragmented, and there's a lack of governance and business alignment to provide a true picture of performance and risk.
"Everyone has models, but you may have modeled things when the products were first developed," he says. "Risk managers' systems don't capture all the relevant elements."
In the wake of the subprime crisis, PWC reports its financial services clients are now investing heavily in consolidated business intelligence solutions to give themselves a more comprehensive view of their business opportunities and risks. Early signs of this trend were confirmed in the Ziff Davis Enterprise "Future of IT" survey, which showed financial services IT executives prioritizing the centralization of IT departments, consolidating project management and radically restructuring their IT departments. Business intelligence is financial services' top IT technology priority in 2008, the survey found.
It's not enough to measure or have business intelligence capabilities; you must have the right data and enough insight into the totality of operations so that the results of quantitative analysis are not only actionable but accurate. As the saying goes, "Garbage in, garbage out." It looks like we'll be paying for the pile of subprime garbage for some time.
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