In this editor's humble opinion, one primary cause of the subprime/financial crisis was the fact that banks, capital markets firms, hedge funds, insurance companies and pension plans bought billions of dollars worth of collateralized debt obligations and engaged in credit default swaps around CDOs without knowing much about the underlying securities that were bundled into those products. They trusted the issuer, they believed the AAA ratings from Moody's and Standard & Poors, they saw their peers investing in CDOs and making a fortune, or they simply looked at the high promised returns (where else could they get a guaranteed 12% return?) and salivated. But where the underlying securities (or a portion of them) were subprime mortgages, an analysis of those loans, including the credit history and financial condition of the borrowers, could have saved some of these investors from losing big on these toxic assets.So when we saw today that a new company called BlackBox Logic, founded by several former Fannie Mae executives, is offering a database of loan-level information about mortgages underlying non-agency residential mortgage-backed securities, we immediately saw the value of such a product. It's not the first - Markit, Standard & Poors and Bloomberg are among the providers of mortgage data - but it's addressing a still-lingering need for accurate, timely data of this nature.
BlackBox Logic offers a loan-level data aggregation service called BBx Data that covers the Jumbo A, Subprime and Alt A mortgage markets. It includes more than 7,200 residential mortgage-backed securities, 21 million loans and nearly 600 million remittance records, dating back to 1999.
BlackBox Logic is headquartered in Denver and has offices in New York and Bethesda, Md. It was founded in 2007; the company says it spent more than two years designing and testing a beta version of BBx Data with RMBS researchers, investors and broker/dealers.
The company says that on average, by standardizing formats and populating data gaps, BBx Data's cleansed data provides a 37 percent improvement on important loan characteristics when compared to unformatted, raw data, saving users programming time. BBx Data users have the option of receiving both cleansed and raw data sets.
The product is said to use a dedicated loan modification processing engine to provide users with coverage of current and retrospective modifications, to take into account the current volume and pace of modification activity. Using proprietary logic, the BBx Data production team identified more than 60,000 loan modifications not identified by servicers or reported by trustees.
BlackBox's founders say they allow clients to purchase only the data they need, rather than the full 21-million loan dataset. BBx Data can be accessed by sector, vintage, deal/CUSIP, state, ZIP Code, MSA or other extract options; customers that choose to select partial datasets pay only for what they need.
BlackBox Logic offers users three ways to access the BBx Data dataset. Users can access BBx Data through Crystal Logic, a proprietary web-based interface and analytics program, for bond-level data extraction, collateral manipulation and research. Crystal Logic offers the ability to develop custom analytics, with tools including a multi-tiered dashboard enabling analysis by deal, bond or portfolio; historical performance reporting based on more than 30 key indicators; monthly roll-rate analysis; payment velocity overviews; advanced cohort creation; and collateral drill-down and loan-level detail views, among others.
BBx Data from BlackBox Logic also can be accessed through the web-based 1010data interface.
Users that couple loan level data with in-house analytic workflow can choose to download BBx Data directly from secure servers. Custom data delivery options are also available.
Larry Barnett, chief executive officer of BlackBox Logic, spent 12 years at Fannie Mae, where he was vice president for secondary mortgage trading operations. William Pugh, chief technology officer, also worked at Fannie Mae. Marty Schwartz, lead data modeler, also worked at Fannie Mae, for 14 years.