Perhaps if you’ve got a hammer, everything looks like a nail, but it does strike us here at Aster that one of the underlying causes of the liquidity crisis is a problem with data and analytics. After all, the reason that banks don’t want to lend to each other anymore is that they don’t trust that the other banks really know the value of their mortgage-backed securities (MBS) –mainly because they themselves don’t trust the value of their own.
So why is this? Why is it hard for banks to understand the value of a particular mortgage backed security? Simple –a bank holding those MBS’s doesn’t have access to the granular data on the underlying individual assets (the mortgages, with their underlying properties and payees) that make up the pooled asset. Simple questions such as “In what zip codes are the properties backing these mortgages, and by what percentage have median property prices changed since the mortgage was issued?” can’t easily be answered. In other words, unlike stocks, for which you have easy access to a company’s full financial statements to value, the underlying value of an MBS is difficult to figure out because the data needed to price the underlying asset simply is unavailable.
The reason for this is that traditional MBS modeling has focused much more on the behavior of those securities under varying interest rate scenarios because, historically, the ratio of pre-payment vs. held-to-term of those mortgages has been the big swing factor in how much those securities were worth. When interest rates fall, people are more likely to pay off or refinance their existing mortgage, and when interest rates rise, people are more likely to hold onto their existing (now lower interest rate) mortgage. The default rate on the underlying mortgage was traditionally taken as a given, and not predictively modeled at all.
People sometimes ask us –what is the price for not implementing a world-class analytics capability. I think now we can answer, “$700 billion, and counting.” What has emerged to be crucial in this crisis are two pieces of data that are not tracked by standard models: 1) Is the current property value above or below the value of the mortgage(s) on the property, and 2) Can a mortgage holder afford his or her mortgage payments?
This has to change. If mortgages are going to continue to be securitized in the future, it will be necessary to have valuation models that track details on the underlying real assets (specifically, the property values, and the owners’ ability to pay) on at least a monthly, if not more frequent basis. We would propose a national centralized registry for the mortgage securitization industry that matches property locations to mortgage pools, and provides a number of valuation models for those properties (analogous to Zillow’s property valuation model). It would also provide ongoing tracking of either a FICO score or more granular credit quality information for those mortgage holders. In addition –and this is crucial – a centralized repository of the detailed data disclosed by the mortgage applicant in the original approved mortgage application needs to be retained in this registry. On top of this shared database, financial services companies would be free to create valuation and pricing models based on whatever predictive drivers they belief influence the default and payment rates.
Obviously such a centralized repository would require bullet-proof security and privacy handling, but such a registry needs to be established if we’re not going to fall into this mess all over again. Similar registries would be appropriate for student loan pools and other securitized loan categories such as auto-loans. Is anyone else thinking that this is all a matter of data?
