The MBS market has long recognized the valuable prepayment characteristics associated with certain loan and borrower attributes such as loan size, Alternative-A documentation, and New York geography. In 1997 the practice of harvesting these loans for separate pooling took on new dimensions as the universe of loans with small balances was demonstrated to have better convexity characteristics than typical TBA pools. Today loan filtering takes place at nearly every stage of the pooling and delivery process as originators, dealers and the agencies identify, segment and extract a premium for (or place in portfolio) loans with the best prepayment characteristics. Indeed, as a first step in the pooling process, originators and dealers now routinely screen raw loan files for a range of characteristics that command a premium to TBA. Even at the pool level the market has become much more discriminating with respect to WAC and seasoning ("low WAC" pools will be addressed at the end of this report).
In the context of the traditional TBA market, this adverse prepayment selection increases the probability that a TBA investor (who by definition is at the end of the "food chain") will be delivered a pool that is more negatively convex than the aggregate universe of pools with similar age and weighted average coupon (WAC). This fact has been highlighted by the current refinancing event where large-scale filtering has inflated true TBA prepayments relative to the universe. This presents TBA investors with two fundamental problems when assessing the prepayment risk of TBApools. First, actual prepayments for the true TBA sector are masked by agency prepayment reports that reflect the entire universe of MBS in a given coupon/vintage bucket. Second, since prepayment models are estimated on aggregate data rather than true TBA data, there is the potential for significant modeling error when using these models to forecast prepayments for TBAs. We will address both of these concerns in this report.