Abstract
Investors in pools of single-family mortgage loans may have only limited information about the individual loans within a pool. Would more information be useful? The authors use data on individual loans to estimate a model of sales, refinancings, and defaults. They construct hypothetical loan pools and examine their prepayment sensitivity to collateral and credit information not universally made available to investors. Simulations show that loan-level data can be extremely valuable in predicting pool durations. In particular, information on the distributions of homeowners' loan-to-value ratios—and to a lesser extent on their credit scores—can be quite important in distinguishing fast-paying from slow-paying pools.
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