This paper documents the agency costs resulting from the deeper tranching of subprime residential mortgage pools. Mortgage servicers are less likely to renegotiate delinquent loans collateralizing a greater number and variety of tranches. We find that an interquartile increase in tranching reduces mortgage servicers’ probability of loan renegotiation by 14% relative to the mean. This effect is concentrated in mortgages with greater ambiguity surrounding the loan value maximizing action. Overall, our results support the notion that tranching worsens agency frictions by increasing coordination costs among investors and impeding their monitoring of the agent.

We introduce a novel application of machine learning to compare pooling and servicing agreements (PSAs) that govern commercial mortgage-backed securities. In contrast to the view that the PSA is largely boilerplate text, we document substantial variation across PSAs, both within- and across-underwriters and over time. A part of this variation is driven by differences in loan collateral across deals. Additionally, we find that differences in PSAs are correlated with ex post loan and bond performance. Collectively, our analysis suggests the importance of examining the entire governing document, rather than specific components, when analyzing complex financial securities.