An Examination of Mortgage Loan Servicing Rights in the Aftermath of the Subprime Mortgage Crisis of 2006
Bibliographic record
Abstract
This study examines whether mortgage servicing firms are capitalizing mortgage loan servicing rights (MSRs) consistent with the FASB’s objective of fair value accounting. The FASB issued SFAS No. 156, “Accounting for Servicing of Financial Assets - an Amendment of FASB Statement No. 140,” and SFAS No. 157, “Fair Value Measurements,” which clarified that MSRs be capitalized at their fair value. Fair value would imply that only market-related value assumptions influence the capitalization of MSRs. A previous study examined this issue prior to the 2006 financial crisis. That study found that non-market, firm-specific characteristics consistent with Positive Accounting Theory (PAT), that should have no effect on the value of MSRs, did have a statistically significant influence on the capitalization of MSRs. This issue has not been examined post-SFAS No. 156 and 157 or the financial crisis of 2006. We reexamine the issue with data gathered from 2007 through 2010. The evidence suggests that the PAT characteristics that influenced the capitalization of MSRs prior to the accounting clarification and the financial crisis do not influence the capitalization of MSRs during the post-crisis period under examination, as predicted by PAT. However, the data do present some evidence suggesting that achieving the objective of “fair value” may not be happening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".