loss given default

Loss given default is the share of principal actually lost once a defaulted loan liquidates, net of recoveries.

Freddie Mac's Single-Family Loan-Level Dataset publishes per-loan actual loss detail across about 56 million loans, which makes it a standard basis for estimating LGD models.

In this catalog it appears concretely: - Freddie Mac licensing.terms_notes — "$26,250 license fee".

Conventions decide whether two sources can be joined at all. Buy one side of an analysis that ignores this convention and every reconciliation step — calendar alignment, unit conversion, entity matching — lands on your team instead of the vendor's.

The failure mode is concrete: the label appears in a listing, the delivered files tell a different story, and the gap surfaces mid-project when fixing it is most expensive.

You rarely have to take a vendor's word for it. 82.4% of the 1,744 datasets Datadory catalogs are free to access, and Freddie Mac licensing.terms_notes lets you inspect the real artifact before any budget is committed.

Treat every claim of this attribute as testable:

1. Open Freddie Mac licensing.terms_notes and confirm its record — "$26,250 license fee" — against the files you actually receive.
2. Pin down update cadence in writing. Across this catalog, 22.6% of 1,744 datasets refresh daily and 64 still arrive only through a manual request form, so ask exactly how fresh each release is.
3. Check whether definitions are verified at all. Field definitions are verified for 1495 of 1,744 datasets (85.7%), and any source you license should meet that bar.
4. Price the delivery route before the license. In this catalog bulk download is the most common access method (725 datasets) ahead of official APIs (574), and 379 sources still require scraping — a maintenance cost that lands on you, not the vendor.

See the term applied to real records: commercial-residential-mortgage-finance data.

Adjacent concepts worth reading next: - delinquency rate - GSE acquisitions - loan-level dataset