What LoanTape Parses and Why It Matters
LoanTape parses public loan data from two sources: SEC filings for auto ABS securitizations and SBA FOIA releases for small business lending. We normalize everything into clean, queryable datasets and update them on a recurring schedule.
Here is what we cover, how big each dataset is, and what fields you get.
| Dataset | Source | Scale | Fields | Coverage | Updates |
|---|---|---|---|---|---|
| ABS-EE asset-level | SEC EDGAR XML | 42.2M ingested assets | 11+ | 2016-present | Monthly |
| Form 10-D distributions | SEC EDGAR | 20 issuer families | 9+ | 2016-present | Monthly |
| ABS remittance | SEC 10-D filings | 20 issuer families | 8+ | 2016-present | Monthly |
| SBA 7(a) loans | data.sba.gov FOIA | 1.2M+ loans | 14+ | FY1991-present | Quarterly |
| SBA 504 loans | data.sba.gov FOIA | 300K+ loans | 12+ | FY1991-present | Quarterly |
ABS-EE asset-level data
Covered registered Auto ABS securitizations file ABS-EE asset-level XML with the SEC under Regulation AB-II. Each disclosed asset in the pool gets its own record. The raw SEC auto-loan schema uses variable names like obligorCreditScore, originalLoanAmount, paymentToIncomePercentage, vehicleManufacturerName, reportingPeriodActualEndBalanceAmount, currentDelinquencyStatus, chargedoffPrincipalAmount, and zeroBalanceCode. LoanTape preserves those SEC names and also maps them into normalized aliases and derived analysis fields where appropriate.
| Field | What it tells you |
|---|---|
obligorCreditScore |
Borrower credit quality when the loan was underwritten |
originalLoanAmount |
Loan size at funding |
reportingPeriodActualEndBalanceAmount |
How much is still owed |
paymentToIncomePercentage |
Monthly payment as a share of borrower income |
vehicleManufacturerName |
Vehicle make or manufacturer |
vehicleModelYear |
Vehicle vintage |
reportingPeriodInterestRatePercentage |
Current coupon on the loan |
remainingTermToMaturityNumber |
Months left until maturity |
currentDelinquencyStatus |
Current, 30 DPD, 60 DPD, 90+ DPD, etc. |
obligorGeographicLocation |
Borrower location for regional risk analysis |
chargedoffPrincipalAmount |
Principal charged off in the period |
zeroBalanceCode |
Why the loan exited: payoff, chargeoff, repurchase, or other zero-balance event |
At the July 2026 coverage snapshot, the warehouse had ingested 42.2 million Auto ABS assets across 20 tracked issuer families. The classified reporting layer contained 35.4 million loan contracts and 6.7 million lease contracts.
| Coverage measure | Count |
|---|---|
| Assets ingested | 42.2M |
| Classified loan contracts | 35.4M |
| Classified lease contracts | 6.7M |
| Tracked issuer families | 20 |
Form 10-D distribution reports
Form 10-D is the distribution report filed for covered Auto ABS transactions. It reports what happened in the pool during the period: how much was collected from borrowers, how many assets went delinquent, how losses flowed through the waterfall, and how much each tranche of bondholders received.
We have normalized 10-D data across 20 tracked issuer families going back to 2016, updated monthly. Issuers structure remittance disclosures differently, so we map the supported fields to a common schema. You can compare Ally to Toyota to Exeter without doing that reconciliation by hand.
Remittance data
We extract remittance-level data from the 10-D filings. That is 8+ fields per reporting period:
| Field | What it tells you |
|---|---|
| Beginning/ending pool balance | How fast the pool is paying down |
| Collections | Cash received from borrowers that month |
| Net losses | Charged-off balances minus recoveries |
| Cumulative net loss (CNL) | Total losses since the deal closed |
| Prepayment speed (CPR) | How fast borrowers are paying off early |
| Delinquency buckets | Counts and balances by 30/60/90+ days past due |
| Servicer advances | Amounts the servicer fronted to cover shortfalls |
| Excess spread | Interest collected minus interest owed to bondholders |
Broken out by issuer and reporting period, this gives you a monthly time series of trust economics going back to 2016.
SBA 7(a) loan data
We parse SBA 7(a) loan performance data published through FOIA requests on data.sba.gov. The 7(a) program is the SBA's primary lending program: general-purpose business loans up to $5 million. Our dataset contains every 7(a) loan approved since fiscal year 1991, over 1.2 million loans with $31 billion approved in FY2024 alone.
SBA 504 loan data
The 504/CDC program covers long-term fixed-rate financing for commercial real estate and heavy equipment. We have 300,000+ loans in this dataset. The 504 program uses a three-party structure: a Certified Development Company provides the SBA-backed portion, a third-party lender covers the senior debt, and the borrower contributes equity.
Fields across both SBA programs
| Field | 7(a) | 504 | What it tells you |
|---|---|---|---|
| Approval amount | Yes | Yes | Loan size at approval |
| Gross chargeoff | Yes | Yes | Amount written off on default |
| Interest rate | Yes | Yes | Coupon at origination |
| Loan term (months) | Yes | Yes | Maturity length |
| NAICS code | Yes | Yes | Industry classification (mapped to 20 sectors) |
| Lender name | Yes | Yes | Originating bank or CDC |
| Borrower state | Yes | Yes | Geographic location |
| Business type | Yes | Yes | Corporation, LLC, sole proprietor, etc. |
| Jobs supported | Yes | Yes | Jobs reported at approval |
| SBA guarantee % | Yes | No | Portion backed by the government |
| Revolving flag | Yes | No | Line of credit vs. term loan |
| CDC name | No | Yes | Certified Development Company |
| Third-party lender | No | Yes | Senior debt holder in the 3-party structure |
| Project county | No | Yes | County-level geography |
Chargeoff rates by industry
The data gets interesting when you cut it by industry. NAICS sectors have very different chargeoff profiles in the 7(a) program:
| NAICS sector | Example businesses | Relative chargeoff risk |
|---|---|---|
| Accommodation & Food Services (72) | Restaurants, hotels | High |
| Retail Trade (44-45) | Stores, dealerships | Above average |
| Construction (23) | Contractors, builders | Above average |
| Healthcare (62) | Clinics, dental offices | Below average |
| Professional Services (54) | Law firms, consultants | Low |
| Finance & Insurance (52) | Brokerages, agencies | Low |
That kind of breakdown is hard to get from the raw CSVs without cleaning up inconsistent NAICS codes and normalizing vintage-level cohorts.
What we do with it
The pipeline runs daily for auto ABS and quarterly for SBA data. Every time a new SEC filing hits EDGAR or the SBA updates their FOIA files, we parse, validate, and load the data.
On the analytics side, we build visualizations from these datasets: vintage loss curves that show how different origination years are aging, Markov chain transition matrices that model how loans move between Current, 1-29 DPD, 30 DPD, 60 DPD, 90+ DPD, Charged Off, and Cash Collected states, roll-rate heatmaps, loss-to-liquidation analysis, and FICO distribution breakdowns by issuer. You can see examples in our chart gallery.
This blog is where we write about what the data shows. Delinquency trends by issuer. How subprime pools compare to prime. Which SBA lenders have the best track records. What vintage curves tell you about credit tightening. Every number in every article comes from the pipeline.
Who this is for
We built LoanTape because we needed a clean, normalized version of these public datasets and couldn't find one. If you work in structured finance, credit risk, or SBA lending and have spent time wrestling with raw EDGAR XML or inconsistent SBA CSVs, you already know why this exists.
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