Methodology

How we collect, clean, and analyze franchise data to produce the most comprehensive independent franchise intelligence available.

Data Sources

Franchise Disclosure Documents (FDDs)

15,458 filings

Official franchise disclosure documents filed with state regulatory agencies. We source FDDs from state filing portals (California, Minnesota, Wisconsin, Illinois, New York, and others), the NASAA Cooperative Filing Program, and direct franchisor submissions. Each FDD contains 23 disclosure items including fees, financial performance, litigation history, and unit counts.

SBA 7(a) Loan Data

89,000+ loans

U.S. Small Business Administration loan performance data for franchise-related 7(a) loans. This dataset includes loan amounts, approval dates, and default/status outcomes - the only objective, third-party measure of franchisee financial success at scale.

Franchise Unit Counts

5,700+ brands

Multi-year unit count data from FDD Item 20, tracking openings, closures, transfers, and company-owned vs. franchised units over time. We maintain longitudinal records for 3,100+ brands spanning 2+ years.

Extraction Pipeline

1
PDF Ingestion
FDDs are ingested as PDF documents. We extract text from each page, segment by Item number (Items 5-21), and identify table structures for financial data.
2
Structured Extraction
Using a combination of pattern matching and AI-assisted extraction (Gemini Flash Lite for bulk processing, with human review for edge cases), we extract structured fields: franchise fees, investment ranges, royalty rates, ad fund rates, territory definitions, and Item 19 financial data.
3
Deduplication & Reconciliation
Many brands file FDDs in multiple states or across multiple years. We deduplicate filings using brand name normalization, franchisor legal entity matching, and filing date comparison to maintain one canonical record per brand per year.
4
Unit Economics Calculation
From extracted Item 19 data, we compute derived metrics: cash-on-cash return (annual franchisee cash flow / total investment), implied franchisee EBITDA, payback period, and royalty burden (royalty + ad fund as % of revenue).
5
Industry Classification
Each franchise is classified into one of 30+ industry categories using franchisor self-reported SIC codes, NAICS codes, and brand description analysis. Classifications are reviewed quarterly.
6
Quality Scoring
Every record receives a data quality score (0-100) based on completeness of extraction, number of FDD years available, Item 19 presence, and SBA loan data availability. Scores below 50 are flagged for re-extraction.

How We Calculate Key Metrics

Cash-on-Cash Return
Annual Franchisee Cash Flow ÷ Total Investment
Estimated annual return on invested capital. Franchisee cash flow is derived from Item 19 median revenue less estimated operating expenses, royalties, and ad fund contributions.
Royalty Burden
(Royalty Rate + Ad Fund Rate) ÷ Estimated Revenue
Total recurring fees paid to the franchisor as a percentage of revenue. A high burden doesn't necessarily mean a bad deal - it depends on the value of the support and brand provided.
Payback Period
Total Investment ÷ Annual Franchisee Cash Flow
Estimated years to recover the initial investment. Calculated from the investment midpoint and median franchisee cash flow.
SBA Default Rate
Defaulted SBA 7(a) Loans ÷ Total SBA 7(a) Loans (by brand)
The percentage of SBA-guaranteed loans for a specific franchise brand that have defaulted. This is an objective measure of franchisee financial distress - independent of franchisor-reported data.
Net Unit Growth
(New Units − Closed Units) ÷ Prior Year Total Units × 100
Year-over-year percentage change in total franchised units. Sourced from Item 20 of the FDD.
Implied Franchisee EBITDA
Median Revenue × (1 − Royalty Burden − Est. Operating Margin)
Estimated earnings before interest, taxes, depreciation, and amortization for a median-performing franchisee. This is a model, not a reported figure.

Coverage & Freshness

5,792
Franchise Brands
15,458
FDD Filings
1,457
Item 19 Financial Disclosures
3,168
Brands with Longitudinal Data
89,000+
SBA Loan Records
30+
Industry Classifications

Our corpus is updated weekly with new FDD filings from state regulatory portals. We prioritize extraction of brands with the most franchise locations and highest search interest. The SBA loan dataset is updated quarterly from public SBA data releases.

Limitations & Disclaimers

This is not legal or financial advice. FranchiseIQ provides data analysis and tools for franchise due diligence. We are not attorneys, accountants, or investment advisors. All investment decisions should be made with professional counsel.

Item 19 data is self-reported by franchisors. Not all franchisors include financial performance representations in their FDDs. When they do, the data reflects their chosen methodology, timeframe, and sample - which may be favorable to the brand.

Derived metrics are estimates. Cash-on-cash returns, payback periods, and implied EBITDA are calculated from reported data using standard formulas. Actual results vary significantly by market, operator, and timing.

SBA default rates reflect loan performance, not franchisee profitability. A low default rate means franchisees are generally able to service their debt - it doesn't mean they're generating strong returns. Conversely, some profitable franchisees may default for reasons unrelated to business performance.

Questions about our methodology? Get in touch →