Franchise Cost Dataset (2025 FDDs)

Structured cost data for 172 U.S. franchise brands, extracted from publicly available Franchise Disclosure Documents (FDDs).

Every franchisor is legally required to file an FDD before selling franchises. These documents contain the real numbers — initial investment ranges, royalty rates, advertising fees, unit counts, financial performance data, and litigation history. This dataset extracts the key data points into a single, comparable format.

What's in the data

Each row represents one franchise brand with 33 fields:

Field	Description
brand_name	Brand name (e.g., McDonald's, Subway)
category	Industry category (QSR, Home Services, Fitness, etc.)
franchise_fee_low/high	Initial franchise fee range ($)
investment_low/high	Total initial investment range from Item 7 ($)
royalty_type	Percentage, flat fee, or greater-of
royalty_value	Royalty rate or amount
ad_fund_value	Advertising/marketing fund contribution
has_item19	Whether the FDD includes financial performance data
average_gross_revenue	Average unit revenue (if disclosed)
median_gross_revenue	Median unit revenue (if disclosed)
total_units	Total franchise + company-owned locations
net_growth_rate_pct	Year-over-year unit growth
health_score	Composite score (0–100) based on growth, fees, and risk
fee_burden_pct	Total ongoing fees as % of revenue
payback_period_years	Estimated investment payback period
litigation_total_cases	Number of legal cases in FDD
litigation_risk_level	Risk classification

Full field list in the CSV header.

Files

data/
  franchise-costs-2025.csv    # 172 brands, 33 columns
  franchise-costs-2025.json   # Same data in JSON format

Categories

Category	Brands	Avg. Investment Range
QSR	32	$500K – $2.5M
Home Services	31	$80K – $250K
Food	17	$300K – $1.5M
Fitness	14	$150K – $500K
Automotive	14	$200K – $600K
Education	12	$80K – $300K
Personal Services	12	$100K – $350K
Pet	8	$150K – $500K
Senior Care	3	$80K – $200K
+ 7 more	29	varies

Quick start

Python:

import pandas as pd

df = pd.read_csv("data/franchise-costs-2025.csv")

# Cheapest franchises to open
df.nsmallest(10, "investment_low")[["brand_name", "category", "investment_low", "investment_high"]]

# Average royalty by category
df.groupby("category")["royalty_value"].mean().sort_values()

# Brands with Item 19 financial data
df[df["has_item19"] == "yes"][["brand_name", "median_gross_revenue", "health_score"]]

R:

df <- read.csv("data/franchise-costs-2025.csv")
head(df[order(df$investment_low), c("brand_name", "category", "investment_low")], 10)

Source & methodology

All data extracted from 2025 FDD filings sourced from state regulatory databases (primarily Minnesota Department of Commerce CARDS system). Extraction was done programmatically with manual verification of key fields.

For an interactive version with filtering, comparison tools, and full brand detail pages, see franchisevs.com.

The FranchiseVS API also provides this data programmatically:

curl https://franchisevs.com/api/brands
curl https://franchisevs.com/api/brands/mcdonalds

License

This dataset is released under the CC BY 4.0 license. You're free to use, share, and adapt it for any purpose — just give attribution.

Suggested citation:

FranchiseVS. (2025). Franchise Cost Dataset: Structured FDD data for 172 U.S. franchise brands. https://franchisevs.com

Related

• FranchiseVS — interactive franchise cost comparison tool
• FranchiseVS API — REST API for franchise data
• 2025 Franchise Cost Report — analysis of investment trends across 170+ brands