SERVPRO’s FDD Item 19 is the section of the Franchise Disclosure Document that contains financial performance representations, the data that many prospective franchisees consider the most important information in the entire FDD. For disaster restoration franchise buyers investing $221,350 to $255,000 or more, Item 19 is supposed to answer the fundamental question: how much money can I expect to make? At Lopes Law LLC, we regularly analyze SERVPRO’s Item 19 data for prospective franchisees, and the most important advice we give is this: Item 19 answers the revenue question, not the profitability question, and the distinction is worth hundreds of thousands of dollars.

This guide explains what SERVPRO discloses in Item 19, how to interpret the data accurately, why average revenue figures are particularly misleading in the disaster restoration industry, and what financial modeling you need to do beyond what the FDD provides.

What Is FDD Item 19 and What Does SERVPRO Disclose?

Item 19 of any Franchise Disclosure Document is titled “Financial Performance Representations.” The FTC Franchise Rule (16 C.F.R. Part 436) allows, but does not require, franchisors to include financial performance data. If a franchisor chooses to make financial performance representations, it must do so in Item 19 and must have a reasonable basis for the claims.

SERVPRO includes an Item 19 in its FDD, which gives prospective franchisees access to historical revenue data for the franchise system. The disclosure typically includes gross revenue figures broken down by various categories, which may include revenue ranges, percentile breakdowns, averages, and medians for franchised locations.

What Item 19 does not include is equally important: operating expenses, net profit margins, owner compensation, labor costs, equipment depreciation, insurance expenses, or any other cost data that would allow you to calculate actual profitability. Item 19 tells you the top line. It does not tell you the bottom line.

Why Is Average Revenue Especially Misleading for SERVPRO?

Average revenue is a misleading metric in any franchise system, but it is particularly unreliable in disaster restoration for three interconnected reasons.

First, revenue is event-driven and highly variable. A SERVPRO franchise in a hurricane-prone area that experienced a major storm in the data year may have generated $2 million or more in revenue. A franchise in a stable climate market may have generated $300,000. Both are included in the average, but their situations are not comparable. A single catastrophic weather event can pull the system average up dramatically without reflecting the typical experience of the majority of franchisees.

Second, the system includes locations of vastly different ages. A SERVPRO franchise that has been operating for 15 years with established insurance relationships and trained crews is not comparable to a franchise that opened 18 months ago and is still building its referral network. The system average includes both, creating a composite figure that does not accurately represent either group.

Third, territory size and population density create enormous variation. A SERVPRO franchise serving 200,000 people in a densely populated suburban market has fundamentally different revenue potential than a franchise serving 50,000 people in a rural area. Both are part of the same system and both contribute to the system average, but their financial realities are worlds apart.

How Should You Analyze SERVPRO’s Item 19 Data?

To extract meaningful insights from SERVPRO’s Item 19, follow this analytical framework:

Step 1: Identify the Data Set

Determine how many franchise locations are included in the Item 19 data, what time period the data covers, and whether any locations were excluded. A data set that excludes locations open less than one year, locations that closed during the period, or locations in certain geographic regions may present a rosier picture than the complete system.

Step 2: Compare Average to Median

If both figures are available, the gap between the average and the median tells you how skewed the distribution is. A large gap means a small number of high-performing locations are pulling the average up. The median is a more reliable indicator of the typical franchisee’s experience.

Step 3: Examine the Distribution

If Item 19 provides quartile data or revenue ranges, determine what percentage of locations fall at each level. Pay particular attention to the bottom quartile, which shows the revenue level of the lowest-performing 25% of the system. If you are a new franchisee in an average territory, the bottom quartile is a more realistic basis for conservative financial modeling than the system average.

Step 4: Match to Your Territory

Identify the locations in the data set that most closely resemble your proposed territory in terms of population, geography, climate risk, and market maturity. The system average for a national network of 2,000+ locations is far less relevant than the performance of the 10 to 20 locations most similar to yours.

How Do You Calculate Actual Profitability From Item 19 Revenue Data?

Because Item 19 provides revenue data but not expense data, you need to build your own profitability model. Here is a framework for modeling a SERVPRO franchise based on Item 19 revenue and the fee data from Item 6:

Line Item	Conservative ($400K Revenue)	Moderate ($700K Revenue)
Gross Revenue	$400,000	$700,000
Royalty (Graduated)	~8% = ($32,000)	~5% = ($35,000)
Advertising (3%)	($12,000)	($21,000)
Equipment / Supplies	($25,000)	($45,000)
Labor (35-45%)	($160,000)	($280,000)
Vehicle / Fuel	($20,000)	($35,000)
Insurance	($15,000)	($20,000)
Facility / Office	($18,000)	($24,000)
Technology / Admin	($8,000)	($12,000)
Estimated Owner Income	$110,000	$228,000

These are illustrative estimates. Actual expenses vary significantly by market, territory, staffing model, and operational efficiency. The critical point is that Item 19 revenue data alone does not tell you what you will earn. You must subtract the full expense structure to arrive at a realistic owner income figure.

What Are the Common Mistakes When Interpreting SERVPRO’s Item 19?

• Using the average as your projection: The average includes top performers and outliers. Model your financials using the median or, better yet, the 25th percentile for conservative planning.
• Ignoring the graduated royalty effect: Because SERVPRO’s royalty is 3% to 10%, the royalty burden at lower revenue levels is proportionally much higher. New franchisees generating $300,000 to $400,000 pay a far higher effective royalty rate than established franchisees generating $1 million+.
• Not accounting for revenue variability: Disaster restoration revenue is inherently unpredictable. Model a downside scenario (a year with no major weather events) alongside the average to stress-test your cash flow.
• Overlooking the equipment sourcing premium: The mandatory equipment purchasing from SERVPRO distribution, detailed in Item 8, creates an ongoing cost premium that reduces profitability below what a standard expense model would suggest.
• Comparing to other franchise categories: A SERVPRO franchise generating $500,000 in revenue is not comparable to a retail franchise generating $500,000. The cost structures, labor requirements, and capital needs are fundamentally different.

The franchise industry has repeatedly demonstrated the consequences of over-optimistic financial projections. Dickey’s Barbecue Pit accumulated 27 SBA loan defaults, and Patrice & Associates experienced a 31% failure rate, with unrealistic revenue expectations contributing to financial distress in both cases.

A Scenario: When Item 19 Analysis Changed the Investment Decision

A prospective SERVPRO franchisee came to our firm after a discovery day where the franchisor presented the system’s average revenue figures. The buyer was excited about the revenue potential and had begun the SBA loan application process based on projections tied to the system average.

When we analyzed the Item 19 data in detail at Lopes Law LLC, we found that the system average was significantly inflated by a small number of high-revenue locations in storm-prone regions. The median revenue was approximately 30% lower than the average. The buyer’s proposed territory was in a stable climate region with limited storm exposure, making the system average a poor proxy for expected performance.

We modeled the buyer’s projected revenue using comparable locations in similar markets and applied the full Item 6 fee structure, including the graduated royalty at startup-phase rates and the equipment sourcing premium. The resulting owner income projection was approximately $55,000, well below the $120,000 the buyer had estimated using the system average.

The buyer ultimately negotiated a larger territory with SERVPRO and restructured the financing plan to account for a longer ramp-up period. That decision, informed by accurate Item 19 analysis, positioned the buyer for success rather than setting expectations that the business could not meet.

For franchisees already in the SERVPRO system who are underperforming relative to Item 19 projections, our franchise exit audit can evaluate your options and help you determine the best path forward.

Why Legal Review of Item 19 Is Essential

Item 19 data requires careful interpretation to avoid overestimating revenue potential. A franchise attorney can analyze the data methodology, identify assumptions that may not apply to your specific territory, model the complete fee structure against projected revenue, and compare the financial opportunity to other franchise investments you may be considering.

At Lopes Law LLC, our flat-fee FDD validation review is $3,000 and covers all 23 items, with detailed Item 19 financial analysis that models revenue data against the Item 6 fee structure for your specific territory. For more about our review process, see our guide on what to expect from an FDD review.

Frequently Asked Questions About SERVPRO FDD Item 19

Reach out, we are friendly. Call now for a free consultation at (267) 777-9117 or schedule your free 20-minute consultation online.

Last updated: March 11, 2026