Illinois Home Services M&A Data Guide: Defensible 2026 Benchmarks

The honest 2026 bottom line

This site does not currently possess a disclosed, statistically representative dataset of Illinois closed home-services transactions. Accordingly, this guide does not publish an “average Illinois multiple.” That restraint is intentional. A precise-looking number without sample size, period, deal size, earnings definition and closed-versus-asking status is not a benchmark.

Use public data to understand market structure and operating context. Use verified closed deals to infer transaction pricing. Use current listings only as a measure of supply and seller expectations unless a separate source confirms the closing outcome.

Five evidence layers

What the public datasets actually measure

County Business Patterns is an annual series for establishments with paid employees. It includes establishment counts, employment and payroll by industry and geography. Its latest release timing lags the current market, and nonemployer businesses are not the same population. QCEW covers the large majority of US jobs reported by employers and supplies quarterly industry employment and wage data. It is useful for labor-market context, not transaction value.

Before extracting a count, define the NAICS codes. “Home services” is not one official industry. Plumbing/HVAC contractors, roofing contractors, landscaping, exterminating services and restoration-related businesses appear in different classifications; diversified companies may be coded by their primary activity. Disclose every included and excluded code.

Build a comparable closed-transaction set

1. Confirm closing: exclude active, withdrawn and expired listings unless analyzing supply.
2. Define price: distinguish enterprise value, asset price, real estate, inventory, assumed debt, earnouts and contingent consideration.
3. Normalize earnings: record whether the denominator is reported SDE, reviewed SDE, EBITDA or adjusted EBITDA.
4. Set period: record fiscal year, trailing period and closing date. Do not mix boom-year storm revenue with normalized revenue without disclosure.
5. Segment: trade, geography, size, recurring revenue, commercial/residential mix, owner dependence and licensing risk.
6. Show distribution: sample size, median, quartiles, minimum/maximum and reasons for exclusions are more informative than an isolated average.

Normalize before comparing

A $300,000 SDE owner-operator and a $1.5 million adjusted-EBITDA platform are not direct comparables. The smaller company's SDE may include the working owner's compensation; the larger company's EBITDA may include a management team. A transaction with owned real estate, inventory and significant working capital also differs from a cash-free, debt-free asset purchase without real estate.

For weather-exposed trades, create both reported and weather-normalized revenue views. For project businesses, examine backlog, work in process and margin fade. For agreement-based services, analyze active contracts, renewal cohorts and deferred service obligations. These operational attributes explain dispersion; they are not automatic percentage premiums.

Minimum benchmark disclosure

• Data owner and permitted use.
• Extraction date and transaction period.
• Geography and included NAICS/trades.
• Closed, asking or mixed status.
• Price and earnings definitions.
• Sample size before and after exclusions.
• Distribution statistics and material outliers.
• Known missing data and selection bias.

How a seller should use benchmarks

Benchmarks are a reasonableness check, not a substitute for company-specific analysis. Start with normalized earnings and cash conversion, then evaluate transferability: management depth, licensing, customer/technician concentration, service agreements, fleet condition, backlog quality and required working capital. A defensible conclusion explains why the company is similar to or different from the observed set.

Update protocol

Refresh public operating datasets when new releases become available. Refresh transaction observations by closing quarter and retain the prior version so users can see what changed. Never silently replace an asking-price record with an assumed closing price. The methodology is the durable asset; a headline multiple is perishable.

Recommended transaction-data schema

One row per closed transaction should include a unique nonidentifying ID, source, permission, announcement/closing date, state and metro, trade, NAICS where known, asset/equity structure, price components, real estate, inventory, working capital, assumed debt, earnout terms, reported revenue, normalized revenue, SDE, EBITDA, adjustment standard, employee count, recurring-revenue definition, owner role and data-confidence flags. Preserve original source fields next to normalized fields.

Calculation rules that prevent false precision

• Use enterprise value only when debt, cash and assumed liabilities are treated consistently.
• Calculate a multiple only when numerator and denominator periods are aligned.
• Do not mix broker-reported SDE with buyer-reviewed adjusted EBITDA without a separate category.
• Do not impute an undisclosed closing price from the last asking price.
• Do not treat seller financing face value as cash without recording terms and collectability.
• Present nominal transaction values unless a defined inflation adjustment serves a stated purpose.

Confidence grading

Small samples and confidentiality

Illinois trade/geography/size cuts can quickly produce fewer than five observations. Do not publish a distribution that could identify a confidential seller or imply stability. Combine periods or regions only when economically defensible and label the broader population. If the sample is too small, publish the methodology, operating context and a qualitative range of observed structures without a numerical multiple.

Publication checklist

Before calling an output a benchmark report, obtain data-owner permission, deduplicate, reconcile price and earnings definitions, document exclusions, grade confidence, review confidentiality, calculate distribution statistics, conduct an outlier review and have a second reviewer reproduce a sample. Publish a data date and correction channel. The current page remains a methodology guide until that evidence threshold is met.

Refresh and correction protocol

Freeze each published dataset with an extraction date, source version and calculation file. On refresh, append new observations, re-run duplicate and confidence checks, and compare revisions to prior records before recalculating distributions. Record whether a historical value changed because better evidence arrived or because the market changed; those are different findings.

Give readers a field dictionary, inclusion period, sample counts before and after exclusions, and a way to report a suspected error. Reproducibility is more valuable than a larger-looking sample assembled from listings, rumors and incomparable definitions.

Primary sources and review notes