The State of US Fast-Food Store Closures in 2026: QSR Footprint Trends

US fast food is not contracting evenly. Behind a roughly flat-looking headline, some QSR segments are closing units faster than they open them, and the losses cluster in specific regions and store formats.

This report turns public store-location data, tracked over time across 40+ US QSR brands, into a clear picture of who is closing, where, and why the national number hides the real story. It is written for the teams that plan real-estate, supply, and franchise strategy around the QSR footprint.

Key findings at a glance

Three patterns stand out across the footprint data. (Figures below are illustrative previews - the full report breaks them down by brand, segment and metro.)

Key finding 1: which segments are contracting

Closures are concentrated in older formats. Legacy sit-down concepts and dine-in pizza show the highest closure rates, while drive-thru-led chicken and coffee/snack formats are the most resilient.

The pattern tracks a broader shift toward smaller, drive-thru and digital-first footprints - chains are closing aging dine-in boxes and, in many cases, reopening leaner formats elsewhere, which a simple count of closures alone would miss.

Key finding 2: where closures concentrate

Closures are far from uniform geographically. The sample below shows net unit change by region (illustrative).

The South is the one region adding units on net, while the Midwest and Northeast carry most of the contraction - exactly the kind of regional split that a national average erases.

Key finding 3: openings still offset much of the loss

Closures grab headlines, but openings matter just as much. Several brands are net-closing dine-in units while net-opening smaller drive-thru or delivery-only formats, so the footprint is reshaping more than it is simply shrinking. Reading closures without openings overstates the decline; reading both reveals where demand is migrating.

What the underlying data looks like

The report is built from store-location records tracked over time, like the one below - the structure buyers receive in a sample.

{
  "brand": "Example QSR",
  "segment": "pizza",
  "store_id": "EQ-44120",
  "city": "Chicago", "state": "IL",
  "lat": 41.8781, "lng": -87.6298,
  "status": "closed",
  "first_seen": "2024-03-10",
  "last_seen": "2026-05-22",
  "format": "dine_in",
  "captured_at": "2026-06-29T09:00:00Z"
}

  

Aggregated to a brand-and-region view, the same data rolls up into a flat file analysts can model on:

brand,segment,region,closures,openings,net_pct
Example QSR,pizza,Midwest,84,31,-4.1
Example Burgers,burgers,Northeast,52,40,-2.2
Example Chicken,chicken,South,18,46,+3.0
Example Coffee,coffee,West,22,29,+1.4

  

Methodology & data

The findings are based on publicly available store-location data tracked across US QSR brands over time in 2026, then aggregated by brand, segment and region. Closures and openings are inferred from changes in published locations. No personal data is involved. The full report details the brands, regions and how each metric is calculated.

Frequently asked questions