Between late 2025 and the first half of 2026, some of America’s most recognizable restaurant brands announced plans to shut down hundreds of locations. Wendy’s confirmed closures of up to 358 stores. Jack in the Box revealed plans to shutter as many as 200 restaurants. Noodles & Company, Red Robin, Arby’s, and several Burger King franchisees added to the growing list. The wave of restaurant shutdowns sweeping the United States is not just a headline; it is a measurable, data-driven shift that carries real consequences for investors, franchisees, real estate developers, and the millions of consumers who rely on these chains daily.
But here is the critical question most analysts overlook: how do you actually track, quantify, and predict these closures before they happen? The answer lies in structured data. Location datasets, financial filings, consumer traffic patterns, and menu pricing trends all paint a picture of which chains are thriving and which are contracting. For businesses that depend on accurate, timely intelligence about the fast food landscape, fast food chain data scraping has become an essential strategy for staying ahead of these shifts.
This blog breaks down the latest fast food chain closure report numbers, examines the forces driving these shutdowns, and explores how web scraping and structured location data are helping analysts, investors, and competitors turn closures into actionable opportunities.
Fast Food Chain Closures: What the Data Reveals About America’s Shifting Restaurant Landscape
The Scale of Fast Food Chain Store Closures in 2025 and 2026
The U.S. fast food industry generated approximately $412.7 billion in revenue in 2025, according to IBISWorld, growing at a compound annual growth rate of 3.7% over five years. That topline growth, however, masks a significant restructuring happening underneath. Dozens of chains are simultaneously expanding in some markets while aggressively contracting in others. The result is a net closure trend that has accelerated sharply since mid-2025.
Wendy’s, one of America’s top five restaurant chains by location count, announced in November 2025 that it would evaluate its entire U.S. portfolio of roughly 6,000 locations. By early 2026, the company confirmed plans to close between 298 and 358 restaurants, representing 5% to 6% of its domestic footprint. This came on top of 140 closures already announced in 2024. The initiative, branded “Project Fresh,” targets locations with outdated technology and consistently underperforming sales.
Jack in the Box filed an SEC 8-K disclosure confirming at least 72 store closures completed in 2025, with plans to shutter 80 to 120 additional locations by mid-2026. The total could reach 150 to 200 closures under its “Jack on Track” restructuring plan. Noodles & Company announced approximately 50 closures, with 30 completed before the end of 2025 and the remaining 20 slated for 2026. Red Robin outlined a long-term turnaround plan involving roughly 70 restaurant closures, about 14% of its total locations, over five years.
These are not isolated events. Arby’s quietly closed at least 14 locations across eight states in 2025 without issuing a formal closure plan. A major Burger King franchisee operating 57 restaurants filed for Chapter 11 bankruptcy in April 2025. Salad and Go exited the Texas and Oklahoma markets entirely, closing 32 locations by January 2026. MOD Pizza, Subway franchisees, and several Del Taco operators also reduced their footprints through closures or bankruptcy proceedings.
Which Fast Food Chains Are Closing in the USA?
To put the scale of fast food chain closing in USA into perspective, here is a consolidated look at the most significant closure announcements reported between late 2025 and early 2026:
Combined, these closures represent well over 1,000 fast food and fast-casual locations exiting the U.S. market within a span of roughly 18 months. And this table captures only the most prominent cases; hundreds of smaller, unreported closures from independent franchisees and regional operators add to the total.
Why Are Major Fast Food Chains Closing Locations?
Rising Costs, Declining Traffic, and the Profitability Squeeze
The driving forces behind the current wave of QSR closures are interconnected and structural, not merely cyclical. Food-away-from-home prices increased 3.7% between September 2024 and September 2025, according to the U.S. Bureau of Labor Statistics. While that figure may seem modest, it compounds years of cumulative increases that have fundamentally altered consumer behavior. A QSR Magazine survey found that 62% of Americans reported eating less fast food in 2024, citing affordability concerns.
Traffic across the food service industry dropped by 1% in the quarter ending June 2025, per Circana’s consumer tracking data. That single percentage point translates into millions of lost transactions nationwide. When combined with rising labor costs (particularly in states like California, which enacted higher minimum wages for fast food workers), ingredient inflation, and escalating commercial real estate expenses, many individual restaurant locations simply cannot generate sufficient returns.
As Restaurant Business Online summarized: sales have been weak, costs have increased, and profitability has taken a hit. That dynamic forces corporate leadership teams and franchisees alike to make difficult portfolio decisions, closing locations that drag on system-wide performance to free up capital for investment in stronger markets.
Fast Food Chain Closure Report: Key Numbers and Trends
Several patterns emerge when you analyze the fast food chain closure report data across multiple brands. First, closures disproportionately affect franchisee-operated locations rather than company-owned stores. Franchisees bear the direct weight of rising local operating costs and often have less financial flexibility than corporate parents. The Burger King franchisee bankruptcy and the exodus of Subway franchisees who walk away from locations after leases expire both illustrate this pattern.
Second, closures tend to cluster geographically. Chains do not close stores evenly across all markets. Instead, they exit or contract in regions where unit economics have deteriorated most, often secondary and tertiary markets with lower population density or intensifying competition. Arby’s closures, for example, spanned eight states including Tennessee, Florida, California, and several mid-Atlantic states.
Third, many closures follow previous closure rounds. Wendy’s 2026 plan came on top of 140 closures from 2024. Jack in the Box’s current restructuring extends an earlier contraction phase. This pattern suggests that initial closures often do not go deep enough to stabilize a brand’s economics, leading to successive rounds of portfolio optimization.
Key Insight: The fast food industry is not shrinking overall. The global market is projected to grow at roughly 5% to 7% CAGR through 2030, reaching well over $1 trillion. However, growth is concentrating among fewer, stronger locations and digitally mature brands, while weaker units are being culled at an accelerating pace.
How Fast Food Datasets Power Smarter Business Decisions
What Fast Food Chain Data Scraping Reveals About Market Shifts
Publicly available information about restaurant closures is scattered, delayed, and incomplete. Official announcements often arrive weeks or months after locations have already gone dark. SEC filings provide aggregate numbers but rarely include specific addresses. Local news reports cover individual shutdowns without connecting them to broader patterns. This is precisely where fast food chain data scraping delivers a decisive advantage.
Web scraping enables systematic, automated collection of location-level data from restaurant chain websites, review platforms, mapping services, business directories, and public regulatory filings. By monitoring these sources continuously, analysts can detect closures in near-real-time, often before any official announcement is made. A location that disappears from a chain’s store locator, stops receiving new customer reviews, or shows updated “permanently closed” status on Google Maps all serve as early closure signals that scraping can capture at scale.
Beyond closure detection, fast food datasets assembled through web scraping provide a rich foundation for competitive analysis. Menu pricing trends, promotional activity, customer sentiment from reviews, operating hours changes, delivery availability, and even job postings (or the absence of them) all contribute to a composite view of brand health at the individual store level.
Real-Time Location Monitoring Through Web Scraping
Consider what a structured fast food location dataset looks like when built through automated scraping:
When this data is collected at scale across thousands of locations and tracked over time, patterns emerge that are invisible to anyone relying on news reports alone. A chain might reduce operating hours at 40 locations three months before any closure announcement. Review volumes might drop sharply at stores slated for shutdown. Job postings might vanish from specific zip codes weeks before the corporate press release. These are the early warning signals that structured restaurant location data makes visible.
Use Cases: Who Needs Fast Food Chain Closure Data?
Investors, Analysts, and Franchisees
For equity analysts covering publicly traded restaurant companies like Wendy’s, Jack in the Box, and Restaurant Brands International, a comprehensive fast food chain closure report built from scraped data offers a granularity that earnings calls and SEC filings cannot match. Instead of waiting for quarterly disclosures, analysts can monitor closure pace in real time, compare announced targets against actual shutdowns, and identify geographic concentration risk before it surfaces in financial results.
Franchisees evaluating whether to acquire or divest locations benefit enormously from closure trend data. If a brand is systematically exiting a particular region, that signals either a market-level problem (declining population, rising costs) or a brand-level retreat that could leave remaining operators with less marketing support and supplier leverage. Conversely, a competitor’s closure in a strong market creates an acquisition or expansion opportunity.
Real Estate Developers and Competitors
Commercial real estate professionals track fast food chain store closures to identify newly available sites in high-traffic corridors. A former Wendy’s or Jack in the Box location often comes equipped with drive-through infrastructure, commercial kitchen buildouts, and established traffic patterns, all of which reduce development costs for the next tenant. Scraped closure data, paired with location coordinates and trade area demographics, helps developers move quickly on these opportunities.
Competing fast food and fast-casual brands use closure intelligence for strategic expansion planning. When a market loses a major chain presence, consumer demand does not disappear; it redistributes. Brands that can identify these gaps early and deploy resources accordingly gain a measurable first-mover advantage.
Technical Deep Dive: Building a Fast Food Chain Closure Tracker with Data Scraping
Data Sources and Schema for Fast Food Datasets
A robust fast food chain closing tracker starts with identifying the right publicly available data sources. Chain-operated store locator pages are the primary source for location-level data, as they are typically updated when stores open or close. Google Maps and Google Business Profiles provide independent verification of operating status, hours, and customer activity. Review platforms like Yelp contribute sentiment data and review velocity metrics. Delivery platform listings on DoorDash, Uber Eats, and Grubhub offer an additional confirmation layer, since closed locations are removed from these platforms relatively quickly.
A well-structured data schema for tracking fast food closures would typically include these fields: chain name, store ID, street address, city, state, ZIP code, latitude, longitude, operating status (active, temporarily closed, permanently closed), last confirmed active date, closure detected date, source of closure signal, franchise or company-owned flag, and any available financial indicators such as review count trajectory.
This schema allows analysts to run queries such as: “Show me all Wendy’s locations in Ohio that transitioned from active to closed between January and March 2026” or “Identify all fast food locations within a 5-mile radius of a specific address that have closed in the past 12 months.” These queries power investment models, real estate prospecting workflows, and competitive intelligence dashboards.
Automating the Fast Food Chain Closure Report Pipeline
Manual tracking of fast food chain store closures is impractical at scale. The U.S. has approximately 215,000 fast food restaurant locations, according to IBISWorld. Monitoring even a subset of major chains across their full location networks requires automated scraping infrastructure capable of handling thousands of pages per day while respecting source websites’ terms of service and rate limits.
Xwiz Analytics specializes in building exactly this type of automated data pipeline. The process involves scheduled scraping of target sources, data normalization and deduplication, change detection algorithms that flag status transitions, and structured output delivery in formats like CSV, JSON, or direct database integration. The result is a living closure intelligence feed that updates continuously rather than quarterly, giving stakeholders a persistent, real-time view of the market.
For organizations that need historical depth in addition to current monitoring, Xwiz can also build backfill datasets that reconstruct closure timelines from archived web data, public records, and aggregated source data. This historical context is critical for trend analysis and predictive modeling.
Why Xwiz Analytics for Fast Food Chain Data Scraping
Tracking restaurant closures at scale requires more than a basic scraping script. It demands infrastructure that can handle tens of thousands of pages daily, intelligent change detection that distinguishes a temporary closure from a permanent one, and data quality processes that eliminate duplicates and false positives. This is where Xwiz Analytics operates.
Xwiz has built deep expertise in location data scraping across the restaurant, retail, and hospitality sectors. The team delivers structured fast food datasets that include verified addresses, geolocation coordinates, operating status, and enrichment fields such as review scores, menu data, and delivery availability. Every project is tailored to the client’s specific needs, whether that means monitoring 10 chains or 100, covering a single state or the entire U.S.
All data collection follows GDPR-compliant and DMCA-protected practices, scraping only publicly available information. Xwiz provides data in client-preferred formats with flexible delivery schedules, from one-time extractions to ongoing monitoring feeds. For analysts, investors, and businesses that need reliable, large-scale fast food location intelligence, Xwiz Analytics delivers accuracy, speed, and scale.
Frequently Asked Questions
Which fast food chains are closing the most locations in 2026?
Wendy’s leads with plans to close 298 to 358 U.S. locations in the first half of 2026 under its Project Fresh initiative. Jack in the Box follows with up to 200 total closures expected by mid-2026. Red Robin and Noodles & Company have also announced significant reductions in their store counts.
What causes fast food chain closures in the USA?
The primary drivers include rising labor and food costs, declining consumer traffic, increased competition from fast-casual and digital-first brands, and lease expirations on underperforming locations. Many chains grew too aggressively during earlier periods and are now correcting their footprint.
How can web scraping track fast food chain store closures?
Web scraping automates the collection of location data from chain websites, Google Maps, review platforms, and delivery apps. By monitoring changes in operating status, review activity, and listing availability, scraping tools can detect closures in near-real-time, often before official announcements.
What data fields are included in a fast food closure tracking report?
A comprehensive closure report typically includes chain name, store address, city, state, ZIP code, GPS coordinates, operating status, closure detection date, data source, and franchise vs. company-owned classification. Enrichment fields like review scores and delivery availability can also be included.
Where can I get accurate fast food datasets for market research?
Xwiz Analytics provides custom location datasets built through systematic web scraping of publicly available sources. Datasets are delivered in structured formats like CSV and JSON, covering location data, menu pricing, reviews, and operating status across major U.S. fast food chains.
Is fast food chain data scraping legal?
Scraping publicly available data is generally permissible when done in compliance with website terms of service, applicable data protection regulations like GDPR, and DMCA guidelines. Xwiz Analytics follows all relevant compliance standards and collects only publicly accessible information.
How often should fast food closure data be updated?
For active monitoring and investment analysis, weekly or bi-weekly updates are recommended. For market research and strategic planning, monthly updates typically provide sufficient granularity. Xwiz Analytics offers flexible delivery schedules based on client requirements.
Conclusion
The current wave of fast food chain closures across the United States represents a significant restructuring of one of the country’s largest industries. With over 1,000 locations from major brands scheduled for shutdown between 2025 and 2026, the implications reach far beyond displaced diners. Investors, franchisees, real estate professionals, and competing brands all stand to gain or lose depending on how quickly they can access and act on accurate closure data.
Structured location intelligence built through professional web scraping transforms scattered news reports and delayed filings into a real-time intelligence layer. Whether you need to track closure pace for an investment thesis, identify available QSR sites for development, or monitor competitor contraction for expansion planning, the right data partner makes the difference between reacting to headlines and anticipating them.
Xwiz Analytics helps businesses harness the power of fast food chain data scraping to build custom closure trackers, location databases, and competitive intelligence feeds. If your organization needs reliable, large-scale fast food data, reach out to the Xwiz team to discuss a tailored solution.
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