15,000 Closures and a Misdiagnosis Problem
Coresight Research tracked roughly 15,000 US store closures in 2025, more than double the 7,325 closures recorded in 2024. Coresight's outlook projects another elevated year in 2026.
The instinct is to call these location failures. Some were. A store in the wrong trade area, with the wrong co-tenants, or in a market that was already saturated. Location data would have flagged these before the lease was signed.
But a large chunk of those closures had nothing to do with location at all. Bed Bath and Beyond didn't close 400 stores because they picked bad addresses. Dollar General didn't shutter 96 stores because it couldn't read a demographic report. Red Lobster's 99 closures in 2024 weren't a real estate problem. Macy's isn't closing 150 locations because the malls they're in were always bad.
The data shows patterns in where failures cluster. It also has real blind spots. Get the diagnosis wrong and the response is wrong: call a location problem an operations failure and you'll renew a lease you should have exited. Call an operations failure a location problem and you'll abandon a site that just needed a better operator. Knowing which is which matters for any team running retail location failure analysis on a portfolio or a new site.
Three Kinds of Store Location Failure
From a site selection standpoint, closed stores fall into three categories, and only one of them is a location data problem.
Three categories of retail closure. Each one has a different relationship to location data. Only the first is a pure site selection problem.
1. The Location Was Wrong from the Start
The site never had the fundamentals. Wrong demographics for the concept. Competitive saturation in the trade area. Poor visibility or access. Insufficient traffic volume. These are the failures that a scoring framework catches before a lease is signed.
Dollar General's 96 closures in early 2025 partly fit here. CEO Todd Vasos said the closures were "predominantly in urban and metro settings" where operating costs made profitability impossible. Dollar General's model works in low-cost, low-density markets. Urban locations with higher rents, higher labor costs, and different competitive dynamics were a poor fit from the start.
The tell: when you re-score these locations using your current methodology, the score is low. The data was there. Someone either didn't have it or didn't use it.
2. The Location Changed
The site was strong when the lease was signed. Then the grocery anchor left. The highway got rerouted. A competitor opened across the street. The neighborhood's demographics shifted over a five-year lease term.
This is the anchor departure scenario we covered in co-tenancy strategy. When a major tenant in a center closes, remaining retailers typically see sales pressure as foot traffic declines across the property. ICSC research has documented this pattern across multiple center types.
Macy's closures partially fit this pattern. Most of the 150 stores being shuttered through 2028 are in regional malls that have seen significant decline. When Macy's was the anchor, the mall thrived. When other anchors left and foot traffic dropped, Macy's became the last major tenant in a dying center. The location changed around the store.
The tell: the location scored well at signing but scores poorly now. A portfolio audit that re-scores existing locations catches this drift before the lease renewal decision.
3. The Problem Wasn't the Location
This is the category that location data misses entirely. The store failed because of operational decisions, strategic pivots, or execution problems that had nothing to do with the address.
Bed Bath and Beyond is the textbook case. The company closed roughly 400 of its 760 stores. But Bed Bath didn't fail because of bad site selection. It failed because it replaced national brands with untested private labels, eliminated the coupons that drove store visits, was late to e-commerce, damaged vendor relationships, and spent $11.8 billion buying back its own shares instead of investing in the business.
The foot traffic data at those locations would have shown declining visits. A site scoring model would have shown dropping performance. But the root cause wasn't the location. The root cause was product strategy and capital allocation. No amount of location intelligence prevents a company from making those decisions.
The tell: when you compare performance to similar locations by the same brand, ALL locations declined at similar rates. The pattern is systemic across the brand. Red Lobster looked the same way before its 2024 bankruptcy. Stores from suburban Florida to coastal California declined together because the operating model, not any single trade area, was failing.
What Location Data Actually Predicts
Location data is good at predicting certain kinds of failures and blind to others. Knowing which is which prevents both over-reliance and under-investment.
Site selection data is precise about some failure modes and silent on others. The blind spots come from using the wrong tool for the job.
Data predicts well:
- Demographic mismatch. A luxury concept in a value-oriented trade area, or a QSR in a residential neighborhood with no daytime population. The five lenses framework catches this through demographic fit scoring.
- Competitive saturation. Too many of the same concept in the same trade area. The saturation analysis framework quantifies this: calculate demand, count supply, check the ratio.
- Cannibalization. Your own stores eating each other's revenue. Dollar General opened 725 stores in 2025 while closing 96. Even at that scale, some locations cannibalized existing stores. Territory design prevents this with buffer zones and drive-time boundaries.
- Access and visibility failures. A site behind a building, off the main road, with poor ingress. Traffic count data and visibility scoring catch these.
- Anchor departure risk. A center that depends on one tenant for 40% of its traffic is a fragile location. Co-tenancy evaluation quantifies this dependence.
Data predicts poorly:
- Operational execution. Staffing, inventory management, customer experience, store cleanliness. A great location with a terrible operator still fails.
- Strategic shifts. When a brand changes its product mix, pricing strategy, or target customer mid-lease, the location's fit changes with it.
- Macro disruptions. Pandemics, interest rate spikes, sudden category shifts. No historical data predicted the speed of e-commerce adoption in 2020-2021.
- Brand perception changes. A brand that loses customer trust (quality problems, scandals, cultural shifts) will see declining traffic across all locations, regardless of site quality.
Location data prevents the preventable failures. Not all failures are preventable.
The $232 Million Lesson
Dollar General took $232 million in charges related to its 2025 store closures. That money paid for leases, build-outs, inventory, and staffing at locations that didn't work.
But here's the part that rarely makes the headline: alongside those closures, Dollar General opened 725 new stores that same year, ending 2025 with roughly 600 more locations than it started with. The company is pruning at scale.
This is what portfolio management looks like when you treat it as an ongoing process rather than a one-time decision. Whether you're managing 20 locations or 20,000, the right question is the same: how quickly do you identify the ones that aren't working, and how cheaply can you exit?
That's where portfolio re-scoring matters. If you're re-evaluating your existing locations on the same framework you use to pick new ones, the underperformers surface before the lease renewal comes up. You get to decide: fix, renegotiate, relocate, or close. That decision is better made 18 months before lease expiration than 6 months after you've noticed declining sales.
Five Site Selection Mistakes the Data Catches Early
If you're running retail location failure analysis on your own portfolio, these are the signals that surface before the P&L makes them obvious.
1. Same-store sales declining while the trade area is growing. If the market is getting bigger but your store is shrinking, the location isn't capturing the growth. Something about the site (access, visibility, co-tenant changes, competitive entry) is deflecting the new demand. Pull the demographic data for your trade area and compare year-over-year population and income growth against your sales trend. A practical alert threshold we recommend: when trade-area population or income growth outpaces same-store sales by more than 5 percentage points for two consecutive quarters, the location warrants investigation.
2. Your true trade area is shrinking. When you measure your trade area and compare it to 12 or 24 months ago, a shrinking radius means a closer competitor is pulling customers away. You're losing ground from the edges inward. This is especially common in markets where a new entrant opened on the far side of your trade area and captured the customers who used to drive past them to reach you.
3. A new competitor opened within your primary trade area. Not just in the same city, but within the drive-time boundary that captures 60-70% of your customers. Run the saturation analysis. If the supply-demand ratio crossed a threshold, your revenue ceiling just dropped. The impact often takes 6-12 months to fully materialize, so a competitor opening today may not show up in your numbers until next quarter.
4. The center's occupancy is declining. Empty storefronts in your center mean declining foot traffic for everyone. Track the trajectory, not the snapshot. A center at 85% occupancy and rising is different from 85% and falling. Talk to the landlord about the leasing pipeline. If vacancies are being filled with short-term tenants or temporary uses, the center may be in structural decline regardless of what the current occupancy number shows. That trajectory should trigger a lease-renewal timeline review: how many months until your renewal, and what does an exit cost today versus in 18 months?
5. Your predicted score no longer matches your actual performance. If the model said this should be a B location and it's performing like a D, something the model doesn't capture has changed. That gap is the investigation trigger. Re-score the location, compare it to the original evaluation, and identify which lenses moved. Did competition increase? Did demographics shift? Did traffic patterns change? The delta between predicted and actual tells you where to look.
The Honest Limitations of Retail Location Analysis
Location data didn't prevent Bed Bath and Beyond from dismantling its own brand. It didn't prevent WeWork from signing leases at valuations that required permanent growth. It won't prevent the next company from making strategic decisions that override what the data says.
What it does prevent: signing a lease in a saturated market because nobody checked, building a store with the wrong demographics because the team relied on gut feel, and missing the slow decay of an existing location because nobody re-scored the portfolio.
The 15,000 closures in 2025 were a mix of preventable and unpreventable failures. Location data catches the preventable ones. For the rest, you need good operators, sound strategy, and the humility to close what isn't working before the losses compound.
Where to Start
Three frameworks from this analysis apply directly to portfolio evaluation: the five lenses scoring methodology for diagnosing which lens moved, portfolio re-scoring for catching drift before lease renewal, and saturation analysis for quantifying competitive pressure. Start with whichever matches your most pressing question.
Frequently Asked Questions
What percentage of retail failures are caused by bad location selection?
There's no clean percentage, and that's the point. Retail closures are almost always multi-factor — a weak location with a strong operator might survive where a strong location with a weak operator won't. No study isolates location as the sole cause because it's always interacting with operations, strategy, and timing. In practice, portfolio post-mortems typically find a mix: some closures with a clear location component (wrong demographics, competitive saturation, poor access) and others driven by factors that no site data would have changed. Even within "location failures," the cause is often a location that changed over the lease term rather than one that was wrong from the start.
How can I tell if my underperforming store has a location problem or an operations problem?
Compare it to your other stores with similar location profiles. If stores in similar trade areas with similar demographics are performing well, the problem is likely operational (staffing, merchandising, local marketing). If stores in similar trade areas are ALL underperforming, the problem may be strategic (wrong concept for that market type). If this specific store scores poorly on a re-evaluation while similar stores score well, the location itself has a problem.
Should I use location data to decide which stores to close?
Yes, but not as the only input. Re-score your portfolio using the same framework you'd use for a new site. Locations that score poorly on the current model are candidates for closer examination. But also check lease terms, renovation costs, brand presence value, and whether the location serves a strategic purpose (market presence, distribution, brand awareness) beyond direct profitability.
How often should I re-evaluate existing store locations?
At minimum, annually. If your market is changing quickly (new competitors, demographic shifts, anchor departures), quarterly re-scoring of at-risk locations is worth the effort. The goal is to spot declining locations before the P&L makes it obvious, giving you time to renegotiate, relocate, or plan an orderly exit instead of a forced closure.