Retail void analysis identifies the categories a trade area wants but lacks. Landlords use it to create a prospect list for a vacancy, while retailers use it to rank markets for entry. The term refers to two different calculations: one counts stores, and the other counts dollars. They don't always produce the same answer.
In November, a leasing team will have 4,200 square feet go vacant and needs chains to call. An expansion team has budget for six openings next year, yet its map shows no obvious next market. Both teams describe the work as void analysis, even though they may be using different math without realizing it.
What is retail void analysis?
Retail void analysis compares the business mix in a trade area with that of a benchmark area, showing which categories are absent. The result is a list of missing business types, weighted according to how well local demand could support each one. Landlords use it to focus tenant outreach, while retailers use it to identify target markets.
The concept has been around for years, but the name is newer. Mapping software made it a defined method. In Esri's tutorial, you establish a reference area you know well, either because one of your stores is there or because you know it performs, and an analysis area under consideration. Compare the business categories in each, adjust for population, and the categories found in one but absent from the other are your voids.
That's the version most analysts are taught. But it isn't the only version being sold.
Two calculations wear the same name
The count method is the original form of void analysis. It measures business presence. For a trade area, the question is whether it has a pet supply store, an urgent care, or a quick-service breakfast concept. Stores are the unit, with a yes-or-no result for each category, normalized against households to make a small town comparable with a metro submarket.
The dollar method is retail gap analysis, also known as leakage and surplus analysis. It compares household spending in a category within the trade area with sales from nearby stores in that category. It estimates both amounts and subtracts one from the other. Esri publishes the result as a leakage surplus factor that runs from positive 100 to negative 100. Positive means local demand exceeds supply, sending money elsewhere. Negative means supply exceeds local demand, and the area draws shoppers from outside.
The distinction is important because each method breaks down in the opposite way. A count-based approach misses categories that exist but are seriously underserved: one struggling store is counted the same as five healthy stores. The dollar method can identify that condition, but it requires two levels of estimation that the count method does not. Neither method can tell when a category is missing for a valid reason.
When a broker or vendor gives you a void report, ask first which calculation produced it.
Where the numbers actually come from
On the demand side, the usual calculation takes the number of households within the trade area and multiplies it by category spending per household. That spending measure ultimately comes from the Bureau of Labor Statistics Consumer Expenditure Survey, whose latest complete annual dataset is 2024, released in December 2025.
Supply-side inputs include business location and sales data. Public establishment counts are sourced from the Census Bureau's County Business Patterns, with the 2023 vintage most recently released in June 2025. Since Census doesn't publish store-level sales, commercial vendors add their own sales estimates.
Those dates have two implications. Government inputs lag the market under review by one to two years. That's acceptable for a structural read, but not for a fast-moving corridor. Also, sales figures in most void reports are estimates built on other estimates.
The second point has a specific source. Retail analyst N. David Milder argued in 2016 that leakage analyses warrant serious caution, citing business listing database error rates of 20 to 40 percent, particularly among small independents, along with annual retail churn of 5 to 30 percent that quickly makes listings outdated. His most consequential point, and the one that has aged best, was that consumer expenditure data records what households spend in a category, but not whether they spent it in a store or online.
None of that means the method has no use. It is for screening, not an underwriting input, which is roughly how good teams already use it.
Landlords fill a space. Retailers find one.
The mechanical distinction depends on which half of the problem remains fixed.
The property stays fixed for the leasing team, landlord, or developer. The work covers one trade area across every category. A retailer database is scanned for named brands that aren't there and whose typical site profile fits what the center offers. Vendors in this area rate the fit and, in some cases, provide the broker contact for each chain. The resulting list is a call sheet.
The retailer, franchisee, or expansion team keeps one category, yours, consistent across every trade area. Markets are compared by category density per household against a benchmark market where your format already works. The result is a ranking of candidate markets.
The same split runs through the rest of both processes, from which disqualifiers count to how long the hold lasts. How developers and landlords approach sites differently follows it past the void question into rent, tenant mix, and what a developer can pay for the ground.
This is where teams make the mistake. Tango Analytics is explicit in its own guidance: void analysis identifies opportunities at the market level, not specific locations. A ranked market list begins the site work; it doesn't complete it. For every candidate address, you still need to draw a trade area, assess overlap with existing stores, and score the parcel itself.
Skipping that step turns a sound market assessment into a poor lease.
Four tests a void has to survive
Is the demand estimate local, or a national average dressed up? Spending by category and household is shaped by income, age mix, and household size. Applying a national table to a trade area with different demographics will create demand that doesn't exist. Check whether the vendor adjusts for local characteristics or only applies a multiplier.
Is the spending leaving the area, or leaving the map? This failure mode appears when category demand is served outside the store network. If a household buys the category online or receives it by delivery, the spending still registers in that category, while the local store base continues to report zero sales. The model therefore sees a void, even though the household is already being served and no storefront is involved.
Has somebody already tried this here? Check the closed unit in your category on that same corridor before you get encouraged. It is the least expensive evidence you'll find, but the model doesn't include it. If an operator failed there two years ago, that gap is a scar rather than an opening.
Can you get a site that works? The analysis does not include zoning, parcel dimensions, parking ratios, drive-through rules, or rent. In one trade area, a category can be genuinely absent while also being genuinely unbuildable. Vendors in this field make the same point: an empty location can signal unmet demand, or it can show that the market already tested the site and found no demand.
Multi-unit operators should add a fifth test. When a void is next to stores you already operate, some of the revenue it would capture is revenue you already have. Cannibalization analysis identifies that before the site tour, rather than afterward.
The trade area boundary decides the answer
Every figure above is tied to a boundary someone drew. Change that boundary, and the demand estimate and competitor count change too.
A three-mile ring and a ten-minute drive-time polygon drawn from the same address can routinely produce population counts at least twice as far apart, since the ring includes households on the other side of a freeway while the polygon excludes them. Apply void analysis to the ring and you get one result; apply it to the drive-time polygon and you get another. Neither result is inherently incorrect. Each reflects a different question, which is why mixing boundary methods across a candidate list produces a ranking nobody should trust.
Choose one method based on how your format actually attracts customers, and apply it consistently across every market in the comparison. With inconsistent boundaries, the resulting ranking reflects methodology, not opportunity.
What tight vacancy does to a void
The search for a void is one task; getting into it is another, and that part has grown more difficult.
National retail vacancy is at 6.0 percent, according to Cushman and Wakefield's Q2 2026 MarketBeat, published in July 2026. Average asking rent is $25.65 per square foot, a 2.2 percent year-over-year increase. CoStar's May 2026 forecast projects vacancy peaking in the mid-4.4 percent range before stabilizing.
Inventory scope is the most likely source of the difference. Cushman and Wakefield has long reported on a shopping-center universe that excludes malls and freestanding single-tenant retail, whereas CoStar covers a broader base that includes both. Freestanding space under long-term net leases is also close to fully occupied. That remains the conventional explanation, not a settled conclusion, since none of these firms publishes a side-by-side reconciliation of its counts. Use both figures as directional, and anchor your view to the definition that matches your own portfolio.
Both sides agree on the direction. Space is scarce, and new supply remains limited. That increases the value of a void report for a leasing team: a category gap gives you a reason to call a chain with options. For an expansion team, the implication is the reverse. Identifying the right market is the easier part; the space there may not exist at the rent your model assumed. Void analysis shows you where to look, but not whether you can get in.
Where this fits in a real workflow
Void analysis screens first. From a country, it produces a shortlist of markets; from a category list, a call sheet. The work afterward is where the money gets decided.
In practice, that means trade areas follow the road network instead of forming circles, candidate sites are assessed against one another rather than individually, overlap is measured against the stores you already operate, and the score can be opened and challenged in a committee room. GrowthFactor's market planning handles this with catchments based on real drive times, population and income read from within each shape, and a five-site plan showing how those sites affect one another. Exploring a market never affects your deal pipeline.
Cavender's used that workflow to increase annual new-store openings from 9 to 27, reviewing more than 2,000 sites in the process. The void read identified the markets; site work made those openings hold up.
The tooling handles the analysis. Your team still brings knowledge of the landlord, local politics, and the deal you can actually get, and a dataset can't provide that part.
When the market read is what a team lacks, market saturation analysis starts with the same map but works backward: it identifies when a trade area is full, not when it is empty. Running both keeps teams from treating an empty market as an easy one.
Frequently Asked Questions about Retail Void Analysis
What is the difference between void analysis and retail gap analysis?
Void analysis counts businesses. It compares which categories are present in a benchmark area against the area you are studying and returns the categories that are absent. Retail gap analysis counts dollars. It estimates category spending by households in the trade area, subtracts estimated category sales at nearby stores, and returns the difference. Vendors sell both under the void analysis name, so ask which calculation produced the report.
Does a void in a trade area always mean an opportunity?
No. A category can be absent for a reason that has nothing to do with demand. Zoning may not allow the format, the parcel sizes may not fit your build, an operator may have already tried and closed, or online ordering may already serve the spending the model counted as unmet. Treat every void as a hypothesis and check the supply-side reasons before you tour anything.
What data does a retail void analysis need?
Three inputs. A trade area boundary, which changes the answer more than most teams expect. A business location database covering the categories you care about, tagged by NAICS or a vendor category scheme. And a demand estimate, usually household counts multiplied by category spending per household derived from the Bureau of Labor Statistics Consumer Expenditure Survey. The dollar version also needs a sales estimate for the stores already there.
Can void analysis tell you which specific site to lease?
Not on its own. Void analysis works at the market or trade-area level, so the output is a ranked list of places worth looking at rather than an address. Turning a ranked market into a signed lease takes a separate pass: draw the trade area for each candidate site, check overlap with your existing stores, and score the specific parcel on access, visibility, co-tenants, and rent.
How does GrowthFactor compare to Placer.ai for void analysis?
Placer.ai publishes a void analysis guide and ranks potential tenants for a property using what it calls a Relative Fit Score, drawn from foot traffic and co-tenancy patterns. GrowthFactor covers what comes after the market read: trade areas on real drive times, candidate sites scored against each other and against your existing stores, and the deal tracked through to signing. Books-A-Million, the #2 book retailer in the US, screens more than 3,000 sites a year through it. Plenty of teams run both.
