A Shopping Center Is an Underwriting Bet on the Trade Area Around It
Published at May 5, 2026 ... views
The thing I keep finding interesting about retail real estate is how much of the underwriting happens before anyone draws a single building.
In office or apartments, you can do meaningful design work before you've fully proven the demand — the building itself is mostly generic, and you can adjust pricing or finishes later. In industrial, the spec is mostly fixed by the tenant once you have one signed.
In retail, the building is downstream of an entirely different question: what does the trade area around this site look like, and is there enough demand inside it to support the kind of center we want to build?
If the answer is yes, the building design becomes relatively straightforward. If the answer is no — or if you misjudge it — the building doesn't matter, because there aren't enough customers in the catchment area to make it work.
A shopping center is really an underwriting bet on the trade area around it. The trade area determines how many people you can plausibly reach, what they spend money on, what they're missing today, and whether the project can hit the sales-per-square-foot threshold that makes the rent pencil. Almost every other decision — anchor selection, tenant mix, building size — flows from that analysis. Get the trade area wrong and no amount of architecture can save the project.
That's the angle I want to work through here, and at the end I'll walk through how a real developer applied it to a site in the Bay Area.
The trade area is the actual product
A trade area is the geographic area from which a retail center reasonably draws its customers. In theory, it's just a circle on a map. In practice, the circle is heavily distorted by three things: drive time, competition, and physical barriers.
Drive time matters because customers behave differently for different categories. People won't drive 20 minutes for milk, but they'll drive 30 minutes to a destination mall. So a neighborhood center has a smaller effective trade area than a regional mall, even if the raw radius looks similar on paper.
Competition matters because every existing retail center in the same trade area is already capturing some share of the demand. If there are already two community centers within five miles of your site, those centers are absorbing customers who would otherwise be available to you.
Physical barriers matter because a circle on a map doesn't account for freeways, rivers, hills, or just lousy traffic patterns. In San Diego, Mission Valley acts as a barrier — a center in Linda Vista can't really pull customers from Hillcrest because the geography between them makes the drive painful even when the distance is short.
That's why trade area maps look more like asymmetric blobs than circles. The shape reflects how customers actually move, not how the map looks.
The 55–65% rule and capture rates
Once you've defined the trade area, you have to estimate how much of the available demand inside it you'll actually capture. This is the part of retail underwriting where developers either get honest or get into trouble.
The standard rule of thumb is that a primary trade area generates 55–65% of a center's total sales. Secondary and peripheral areas — places further out, where customers occasionally come from for specific reasons — make up the rest.
The dangerous mistake is over-relying on the secondary trade area. If your pro forma assumes that 40% of your sales will come from outside the primary area, you're betting that customers will actively choose your center over their local options for the long haul. That's possible — there are real reasons people drive farther for a particular store — but it's a much harder bet to win than capturing your local market well.
A useful framing: it's like betting on the river card in Texas Hold'em. You can win that way occasionally, but if your whole strategy depends on the very last card flipping over right, you're going to lose more often than not. Build your underwriting on the primary trade area math, and treat secondary captures as upside.
What "captures the project" actually means depends on the rent and the tenant mix. A tenant signing a lease has their own underwriting — they need a certain sales-per-square-foot number to make the location viable for them. If your trade area math suggests you can support that sales-per-square-foot for the tenant categories you want, the project pencils. If not, either the rent comes down (which breaks your pro forma) or the tenant doesn't come.
Demographics and the socioeconomic question
Trade area math assumes the people inside the circle have money to spend. Whether that's true depends on the demographic composition of the area.
The basic demographic data developers look at:
- Population and population growth — how many people, and is the number rising or falling?
- Household income — both median and average, with attention to the income distribution
- Household size and tenure — how many people per household, and whether they own or rent
- Age, education, and ethnicity — different demographics have different shopping patterns
- Job and industry mix — what kind of jobs people in the area hold, which proxies for spending power
The piece that gets uncomfortable but has to be acknowledged is the socioeconomic geography. In most US cities, retail follows higher-income neighborhoods more aggressively than lower-income ones. Lenders, equity partners, and tenants all do their own diligence on the area, and the data points they care about — household income, job growth, household formation rates — don't distribute evenly across a metro.
In San Diego, the unofficial dividing line is Interstate 8. North of the 8 is where most major retail development happens. South of the 8 — particularly Southeast San Diego — has a single major regional shopping center despite years of effort by community advocates to attract more.
The reason isn't lack of demand from people who live there. The reason is that lenders and tenants both gravitate toward higher-income areas because the underwriting metrics are stronger there. A tenant looking at a site is asking "can I hit my sales-per-square-foot target here?" A lender is asking "is this center going to perform consistently enough to service the debt?" The demographic data answers both questions, and the answers tend to favor wealthier areas.
It's not a fair pattern, but it is a real one — and it's part of the diligence developers and analysts have to do honestly.
Leakage: when customers travel for what they can't get nearby
One of the most useful concepts in trade area analysis is leakage.
Leakage is when customers in a trade area are spending money outside it — driving to other areas to buy things their local stores don't offer. If you can identify leakage in a category, you've identified a potential opportunity.
The way to detect leakage is to compare what customers in a trade area spend by category against what local retailers in that trade area sell by category. If customers are spending $X on home improvement but local stores only sell $Y of home improvement, the difference is leakage — money flowing out to a Home Depot or Lowe's somewhere else.
A real personal example of this: there are two Home Depots closer to my house than the one I actually shop at, but I drive an extra six minutes to the one on Sports Arena Boulevard because it's a slightly bigger store and I know where everything is. Multiply that kind of small individual decision across thousands of customers, and you get measurable leakage that a smart developer can sometimes capture by offering a better-located version of what people are already willing to drive for.
Sources for this kind of data include the US Census Bureau, the California State Board of Equalization (for state sales tax data), and third-party services like Cushman & Wakefield, Costar, and Claritas Nielsen — the last of which is expensive but very specialized for retail.
Sales by category — what's growing, what's flat
Even after the trade area and demographics check out, retail demand isn't uniform across categories. Some categories grow steadily; others are in long-term decline.
The big categories developers track:
| Category | Trajectory |
|---|---|
| General merchandise (Target, Walmart) | Stable |
| Food and beverage | Steady, growing slightly |
| Clothing and apparel | Mixed; recovering post-pandemic |
| Furniture and home goods | Growing |
| Restaurants and dining | Growing — driver category |
| Services (haircuts, nails, dry cleaners) | Growing |
| Department stores | Long-term decline |
| Electronics and books | Mostly migrated to online |
This matters for site selection because the demand math has to be done by category. Even if the trade area has plenty of disposable income, building a shopping center anchored by a category in long-term decline is "fighting the last war." That phrase stuck with me from this material — you don't want to be very good at the thing nobody wants anymore.
A developer evaluating a site is asking, by category: how much of the local demand is unmet? Which categories are oversupplied by existing competition? Where's there room for a new entrant? Those answers shape both the kind of center to build and the tenant mix to assemble.
A few things I'm taking away
- Retail underwriting is mostly trade area analysis — the building is downstream of whether the surrounding catchment can support the rents you need to charge.
- Trade areas are not circles — they're asymmetric blobs distorted by drive time, competition, and physical barriers like freeways, rivers, and hills.
- The 55–65% rule is a useful discipline: assume your primary trade area carries the project, and treat secondary captures as upside rather than baseline.
- The socioeconomic geography of a metro tilts new retail toward higher-income areas, because lenders and tenants both gravitate to where the underwriting metrics are stronger.
- Robert Gibbs' Principles of Urban Retail Planning and Development is the standard reference for the methodology underlying this post — trade area definition, primary-vs-secondary, capture rate discipline, and category trajectories.
- Esri's Tapestry Segmentation classifies US neighborhoods into 60 demographic segments at the ZIP-code level, which is the demographic instrument retail underwriters actually use day-to-day. Free at the ZIP-lookup level for casual use.
- Leakage is one of the most useful diagnostic concepts — when customers consistently leave the trade area to buy a category, that's a signal of unmet local demand a developer can sometimes capture.
- Retail demand has to be analyzed by category, not in aggregate. The NRF Top 100 Retailers list is a useful annual scan for which categories are growing, which are flat, and which are eroding — the inputs to "fighting the last war" diagnoses.
- Tenants do their own underwriting on every site — they need their own sales-per-square-foot target to be hittable, and if your trade area math doesn't support that, they won't sign no matter how nice the building is.
What this gets you ready for
This post is the framework half. The companion post — applying the trade-area math to a real East Bay shopping center — walks the entire underwriting flow on a Fremont site: competition inventory, purchasing-power estimate, supportable sales per square foot, and the rent number that actually decides whether the deal pencils.
The interesting part of retail development happens in spreadsheets long before anyone breaks ground. The site that becomes a shopping center is the one where the math worked — not the one with the prettiest concept. Once the math works, the design follows. And once the design follows, the leasing follows.
This post is the first of two on retail trade-area analysis in my ongoing series — Real Estate Development. The companion post covers the Fremont East Bay case study that applies this framework end-to-end. Earlier in the retail arc: the percentage rent structure that defines retail leases and what shopping centers have to sell that Amazon can't.
Sources
- Robert J. Gibbs — Principles of Urban Retail Planning and Development (Wiley, 2012) — the standard reference for trade-area methodology, primary vs secondary trade areas, capture-rate discipline, and category trajectories. The framework in this post is built on Gibbs's methodology. https://www.amazon.com/Principles-Urban-Retail-Planning-Development/dp/0470488220
- Esri — ArcGIS Tapestry Segmentation — the demographic clustering tool retail underwriters use day-to-day. Free ZIP-level lookup; paid for the full Business Analyst suite. https://www.esri.com/en-us/arcgis/products/data/data-portfolio/tapestry-segmentation
- National Retail Federation — Top 100 Retailers (2025 list) — annual scan of retailer performance by category; useful for "what's growing, what's flat" reads. https://nrf.com/research-insights/top-retailers/top-100-retailers/top-100-retailers-2025-list
- NRF — Hot 25 Retailers (fastest-growing) — companion list of fastest-growing retailers, for emerging-category signals. https://nrf.com/blog/2025-hot-25-retailers
- US Census Bureau, California State Board of Equalization, Cushman & Wakefield, Costar, Claritas Nielsen — the data sources Moncrief mentions for trade-area work. The first two are free; the others are paid.