Every retail real estate team has a set of criteria they use to evaluate potential locations. Some teams have these criteria written down in a formal scoring model. Others carry them as institutional knowledge that lives in the heads of experienced deal-makers. Either way, the criteria determine which sites get approved and which get passed on.

The problem is that many criteria frameworks were built before foot traffic data, trade area analytics, and competitive benchmarking were available. They rely heavily on qualitative assessment (“the center feels busy,” “the co-tenancy looks strong”) because quantitative measurement was not possible at the time they were designed.

Modern data changes what is measurable. This guide covers the 10 criteria that matter most for retail site selection, what “good” looks like for each one, and how to measure each criterion with data rather than judgment alone. For the full site selection process these criteria feed into, see our retail site selection guide.

The 10 criteria

1. Foot traffic volume and trend

What it measures: How many people visit the candidate location and its immediate surroundings, and whether that number is growing, stable, or declining.

Why it matters: Traffic is the most direct predictor of a store’s revenue potential. A location with 10,000 weekly visitors has a fundamentally different ceiling than one with 2,000, assuming the visitor profile is comparable.

What good looks like: This is category-dependent. A QSR needs high-volume, high-frequency traffic. A luxury retailer needs lower volume but higher-quality traffic. The right benchmark is not an absolute number but a comparison against your existing stores that perform well and against comparable sites in the market.

How to measure it: Foot traffic data platforms (PassBy, Placer.ai) provide weekly and monthly visit counts with historical trends. Compare the candidate site’s traffic volume and trend against your top-performing existing locations. If the candidate receives 30% less traffic than your average successful store, that gap needs to be explained by other factors (better demographics, less competition) or the site should be deprioritized.

PassBy provides 5+ years of historical data across 1.5 million US locations, plus 90-day predictive forecasts. For details on how foot traffic data works, see foot traffic data: the complete guide.

2. Demographic alignment

What it measures: Whether the population in the trade area matches your target customer on income, age, household composition, education, and other relevant dimensions.

Why it matters: A high-traffic location where the surrounding population does not match your customer profile will underperform regardless of the traffic volume. A streetwear brand in a retirement community or a luxury jeweler in a college town faces a structural mismatch.

What good looks like: Define your ideal customer demographic profile from your best-performing stores. The candidate site’s trade area demographics should match that profile within an acceptable range. For most retailers, income alignment and age alignment are the two most predictive factors.

How to measure it: Census data provides the baseline. PassBy overlays observed visitor demographics that capture who actually visits the area, not just who lives nearby. The distinction matters: a location near a business district may have a residential population that differs significantly from its daytime visitor population.

3. Psychographic fit

What it measures: Whether the lifestyle characteristics, values, and shopping behaviors of the trade area population align with your brand positioning.

Why it matters: Demographics tell you the factual profile of the population. Psychographics tell you how they spend their time and money. Two neighborhoods with identical income and age profiles can have completely different spending patterns: one oriented toward dining and entertainment, the other toward home improvement and outdoor recreation. Your brand fits one but not the other.

What good looks like: PassBy’s psychographic data reveals lifestyle indexes for trade area visitors. A fitness-oriented apparel brand should look for trade areas that index high on health and wellness. A value-oriented retailer should look for trade areas that index high on price sensitivity and practical shopping behavior.

How to measure it: Psychographic profiling from foot traffic data platforms uses observed behavioral patterns (what other stores do visitors frequent, how do they spend their time) rather than survey-based estimates. This behavioral approach is more predictive than self-reported psychographic data.

4. Competitive landscape

What it measures: Who else operates in the candidate location’s trade area, how many competitors exist, and how they are performing.

Why it matters: The right level of competition depends on your strategy. Some competition is healthy (cluster effects draw more category-specific traffic). Too much competition in a limited-demand market leads to traffic splitting and underperformance for everyone.

What good looks like: Complementary competitors nearby (cluster effect) are generally positive. Direct competitors who are performing well indicate strong demand. Direct competitors who are declining may signal a weakening market or an opportunity to capture their customers. A saturated market with multiple strong competitors and limited demand is a red flag.

How to measure it: Foot traffic data reveals exactly how many visits each competitor location receives, whether they are growing or declining, and how their traffic compares to the market average. PassBy’s competitive benchmarking in Almanac compares up to five brands simultaneously. See competitive intelligence for the full framework.

5. Trade area characteristics

What it measures: Where the candidate location’s visitors come from, how far they travel, and how the trade area compares to your existing stores’ catchments.

Why it matters: A store’s trade area defines its customer pool. A trade area that extends 20 minutes in every direction serves a fundamentally different population than one that extends 8 minutes. Understanding the trade area shape, size, and density predicts the addressable market.

What good looks like: The candidate site’s trade area should be large enough to support your required traffic volume and contain sufficient population that matches your demographic criteria. Compare against your successful existing stores to benchmark what an adequate trade area looks like for your brand.

How to measure it: PassBy builds observed trade areas from actual visitor movement data rather than theoretical drive-time radii. This is more accurate because it captures real behavior: a location might draw visitors from 15 minutes north but only 5 minutes south due to a highway barrier, a pattern that drive-time circles cannot detect. See trade area analysis and catchment area analysis.

6. Cannibalization risk

What it measures: How much of the candidate store’s projected traffic would come from your existing locations rather than being incremental to your network.

Why it matters: A new store that appears to generate 5,000 weekly visits but transfers 2,000 of them from a sibling store adds only 3,000 net visits to the network. The financial model needs to reflect the net figure, not the gross.

What good looks like: Most retailers set a maximum acceptable cannibalization rate between 15% and 30%, depending on category and strategy. Below 15% is ideal. Above 30% should trigger serious scrutiny. The threshold should be calibrated to your specific economics: at what cannibalization rate does the new store still deliver positive ROI?

How to measure it: Trade area overlap analysis compares the candidate site’s projected trade area against your existing stores’ observed trade areas. The percentage of overlap predicts the cannibalization rate. See our dedicated cannibalization guide for the methodology and formulas.

7. Co-tenancy and adjacency

What it measures: What other retailers, restaurants, services, and anchors operate in the immediate vicinity, and whether they generate complementary traffic.

Why it matters: Retail is not a solo activity. A store benefits from being surrounded by businesses that attract the same customer. A women’s fashion retailer near Sephora benefits from beauty shoppers who cross-shop. A coffee chain near a gym benefits from the morning fitness crowd. The wrong co-tenancy (discount tenants adjacent to a premium brand) can undermine positioning.

What good looks like: Strong anchors with growing traffic, complementary category tenants that share your customer profile, and limited direct competitors within the immediate vicinity. Cross-visitation data quantifies these adjacency effects: it shows what percentage of one tenant’s visitors also visit another.

How to measure it: PassBy’s cross-visitation data reveals which stores share visitors. In Almanac, you can analyze cross-shopping patterns for any center or retail cluster to see which co-tenants drive the most complementary traffic. For a walkthrough, visit the help center.

8. Accessibility and visibility

What it measures: How easy it is for customers to reach the location by car, public transit, and on foot, and how visible the storefront is to passing traffic.

Why it matters: A location that is difficult to access or invisible from the main road will underperform its traffic and demographic potential. Parking availability, ingress/egress quality, transit proximity, and storefront frontage all contribute.

What good looks like: This is one criterion where on-the-ground assessment remains essential. Data tells you what the trade area and traffic look like. A physical visit tells you whether the parking lot is navigable, whether the storefront is visible from the approach road, and whether the entrance is intuitive. Both are necessary.

How to measure it: Traffic data provides the quantitative layer (how many people are in the area). The qualitative layer (how easy it is to get from “in the area” to “in the store”) requires a site visit at different times and days. The best practice is to use data to shortlist candidates, then validate accessibility and visibility in person.

9. Financial viability

What it measures: Whether the projected revenue from the location supports the total cost of occupancy, including rent, CAM charges, buildout, and operating expenses.

Why it matters: A location can meet every other criterion and still fail if the economics do not work. A prime high-traffic location with premium rent may not generate enough incremental revenue over a lower-cost location to justify the price difference.

What good looks like: Rent-to-revenue ratios vary by category, but most retailers target occupancy costs at 8-15% of projected revenue. The financial model should use net incremental traffic (after cannibalization adjustment) multiplied by your historical conversion rate and average transaction value to project revenue. Foot traffic data provides the traffic input; your internal data provides conversion and ATV.

How to measure it: Foot traffic benchmarks from PassBy let you compare the candidate site’s traffic against comparable locations where you already know the economics. If a candidate site receives traffic comparable to one of your stores that generates $2M in annual revenue, and the candidate’s rent is 20% higher, the model needs to show where the incremental revenue comes from to justify the premium.

10. Growth trajectory

What it measures: Whether the location and its surrounding trade area are improving or deteriorating over time.

Why it matters: A 10-year lease means you are committing to a location for a decade. A trade area that is growing (new residential development, increasing foot traffic, rising incomes) will support the store better in year 5 than in year 1. A trade area that is declining (population loss, declining traffic, store closures) will make the store harder to operate every year.

What good looks like: Positive or stable foot traffic trends at the site and surrounding area over 3-5 years. Population growth or stability in the trade area. No major infrastructure changes (highway rerouting, mall closures) that could disrupt traffic patterns.

How to measure it: PassBy provides 5+ years of historical foot traffic data and 90-day predictive forecasts. Combine traffic trends with demographic trend data (census projections, building permits, zoning changes) to build a forward-looking view of the trade area’s trajectory.

Turning criteria into a scoring model

The 10 criteria above become most useful when formalized into a weighted scoring model that your team applies consistently across every candidate site.

A simple approach: score each criterion on a 1-5 scale, apply a weight that reflects your brand’s priorities, and calculate a composite score. A QSR brand might weight foot traffic volume and accessibility most heavily. An apparel brand might weight demographic alignment and psychographic fit. A pharmacy might weight trade area density and competitive landscape.

The scoring model does two things: it forces every site to be evaluated against the same framework, reducing inconsistency between evaluators, and it creates a quantitative record that justifies decisions to committees, boards, and franchisees.

The criteria and weights should be calibrated against your own data: score your top 10 existing stores on the same framework and check whether the model would have predicted their success. If the model scores your worst-performing stores as highly as your best, the weights need adjusting.

Getting started

PassBy’s Almanac platform provides the data that feeds criteria 1-7 and 10: foot traffic volume and trends, demographics, psychographics, competitive landscape, trade area analysis, cannibalization modeling, co-tenancy analysis, and growth trajectories. Criteria 8 and 9 require on-the-ground assessment and financial modeling that complement the data.

For teams building or refining their site selection scoring model, the Test & Learn tier provides 90 days of Almanac access to test the data against your existing portfolio. See pricing →

For a walkthrough of how Almanac supports each criterion, visit the help center or book a demo →.

FAQ

What are the most important criteria for retail site selection? The most important criteria depend on the retail category. For most retailers, foot traffic volume and trend, demographic alignment, competitive landscape, and financial viability are the four most heavily weighted factors. For expanding chains, cannibalization risk becomes critical. For brands where positioning matters (apparel, luxury), psychographic fit and co-tenancy often carry more weight than raw traffic volume.

How many criteria should a site selection model include? Ten criteria provide comprehensive coverage without becoming unwieldy. Some teams use fewer (focusing on the 5-6 that matter most for their category) and some use more (adding category-specific factors). The key is consistency: every site should be evaluated against the same set of criteria with the same weightings.

How do you weight site selection criteria? Weight the criteria based on what predicts success for your specific brand. Score your top-performing existing stores on each criterion, then check which criteria most strongly differentiate your best stores from your worst. Those criteria should receive the highest weights. Most retailers review and recalibrate their weights annually.

What’s the difference between demographics and psychographics in site selection? Demographics describe factual population characteristics: income, age, household size, education, employment. Psychographics describe behavioral and lifestyle characteristics: shopping preferences, leisure activities, media consumption, brand affinities. Two neighborhoods with identical demographics can have very different psychographic profiles, which predicts different retail performance.

How do you measure site selection criteria with data vs judgment? Of the 10 criteria in this guide, 8 can be measured primarily with data (foot traffic, demographics, psychographics, competition, trade area, cannibalization, co-tenancy, growth trajectory). Two (accessibility/visibility and financial viability) require a combination of data and qualitative assessment. The goal is not to eliminate judgment but to reserve it for the criteria where it genuinely adds value.