Tenant Mix Calculator

Tenant mix describes how the businesses in a property are distributed across categories. This calculator uses four fixed categories: retail, food and beverage, services, and entertainment. Assign each tenant to exactly one category, even if its activities overlap. Use a consistent rule, such as its main activity, when comparing snapshots.

The inputs describe an entered snapshot, not a recommended leasing plan. Enter whole tenant counts, nonoverlapping leased areas in square feet, and annual revenue for the same period. Choose one revenue basis throughout: for example, landlord rental income or tenant sales. Mixing those measures makes the category comparison misleading.

• Retail: clothing, electronics, home goods and similar stores.
• Food and beverage: restaurants, cafés and food outlets.
• Services: banks, salons and other service businesses.
• Entertainment: cinemas, arcades or venues classified consistently under your chosen rule.

Separating counts, area and revenue helps you ask different questions. A category can contain many small units but occupy little floor area, or contain one large tenant that contributes a substantial part of entered revenue. Those differences are useful context; they do not identify which lease to sign.

• Count concentration: See how much the entered tenant population depends on each category.
• Space allocation: Compare category floor areas with the property's total leasable area.
• Revenue exposure: Identify categories contributing more of the same annual revenue measure.
• Planning questions: Combine the snapshot with actual lease terms, costs, local demand, access and visitor data before making a commercial decision.

The tool does not estimate property value, future foot traffic, vacancy risk or profit. Those outcomes depend on information beyond these fields.

Let N be the total entered tenant count, T the total leasable area, A the sum of the four entered leased areas and R the total annual revenue. For a category with count n, area a and revenue r:

• Tenant share: n ÷ N × 100. It is N/A when N is zero.
• Area share: a ÷ T × 100. The four shares can sum to less than 100%; their area total cannot exceed T.
• Revenue share: r ÷ R × 100. It is N/A when R is zero.
• Category revenue density: r ÷ a, in annual revenue per square foot. It is N/A whenever a is zero, including when positive revenue has been entered.
• Property revenue densities: R ÷ T uses all leasable area; R ÷ A uses only entered allocated area and is N/A when A is zero.

Diversity complement: 1 − Σp², where p = n ÷ N for each of the four categories. NIST defines Simpson concentration as Σp²; this calculator displays its complement, so a larger number means less concentration by tenant count. The result is N/A when N is zero, 0 when all tenants are in one category and at most 0.75 when all four counts are equal. It is an index, not a percentage or a target for an ideal property. With k nonempty categories, its largest possible value is 1 − 1/k.

Worked example: 10 retail, 4 food, 4 service and 2 entertainment tenants total 20. Their shares are 50%, 20%, 20% and 10%, giving 1 − (0.50² + 0.20² + 0.20² + 0.10²) = 0.66. For 10,000 sq ft total leasable area and category areas 5,000, 2,000, 1,500 and 500 sq ft, entered allocated area is 9,000 sq ft and unallocated area is 1,000 sq ft. Annual revenues of $100,000, $50,000, $30,000 and $20,000 total $200,000: $20.00 per total leasable sq ft or $22.22 per entered allocated sq ft. These are illustrative inputs, not a benchmark.

Method source: NIST Dataplot: Simpson Diversity Index gives the count/proportion concentration definition and requires unique groups. E. H. Simpson, Measurement of Diversity (1949) is the original paper. Sources checked 2026-09-30. The four-category maximum and worked example above follow from the displayed arithmetic; neither source establishes retail allocation targets.

Use these outputs as a starting point for investigation. Keep category rules, revenue basis and area definitions the same between snapshots, and note any reclassification or incomplete data.

• Anchor tenants: Examine an anchor's lease terms and actual visitor data; its category count alone does not measure its effect.
• Category balance: Compare changes in counts, area and revenue rather than aiming at an unsupported universal ratio.
• Complementary uses: Test ideas about neighboring businesses against local customer behavior and property constraints.
• Space use: Consider occupancy costs, unit configuration and common areas alongside revenue density. Revenue per area is not profit per area.
• Market context: Use local demand and lease economics to evaluate a proposal. The calculator does not select an optimal mix.