7.4 Geospatial Analysis: Intellisite, Drive-Time Trade Areas & Retail Gap Modeling

7.4 Geospatial Analysis: Intellisite, Drive-Time Trade Areas & Retail Gap Modeling

[!NOTE] A published CI 102 objective. The CCIM Institute states that by the end of CI 102 you will be able to perform state-of-the-art geospatial analyses and to use Intellisite to map demographics, traffic, business density, and consumer spending — and turn that data into a market call. Sections 7.1 through 7.3 supplied the concepts: psychographic segmentation, physical site evaluation, and regulatory screening. This section supplies the tooling and the geometry that turn those concepts into a defensible, data-backed recommendation.

The Institute's Geospatial Platform

The CCIM Institute delivers geospatial capability to members through Intellisite, which succeeded the long-running Site To Do Business (STDB) platform. Intellisite bundles Esri mapping and demographic data with commercial real estate workflows, and the CI 102 case studies are built to be worked inside it across all four major property types.

The underlying data engine is Esri Business Analyst — the same platform that produces Business Analyst Online (BAO) reports and Tapestry Segmentation, the household classification system covered in Section 7.1. Candidates should understand what the platform does, not merely which buttons it exposes, because the exam tests interpretation.

---

Trade Area Geometry: Rings Versus Drive Times

This is the single highest-value geospatial concept on the exam, and it connects directly to the trade area hierarchy from Section 6.3.

Concentric Radius Rings

A ring is a circle of fixed radius drawn around the site — the familiar 1-, 3-, and 5-mile report. It is fast, universally understood, and reproducible, which is why lenders and brokers still request it.

Its defect is a hidden assumption: that a customer can travel equally easily in every direction. A ring drawn on a site bounded by a river, a rail yard, a limited-access freeway, a military reservation, or a mountain ridge counts thousands of households that cannot practically reach the property.

Drive-Time and Drive-Distance Polygons

A drive-time polygon traces the actual road network outward from the site until a travel-time budget is exhausted, producing an irregular shape that stretches along arterials and collapses against barriers. Drive-distance polygons do the same on network mileage rather than minutes.

[!WARNING] The barrier trap. A three-mile ring around a site on the east bank of a river with one bridge two miles north may capture 42,000 households. The corresponding ten-minute drive-time polygon may capture 24,000 — and the missing 18,000 are on the far bank with a fifteen-minute detour. Underwriting demand from the ring overstates the trade area by 75 percent and will produce an unsupportable capture rate.

Exam-safe practice: deliver rings and drive times side by side. When they diverge materially, the divergence is itself the finding, and the drive-time figure governs the demand model.

Custom Polygons and Geocoding

Two supporting techniques appear throughout CI 102 casework:

• Custom polygons are hand-drawn trade areas following real boundaries — a school attendance zone, a section of highway between two interchanges, a neighborhood bounded by arterials. Use them when local knowledge beats any automated geometry.
• Geocoding converts a list of addresses into mapped points. Plotting a client's actual customer addresses or loyalty-card data and drawing the polygon that contains 60 to 70 percent of them produces an empirically observed primary trade area rather than a modeled one — the strongest evidence available.

---

Retail Gap Analysis: Leakage and Surplus

Retail gap analysis is where geospatial data converts into an investment recommendation.

• Demand is the estimated retail expenditure by households living inside the trade area, by category.
• Supply is the estimated sales generated by establishments located inside the trade area, by the same category.

$\text{Retail Gap} = \text{Demand} - \text{Supply}$

• Positive gap = Leakage. Residents are spending money that local businesses do not capture; those dollars are being exported to competing trade areas. Leakage signals potential unmet demand.
• Negative gap = Surplus. Establishments are selling more than resident households can account for, meaning the area imports customers from outside. Surplus signals an established regional destination — and a crowded competitive field.

The Leakage/Surplus Factor

Raw dollar gaps cannot be compared across trade areas of different sizes, so analysts normalize:

$\text{Leakage/Surplus Factor} = \frac{\text{Demand} - \text{Supply}}{\text{Demand} + \text{Supply}} \times 100$

The result runs from +100 (complete leakage — no local supply at all) to -100 (complete surplus). A factor near zero indicates a trade area roughly in balance.

---

Worked Example: Sizing a Home Improvement Opportunity

An analyst evaluates a 12-acre pad in a growing suburban submarket for a home improvement retailer. Intellisite is used to build a 12-minute drive-time polygon, and the Retail MarketPlace report for the Building Material and Garden Equipment category returns:

• Demand (household expenditure): $88,400,000
• Supply (sales by establishments in the polygon): $52,600,000

Step 1: Compute the Retail Gap

$\text{Gap} = \$88{,}400{,}000 - \$52{,}600{,}000 = \$35{,}800{,}000 \text{ of leakage}$

Step 2: Compute the Leakage/Surplus Factor

$\frac{\$35{,}800{,}000}{\$88{,}400{,}000 + \$52{,}600{,}000} \times 100 = \frac{35.8}{141.0} \times 100 = 25.4$

A factor of +25.4 indicates substantial leakage: roughly a quarter of the category's total trade-area activity is escaping to competitors outside the polygon.

Step 3: Apply a Realistic Capture Rate

No single store recaptures all leakage. Competitors will respond, some households will keep shopping near work, and category loyalty is sticky. The analyst underwrites a 65% capture of the identified leakage:

$\$35{,}800{,}000 \times 0.65 = \$23{,}270{,}000 \text{ of supportable sales}$

Step 4: Convert Sales to Supportable Gross Leasable Area

Divide by the category's benchmark sales productivity — here $310 per square foot annually:

$\frac{\$23{,}270{,}000}{\$310/\text{SF}} = 75{,}065 \text{ SF of supportable GLA}$

Step 5: Reconcile Against the Physical and Regulatory Site

The demand model is now cross-checked against Sections 7.2 and 7.3. A 75,000 SF box on 12 acres requires roughly 375 stalls at 5.0 per 1,000 SF, consuming about 131,000 SF of surface parking against a 522,720 SF parcel — feasible. Signalized full-movement access, permitted zoning use, and adequate truck-court depth must all be confirmed before the number is committed to an investment memorandum.

[!IMPORTANT] Leakage is a hypothesis, not a conclusion. A large positive gap can mean unmet demand — or it can mean the category is structurally unservable in that location because a dominant competitor sits just outside the polygon boundary, because the arterial has no signalized left turn, or because zoning prohibits the use. Every geospatial finding must be reconciled with the Market, Location and Site, and Political and Legal analyses of the CCIM Strategic Analysis Model before it becomes a recommendation.

---

Beyond Retail: Geospatial Analysis by Property Type

---

Exam Traps

• Reporting rings where barriers exist. If a river, freeway, rail corridor, or elevation change bisects the trade area, radius-ring demographics are not defensible. Run the drive time.
• Mixing data vintages. Decennial census counts, current-year estimates, and five-year forecasts are three different products. Comparing a decennial count to a competitor's current-year estimate manufactures growth that does not exist. State the vintage on every figure.
• Treating the full gap as capturable. Leakage is the ceiling, never the projection. Apply an explicit, defended capture rate.
• Confusing surplus with strength — or with saturation. A negative gap means the area imports shoppers, which can indicate a proven regional destination or an oversupplied field. Only competitor-level analysis distinguishes the two.
• Double-counting daytime and residential demand. Office-worker lunch demand and resident household demand are different populations measured by different variables. Adding them without adjustment inflates the market.
• Reading a Spending Potential Index as dollars. SPI is indexed to 100 = national average. An SPI of 142 means the trade area spends 42 percent above the national average on that category — it is not a dollar figure and cannot be summed.