Working with Esri Data for Site Selection

Esri is the dominant provider of geographic and demographic data for commercial real estate. Nearly every major retailer, restaurant chain, and CRE brokerage uses Esri data in their site selection process.

Esri's Business Analyst platform provides the demographic backbone of site selection: population, income, age, education, consumer spending, lifestyle segmentation, and market potential — all accessible at custom trade area geographies.

This guide covers the key Esri data layers CRE professionals use and how to apply them effectively in site selection analyses.

Updated Demographics: Current-year estimates and 5-year projections for population, households, income, age, race/ethnicity, education, and employment. These form the foundation of every site report.

Consumer Spending: Estimated annual spending by category (food at home, food away from home, apparel, entertainment, etc.) for any trade area. Powered by the Bureau of Labor Statistics Consumer Expenditure Survey.

Tapestry Segmentation: 67 lifestyle segments that classify every U.S. household based on demographics, socioeconomics, and behavior. The most powerful Esri layer for understanding market fit.

Market Potential Index (MPI): Likelihood measures for hundreds of consumer behaviors and product preferences. Critical for matching a specific concept to a trade area.

Business Locations: Database of all U.S. businesses by NAICS code, used for competitive analysis and market supply assessment.

Traffic Counts: Average daily traffic (ADT) counts for major roads and intersections. Useful for visibility and accessibility analysis.

Tapestry Segmentation is Esri's most valuable layer for CRE site selection because it tells you how people live, not just who they are.

Each of the 67 segments includes: - Demographic profile (age, income, education, household composition) - Housing profile (own vs. rent, housing type, home value) - Spending preferences (where they spend above and below average) - Media and technology habits - Lifestyle and values

How to apply Tapestry in practice:

1. Profile your existing successful locations — what Tapestry segments dominate your best-performing trade areas? 2. Create a target segment profile — rank segments by their affinity for your concept 3. Score candidate sites — measure the concentration of target segments in each trade area 4. Compare and rank — the trade area with the highest concentration of your ideal segments gets the highest score

This approach moves site selection from subjective judgment to data-driven decision-making.

Esri's Consumer Spending data lets you quantify demand at the trade area level for your specific category.

Key spending metrics:

• •Total spending by category: How much are households in the trade area spending on food away from home, fitness memberships, healthcare, etc.?
• •Spending Potential Index (SPI): How does local spending compare to the national average? An SPI of 115 means 15% above average.
• •Per-capita spending: Normalize by population to compare trade areas of different sizes.

Leakage/surplus analysis: Compare the supply of businesses (from Esri Business Locations) to the demand (from Consumer Spending). When demand exceeds supply, you have "leakage" — residents are spending that money outside the trade area. Leakage represents the opportunity for a new entrant.

For example, if a trade area has $50M in annual food-away-from-home spending potential but only $30M in existing restaurant supply, there's $20M in leakage that a new restaurant could capture.

Esri data is most powerful when combined with other data sources for a complete site selection analysis.

Esri + Placer.ai: Esri tells you who lives in the trade area; Placer tells you who actually visits. When the demographic profile (Esri) aligns with the visitor profile (Placer), you have strong market-concept fit.

Esri + Google Places: Esri quantifies demand; Google Places maps the competitive supply. Together, they reveal gaps in the market.

Esri + Financial Models: Esri spending data feeds directly into financial feasibility models. Use category spending potential to project sales, then compare against rent benchmarks to evaluate deal viability.

Platforms like Slant automate this integration, pulling Esri demographics, Placer traffic, and Google competitive data into a unified analysis — eliminating the manual process of collecting, normalizing, and synthesizing data from multiple platforms.