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Retail and Commercial Trade Area Study

Real Estate · ChatGPT, Claude, Gemini · data
85AI Quality /100
93AI Usefulness est. /100
86Eval Confidence /100
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Quality, usefulness and confidence are AI evaluations out of 100 — not user ratings. Community results come from visitors' "worked / didn't work" votes below. How scoring works →

The prompt

Conduct a trade-area and site-suitability study. SITE: [address and coordinates] PROPOSED USE: [retail/office/service/etc.] TARGET CUSTOMER: [profile] TRAVEL MODES AND PEAK HOURS: [details] KNOWN COMPETITORS: [list] ANCHORS AND DEMAND SOURCES: [offices, transit, housing, institutions] AVAILABLE DATA: [links/files] DECISION: [lease, acquire, launch or reject] Define primary, secondary and tertiary catchments using locally appropriate travel time and physical barriers. Analyze population or workforce, spending or business demand proxies, daytime/nighttime demand, access, visibility, parking, transit, planned infrastructure, competitor density and substitutes. For every important number give source, date, geography and whether it is measured, estimated or inferred. Do not import foreign benchmarks without local justification. Score the site with explicit weights and confidence, then show how the decision changes under alternate assumptions. End with fieldwork questions, data gaps and a go/conditional/no-go recommendation.
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