A cafe lease usually locks years of fixed cost, so your site selection process must be stricter than your fit-out planning. This checklist is designed for Australian operators who want a hard go/no-go decision before signing. Work through each step in order and reject any site that fails your minimum thresholds.
This is where founders usually get it wrong: they treat benchmark demand as proof, when it is only a starting hypothesis that still needs local validation.
12 checks
Core checks before lease commitment
8–12%
Healthy cafe rent-to-revenue target band
2–3 sites
Final shortlist size before decision
Step 1: Set your maximum viable rent first
Start with conservative revenue assumptions and calculate maximum monthly rent before visiting sites. If asking rent needs unrealistic customer volume, the site is already a NO.
Run this first before any inspections.
Check rent viability → →Step 2: Verify daypart demand at frontage level
Minimum demand validation protocol
- 1
Run weekday morning counts
- 2
Run weekday lunch counts
- 3
Run one weekend session
- 4
Estimate realistic conversion, not perfect conversion
Step 3: Check competition density and positioning
Count nearby cafes and assess positioning overlap. A validated market with moderate density is often safer than a no-competition strip with weak demand.
Step 4: Stress-test downside before emotion
Downside pre-lease check
Model at least one downside case before signing: revenue -25% in first months. If the site cannot survive that shock without immediate cash stress, renegotiate or reject.
12-point cafe site checklist
Convert this checklist into an address-level decision.
Run full cafe address analysis → →Related reading
Turn this cafe guide into a decision
Validate customer-day demand, rent ratio, and local competition for your exact address before signing.
Run full cafe location analysis →Free pre-lease checklist
Download the quick checklist operators use to avoid signing weak sites without demand and rent validation.
How to read this decision
Interpretation: most bad decisions happen when operators over-trust average-case projections and underweight downside execution risk.
Mini real-world scenarios
A founder who compared two nearby suburbs chose the lower-rent site and reached breakeven sooner because repeat local demand was less volatile.
A location we reviewed last year had healthy median income, but rent reviews were uncapped. Margin disappeared by year two even with stable traffic.
One site showed strong footfall but weak conversion intent. People moved through quickly, and the concept needed destination demand that never formed.
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