Every Locatalyze report combines live API data with AI-estimated benchmarks. Here's exactly what we analyse, where the data comes from, and how we calculate your score.
The process
Address + coordinate pinning
Competitor mapping
Demographic analysis
Rent benchmarking
Model calibration (optional but impactful)
Deterministic financial model
Written analysis & verdict
Data sources
Competitor providers
Competitor locations, ratings and review counts swept live from Google Maps, Geoapify and Foursquare, then confidence-weighted. Scored within a 500m window of your coordinates.
ABS Census 2021
Population demographics, median income, household size and age distribution from the Australian Bureau of Statistics. The most reliable dataset in the report.
Rent benchmarks
Suburb-level rent benchmarks estimated by the AI pipeline from category patterns, suburb context, and commercial-listing signals. Treat as directional guidance, not a market quote.
LocationIQ
Geocoding — converts your typed address into precise coordinates for the analysis. The interactive address map is rendered with Mapbox.
Category trade norms
Daily customers baseline, average ticket size, COGS %, gross margin, and staffing cost ratios segmented by business type. Used as the fallback when you do not provide your own figures.
Deterministic Compute Engine
A rules-based financial model (not AI) that builds the P&L from your calibrated inputs. Formulas are deterministic and documented — no black box outputs.
Narrative layer
Generates the prose sections only — SWOT, market narrative, risk scenarios, and 3-year projection. Financial figures come from the compute engine, not the language model.
Scoring system
The Location Score (0–100) is a weighted composite of five dimensions. Each dimension is scored independently then combined into a final score that determines your GO / CAUTION / NO GO verdict. Every report also shows a separate Data Completeness % and Model Confidence label so you can see how much of the analysis relied on your own inputs versus fallback category norms. This score powers your on-demand address report; our public suburb guides use a separate, suburb-level model — see How suburb location guides are scored below.
20%
weight
Rent Affordability
Rent as a percentage of projected revenue. Under 14% rates as Healthy; 14–18% as Watch (margin buffer thinning); above 22% as Risky. The workable band runs 8%–18% of revenue. This dimension is one of the strongest predictors of long-term viability.
25%
weight
Competition
Competitor density within 500m, weighted by their threat level (ratings, review volume, proximity). Fewer strong competitors = higher score.
20%
weight
Market Demand
Search demand signals, population density, income fit, household growth and demographic alignment for your business category. When no measured demand signal is available, this weight is reduced and redistributed across the other dimensions.
25%
weight
Profitability
Net profit margin after all costs. Calculated from your revenue estimate minus rent, COGS, labour and fixed costs.
10%
weight
Location Quality
Physical location signals: access quality, anchor proximity, and local activity conditions around your specific site.
Economics and location signals support proceeding with normal diligence. The data gives you a basis to act — move to lease negotiation and site visits.
A split read — economics, competition, demand, or location need verification before you commit. Viable with the right execution.
Economics or competition read as unfavourable on the measured inputs. The risk profile does not support proceeding at this time.
Core commercial inputs (e.g. ticket size, demand, or foot traffic) are missing, so the verdict is provisional — not a measured rejection. Re-run once the missing inputs are confirmed; we never present a confident verdict on thin data.
The bands above describe the composite score on its own. The engine can apply a per-axis floor — for example when Profitability or Competition lands at the data floor because key inputs were unmeasured — that makes the displayed verdict harsher than the score alone predicts. When that happens, the report shows a one-line note explaining the override, so the verdict stays auditable.
Suburb guides
Our public suburb guides (under Location guides) use a separate, suburb-level model from the on-demand address report above. Each suburb is rated on five 1–10 location factors, scored for three business types, and blended into a single 0–100 suburb score. Every figure is computed deterministically from the factors — never hand-entered — and a build-time check fails the site if any score is set by hand.
1–10
weight
Demand
Strength of underlying trade demand for the suburb. Higher is better — it lifts every business-type score.
1–10
weight
Rent cost
Rent pressure for the suburb. Higher is worse: expensive rent pulls scores down.
1–10
weight
Competition
How saturated the suburb already is. Higher is worse. These are curated factor levels, not a live competitor scan.
1–10
weight
Seasonality
Revenue volatility across the year. Higher is worse — steadier trade scores better.
1–10
weight
Tourism dep
How tourism-driven the trade is. Contextual: it lifts restaurant and retail scores but counts against cafés, which rely on steady local routine.
The five factors are weighted differently for each business type, then each business-type score is blended into the headline suburb score — café 40%, restaurant 35%, retail 25%. We surface the per-type scores so a suburb that suits a café is not presented as if it suits every concept, and we label the strongest fit.
Café weighting
Demand 40 · Rent cost 28 · Competition 18 · Seasonality 14
Cafés lean hardest on steady local demand and affordable rent; tourism is treated as a risk, not a help.
Restaurant weighting
Demand 32 · Rent cost 22 · Competition 18 · Seasonality 14 · Tourism dep 14
Restaurants can convert tourism and destination trade, so it counts in their favour.
Retail weighting
Demand 28 · Rent cost 22 · Competition 18 · Seasonality 10 · Tourism dep 22
Retail is the most tourism-sensitive of the three and the least exposed to seasonality.
Suburb composite
(Café × 40%) + (Restaurant × 35%) + (Retail × 25%)
The three business-type scores blend into one 0–100 suburb score. Suburb guides rank suburbs by this composite, highest first.
The suburb scores well across demand, rent and competition for the blended business mix.
A workable suburb with trade-offs — the score is held back by rent, competition or seasonality on at least one concept.
The factor mix works against most concepts here. Treat as a stretch unless your model is unusually strong on the weak factor.
Rent viability check
Each suburb guide also shows a rent-viability table across the quoted rent band. As a quick screen it anchors rent at 10% of revenue — so a given rent implies the monthly revenue you would need to keep rent at that level — then converts that to a daily customer target across roughly 26 trading days (a six-day week). This is an illustrative suburb-level helper; your on-demand report uses the full engine, 30 trading days, and your own inputs.
A note on the two verdicts: suburb guides read GO / CAUTION / RISKY (some guides label the lowest tier NO) on the 69 / 60 bands above. Your on-demand address report reads GO / CAUTION / NO GO on the 60 / 40 bands in the previous section. They are deliberately separate models — one ranks suburbs for discovery, the other rates a specific site with your numbers.
Financial model
Our financial model combines your inputs with category baseline data to build a realistic P&L. Here's the logic behind each number.
Base revenue (category norm)
daily_customers_base × hours_multiplier × access_multiplier × avg_ticket × 30
The baseline revenue from which all scenarios are built. We use 30 calendar days per month; reduced trading (e.g. six-day weeks) is reflected through the hours_multiplier rather than the day count, so the model stays consistent across formats. hours_multiplier ranges from 0.45× (weekends only) to 1.35× (all-day). access_multiplier ranges from 0.70× (arcade) to 1.10× (transport hub). avg_ticket uses your entered value if provided, otherwise the category typical.
COGS (Cost of Goods)
~20–45% of revenue (by category)
COGS is derived from the category gross margin, so it varies by format: cafés ~35%, restaurants ~32%, retail ~45%, bars ~28%, takeaway ~40%. Specialist categories sit outside this range (e.g. pharmacy ~65%). Based on common gross margins for Australian operators.
Labour Costs
Category staffing baseline × your staffing scale
Each category carries a monthly staffing baseline — café ~$18,000, restaurant ~$35,000, retail ~$16,000 — which you can scale up or down in the calibration step. Does not include owner salary.
Fixed Costs (for break-even)
Monthly rent + staffing + other fixed costs
Fixed costs are rent, staffing, and other recurring overheads (utilities, insurance, baseline marketing, repairs) — everything that does not scale with covers. COGS is excluded because it is variable. This is the contribution-margin break-even, which avoids double-counting variable costs.
Break-even Customers / Day
Fixed costs ÷ (avg_ticket × (1 − COGS%) × 30)
The minimum daily customers needed to cover fixed costs over 30 days per month (matching the revenue formula). The denominator is the per-customer contribution margin — average ticket less the cost of goods on that sale. Compared against your projected daily demand: if projected > break-even, the location is viable at current inputs. The zero-profit survival floor (rent + overheads only, excluding staff cost) is lower than this number.
Payback Period
Setup cost ÷ Monthly net profit
Months to recover your initial investment. Only shown when monthly net profit is positive and setup cost is greater than $0. Under 12 months is excellent. Over 24 months carries meaningful risk.
3-Year Projection Assumption
Year2 = Year1 × 1.08, Year3 = Year2 × 1.10
Projection growth assumptions use a two-step rate: +8% then +10% when sufficient live revenue signals are available.
Important: Use as a decision-support tool
Locatalyze reports are designed to help you make better-informed decisions — not to replace professional due diligence. Scores and P&L figures come from our deterministic rules engine and category benchmarks; the written narrative is generated by AI on top of those numbers. Neither the engine nor the narrative guarantees trading outcomes.
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