How FairCost Data Works
Most platforms show a number and ask you to trust it. Here is exactly how we build, verify, and quality-control every data point.
Evidence Tiers
Every submission is weighted by evidence quality. Self-reported (weight: 0.3) carries the lowest weight. Written quotes (0.5) are better. Invoice-backed (0.8) and receipt-backed (0.9) submissions are strong. Bank-verified (1.0) carries maximum weight. A single bank-verified submission has 3.3x the influence of a self-reported one.
Scope Normalisation
Two quotes for the same service can differ by $300 just based on scope. We normalise every submission using a scope score system. Base clean = 100 points. Oven deep clean = +20 points. Carpet steam per room = +35 points. This means we compare like-for-like, not just raw dollar amounts. When you see a fair range, it is adjusted for the scope you actually need.
Confidence Scoring
Every data point displays a confidence level. High: 50+ submissions, average evidence weight above 0.7, updated within 30 days, suburb-specific data available. Strong: 20+ submissions, average weight above 0.5. Moderate: 10+ submissions. Low: under 10 submissions, often using city or state averages. We show this so you know how much to trust the number.
Fraud Detection
Every submission passes automated checks. IP pattern analysis detects bulk submissions from the same source. Duplicate invoice template detection catches copy-paste fraud. Repeated users with inconsistent data get flagged. Impossible prices (e.g., $50 for a 4-bed house clean) trigger automatic flags. Fraud-scored submissions require manual admin review before entering the dataset.
Contributor Reputation
Frequent submitters earn reputation tiers: Bronze (3+ verified submissions), Silver (10+), Gold (25+). Higher-reputation contributors' submissions carry slightly more weight. This rewards genuine participants and makes gaming more expensive.
Freshness Decay
Data older than 24 months carries minimal weight in our calculations. Pricing changes over time, and stale data distorts ranges. Our aggregates prioritise recent submissions so the ranges you see reflect current market conditions.
Provider Right of Reply
If a provider's pricing is flagged as expensive, they can submit a scope explanation. 'This quote included trench excavation and after-hours callout.' This context is reviewed by our team and, if valid, adjusts the scope normalisation. We are not anti-provider. We are anti-opacity.
Anti-Gaming Mechanics
Evidence weighting + freshness decay + outlier detection + IP analysis + scope normalisation + contributor reputation + manual review = extremely difficult to manipulate at scale. A provider would need dozens of fake bank-verified submissions across different IPs with realistic scope descriptions to meaningfully shift a range. The cost of gaming exceeds the benefit.
Featured Listings Do NOT Influence Data
Featured providers pay for visibility, not for data manipulation. Their pricing submissions are weighted identically to everyone else's. Their featured status does not affect the fair range, confidence score, or any data on any page. Revenue and data are architecturally separated.
Limitations
All pricing is indicative, not guaranteed. Low-data categories and suburbs may have wide ranges. City-level averages are used when suburb data is insufficient. We are a guidance tool, not a quoting engine. Always get specific quotes for your actual job from licensed, insured providers.
Have questions about our methodology? Get in touch. We are happy to explain any part of our process in detail.