How we get the numbers — and what we won’t pretend to know
For a paid analyst tool, the math has to be transparent. Here is exactly where every figure comes from, how often it refreshes, and how we score our own confidence.
Where the numbers come from
How fresh it is
Different data moves at different speeds. We show each number’s true “as of” date and never smooth over a slow source to look more current than we are.
Confidence, not false precision
Every market carries a confidence score from 0–100, driven by how much real data actually backs it — never cosmetic. Thin coverage shows a low score and a plain warning rather than a confident-sounding guess. When a rent looks contaminated by short-lets, or a yield reads implausibly high, we withhold the number and say so. A bad scrape fails loudly; it never gets dressed up as analysis.
How our analysis compares
Why a full 4-strategy underwrite beats the shortcuts most buyers still rely on.
What we do not claim
- DealPilot AI is research tooling, not financial or investment advice.
- National and regional figures are averages — they smooth over real local variation, which is exactly why the app drills to the city and the neighbourhood.
- Projections are scenarios, not certainties.
- We only publish a market once its real data is in. We never ship a fabricated placeholder.
How the numbers work
Where does DealPilot AI’s market data come from?
Sale and rent prices come from live listing data on the major national portals — Idealista in Portugal, Spain and Italy, Spitogatos in Greece — plus France’s official DVF transaction register, read by real flat size rather than a blended average. House-price and rent-growth trends come from Eurostat and the national statistics offices (INE, ISTAT, ELSTAT, INSEE); macro context comes from the OECD and World Bank; and tax, visa and rules come from official government and tax-authority sources.
How often is the data updated?
Different data moves at different speeds and each figure shows its true “as of” date. Listing prices and rents refresh monthly — DealPilot AI’s edge — with each city showing the exact period it was pulled. The house-price index and rent growth update quarterly, as the statistics offices publish them, and Italy’s OMI reference prices are semi-annual. We never smooth over a slow source to look more current than it is.
How does DealPilot AI handle rental yield and price figures?
Prices and rents are read by real flat size rather than a blended average, so the numbers reflect the type of property you’re actually looking at. National and regional figures are treated as averages that smooth over local variation, which is exactly why the app drills down to the city and neighbourhood level rather than resting on a headline number.
How is the data-confidence score set?
Every market carries a confidence score from 0–100, driven purely by how much real data actually backs it — never cosmetic. Thin coverage shows a low score and a plain warning instead of a confident-sounding guess. When a rent looks contaminated by short-lets or a yield reads implausibly high, DealPilot AI withholds the number and says so. A bad scrape fails loudly; it is never dressed up as analysis.
What does DealPilot AI not claim to predict?
DealPilot AI is research tooling, not financial or investment advice. National and regional figures are averages that smooth over real local variation, and any projection is presented as a scenario, not a certainty. A market is only published once its real data is in — DealPilot AI never ships a fabricated placeholder.
Does DealPilot AI ever publish a market before its data is ready?
No. A market is only published once its real data is in place; DealPilot AI never ships a fabricated placeholder to fill a gap. Where a source is slow, the figure shows its genuine “as of” date rather than a faked fresher cadence, and thin coverage is flagged with a low confidence score and a plain warning.