How Offrly Works

An honest, outcome-oriented view of what Offrly produces, the pipeline behind every estimate, where it's reliable, and how we measure accuracy in public.

Updated 2026-04-29

The short version

Offrly is a free UK property analyst with two tools — AI-graded property search and instant sale or rental valuation. Both run in about 30 seconds, free, with no mandatory signup. The estimate is indicative market guidance — not a regulated valuation, not financial / tax / legal advice.

Below: what we produce, what's in the estimate, what's not, where it's reliable, where it isn't, and how we publish accuracy.

What you get from a valuation

What you don't get: a regulated valuation, a building-condition survey, a financial-advice or tax answer.

What you don't get: a viewing booking, a mortgage decision, an offer process. Offrly is intelligence; the transaction layer is whoever is selling or letting the home.

How we get to a price

Every Offrly valuation runs the same five-stage pipeline. There's no human in the loop.

  1. Find — pull comparable sales and live listings around the target postcode within a search radius. Typically 300–500 comparables make it through this step.
  2. Clean — drop plots and land-only entries, drop price outliers, and drop comparables whose photos couldn't be condition-rated. The AI declines to guess on signal it can't see.
  3. Read — for each surviving comparable, capture distance from the target, beds and baths, property type, tenure, plus what photo-aware AI gleans from listing photos the way a seasoned property analyst would.
  4. Fit — fit a price model on the cleaned, scored comparables. The model weights each feature based on how well it explains observed prices in this specific local market, which is why R² varies between queries.
  5. Apply — plug the target's features into the fitted model to get the estimate, and report R² as the fit quality.

Worked example

A 3-bed mid-terrace in a typical inner-London postcode. The pipeline evaluated 380 comparables within scraping range. Below are the top 10 by weight — the comparables the pricing model leaned on most:

# Comparable Asking / sold Beds/Baths Distance Garden Condition Tenure Why it counted
1 Same road, 4 doors down £640,000 3 / 1 0.04 mi Good Good Freehold Closest tenure-and-type match
2 Adjacent road, mid-terrace £625,000 3 / 1 0.10 mi Avg Good Freehold Strong type match, close
3 Same postcode, end-terrace £680,000 3 / 2 0.18 mi Good Good Freehold End-terrace premium
4 Same postcode, mid-terrace £590,000 3 / 1 0.22 mi Avg Dated Freehold Condition discount
5 Adjacent ward, mid-terrace £655,000 3 / 1 0.31 mi Good Excellent Freehold Renovation premium
6 Same road, 2 doors down £600,000 3 / 1 0.02 mi Avg Dated Freehold Closest, down-weighted on condition
7 Adjacent road, mid-terrace £620,000 3 / 1 0.14 mi Avg Good Freehold Type & tenure match
8 Same ward, mid-terrace £645,000 3 / 1 0.36 mi Good Good Freehold Borderline radius, light weight on distance
9 Same postcode, semi £710,000 3 / 2 0.24 mi Large Good Freehold Type penalty (semi vs terrace)
10 Adjacent ward, mid-terrace £635,000 3 / 1 0.41 mi Avg Good Freehold Furthest in the top 10, lightest weight

Estimate: £625,000. Fit score (R²): 0.87 — the surrounding sales explain 87% of the local price variation, so we're in a tight market.

The model up-weights tenure-matched, type-matched, photo-rich comparables that are physically close. It down-weights comparables that are further away, in a different sub-type, or where photos suggest substantially different garden / condition. Comparables 1, 2 and 7 carry the most signal here; comparable 9 (a semi) gets a type penalty even though it's nearby.

What R² means here

R² (the fit score) is how well the surrounding sales explain price variation in this specific market.

R² is reported on every result card. It is also the input we use ourselves when deciding how much weight to give the model on a per-query basis.

What's IN the estimate

What's NOT in the estimate (and matters separately)

These items genuinely affect what a UK home is worth on the open market — but they don't reliably show up in listing photos or comparable sale prices, so the AI does not see them. Treat them as buyer-side due diligence items, not failures of the estimate:

The right tools for those: the seller's pack, a RICS-qualified surveyor, and your solicitor. Use Offrly's estimate as a fast, free first-pass market answer; use those professionals to close the loop.

Where Offrly is most reliable

Where Offrly is less reliable

When confidence is low, the R² fit score will be low — typically below 0.65. Read the estimate loosely in that case.

Coverage

Offrly covers the United Kingdom only — England, Scotland, Wales and Northern Ireland — subject to live comparable listings being available in the area. We do not cover Ireland, the Channel Islands, the Isle of Man or any non-UK market.

For sold-price intelligence on location pages, England and Wales coverage comes from HM Land Registry Price Paid Data under Open Government Licence v3.0. Scotland and Northern Ireland have separate registers; we use city-level summaries for those.

How we measure accuracy in public

Two transparent views over the same auto-joined pair set, sitting alongside each other:

Both are prediction-vs-outcome reports, not synthetic back-tests, with no human in the loop. Each figure traces back to a real Offrly prediction made strictly before the eventual sold date — predictions never see future sold prices.

Rentals are not in either report — there is no public sold-rent register equivalent to pair predictions against.

What Offrly does not do

Feedback

If you think an Offrly estimate is materially off, let us know via the contact form. Off-by-a-lot examples — with the address or postcode and the headline reason — are the most useful kind of feedback.