W1F has an average sold price of £2,999,216 across 1,056 HM Land Registry transactions covering 285 postcodes. The largest town grouping is London.
Decision summary
W1F postcode area averages £2,999,216 across 1,056 completed sales covering 285 postcodes. Check the median, sample depth and nearby postcodes before using the average as a market signal.
Average £2,999,216 · Median £1,114,500 · 1,056 transactions · +274.3% growth · 285 postcodes
Use the location comparison tool to put W1F postcodes beside other postcode areas, towns or districts before narrowing your shortlist.
Property valuation guide
Professional valuers use recent sold prices as comparable evidence. In the W1F area, the median sold price is £1,114,500 and the average is £2,999,216, based on 1,056 HM Land Registry transactions. Over five years, prices have moved +274.3%, which affects how comparables from earlier years should be weighted.
These are historical completed-sale prices — not a formal valuation, asking price or investment advice. Use them to sense-check an asking price or set expectations before instructing a surveyor.
The average sold price in W1F is £2,999,216, based on 1,056 HM Land Registry transactions across 285 postcodes.
W1F is +655.8% above the England and Wales national average of £396,803.
No. These are historical completed sale prices from HM Land Registry Price Paid Data. They are not current valuations, asking prices or investment advice.
The W1F postcode area covers 285 individual postcodes with 1,056 registered sales. The largest town grouping is London.
All sold price figures on this page come from HM Land Registry Price Paid Data for England and Wales, published under the Open Government Licence v3.0. Contains HM Land Registry data © Crown copyright and database right. These are historical completed sale prices — they are not current valuations, not forecasts and not investment advice. Past sale prices do not guarantee future values. Always seek independent professional advice before making property or financial decisions. Read the full methodology for details on data cleaning, grouping and limitations.