W1K has an average sold price of £3,357,462 across 2,927 HM Land Registry transactions covering 450 postcodes. The largest town grouping is London.
Decision summary
W1K postcode area averages £3,357,462 across 2,927 completed sales covering 450 postcodes. Check the median, sample depth and nearby postcodes before using the average as a market signal.
Average £3,357,462 · Median £1,400,000 · 2,927 transactions · +412.6% growth · 450 postcodes
Use the location comparison tool to put W1K 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 W1K area, the median sold price is £1,400,000 and the average is £3,357,462, based on 2,927 HM Land Registry transactions. Over five years, prices have moved +412.6%, 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 W1K is £3,357,462, based on 2,927 HM Land Registry transactions across 450 postcodes.
W1K is +746.1% 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 W1K postcode area covers 450 individual postcodes with 2,927 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.