SK9 has an average sold price of £337,466 across 27,392 HM Land Registry transactions covering 1,080 postcodes. The largest town grouping is Wilmslow.
Decision summary
SK9 postcode area averages £337,466 across 27,392 completed sales covering 1,080 postcodes. Check the median, sample depth and nearby postcodes before using the average as a market signal.
Average £337,466 · Median £297,250 · 27,392 transactions · +87.3% growth · 1,080 postcodes
Use the location comparison tool to put SK9 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 SK9 area, the median sold price is £297,250 and the average is £337,466, based on 27,392 HM Land Registry transactions. Over five years, prices have moved +87.3%, 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 SK9 is £337,466, based on 27,392 HM Land Registry transactions across 1,080 postcodes.
SK9 is -15.0% below 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 SK9 postcode area covers 1,080 individual postcodes with 27,392 registered sales. The largest town grouping is Wilmslow.
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.