Most and least expensive streets

London, W10


London, W10 has a total of 166 streets. The most expensive street in W10 is Conlan Street, with an average sale price of £6,750,000. The least expensive street in W10 is Princess Louise Walk, with an average sale price of £170,000.



Filter by number of transactions

Most expensive streets

Rank Street Transactions (Past 5 years) Average price
1 Conlan Street

W10 5AP | W10 5AR
2 £6,750,000
2 Wornington Road

W10 5FP | W10 5FW | W10 5LD | W10 5PT | W10 5QD | W10 5QE | W10 5QF | W10 5QG | W10 5QJ | W10 5QP | W10 5QQ | W10 5QW | W10 5RE | W10 5RF | W10 5YA | W10 5YF
8 £5,042,560
3 St. Lawrence Terrace

W10 5SR | W10 5ST | W10 5SU | W10 5SX
7 £3,329,500
4 Elkstone Road

W10 5NT
2 £3,250,000
5 Exmoor Street

W10 6BA | W10 6BB | W10 6BD | W10 6BE | W10 6BF | W10 6DZ
3 £3,072,000
6 Finstock Road

W10 6LT | W10 6LU
2 £2,845,000
7 Kingsbridge Road

W10 6PU | W10 6QF
1 £2,650,000
8 Kensal Road

W10 5BE | W10 5BF | W10 5BJ | W10 5BL | W10 5BN | W10 5BS | W10 5BZ | W10 5DA | W10 5DB | W10 5DE | W10 5DF | W10 5DG | W10 5JU
8 £2,619,625
9 Shalfleet Drive

W10 6HP | W10 6UB | W10 6UE | W10 6UF
11 £2,579,714
10 Wallingford Avenue

W10 6PX | W10 6PY | W10 6PZ | W10 6QA | W10 6QB
11 £2,369,000


Least expensive streets

Rank Street Transactions (Past 5 years) Average price
1 Princess Louise Walk

W10 6DS
2 £170,000
2 Maple Walk

W10 4NN
1 £175,000
3 Dalgarno Way

W10 5EL | W10 5EN | W10 5EW | W10 5HG
2 £236,250
4 Bosworth Road

W10 5EG | W10 5EH | W10 5EQ
1 £260,000
5 Shrewsbury Street

W10 5DP | W10 5DR | W10 5JD
2 £268,500
6 Salters Road

W10 5YP
2 £310,000
7 Webb Close

W10 5QB
1 £345,000
8 Rowan Walk

W10 4JJ
3 £357,033
9 John Fearon Walk

W10 4NT | W10 4NX
2 £372,500
10 Acklam Road

W10 5JJ | W10 5QZ | W10 5YG | W10 5YU | W10 5YX
1 £380,000


Street ranking notes

Average street prices are calculated using all sales in the HM Land Registry data from the past 5 years. It is important to note that as homes are demolished and built these prices will fluctuate over time accordingly. Use the transactions number filter to avoid any anomalous data caused by low transaction numbers.