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Most and least expensive streets

London, W7

London, W7 has a total of 167 streets. The most expensive street in W7 is Boston Road, with an average sale price of £2,056,235. The least expensive street in W7 is Broadway, with an average sale price of £106,500.

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Most expensive streets

Rank	Street	Transactions (Past 5 years)	Average price
1	Boston Road W7 2AA \| W7 2AD \| W7 2AR \| W7 2AS \| W7 2AT \| W7 2AX \| W7 2AY \| W7 2DG \| W7 2EP \| W7 2ER \| W7 2ES \| W7 2ET \| W7 2HJ \| W7 2HN \| W7 2HP \| W7 2HR \| W7 2HW \| W7 2JU \| W7 2QE \| W7 3QJ \| W7 3QL \| W7 3SA \| W7 3SB \| W7 3SH \| W7 3SJ \| W7 3TR \| W7 3TS \| W7 3TT	45	£2,056,235
2	Cawdor Crescent W7 2DA \| W7 2DB \| W7 2DD	7	£954,900
3	Christopher Avenue W7 2BN	3	£940,000
4	Manor Court Road W7 3EJ \| W7 3EL \| W7 3HD	10	£900,663
5	Wellmeadow Road W7 2AL \| W7 2AZ	2	£877,500
6	Alwyne Road W7 3EN	1	£850,000
7	Croft Gardens W7 2JQ	5	£837,000
8	Chepstow Road W7 2BG	3	£836,833
9	Southdown Avenue W7 2AE \| W7 2AF \| W7 2AG \| W7 2AQ	14	£816,123
10	Golden Manor W7 3EE \| W7 3EF \| W7 3EG \| W7 3EH \| W7 3EQ \| W7 3HB	11	£813,204

Least expensive streets

Rank	Street	Transactions (Past 5 years)	Average price
1	Broadway W7 3JA \| W7 3SS	2	£106,500
2	Walker Close W7 3NB	4	£136,250
3	Cheyne Path W7 1QR \| W7 1QS \| W7 1QT	2	£197,000
4	Brownell Place W7 3AZ	7	£230,321
5	Cambridge Road W7 3DU \| W7 3DY \| W7 3PA \| W7 3PD	12	£249,498
6	Shirley Gardens W7 3PT \| W7 3PU	9	£266,063
7	Copley Close W7 1AZ \| W7 1BA \| W7 1BB \| W7 1BZ \| W7 1JT \| W7 1JU \| W7 1JX \| W7 1JY \| W7 1JZ \| W7 1PY \| W7 1PZ \| W7 1QA \| W7 1QE \| W7 1QF \| W7 1QG \| W7 1QH \| W7 1QJ \| W7 1QL \| W7 1QN \| W7 1QP \| W7 1QQ \| W7 1QW	9	£266,821
8	Riverside Close W7 1BY	11	£285,409
9	Tennyson Road W7 1LH \| W7 1LN \| W7 1LW	11	£289,430
10	Jasper Avenue W7 3BF	4	£292,750

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.