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Most and least expensive streets

Southend-on-sea, SS1

Southend-on-sea, SS1 has a total of 224 streets. The most expensive street in SS1 is Elmer Approach, with an average sale price of £6,618,000. The least expensive street in SS1 is Tylers Avenue, with an average sale price of £31,225.

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Most expensive streets

Rank	Street	Transactions (Past 5 years)	Average price
1	Elmer Approach SS1 1LR \| SS1 1LW \| SS1 1NE	1	£6,618,000
2	Thorpe Bay Gardens SS1 3HL \| SS1 3NP \| SS1 3NR \| SS1 3NS \| SS1 3NW	3	£1,633,333
3	Western Esplanade SS1 1EE	3	£1,488,782
4	London Road SS1 1NT \| SS1 1NW \| SS1 1NX \| SS1 1NZ \| SS1 1PA \| SS1 1PE \| SS1 1PF \| SS1 1PG \| SS1 1PH \| SS1 1PJ \| SS1 1PL \| SS1 1PP \| SS1 1PQ \| SS1 1PR \| SS1 1PW \| SS1 1QH \| SS1 1TJ	16	£1,297,100
5	High Street SS1 1DE \| SS1 1DF \| SS1 1DG \| SS1 1DQ \| SS1 1HS \| SS1 1HT \| SS1 1HZ \| SS1 1JD \| SS1 1JE \| SS1 1JF \| SS1 1JG \| SS1 1JN \| SS1 1JS \| SS1 1JT \| SS1 1JX \| SS1 1LB \| SS1 1LH \| SS1 1LL \| SS1 1LN \| SS1 1LQ	56	£980,486
6	Market Place SS1 1DA	1	£820,000
7	Cashiobury Terrace SS1 1EZ	1	£820,000
8	Daines Close SS1 3PG	2	£762,500
9	Burges Road SS1 3AX \| SS1 3AY \| SS1 3FW \| SS1 3HT \| SS1 3HU \| SS1 3JJ \| SS1 3JL \| SS1 3JN \| SS1 3JP	22	£755,234
10	Burges Terrace SS1 3BD	2	£747,500

Least expensive streets

Rank	Street	Transactions (Past 5 years)	Average price
1	Tylers Avenue SS1 2AD \| SS1 2AP \| SS1 2AW \| SS1 2BA \| SS1 2BB \| SS1 2BE	1	£31,225
2	Lancaster Crescent SS1 2NU	12	£64,926
3	Jetty Mews SS1 2AG	1	£85,000
4	Pier Approach SS1 2EH	1	£90,000
5	Stanley Road SS1 2HB	8	£112,250
6	Old Southend Road SS1 2HA	7	£121,810
7	Chichester Road SS1 2JP \| SS1 2JU \| SS1 2JZ \| SS1 2NH	4	£122,750
8	Gordon Place SS1 1JU \| SS1 1NH \| SS1 1NP	43	£129,778
9	Toledo Road SS1 2DW \| SS1 2EE	7	£130,506
10	Albert Road SS1 2HF	6	£131,833

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.