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Most and least expensive streets

Luton, lu1

Luton, lu1 has a total of 340 streets. The most expensive street in lu1 is Kimpton Road, with an average sale price of £40,000,000. The least expensive street in lu1 is The Mall, with an average sale price of £30,000.

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Most expensive streets

Rank	Street	Transactions (Past 5 years)	Average price
1	Kimpton Road LU1 3LD	1	£40,000,000
2	Regent Street LU1 5FA	1	£20,150,000
3	Church Street LU1 3JE \| LU1 3JF \| LU1 3JG	1	£10,500,000
4	Cumberland Street LU1 3BP	1	£6,500,000
5	Bute Street LU1 2BU \| LU1 2EP \| LU1 2EY \| LU1 2WA \| LU1 2WB \| LU1 2WD \| LU1 2WE \| LU1 2WF \| LU1 2WG \| LU1 2WH \| LU1 2WJ \| LU1 2WL \| LU1 2WN	4	£6,344,524
6	Latimer Road LU1 3UZ \| LU1 3XA \| LU1 3XD \| LU1 3XE \| LU1 3XQ	13	£6,239,001
7	Capability Green LU1 3AE \| LU1 3BA \| LU1 3LG \| LU1 3LS \| LU1 3LU \| LU1 3PE \| LU1 3PG	13	£5,556,231
8	Windsor Walk LU1 5DP \| LU1 5DR \| LU1 5DS	1	£2,800,000
9	Stuart Street LU1 2SA \| LU1 2SJ \| LU1 2SL \| LU1 2SW \| LU1 5BL \| LU1 5FW \| LU1 5FX	1	£2,570,000
10	Oxford Road LU1 3AX \| LU1 3DQ	31	£2,074,506

Least expensive streets

Rank	Street	Transactions (Past 5 years)	Average price
1	The Mall LU1 2BG \| LU1 2LJ \| LU1 2LL \| LU1 2LP \| LU1 2SZ \| LU1 2TA \| LU1 2TB \| LU1 2TD \| LU1 2TE \| LU1 2TF \| LU1 2TH \| LU1 2TJ \| LU1 2TL \| LU1 2TN \| LU1 2TP \| LU1 2TQ \| LU1 2TU \| LU1 2TW \| LU1 2ZS	1	£30,000
2	Bridge Street LU1 2NB \| LU1 2NF	1	£41,400
3	Moor Street LU1 1EZ \| LU1 1HA	2	£56,785
4	Bailey Street LU1 3DT \| LU1 3DU	5	£110,417
5	Inkerman Street LU1 1BP \| LU1 1JB \| LU1 1JD	5	£113,523
6	Windsor Street LU1 3UA \| LU1 3UB \| LU1 5DT \| LU1 5DY	16	£115,362
7	Chase Street LU1 3QZ	6	£117,083
8	Delphine Close LU1 5RE	1	£119,995
9	Market Square LU1 5RD	3	£123,333
10	Ruthin Close LU1 5EJ \| LU1 5EL \| LU1 5EN	8	£127,583

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.