The Quartz- Lexa Residensi

Lexa Residence, Jalan 34/26, Wangsa Maju, 53300 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia

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From
RM 600K
Size
800 sqft

Property Transactions

5 subsales grouped by size · Serviced Apt →

Median
RM 460,000
PSF
RM 659
Price Size
650 sqft
Serviced Apt
RM 450,000
Level 24
667 sqft · RM 674 PSF
RM 460,000
Level 19
646 sqft · RM 712 PSF
RM 440,000
Level 26
667 sqft · RM 659 PSF
850 sqft
Serviced Apt
RM 550,000
Level 20
861 sqft · RM 639 PSF
900 sqft
Serviced Apt
RM 550,000
Level 19
883 sqft · RM 623 PSF
Legend Recent Highest Price Highest PSF

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The Quartz- Lexa Residensi
© OpenStreetMap · CARTO

Lexa Residence, Jalan 34/26, Wangsa Maju, 53300 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia

Maps

The Quartz- Lexa Residensi in Kuala Lumpur, Kuala Lumpur recorded 5 subsale transactions in 2022, with a median price of RM 460K and a median price per square foot (PSF) of RM 659.

This area contains both residential and commercial properties. View 14 residential properties or 3 commercial properties separately for more focused analysis.

Price remained flat, and PSF growth was PSF remained flat. The median price is RM 460K, with most transactions falling within a stable range of RM 440K to RM 509K, and a typical market range of RM 440K to RM 550K.

Most transactions involved serviced apartment, though some variety exists in the market.

For price per square foot, the median is RM 659, with most transactions between RM 629 and RM 690. The usual range is RM 640.31 to RM 678.31, showing that most units are priced quite close to each other. A typical spread (IQR) of RM 38.00 and an average deviation (MAD) of RM 31 indicate a highly stable PSF trend across properties.

While the area has shown positive growth trends, price variations suggest a more dynamic market. This presents opportunities for investors comfortable with moderate volatility. Significant price variations suggest comparing multiple properties and timing the market carefully. Limited transaction history suggests carefully evaluating comparable sales data.