Bandar Sungai Long (Forest Green Park)

Jalan Sungai Long, Bandar Sungai Long, 43000 Kajang, Selangor, Malaysia

Property Transactions

2 subsales grouped by size

Median
RM452,500
PSF
RM619
Period
transactions middle 50% (P25–P75)
950 sqft
Condo
RM285,000
Level 5
958 sqft · RM297 PSF
1,000 sqft
Condo
RM620,000
Level 4
1,001 sqft · RM619 PSF
Legend Recent Highest Price Highest PSF

Selling in Bandar Sungai Long (Forest Green Park)? Homes here sold for a median of RM452,500.

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Jalan Sungai Long, Bandar Sungai Long, 43000 Kajang, Selangor, Malaysia

Maps

Bandar Sungai Long in Hulu Langat, Selangor recorded 2 subsale transactions between 2021 and 2026, with a median price of RM453K and a median price per square foot (PSF) of RM619.

Nothing is listed in Bandar Sungai Long (Forest Green Park) right now. Browse properties for sale in Bandar Cheras and properties for rent in Bandar Cheras nearby, then use the 2 sales recorded here to judge whether an asking price is fair.

This area consists exclusively of residential properties, with no commercial listings recorded.

Price remained flat, and PSF growth was PSF remained flat. The median price is RM453K, with most transactions falling within a stable range of RM285K to RM620K, and a typical market range of RM369K to RM536K.

Most transactions involved condominium/apartment, with minimal variety in property types.

Price per square foot shows a median of RM619, though individual units vary from RM458 to RM780 in the core range. The broader market spans RM538.86 to RM699.86, indicating diverse property characteristics. A wider spread (IQR: RM161.00) and deviation (MAD: RM161) indicate significant PSF variations, likely due to diverse property types or conditions.

While the area has shown positive growth trends, price variations suggest a more dynamic market. This presents opportunities for investors comfortable with moderate volatility. Some price volatility exists, making thorough market research essential before transacting. Limited transaction history suggests carefully evaluating comparable sales data.