Taman Bukit Rahman Putra

Bukit Rahman Putra, 47000 Sungai Buloh, Selangor, Malaysia

Property Transactions

3 subsales found

Median
RM1,210,000
PSF
RM378
Price Size
Period
transactions middle 50% (P25–P75)
RM1,500,000
Brp 5/5
3,200 sqft RM469 PSF
RM950,000
Brp 5/5
3,200 sqft RM297 PSF
RM1,210,000
Jalan Brp 5/8
3,200 sqft RM378 PSF
Legend Recent Highest Price Highest PSF

Selling in Taman Bukit Rahman Putra? Homes here sold for a median of RM1,210,000.

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Market Snapshot

RM1,210,000

RM378 psf

Median transaction price

RM1,800,000

RM970 psf

Median transaction price

Taman Bukit Rahman Putra
© OpenStreetMap · CARTO

Bukit Rahman Putra, 47000 Sungai Buloh, Selangor, Malaysia

Maps

Taman Bukit Rahman Putra in Petaling, Selangor recorded 3 subsale transactions between 2021 and 2026, sized between 3,175 and 3,224 sqft, with a median price of RM1.21 million and a median price per square foot (PSF) of RM378.

This area contains both residential and commercial properties. View 45 residential properties or 5 commercial properties separately for more focused analysis.

Price remained flat, and PSF growth was PSF remained flat. The median price is RM1.21 million, with most transactions falling within a stable range of RM985K to RM1.43 million, and a typical market range of RM950K to RM1.50 million.

Most transactions involved 2 - 2 1/2 storey terraced, with high diversity across multiple property types.

The median PSF stands at RM378, with core pricing between RM308 and RM448. Market pricing typically extends from RM320.64 to RM435.64, reflecting moderate variation in unit pricing. The spread of RM115.00 (IQR) and deviation of RM70 (MAD) suggest moderate price variations reflecting different property features.

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.