Taman Pandan Indah

22, Jalan Pandan Indah 2/14a, Pandan Indah, 55100 Ampang, Wilayah Persekutuan Kuala Lumpur, Malaysia

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From
RM600K
Size
800 sqft

Property Transactions

2 subsales grouped by size

Price Size
Period
transactions middle 50% (P25–P75)
600 sqft
Office
RM68,000
Level 2
615 sqft · RM111 PSF
RM80,000
Level 2
615 sqft · RM130 PSF
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Market Snapshot

RM300,000

RM356 psf

Median transaction price

RM74,000

RM120 psf

Median transaction price

Low

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Gross yield, median rent and sale psf — worked out from transacted prices and asking rents.

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Taman Pandan Indah
© OpenStreetMap · CARTO

Taman Pandan Indah, 22, Jalan Pandan Indah 2/14a, Pandan Indah, 55100 Ampang, Wilayah Persekutuan Kuala Lumpur, Malaysia

Maps

Taman Pandan Indah in Hulu Langat, Selangor recorded 2 Office Lot properties subsale transactions between 2021 and 2026, with a median price of RM74K and a median price per square foot (PSF) of RM120.

This area contains both residential and commercial properties. View 173 residential properties or 12 commercial properties separately for more focused analysis.

Price remained flat, and PSF growth was PSF remained flat. The median price is RM74K, with most transactions falling within a stable range of RM68K to RM80K, and a typical market range of RM68K to RM80K.

Within the Office Lot category, condominium/apartment dominated the market, with high diversity across multiple property types.

For price per square foot, the median is RM120, with most transactions between RM111 and RM130. The usual range is RM35.84 to RM204.84, showing that most units are priced quite close to each other. With an IQR of RM169.00 and MAD of RM10, the PSF demonstrates reasonable consistency across the market.

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.