Taman Pandan Indah

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

NEW LAUNCH New Condo in PJ Damansara
Ad Sponsored

New Condo in PJ Damansara

Pet friendly condo. 10mins to 1Utama

From
RM 600K
Size
800 sqft

Property Transactions

1 subsales grouped by size

Median
RM 750,000
PSF
RM 774
950 sqft
4-Sty Shop
RM 750,000
Jalan Pandan Indah 4/1A
969 sqft · RM 774 PSF
Legend Recent Highest Price Highest PSF
Selling in Taman Pandan Indah? Post a Free Ad

Market Snapshot

RM 310,000

RM 326 psf

Median transaction price

RM 750,000

RM 774 psf

Median transaction price

Low

Rental Yield Data

Login to view this data.

Login to View
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 1 4-Storey Shop properties subsale transactions between 2021 and 2026, with a median price of RM 750K and a median price per square foot (PSF) of RM 774.

This area contains both residential and commercial properties. View 387 residential properties or 130 commercial properties separately for more focused analysis.

Price remained flat, and PSF growth was PSF remained flat.

Within the 4-Storey Shop category, condominium/apartment dominated the market, with high diversity across multiple property types.

For price per square foot, the median is RM 774, with most transactions between RM 774 and RM 774. The usual range is RM 688.19 to RM 860.19, showing that most units are priced quite close to each other. With an IQR of RM 172.00 and MAD of RM 0, 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. Some price volatility exists, making thorough market research essential before transacting. Limited transaction history suggests carefully evaluating comparable sales data.