Sri Angsana Hilir Condominium

A,B & C, Sri Angsana Hilir Condominium, Jalan Hilir 3, Desa Pandan, 55100 Kuala Lumpur, Selangor, Malaysia

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From
RM 600K
Size
800 sqft

Property Transactions

3 subsales grouped by size

Median
RM 105,000
PSF
RM 175
Price Size
Period
transactions middle 50% (P25–P75)
600 sqft
Flat
RM 105,000
Level 2
600 sqft · RM 175 PSF
650 sqft
Flat
RM 95,000
Level 2
672 sqft · RM 141 PSF
1,350 sqft
2-Sty Terrace
RM 800,000
Jalan Hilir 5
1,367 sqft · RM 585 PSF
Legend Recent Highest Price Highest PSF

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Market Snapshot

Residential

RM 105,000

RM 175 psf

Median transaction price

Taman Sri Ampang Hilir / Angsana Hilir
© OpenStreetMap · CARTO

A,B & C, Sri Angsana Hilir Condominium, Jalan Hilir 3, Desa Pandan, 55100 Kuala Lumpur, Selangor, Malaysia

Maps

Sri Angsana Hilir Condominium in Hulu Langat, Selangor recorded 3 subsale transactions between 2021 and 2026, with a median price of RM 105K and a median price per square foot (PSF) of RM 175.

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 RM 105K, with most transactions falling within a stable range of RM 95K to RM 435K, and a typical market range of RM 95K to RM 129K.

Most transactions involved low-cost flat, though some variety exists in the market.

Price per square foot shows a median of RM 175, though individual units vary from RM 0 to RM 377 in the core range. The broader market spans RM 158.76 to RM 191.26, indicating diverse property characteristics. A wider spread (IQR: RM 32.50) and deviation (MAD: RM 202) 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. Significant price variations suggest comparing multiple properties and timing the market carefully. Limited transaction history suggests carefully evaluating comparable sales data.