| Road | Price | PSF | Size | Date | Type |
|---|
|
Jalan Perindustrian Silibin 1
|
RM 720,000
|
RM 144
|
5,005 sqft
|
|
|
|
Jalan Perindustrian Silibin 1
|
RM 628,000
|
RM 125
|
5,005 sqft
|
|
|
Prsn Perindustrian Silibin 1
|
RM 1,250,000
|
RM 130
|
9,601 sqft
|
|
|
|
Persiaran Perindustrian Silibin 2
|
RM 1,300,000
|
RM 135
|
9,601 sqft
|
|
|
|
Prsn Perindustrian Silibin 1
|
RM 1,190,000
|
RM 124
|
9,601 sqft
|
|
|
|
Prsn Perindustrian Silibin 2
|
RM 1,190,000
|
RM 124
|
9,601 sqft
|
|
|
|
Jalan Perindustrian Silibin 2
|
RM 1,180,000
|
RM 123
|
9,601 sqft
|
|
|
Jalan Perindustrian Silibin 1
|
RM 1,046,000
|
RM 81
|
12,895 sqft
|
|
|
Jalan Perindustrian Silibin 1
|
RM 1,054,000
|
RM 80
|
13,100 sqft
|
|
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Market Snapshot
CommercialRM 1,180,000
RM 124 psfMedian transaction price
Silibin Light Industrial Park in Kinta, Perak recorded 9 subsale transactions between 2021 and 2026, with a median price of RM 1.18 million and a median price per square foot (PSF) of RM 125.
This area consists exclusively of commercial properties, with no residential listings recorded.
Price remained flat, and PSF growth was PSF remained flat. The median price is RM 1.18 million, with most transactions falling within a stable range of RM 1.06 million to RM 1.30 million, and a typical market range of RM 1.11 million to RM 1.25 million.
Most transactions involved semi-detached factory/warehouse, with minimal variety in property types.
For price per square foot, the median is RM 125, with most transactions between RM 119 and RM 131. The usual range is RM 121.97 to RM 128.97, showing that most units are priced quite close to each other. A typical spread (IQR) of RM 7.00 and an average deviation (MAD) of RM 6 indicate a highly stable PSF trend across properties.
Overall, the market in this area appears stable with consistent appreciation, making it an attractive option for both investors and homebuyers. The consistent property type and stable pricing make it easier to assess value and compare trends. Limited transaction history suggests carefully evaluating comparable sales data.