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SG Property Intel
CondominiumFreeholdRest of Central RegionInvestment score 38

Freesia Woods

SUNSET WAY · Singapore 120116 · D05

Median PSF · 12m

S$1,348

Median price · 12m

S$2.88M

Latest sale

S$2.70M

Jul 2026

1-year change

-19.8%

Sales · 12m

2

10 all time

Gross yield

2.02%

9 leases

Price history

How this development has traded

Median transacted price and price per square foot by quarter, from every sale on record here.

Change over window
-12.0%
Annualised
-7.1%
Transactions
10
1 year
-19.8%
3 years p.a.
-5.0%
5 years p.a.
Median size
1,432 sqft

Against the neighbourhood

How it compares within D05

Pasir Panjang, Clementi New Town, West Coast. All figures are medians over the last 12 months for private homes in this district.

District analytics
MeasureFreesia WoodsD05 medianDifference
Median PSFS$1,348S$1,890-28.7%
Median priceS$2.88MS$1.74M+65.4%
Gross rental yield2.02%3.84%-1.8 pts
1-year price change-19.8%+6.7%-26.5 pts

This development trades about 29% below the D05 median price per square foot.

Rental

Rent and yield

From URA private residential rental contracts.

Median monthly rent

S$4,800

Contracts · 12m

9

Implied annual rent

S$58K

Gross yield

2.02%

Unit typeContractsMedian rent
3 bedroom6S$5,200
2 bedroom3S$3,800

Investment analysis

Investment score 38 / 100

Each component is a percentile rank against every other private development in the database. This is an analytical indicator, not financial advice.

38/100

100% of the scoring weight measurable

Every bar is a percentile rank, so 70 means this development sits above roughly 70% of its peers on that factor. Factors we cannot measure are excluded and the remaining weights are rebalanced, rather than scored as zero.

  • Valuationweight 22%90

    How its median price per square foot compares with its own district.

  • Liquidityweight 15%69

    Transactions in the last 12 months — how easily it trades.

  • Rental yieldweight 20%22

    Gross yield against every other development in its segment.

  • Supply riskweight 10%19

    Upcoming units recorded in the same district. Higher score means less competing supply.

  • Price momentumweight 18%7

    Three-year annualised change in median PSF, or one year where three is unavailable.

  • Locationweight 15%3

    Distance to the nearest rail station, plus schools within 1 km. Nearest is Clementi MRT at 1.3 km.

The investment score is an analytical indicator computed from transaction and location data. It is not a recommendation, a forecast, or financial advice.

Location

Nearby amenities

Rail stations

  • Clementi MRT1.3 km
  • Dover MRT1.6 km
  • King Albert Park MRT1.8 km
  • Beauty World MRT1.9 km

Schools within 2 km

  • Pei Tong Primary Schoolprimary1.0 km
  • School Of Science And Technology, Singaporesecondary (s1-s4)1.3 km
  • Nan Hua Primary Schoolprimary1.3 km
  • Clementi Primary Schoolprimary1.4 km
  • Clementi Town Secondary Schoolsecondary (s1-s5)1.5 km
  • Bukit Timah Primary Schoolprimary1.6 km

Distances are straight-line. Proximity does not determine school admission eligibility.

Supply

Upcoming competition

No upcoming supply is recorded nearby. URA pipeline data requires an API key, which is not configured, so this section may be incomplete.

The record

Transactions at this development

Browse all transactions
MonthTypePriceSizePSFFloorLease left
Jul 2026CondominiumS$3,050,0002,680 sqftS$1,13801-05
Jul 2026CondominiumS$2,700,0001,733 sqftS$1,55801-05
Feb 2025CondominiumS$2,300,0001,421 sqftS$1,61901-05
Oct 2024CondominiumS$2,400,0001,378 sqftS$1,74201-05
May 2024CondominiumS$2,720,0001,722 sqftS$1,57901-05
Mar 2023CondominiumS$2,270,0001,442 sqftS$1,57401-05
Aug 2022CondominiumS$2,450,0001,432 sqftS$1,71101-05
Aug 2022CondominiumS$2,250,0001,227 sqftS$1,83401-05
Feb 2022CondominiumS$1,780,0001,227 sqftS$1,45101-05
Jan 2022CondominiumS$2,310,0001,432 sqftS$1,61401-05

Important

Property values shown are estimates generated from available transaction and market data and are not official valuations. Actual transaction prices may differ. Data availability and accuracy depend on the underlying source datasets. Users should conduct their own due diligence and seek professional advice where appropriate.