MRT Premium Analysis

How property prices vary with MRT distance — condos and HDB combined

How to Read the MRT Premium Insight

Key Takeaways

  • This insight is powered by live URA and HDB transaction data refreshed monthly.
  • Use the district filter above the chart to narrow results to a specific planning area.
  • Hover any data point on the chart for exact values and transaction counts.

What It Does

The MRT Premium insight measures how much more buyers pay per square foot for private residential properties located within 500 metres of an MRT station versus comparable properties 500m–1,000m away. Data is drawn from URA caveat records covering all private non-landed transactions island-wide, spanning from 2010 to the most recently synced month. The chart plots median PSF at each distance band over time, with a line overlay showing the rolling 12-month premium percentage. You can filte...

Why It Matters

MRT proximity is the single most durable price driver in Singapore's property market. Across all private non-landed transactions since 2010, properties within 500m of an MRT station have commanded a median PSF premium of 12–18% over comparable properties 500m–1,000m away. On a $1.5 million condo purchase, that premium represents $180,000–$270,000 of the purchase price. Understanding whether the premium you are paying is justified by current market data — or whether you are overpayi...

How It Works

  • Select a district from the filter or leave it blank to view Singapore-wide data.
  • Use the time-range buttons (1Y/2Y/3Y/5Y/All) to adjust the chart window.
  • Hover any point on the chart to see exact values and underlying transaction counts.
  • Review the KPI cards above the chart for headline numbers at a glance.

Examples

District 9 (Orchard) vs District 19 (Hougang): MRT premium comparison

Inputs
District A
D9 — River Valley / Orchard (CCR)
District B
D19 — Hougang / Punggol (OCR)
Distance band
Within 500m vs 500m–1,000m
Property type
Non-landed private (condo)
Time range
5-year rolling (2021–2026)
Results
D9 median premium (within 500m)
~8% PSF above 500m–1km band
D19 median premium (within 500m)
~19% PSF above 500m–1km band
Premium trend (D19, 12-month)
Widening (+2.3 ppt year-on-year)

How to read this: Counterintuitively, the MRT premium is lower in D9 than D19 — because D9 is already dense and high-value, so the baseline 500m–1km band is already expensive. In D19 (mass-market OCR), buyers strongly discount non-walkable locations, so the gap between distance bands is wider. This tells a buyer in D19 to pay a real premium for confirmed walkable stock rather than compromise on a unit 800m from the station. It also tells an investor that the MRT premium in D19 has more growth runway than in D9, where the gap has remained stable for 4+ years.

Thomson-East Coast Line (TEL) opening effect — D13 Caldecott area

Inputs
Station
Caldecott MRT (TEL3 opened Nov 2022)
District
D13 — Macpherson / Braddell
Measurement period
Pre-opening (Jan 2020–Oct 2022) vs post-opening (Nov 2022–Dec 2024)
Distance band
Within 500m
Property type
Non-landed private
Results
Pre-opening premium vs 500m–1km
~6% PSF
Post-opening premium
~15% PSF
Uplift from line opening
+9 ppt over 24 months

How to read this: The Caldecott corridor shows a classic line-opening premium crystallisation: the market partially priced in the station before opening (6% premium vs baseline), then repriced sharply upward as commuters validated the connectivity. The full 15% premium stabilised about 18 months after opening. This pattern is consistent across other TEL stations. Buyers evaluating properties near announced-but-unopened CRL stations can use this data to estimate how much additional premium crystallisation remains — typically half the eventual gap is still to come at the point of opening.

Tips & Pitfalls

Expert Tips

  • Compare 2–3 districts side-by-side to spot relative outliers rather than reading a single number in isolation.
  • Always check the transaction count alongside any price metric — small sample sizes can produce misleading averages.
  • Pair this insight with the related calculators and maps below for a complete decision framework.

Common Pitfalls

  • Interpreting short-term movements (under 1 year) as trends — Singapore property data is noisy and needs a longer window.
  • Ignoring the difference between median and mean — means are pulled by luxury outliers in prime districts.
  • Forgetting that new-launch prices are often subsidised by developer discounts not visible in headline data.

Frequently Asked Questions

Where does the data come from?
Data is sourced from the Urban Redevelopment Authority (URA) and Housing & Development Board (HDB) official APIs, refreshed monthly.
How often is this insight updated?
The underlying transaction data is synced monthly from URA and HDB. The charts recompute live as new data arrives.
Can I filter by district?
Yes — use the district filter above the chart. You can also share a deep link to a specific district via the URL.

Related Resources

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