Lease Decay Effect

How remaining lease affects property prices

How to Read the Lease Decay 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 Lease Decay insight tracks how the PSF discount on 99-year leasehold properties increases as remaining lease diminishes, based on actual URA transaction data segmented by remaining lease bands: 90yr+, 80–89yr, 70–79yr, 60–69yr, 50–59yr, 40–49yr, and below 40yr. For each lease band, the chart shows median PSF relative to the equivalent freehold median PSF in the same district and property type — so a 70–79yr leasehold in D15 is compared to freehold properties in D15, not t...

Why It Matters

The lease decay risk is the most systematically underpriced risk in Singapore property investment. A 99-year leasehold property purchased at 60 years remaining is worth approximately 80–85% of an equivalent freehold property in the same location at current market pricing — but that discount will widen to approximately 50–60% of freehold when 40 years remain, and below 30% when 30 years remain. The compounding nature of this discount means that buyers who purchase a 60-year leasehold ...

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

D15 leasehold decay curve: 70yr threshold effect in practice

Inputs
District
D15 — East Coast / Katong
Metric
Leasehold PSF as % of freehold PSF by remaining lease band
Property type
Non-landed private residential
Results
90yr+ remaining lease
95% of FH PSF (−5% discount)
80–89yr remaining
92% of FH PSF (−8% discount)
70–79yr remaining
86% of FH PSF (−14% discount)
60–69yr remaining
81% of FH PSF (−19% discount)

How to read this: The D15 decay curve shows a meaningful acceleration in the discount as remaining lease crosses 80yr (discount widens from 5% to 8%) and again as it crosses 70yr (from 8% to 14%). This 6-percentage-point acceleration at the 70yr threshold is the bank valuation effect — lenders apply larger haircuts to sub-70yr properties, reducing LTV and therefore the effective buyer pool. For an owner of a D15 development currently at 72 years remaining, the unit is 3 years from crossing the 70yr threshold and incurring the acceleration. Selling at 72 years remaining versus 68 years remaining will likely deliver 5–6% higher PSF — a significant difference that compounds directly into exit proceeds.

Comparing empirical vs SLA discount: when market discounts more than theory

Inputs
Development
D19 leasehold condo, 58 years remaining
SLA table discount
~15% below freehold (theoretical)
Market observed
D19 transactions show −22% vs equivalent FH in same district
Results
SLA theoretical discount
−15%
Empirical market discount
−22%
Gap
−7% additional market discount vs SLA tables
Interpretation
Market more pessimistic on lease than SLA tables — likely CPF eligibility concern

How to read this: When the empirical market discount exceeds the SLA theoretical discount, it signals that market participants are pricing in a risk the SLA tables do not fully capture — in this case, concern about CPF financing eligibility for the youngest potential buyers. At 58 years remaining in 2026, a 30-year-old buyer would have the property run out of lease at age 88 — within the CPF requirement margin, but barely. Some banks and CPF members are more conservative, restricting financing. This creates a thinner buyer pool that pushes prices below the SLA-theoretical level. The lease decay chart surfaces this real-world discount directly from transaction data, without relying on SLA tables that may not reflect current market sentiment.

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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