Walkability Score

Neighbourhood walkability score distribution

How to Read the Walkability Score 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 Walkability Score insight ranks all 3,400+ ShiokNest-tracked developments on a 0–100 walkability scale computed from seven amenity categories within a 500m walking radius: MRT station (weighted 25%), hawker centres (15%), supermarkets (15%), parks and green space (15%), schools (15%), clinics and pharmacies (10%), and shopping malls (5%). Each category uses Haversine distance to the nearest facility, with a distance-decay function that assigns full score for facilities within 100m an...

Why It Matters

Walkability is one of the strongest quantifiable determinants of residential rental demand and long-term price resilience. Tenants pay premiums for walkable developments because walkability saves time and reduces transportation expenditure — a development within 200m of an MRT and a hawker centre and a supermarket delivers genuine lifestyle efficiency that tenants value and demonstrate willingness to pay for. The Walkability Score quantifies this precisely, allowing an investor to confir...

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

D12 (Toa Payoh) development: high walkability at OCR pricing

Inputs
Development
D12 condo near Toa Payoh MRT, ~300 units
Segment
RCR
Overall score
79/100
Results
MRT component (25% weight)
23/25 — MRT entrance 180m walk
Hawker centre component (15%)
14/15 — Toa Payoh Hub hawker within 200m
Supermarket component (15%)
13/15 — NTUC within 220m
School component (15%)
9/15 — nearest primary school 680m
Overall vs D9 comparable
D9 development scores 82 — only 3 points higher, at ~40% more PSF

How to read this: This D12 development scores 79/100 on walkability — nearly equivalent to the D9 development at 82/100 — yet sells at approximately 40% lower PSF because it lacks the Orchard/River Valley address. For a tenant or owner-occupier who values daily walkability (MRT, hawker, supermarket) over prestige location, the D12 development delivers near-equivalent lifestyle at substantially lower cost. The school component (9/15) is the one weakness — if school proximity is a priority, check the specific primary schools within 1km before buying. The walkability score breakdown makes this trade-off visible in 10 seconds.

Verifying an "MRT-convenient" claim: 400m vs 800m reality check

Inputs
Claim
Agent describes development as "MRT convenient, near Buona Vista"
Development
D5 condo, Buona Vista fringe
MRT component score
7/25
Actual distance
~720m to Buona Vista MRT (Google Maps walking: 9 min)
Results
MRT score interpretation
7/25 = distance 600–800m range (moderate decay zone)
vs agent claim of "near"
"Near" is technically true but meaningfully different from 200m proximity
PSF premium implied
Development priced as if MRT-adjacent; walkability score says otherwise
Alternative 200m MRT dev
D5 development with MRT score 23/25 priced only 3% higher PSF

How to read this: The MRT component score of 7/25 is a precise reality check on the "MRT convenient" claim. A score of 7/25 corresponds to walking distance in the 600–800m band — technically reachable on foot but not the 200–300m proximity that drives a genuine MRT rental premium. An investor buying this development at a price that incorporates a full MRT premium is over-paying for the accessibility characteristic. The walkability score surfaces this distinction immediately and identifies an alternative development 3% more expensive in PSF but with a 23/25 MRT score — genuinely MRT-adjacent and likely to command stronger rental demand from transit-dependent tenants.

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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