ShiokNest Score

Overall property score distribution

How to Read the ShiokNest 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 ShiokNest Score is a composite 0–100 rating that aggregates four independently computed sub-scores — Walkability, Investment Potential, En-Bloc Risk/Opportunity, and Profitability — into a single property-level ranking. Each sub-score draws on different data: walkability uses OneMap POI data for MRT proximity, hawker centres, supermarkets, schools, clinics, and parks; investment potential uses URA transaction velocity, price momentum, gross yield, and lease remaining; en-bloc ris...

Why It Matters

Property search in Singapore involves comparing hundreds of variables across thousands of developments. Most buyers reduce this to a handful of heuristics — location, price, MRT distance — and end up missing properties that are exceptional on the dimensions they cannot easily measure. The ShiokNest Score compresses the most financially meaningful variables into a single ranked list so you can shortlist efficiently, then deep-dive only on the properties that are actually strong candidat...

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

Shortlisting OCR 3BR condos under $1.5M: using the score leaderboard

Inputs
Filter: Market segment
OCR
Filter: Bedroom type
3-bedroom
Filter: Price range
$1M – $1.5M
Filter: District
All OCR (D18–D28)
Filter: Min transactions
20+ (excludes low-data outliers)
Results
Developments shown
~140 matching OCR 3BR condos
Top-scored development (ex.)
ShiokNest Score 81 — D19, FH, MRT 380m, yield 3.6%
Median score for the filtered set
61
Key sub-score trade-off visible
High walkability / moderate investment potential vs. high investment / low walkability split

How to read this: The leaderboard immediately surfaces the top 10–15 developments that are strong across all four dimensions within your budget. Rather than browsing 140 listings, you can investigate the top 20 and ignore the rest. In this example, the highest-scoring D19 freehold development scores 81 because it combines genuine walkability (MRT under 400m, 4 hawker centres within 500m) with solid investment metrics (3.6% gross yield, positive 3-year price momentum). The two developments tied at score 78 score that number differently — one is a walkability champion with average investment metrics, the other is the reverse. The sub-score breakdown makes the distinction immediately clear.

Monitoring an owned property: score change as en-bloc signal

Inputs
Development
A 1980s D10 private estate (freehold, 40-year-old)
Score 18 months ago
ShiokNest Score 58
Score today
ShiokNest Score 71
Sub-score change
En-Bloc sub-score: 42 → 67; others stable
Results
Score increase
+13 points in 18 months
Driving factor
En-bloc sub-score surge
En-bloc signals detected
Adjacent plot sold for redevelopment; plot ratio utilisation below 60%; lease age 40+ years

How to read this: The 13-point composite score increase is almost entirely driven by the en-bloc sub-score, which ShiokNest recomputes quarterly using lease age, plot ratio utilisation, adjacent redevelopment activity, and district historical en-bloc frequency. The three converging signals (adjacent sale, underutilised plot ratio, 40-year age) are precisely the conditions that precede most en-bloc attempts in Singapore. An owner monitoring this score would see the change as a prompt to investigate more deeply — check if a CBRE or Knight Frank collective sale mandate has been filed, review the MCST meeting minutes, and model the minimum reserve price under current GLS benchmarks.

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

Insight Browse all insights Calculator Mortgage Calculator Map Rental Yield Map Guide Property Guides