I've written the full article and saved it to app/research/2026-singapore-property-playbook-young-buyers.md, matching the location and format of your other content files.
What's in it
Structure & length (~4,200 words):
- Hook opening that leads with the HDB-vs-private divergence and the primary keyword ("2026 Singapore property playbook") in the first paragraph
- State-of-market context → 5 verified signal checks → price ladder → policy/financing → buy-vs-rent → district heatmap → 5 concrete moves → caveats → Food for Thought → Hiva CTA
Data integrity — I followed your no-fabricated-data standard closely:
- Used +0.9% final (not the +0.3% flash) and OCR +2.2% throughout, per the research correction
- Flagged the breakeven table and private district heatmap as illustrative models with assumptions stated on-page
- Attributed the 99.co analyst quote and noted broker-source bias
- Kept the "trend vs blip" hedge on the single-quarter HDB dip
Visuals:
- 4 charts — HDB vs private growth (bar), median PSF by region (bar), SORA peak-to-trough (line), HDB town medians (bar)
- 3 Mermaid diagrams — the price ladder (flowchart), buy-vs-rent decision tree, and a buyer's 2026 timeline
SEO: target keywords in headings (e.g. "The 2026 Singapore property playbook", "Where Young Buyers Should Actually Look", district heatmap section), metadata block with category Market Pulse, tags, and seo_keywords.
CTA: single <hiva-cta> block at the very end, bridging to per-project pricing / district scoring / market trends and closing on a declarative sentence.
One thing worth noting: the brief's headline angle said "falling rents," but the research explicitly corrected that — rents turned +0.3% in Q1 2026. I wrote Move #5 around the corrected data ("don't wait for cheaper rent — that window closed") rather than the outdated premise, since that's what the verified findings support.