Last reviewed: May 2026
This page documents how PSF Insight collects data, calculates metrics, generates AI analysis, and maintains editorial standards. We publish this so users, regulators, and reviewers can scrutinise our work.
All data on PSF Insight is sourced from official Singapore government APIs and public datasets. We do not scrape third-party sites, fabricate, or estimate beyond clearly labelled derived metrics.
Data is automatically refreshed every Wednesday at 10am Singapore time via a GitHub Actions workflow that pulls all sources, archives historical snapshots, and rebuilds the production database. URA transaction data typically lags by 3 to 6 weeks from the actual transaction date due to caveat lodging timelines, which is consistent with all Singapore property platforms.
The current dataset covers approximately:
Calculated as transaction price divided by strata area in square feet. URA reports area in square metres which we convert at 10.7639 sqft per sqm. PSF is the universal Singapore property comparison metric.
For project-level and district-level summaries, we report the median PSF rather than the mean. Median is more robust to outliers (one penthouse sale skewing a project's average). For monthly trend lines, we use the median of all transactions in that month.
For the P&L Calculator, we match comparables on: same project, similar unit size (within tolerance, default 50 sqft), similar floor band (when specified), and within the past 12 months. If fewer than 3 comparables exist with strict matching, we progressively relax floor and time constraints to find a usable sample.
Calculated as the median PSF of comparable transactions multiplied by the user's unit size. This is an estimate, not a valuation. Bank valuations may differ based on internal models, condition, and market conditions at valuation date.
For each purchase year, we calculate the average PSF paid that year against the most recent 6 months' average PSF. The percentage difference is the rough capital gain or loss for buyers from that vintage. This is a simplification: it does not account for floor differences, renovation, or specific unit characteristics.
Year-over-year change uses the average PSF of the latest full year against the prior full year. This smooths out monthly volatility but may lag major market turns by 6 to 12 months.
AI-generated analysis on PSF Insight is produced by a large language model (Claude family) accessed via Amazon Bedrock. We do not disclose the specific model version to users in product to keep messaging consistent across model upgrades. The model has a fixed system prompt that grounds it in Singapore property frameworks.
Each AI analysis call passes the following structured context to the model:
The system prompt grounds the model in nine analytical frameworks observed across professional Singapore property analysts:
AI analysis is generated each time, which means two calls with the same inputs may produce slightly different outputs. The model can occasionally misread numbers, hallucinate project names, or apply frameworks inappropriately. We mark AI analysis explicitly as such and append a disclaimer to every response.
Important: AI analysis on PSF Insight is informational and educational, not financial advice. We do not guarantee accuracy of every claim made by the model. Always verify important numbers against primary sources and consult qualified professionals before transacting.
Blog articles on PSF Insight are written and edited by our team. We use AI tooling to assist with research and drafting, but every article is reviewed by a human editor for accuracy, Singapore-specific applicability, and alignment with regulations. We do not publish content without editorial review.
Where articles cite specific numbers (ABSD rates, PSF figures, regulatory thresholds), we link to the primary source where possible: IRAS, MAS, URA, HDB, or government statements. Real project examples are based on publicly disclosed transactions.
Articles are updated when relevant policies change. The "Updated" date on each article reflects the most recent material edit. Cooling measure changes, tax rate revisions, and major regulatory shifts trigger immediate updates.
We do not accept paid placements, sponsored content, or developer-funded articles. We do not receive commissions from real estate agents. We do not promote specific projects or developers in editorial content. Subscription revenue and advertising are our only income streams, and neither influences article content.
If we publish an error, we correct it promptly and note the correction at the bottom of the article. We do not silently edit. Send correction requests to help@northstareducation.io.
We collect minimum data necessary to operate the service. See our Privacy Policy for full details. We do not sell user data, share it with third parties beyond essential infrastructure providers (Google for OAuth, Stripe for payments, AWS for AI), or use it for marketing without consent.
If you find an error, a methodology question, or a process you think we should improve, we genuinely want to hear it. Send to help@northstareducation.io. We respond to substantive feedback within 3 business days.