This page is shared openly with fellow SF agents. If what you are seeing in the field supports or contradicts the working hypothesis, the Feedback tab is exactly for that. Better shared information makes everyone a better advisor.
The city, classified by role
Every subdistrict is placed by its own single-family data (price, overbid intensity, and how house-heavy it is), not by its district as a whole. The top of the market splits in two: Luxury areas price to value and barely overbid, while Prestige areas list low and overbid hard.
Every neighborhood, at a glance
The sparkline carries the shape of the last ten years in the width of a word; the endpoints carry the numbers. Read the level, not the wiggle; single-year counts are small.
Where 2026 stands so far
The numbers behind the answer
The four numbers that set the tone for everything below: how hard buyers are bidding, where they are bidding hardest, how much of it is cash, and what price has done over a decade.
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The market, four ways
Houses vs condos, ten-year price paths, the cash fingerprint, and the top of the market. Pick a view.
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Should a seller bring a listing on now, in the summer, or hold for after Labor Day? Ten and a half years of closings, grouped by the month each one hit the market, say the folklore is mostly wrong, and that the launch date matters more than the season.
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What POTM is
POTM stands for Pulse On Today’s Market, the governed engine behind Paulo’s Pulse. It is built from San Francisco MLS closed sales: more than a decade of records, refreshed each cycle, with every figure traceable to a recorded closed sale by neighborhood and property type. Governed means fixed rules, not one-off spreadsheets: one source of truth, single-family kept separate from condos and TICs, duplicates removed, thin samples flagged.
How it works
Every update follows the same four steps:
- 1Import San Francisco MLS closed sales.
- 2Clean and de-duplicate the records.
- 3Organize by subdistrict, property type, and month, a decade deep.
- 4Compute median price, sale-to-list, days on market, and cash share, always shown with sample size.
Where AI fits
AI helps prepare the analysis. It does not replace judgment. Under Paulo’s direction, Claudio, his AI assistant, cleans the data, builds the charts, and drafts the first read. Paulo reviews the results, verifies the numbers, and adds the context that only comes from working with buyers and sellers every day.
Accountability
Every conclusion on this site is Paulo’s. The line between measured (what the sales records show) and observed (what is happening in the field) is deliberate, and observed is never dressed up as measured.
If a number is wrong, it is Paulo’s to correct. That is what the Feedback tab is for.
The exact rules behind every number here, the data sources, definitions, comparison windows, sample-size discipline, and the limits, live on the Methodology page.
Found a bug, a bad number, or a gap?
This guide gets better with your eyes on it. Flag anything that looks wrong: a figure that does not match what you see in the field, a subdistrict misclassified, or a metric you wish existed. It routes straight to Paulo so it can be corrected or built.
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From the POTM Blog