How hot is the San Francisco housing market, really?

Tech, AI, VC and PE money is buying 50%+ of homes in SF.

We mapped 5,344 buyers over the last three years to their employers and analyzed the trends. Tech has held just under 40% of identified buyers for three years, steady rather than surging. The genuine movement is underneath it: capital is rotating from operators to investors. And the AI money has not come in yet.

Venture capital and private equity is the fastest-growing buyer group in the city, up 1.41 points to 3.8% of buyers. Financial services adds 0.96. Artificial intelligence adds 0.84 to reach 6.6%. On the other side, the operating tech that built the city is receding: internet and e-commerce down 1.32 points, software and IT down 1.27.

San Francisco is not being bought by more tech people. It is being bought by the people who fund them.

San Francisco Buyers by Employment type

We measured what different buyers pay compared to actual value of their homes and how they use cash offers.

Industries with 50+ matched buyers. Sale vs value is the median against a cross-fitted as-of-sale hedonic estimate; Owen index is the median competitiveness score of the homes each group buys; % SFR is the single-family share.

Venture capital and private equity buyers pay the most by a distance, a $2.1M median, and the only large group that closes a full five points above true value (105.0%). Artificial intelligence buyers land at 99.9%, exactly what the homes are worth. Government administration buyers pay the least relative to value (95.2%) on the lowest median price in the table. And the cash champions are not who you think: self-employed and small-business buyers pay cash 42% of the time, nearly double any tech cohort.

Money changes what you can buy. It barely changes what you pay relative to worth, unless you are the one writing the fund checks.

Every neighborhood has a buyer profile. They are nothing alike.

Aggregate shares hide the real structure. At neighborhood level the concentration is extreme, and it is stable enough to price against.

Marina draws 24% of its buyers from venture capital, private equity and finance, nearly two and a half times the citywide rate. Mission Bay is 53.8% tech. AI buyers cluster somewhere else again: Twin Peaks, Potrero Hill and Inner Sunset, all well above the 6.2% citywide share.

Which raises the obvious question. If you know where the money is, do you know where the competition is?

San Francisco by neighborhood: price vs value

We analyzed thirty-two neighborhoods with 50+ sales, 2023–2026, sorted by median sale price. Then we calculated the average overbid (sale vs list) and the true-value premium (sale vs an independent as-of-sale estimate). The biggest overbids are in the mid-priced auction neighborhoods, while almost everywhere sells within a few points of what homes are actually worth.

How to read it. Sale vs list measures the final close price compared to the most recent list price. Sale vs true value measures against a hedonic estimate of each home’s worth in its sale month. For example, a 120% overbid and a 101% true-value result in the same neighborhood - Sunset/Parkside - is the underpricing convention in one line: deep asks, big auctions, fair prices. Medians throughout.

Cash does not buy a discount. It pays a premium.

Everyone in San Francisco believes cash wins. It does not win on price. Across 8,328 sales, adjusted for neighborhood, property type, condition, size, price tier and quarter, cash buyers paid 0.95 points more relative to fair market value than financed buyers, roughly $14,700 on a median sale. Cash here is not a negotiating instrument. It is what wealthy and institutional buyers use to compete for scarce property, and they pay up to do it. The caveat on this analysis: since we don't have data on lost offers, we can't estimate the odds that a cash bid beats a financed one at the same price.

The only place cash buys a discount is the one place financing might genuinely fall through: below average-condition homes, where it takes 2.2 points off, about $34,700.

Every listing takes a position. Most take the same one.

San Francisco Realtors generally follow the same strategy: underprice the listing by 5 to 15% relative to Fair Market Value to invite a bidding war. Does it work?

Of the sellers who asked below fair market value, 40% cleared it. Of those who asked above, 79% did.

Among agents with five or more verified sales, the underpricing habit is strikingly persistent (here is the research on this). Owen allows you to model the pricing strategy of every listing agent in the city and optimize how to bid.

What is a sale-to-list ratio, and what is San Francisco's right now?

A sale-to-list ratio is the price a home actually closed at divided by the price it was listed at, usually shown as a percentage. At 100% the home sold for exactly its asking price. Above 100% (above 1.0) buyers bid the price up, which means the ask was set below what the market would bear. Below 100% the seller took less than the ask, which means the listing started above what buyers would pay. The ratio measures the ask, not the home: two identical houses can post very different ratios purely because their agents chose different starting prices.

In San Francisco the ratio is not one number, it is a spread, and the spread is wide. Across the thirty-two neighborhoods Owen measures with 50+ sales and verified asks, 2023 to 2026, median sale-to-list runs from 99% in Mission Bay and Western Addition up to 120% in Sunset/Parkside, with Excelsior at 116%, West of Twin Peaks at 116%, Portola at 115% and Inner Richmond at 114%. Read that correctly: the highest ratios are not the hottest homes, they are the deepest discounts to start with, and the mid-priced house neighborhoods carry the biggest ones.

List price vs sale price: what the gap really means

The list price is a marketing decision. The sale price is a market outcome. The gap between them is the size of the bet the seller and the listing agent placed when they picked the ask, and it says almost nothing on its own about whether the seller did well. A home that closes 15% over a deliberately low ask and a home that closes 2% over a realistic ask can both land at the same check, and often do.

San Francisco's data makes the point bluntly, because most listings take the same position. 49% of sales fall in what Owen calls the Feel Good Auction: priced roughly 18% below fair market value, closing around 12% below fair market value. The headline reads "sold 10% over asking" while the buyer closes about $154k under what the major valuation sites show as fair value. 23% are Successful Auctions, priced 10% below value and closing 8% above it. 26% are Bold Movers, priced about 10% above value and closing around 15% above it, the best outcome in the set. 2% Overreach, priced 4% above value and closing 3% below. Of the sellers who asked below fair market value, 40% cleared it. Of those who asked above, 79% did.

How should you price a home for sale in San Francisco?

These are the principles Owen's pricing agent applies to an address, in the order it applies them.

1. 1

   Start from value, not from the ask next door

   Owen scores what is knowable before listing: neighborhood, property type, condition, age, size, price tier and commute geometry. Comparable asking prices carry each agent's own pricing convention baked in, so they are the wrong anchor.

2. 2

   Separate the strategy from the property

   Market timing, pricing strategy and the choice of agent are deliberately excluded from the Owen score, because those are the parts you control. Score the home first, then choose the position.

3. 3

   Price the position you actually want

   Underpricing and holding a confident ask are two different strategies with two different distributions of outcome, and the data above shows all four cells the city lands in. Pick one on purpose rather than defaulting to the local convention.

4. 4

   Read your neighborhood's convention before you follow it

   Median sale-to-list runs from 99% to 120% across the city's thirty-two measured neighborhoods. The convention where you are selling is a fact to price against, not a rule to obey.

5. 5

   Bring the evidence to the pricing conversation

   Every number traces back to the transaction it came from, which is what turns a listing meeting from an argument about instinct into a decision. Owen produces pricing analysis, not an appraisal.

Owen is the same AI home buying agent Ownify runs nationally, calibrated here on San Francisco transactions only.

Frequently asked questions

A pricing agent for any San Francisco address.

Everything above is what the market looks like in aggregate. Owen answers it for the one address you care about, before it is listed, in the time it takes to type it.

Owen scores an address on what is knowable before listing: neighborhood, property type, condition, age, size, price tier and commute geometry. Market timing, pricing strategy and the agent are deliberately excluded from the score, because those are the things you control. It is a pricing analysis, not an appraisal.