property nerds® indexlive from the mlsreading for October 18, 2026by the boyenga team at compass

Property Nerds^® Silicon Valley Housing Market Index^™

One number for how competitive the Silicon Valley housing market is, from 0 (cold: buyers have the upper hand) to 100 (hot: sellers do). Read every week from four MLS measures across 31 cities, with San Francisco alongside, and rebuilt month by month from closed sales as far back as the data goes.

63.1/ 100

Warm · sellers have the edge

First reading. Method version 1.0.

Santa Clara County15 cities · warm60.8
San Mateo County16 bay-side cities · warm75.4
San Franciscoreported separately · hot84.9

the four measures

What’s behind this week’s 63.1

Each measure is scored from 0 to 100 on a fixed scale, and the index is their average. The scales never move, so a higher number always means a more competitive market.

Sale-to-list100.1%score 51.1 / 100

Median sale price ÷ final list price, last 90 days. 95% scores 0, 100% scores 50, 105% or more scores 100.

Sold over asking53.1%score 53.1 / 100

Share of sales that closed above list price. The score is the share.

Months of supply2.1score 77.2 / 100

Homes for sale now ÷ the monthly pace of sales. 1 month or less scores 100, 3.5 scores 50, 6 or more scores 0.

Listings with a price cut14.5%score 70.9 / 100

Share of homes for sale asking less than an earlier price. 0% scores 100, 25% scores 50, 50% or more scores 0.

Silicon Valley, October 18, 2026: 3,635 sales in the last 90 days and 2,595 homes for sale; median sale price $1.61M, median $982 per sq ft.

city by city

The index in every city we cover

The same four measures, city by city. Tap a column to sort. Cities with fewer than 20 sales in 90 days are shown for reference and sorted last.

		reading
Millbrae	89.5	hot	116.4%	79.3%	1.2	8.3%	29
South San Francisco	86.9	hot	105.9%	74.3%	1.5	7.9%	74
Los Altos	85.8	hot	104.8%	68.9%	1.2	10.3%	74
Daly City	85.6	hot	105.5%	72.8%	2.2	2.9%	92
San Francisco	84.9	hot	111.4%	73.9%	2.1	6.5%	1,138
Palo Alto	84.1	hot	104.0%	62.8%	1.3	5.5%	129
Burlingame	80.2	hot	103.8%	68.1%	1.0	17.4%	69
San Mateo	78.1	warm	102.9%	59.2%	1.8	5.0%	169
San Bruno	76.1	warm	102.3%	61.7%	1.5	10.3%	60
Sunnyvale	76.0	warm	102.5%	63.0%	1.5	12.2%	200
Redwood City	73.7	warm	103.8%	62.7%	1.8	20.0%	150
Hillsborough	73.3	warm	100.1%	56.0%	1.7	0.0%	25
San Carlos	73.1	warm	102.2%	57.5%	1.5	13.9%	73
Belmont	72.0	warm	101.0%	57.9%	1.3	12.5%	57
Mountain View	71.2	warm	101.2%	60.0%	2.0	9.0%	120
Cupertino	68.7	warm	100.7%	54.6%	2.1	7.4%	77
Menlo Park	67.5	warm	100.0%	48.9%	1.7	7.8%	92
Santa Clara	67.0	warm	100.9%	58.8%	1.8	16.8%	170
Foster City	63.2	warm	100.1%	51.0%	1.5	19.2%	51
Saratoga	62.3	warm	100.0%	52.8%	2.0	16.3%	72
San Jose	58.6	balanced	100.0%	46.5%	2.4	17.5%	1,285
Campbell	58.3	balanced	100.1%	51.9%	3.2	12.8%	81
Gilroy	57.4	balanced	100.0%	48.5%	3.0	14.4%	97
Los Altos Hills	57.4	balanced	98.2%	33.3%	2.1	6.7%	21
Milpitas	56.7	balanced	100.0%	48.9%	2.6	20.0%	92
Morgan Hill	46.5	balanced	99.1%	32.6%	3.4	19.3%	95
East Palo Alto	44.1	balanced	98.2%	32.0%	2.8	26.1%	25
Los Gatos	44.0	balanced	98.3%	33.3%	4.0	14.8%	96
Atherton	78.4	small sample	111.9%	68.8%	3.8	0.0%	16
Monte Sereno	59.9	small sample	100.0%	33.3%	1.8	14.3%	12
Portola Valley	55.5	small sample	98.3%	50.0%	3.4	6.3%	14
Woodside	49.2	small sample	100.0%	38.9%	4.5	11.1%	18

Region readings pool every sale and listing in the region, so they aren’t averages of the city numbers. Sale-to-list and over-asking use sales from the last 90 days; supply and price cuts use the homes for sale on the reading date.

the long view

Sales-side heat, every month since Jan 2012

The sales side of the index, sale-to-list and over-asking, rebuilt from every closed sale in our MLS feed, one month at a time. Same 0–100 scale and bands as the weekly index.

Silicon ValleySanta Clara CountySan Francisco

hottest monthMar 2022

Sales-side score 94.6: median sale-to-list 113.5%, 89.2% sold over asking.

coolest monthDec 2022

Sales-side score 32.1: median sale-to-list 98.4%, 30.4% sold over asking.

this week vs history22.0%

This week’s sales-side score (52.1) is hotter than 22.0% of the 177 months on record.

Monthly sales-side score = (S1 + S2) ÷ 2 from that month’s closed sales, Silicon Valley pooled. Past supply and price cuts can’t be rebuilt, so this line isn’t directly comparable to the weekly four-part index. Sale dates are closing dates, which usually trail the accepted offer by about a month.

the calendar

The over-asking calendar

Each cell is the share of Silicon Valley sales that closed over asking that month. Read across a row for the seasons and down a column for the cycles.

year	jan	feb	mar	apr	may	jun	jul	aug	sep	oct	nov	dec
2012	28	35	39	45	52	56	55	54	58	57	60	59
2013	57	64	69	73	76	73	72	65	63	61	58	53
2014	56	66	71	73	73	69	65	63	62	59	58	55
2015	51	68	75	78	80	78	74	73	69	66	62	62
2016	62	68	75	74	74	69	65	61	56	57	55	52
2017	50	67	72	76	76	74	74	75	73	79	77	77
2018	78	86	85	86	87	82	74	72	63	60	48	42
2019	37	49	53	58	58	55	51	46	46	42	44	41
2020	43	59	67	64	45	47	50	56	56	60	59	55
2021	56	64	71	77	78	79	78	78	75	77	77	72
2022	75	84	89	87	86	73	53	41	43	42	34	30
2023	31	44	53	60	65	66	64	64	66	62	55	48
2024	47	64	71	74	75	72	65	62	62	64	58	51
2025	52	69	72	68	60	56	50	46	51	55	54	47
2026	46	63	66	62	61	57	51	52	55

Share of Silicon Valley sales that closed above list price, by month (percent). Darker means more homes sold over asking.

the seasons

Is there a best month to sell?

The average share of Silicon Valley sales closing over asking in each calendar month, 2012–2025.

Jan51.6%
Feb63.3%
Mar68.8%
Apr70.9%
May70.3%
Jun67.8%
Jul63.8%
Aug61.1%
Sep60.3%
Oct60.2%
Nov57.1%
Dec53.2%

Average share of Silicon Valley sales closing over asking in each calendar month, 2012–2025. Sale dates are closing dates, which usually come about a month after the offer.

the price line

Price per square foot since Jan 2012

What buyers paid for living space, month by month: the median sale price per square foot for Silicon Valley, its two counties and San Francisco.

Silicon ValleySanta Clara CountySan Mateo CountySan Francisco

Silicon Valley median sale price per sq ft averaged $379 in the first 12 months on record and $987 in the last 12 (+160%). Lines are three-month rolling medians of monthly medians; living area as reported to the MLS.

city by year

Every city, every year

The yearly sales-side score for every city we cover, built from each city’s closed sales. Darker is hotter. Sorted by last full year.

city	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026*
South San Francisco	58	88	79	90	81	94	95	85	69	93	76	69	85	84	87
Sunnyvale	71	92	93	93	89	95	90	60	65	89	86	81	90	83	77
Daly City	56	80	77	92	91	90	93	88	75	92	88	80	80	83	86
Millbrae	54	85	89	90	87	88	89	76	50	88	64	81	86	75	84
Santa Clara	61	80	86	92	82	92	90	50	62	87	86	81	86	74	71
Belmont	53	89	90	93	91	93	90	68	70	92	87	60	82	72	68
Cupertino	69	90	91	90	80	91	91	55	61	90	74	85	84	71	72
Mountain View	73	91	91	93	87	93	91	61	52	76	74	69	76	67	73
Los Altos	56	84	89	87	60	87	87	50	50	85	60	52	72	65	69
Foster City	52	87	89	92	85	92	93	59	56	89	82	54	67	63	54
Palo Alto	76	89	89	87	61	86	85	52	47	73	67	49	71	63	75
Burlingame	47	67	68	88	83	90	88	56	48	72	67	44	57	63	76
Milpitas	51	76	68	82	79	93	89	49	57	89	86	68	82	61	59
Campbell	48	74	76	88	63	89	86	48	60	88	81	70	82	61	55
San Bruno	65	81	69	90	91	92	92	85	63	88	78	66	66	59	69
San Francisco											71	48	55	59	85
San Jose	50	69	64	76	66	83	87	48	61	90	78	68	76	58	55
Saratoga	40	55	70	56	54	81	64	41	50	90	71	59	79	57	56
San Carlos	54	89	87	92	80	90	91	65	51	86	68	52	75	54	71
Redwood City	49	72	81	88	75	87	88	52	50	86	73	49	64	54	72
San Mateo	50	86	87	89	85	90	91	64	55	86	64	50	69	53	73
Menlo Park	51	70	76	83	52	65	78	48	44	58	56	46	53	51	57
Monte Sereno	26	37	46	38	40	23	33	34	42	69	71	35	49	50	53
Los Altos Hills	23	27	47	65	22	50	47	30	29	52	48	31	39	50	32
Portola Valley	44	40	61	51	47	59	49	24	21	50	47	37	37	49	26
East Palo Alto	48	61	57	88	74	89	89	50	48	53	76	49	69	48	48
Gilroy	47	55	42	46	45	51	56	39	51	84	67	50	49	45	47
Atherton	26	43	42	44	38	36	30	25	19	30	33	7	31	45	49
Morgan Hill	40	49	38	45	46	49	62	38	50	84	62	46	51	44	46
Hillsborough	15	27	42	46	30	46	44	34	33	48	42	28	43	43	50
Los Gatos	33	45	47	49	44	50	51	37	46	65	54	49	50	42	46
Woodside	25	29	35	44	24	26	28	20	15	43	46	18	31	20	35

Yearly sales-side score (sale-to-list and over-asking), pooled from each city’s closed sales. Blank = fewer than 20 sales that year. * = year to date.

the record

Every reading since the first

The weekly record starts with this reading. Each new week is added here, and earlier readings stay as published.

Silicon ValleySanta Clara CountySan Mateo CountySan Francisco

The readings

reading for	Silicon Valley	Santa Clara Co.	San Mateo Co.	San Francisco
October 18, 2026	63.1	60.8	75.4	84.9

the method

How the index is built

Version 1.0. The formulas are fixed and reproducible from MLS data. Any change gets a new version number and a dated note here.

What it measures: Competition in the resale market: how far over or under list homes sell, how often they sell over asking, how much supply there is relative to sales, and how often sellers cut their price.
Coverage: Silicon Valley = the 31 cities we cover in Santa Clara County (15) and bay-side San Mateo County (16). San Francisco is reported alongside as its own reading.
Data: Homes listed and sold through the MLS, read through our IDX feeds on the reading date. Houses, condos and townhomes only. The same sale reported by more than one feed is counted once (matched on a normalized address and sale date). Sales recorded at under 50% or over 200% of list are treated as data errors and left out.
S1 · Sale-to-list (25%): Median of sale price ÷ final list price for sales in the last 90 days. S1 = (ratio − 0.95) ÷ 0.10 × 100, held between 0 and 100: 95% scores 0, 100% scores 50, 105% or more scores 100.
S2 · Sold over asking (25%): Share of those sales that closed above list price. S2 = share × 100.
S3 · Months of supply (25%): Homes for sale on the reading date ÷ the monthly pace of sales over the last 90 days. S3 = (6 − months) ÷ 5 × 100, held between 0 and 100: 1 month or less scores 100, 3.5 scores 50, 6 or more scores 0.
S4 · Price cuts (25%): Share of homes for sale asking less than an earlier list price. S4 = (0.50 − share) ÷ 0.50 × 100, held between 0 and 100: none scores 100, 25% scores 50, half or more scores 0.
The index: PNI = (S1 + S2 + S3 + S4) ÷ 4, from 0 to 100, to one decimal. Region readings pool every sale and listing in the region, so bigger cities weigh more.
Reading bands: 0–19.9 cold · 20–39.9 cool · 40–59.9 balanced · 60–79.9 warm · 80–100 hot.
Monthly history: Past supply and price cuts can’t be rebuilt from closed sales, so the long-run history uses the sales side only: PNI-S = (S1 + S2) ÷ 2, computed for each calendar month from that month’s closed sales. It starts in the first month with full coverage in our MLS feed.
Small samples: Cities with fewer than 20 sales in 90 days are labeled “small sample”. Their readings are shown for reference; one or two sales can move them a lot.
Revisions: Published readings are not revised. Method changes get a new version number and date. Version 1.0; first reading October 18, 2026.

the data

Data, downloads and how to cite

Every number on this page can be downloaded. Use the files freely with credit; here is what each column means and when new data arrives.

Boyenga Team. “Property Nerds® Silicon Valley Housing Market Index™,” reading for October 18, 2026. https://www.boyengateam.com/silicon-valley-housing-market-index

Download this reading (CSV)Download the monthly history (CSV)Ask for the data behind it

The reading file

area: Silicon Valley, a county region, San Francisco or a city.
index: The four-part index, 0–100.
reading: The band: cold, cool, balanced, warm or hot. Cities under 20 sales say “small sample”.
sale_to_list: Median sale price ÷ final list price, last 90 days (1.000 = 100%).
share_over_asking: Share of those sales above list price (0–1).
months_of_supply: Homes for sale ÷ monthly pace of sales.
share_with_price_cut: Share of homes for sale asking less than an earlier price (0–1).
sales_90_days: Closed sales counted, repeat records removed.
listings_for_sale: Houses, condos and townhomes for sale on the reading date.
median_sale_price: Median closed price, last 90 days.
median_price_per_sqft: Median of sale price ÷ living area, last 90 days.

The monthly history file

month: Calendar month of closing (yyyy-mm).
region: sv = Silicon Valley, scc = Santa Clara County, smc = San Mateo County (bay side), sf = San Francisco.
sales: Closed sales that month, repeat records removed.
median_sale_to_list: Median sale price ÷ final list price.
share_over_asking: Share of sales above list price (0–1).
sales_side_score: (S1 + S2) ÷ 2, 0–100.
median_price_per_sqft: Median of sale price ÷ living area.

Release schedule

Weekly readingA new reading each week, dated the day the data is read. It uses sales closed in the 90 days before that date and the homes for sale on that date.
Monthly historyRebuilt from the full archive each month and adds the newest complete month. Recent months can shift slightly as late-reported sales arrive.
RevisionsPublished weekly readings are never revised. Method changes get a new version number and a dated note.
ArchiveEvery weekly reading is kept and listed in the record above, and each one is saved as a file.

Property Nerds® is a registered U.S. trademark (Reg. No. 5,525,765). Property Nerds® Silicon Valley Housing Market Index™ and Property Nerds Index™ are trademarks of the same owner, published by the Boyenga Team at Compass. Built from listings and sales reported to the MLS through our IDX feeds; shown as reported and not guaranteed.

questions

Questions we hear

What is the Property Nerds® Silicon Valley Housing Market Index™?

A weekly reading from 0 to 100 of how competitive the housing market is across the Silicon Valley cities we cover. It combines four MLS measures: sale-to-list, the share of homes selling over asking, months of supply and the share of listings with a price cut. 50 is balanced; higher favors sellers, lower favors buyers.

Who publishes it?

The Boyenga Team at Compass, the Property Nerds. The method, the data files and every past reading are on this page.

What counts as Silicon Valley here?

The 31 cities we cover in Santa Clara County (15) and bay-side San Mateo County (16).

Why is San Francisco separate?

San Francisco is its own market with its own MLS, and most definitions of Silicon Valley don’t include it. It gets its own reading so you can compare.

Where does the data come from?

From the MLS, through the IDX feeds that power our home search: listings and closed sales reported to the MLS in the cities we cover. Homes sold without an MLS listing aren’t included.

Which homes count?

Houses, condos and townhomes. Land, multi-unit buildings, commercial property and rentals are left out.

How do you handle duplicate records?

The same sale often arrives through more than one MLS feed, sometimes with the street spelled differently. Each sale is counted once, matched on a normalized address and the sale date.

How often is it updated?

Weekly. Each reading uses sales from the prior 90 days and the homes for sale on the reading date. Earlier readings stay as published.

Why 90 days?

It’s recent enough to show turns in the market and long enough to give stable medians in smaller cities.

What do the bands mean?

0–20 cold, 20–40 cool, 40–60 balanced, 60–80 warm, 80–100 hot. A reading of 50 matches homes selling right at list price, about half going over asking, 3.5 months of supply and a quarter of listings with a price cut.

Is a high number good or bad?

Neither. A high reading is good news for sellers and means more competition for buyers; a low reading is the reverse.

How far back does it go?

The weekly four-part index starts October 18, 2026. The sales-side history is rebuilt month by month from closed sales, starting with the first month that has full coverage in our MLS feed.

Why is the history only the sales side?

Closed sales keep their sale price, list price and date, so sale-to-list and over-asking can be rebuilt for any past month. The homes that were for sale, and their price cuts, on a past date can’t be rebuilt reliably, so supply and price cuts start with the weekly index.

Why might these numbers differ from other market reports?

Reports differ in which cities they include, the time window, the property types, whether they use medians or averages, closing or contract dates, and how they handle duplicate records. The index states each of these choices above.

Why do some cities say “small sample”?

They had fewer than 20 sales in 90 days. Their readings are shown for reference, but one or two sales can move them a lot.

Does the index predict prices?

No. It measures how competitive the market is right now, not where prices will go.

Can I download the data?

Yes. This reading and the full monthly history are CSV files in the data section, with a dictionary for every column.

Can I quote it?

Yes. Credit the Property Nerds® Silicon Valley Housing Market Index™ by the Boyenga Team and link to this page. For the data behind a reading, contact us.

more to learn

Go deeper

Home Price HistoryEvery city’s median price and price per sq ft since 2012, and the 2022 dip.
City ScorecardEvery city’s numbers on one spec sheet, with a sortable table.
Data LibraryEvery dataset in one catalog, with methods, downloads and citations.
The $1M LineHow the share of sales under $1M, $2M and more moved in every city since 2012.
Bidding Wars by PriceHow often homes at each price sold over asking, from under $1M to $5M+, every year since 2012.
How Much Over AskingHow many dollars over list homes sold for, by city and price, every month since 2012.
Best Month to SellEach city’s strongest and weakest closing months, from 14 years of sales.
Market PulseHomes for sale, days on market and 12 months of prices for 32 cities.
Leverage LabWho has the upper hand at your exact price.
Offer LabHow far over or under asking homes sold in the last 90 days.
Luxury Market ReportThe same kind of read for homes at $3M and up.
City Match-UpTwo cities side by side, from prices to transfer tax.
Price-Cut RadarThe homes behind the price-cut number.

Based on listings and sales reported to the MLS through our IDX feeds, read on the reading date. Shown as reported and not guaranteed; repeat records are removed. Readings describe the market, not any single home.

talk to a property nerd

What does this week’s number mean for your move?

We’ll put your street, price range and timing in the context of the index and the sales behind it. Commissions are negotiable, and we offer bundled services, so ask us what’s included.

Book a strategy call Call (408) 373-1660 Call (650) 383-8606

Ask a Property Nerd what this week’s reading means for you

Eric & Janelle Boyenga

+1(408) 373-1660

Agent DRE01254724 / 01254725