July Market Still Affected by Spring’s Economic Uncertainty, But Indicators Are Turning Positive & Santa Clara County’s AI Boom Continues
As of early August, measures of economic volatility and uncertainty had plunged from the extreme highs of spring, stock markets shrugged off tariff concerns to hit new highs, consumer confidence continued to tick up, and interest rates dropped to their lowest point in 2025. To a large degree, negative macroeconomic conditions took much of the wind out of the sails of the spring and early summer selling seasons in most of the Bay Area (and the nation). Santa Clara County saw some year-over-year cooling from the extremely heated conditions of last year – mostly due to the considerable increase in listings for sale – but bolstered by the AI boom, it continued as one of the strongest markets in the state and the country. Its median house sales price increased again year-over-year, while most other Bay Area counties saw declines.
If improvements in economic indicators continue (and issues like inflation and employment don’t drastically deteriorate), and if the dramatic effects of the AI boom on the housing markets of Silicon Valley and San Francisco persist, we should see further positive effects on local market conditions moving deeper into the second half of the year.
Statistics are generalities, essentially summaries of widely disparate data generated by dozens, hundreds or thousands of unique, individual sales occurring within different time periods. They are best seen not as precise measurements, but as broad, comparative indicators, with reasonable margins of error. Anomalous fluctuations in statistics are not uncommon, especially in smaller, expensive market segments. Last period data should be considered estimates that may change with late-reported data. Different analytics programs sometimes define statistics – such as “active listings,” “days on market,” and “months supply of inventory” – differently: what is most meaningful are not specific calculations but the trends they illustrate. Most listing and sales data derives from the local or regional multi-listing service (MLS) of the area specified in the analysis, but not all listings or sales are reported to MLS and these won’t be reflected in the data. “Homes” signifies real-property, single-household housing units: houses, condos, co-ops, townhouses, duets and TICs (but not mobile homes), as applicable to each market. City/town names refer specifically to the named cities and towns, unless otherwise delineated. Multi-county metro areas will be specified as such. Data from sources deemed reliable, but may contain errors and subject to revision. All numbers to be considered approximate.
Many aspects of value cannot be adequately reflected in median and average statistics: curb appeal, age, condition, amenities, views, lot size, quality of outdoor space, “bonus” rooms, additional parking, quality of location within the neighborhood, and so on. How any of these statistics apply to any particular home is unknown without a specific comparative market analysis.
Median Sales Price is that price at which half the properties sold for more and half for less. It may be affected by seasonality, “unusual” events, or changes in inventory and buying trends, as well as by changes in fair market value. The median sales price for an area will often conceal an enormous variety of sales prices in the underlying individual sales.
Dollar per Square Foot is based upon the home’s interior living space and does not include garages, unfinished attics and basements, rooms built without permit, patios, decks or yards (though all those can add value to a home). These figures are usually derived from appraisals or tax records, but are sometimes unreliable (especially for older homes) or unreported altogether. The calculation can only be made on those home sales that reported square footage.
