{ "id": "2000-januaryfebruary-cost-of-living", "slug": "2000-januaryfebruary-cost-of-living", "url": "https://dwillis.github.io/uplink/article/2000-januaryfebruary-cost-of-living.html", "year": 2000, "month": "January/February", "monthNum": 1, "headline": "Cost of living", "kicker": "ECONOMIC DATA", "author_name": "Jennifer LaFleur and Michelle Quinn", "author_title": "", "affiliation": "Mercury News", "summary": "A Mercury News analysis of Silicon Valley's wealth distribution reveals economic paradoxes: while roughly 1 in 9 Santa Clara County households are millionaires, most residents aren't particularly wealthy by net worth measures, and only 29 percent can afford median-priced homes compared to 55 percent nationally.", "keywords": [ "wealth", "Silicon Valley", "housing" ], "topics": [ "Business & Economy", "Data & Databases", "Demographics & Census" ], "technologies": [ "Claritas", "Ferret", "Census Current Population Survey" ], "full_text": "When most people think of Silicon Valley, they think of two things: technology and money. If a few national reports are to be believed, nearly everyone in Metro San Jose is a millionaire. Earlier this year, a team of Mercury News reporters set out to find out just how much money is being made in Silicon Valley, how it was distributed and how it has affected people's lives. What our analysis of several private and public databases found was that the valley is out of whack economically compared with the rest of the nation, riding a boom filled with paradoxes. According to one of the most aggressive estimates, 65,000 Santa Clara County households - roughly 1 in 9 - are worth more than $1 million, not including the value of their homes. And yet, only 29 percent of Santa Clara County's households can afford the median-priced home, far below the national rate of 55 percent. And two out of five county households would need to stretch beyond advisable limits to rent the average two-bedroom apartment.\n\nThe bottom line of using any of these types of data sources is to compare the results to other information.\n\nPerhaps most startling: although many people here earn exceptionally high pay compared with the rest of the country, most aren't particularly wealthy. Look at the net wealth of Santa Clara County households - their assets minus their debts - and the distribution closely mirrors the nation's as a whole.\n\nData sources\n\nThis series used several sources of data. Our biggest concern was that the market data models truly reflected Silicon Valley. Because many market surveys cap home values and incomes low values for our area, we compared data from several sources.\n\nWe ended up using wealth measures from Claritas, an Arlington, Va.-based marketing information firm, which estimates income and wealth distributions for the United States, including all ZIP codes and small areas. Estimates are based on a combination of factors: extrapolation of census data, Claritas' Market Audit Survey and other area-specific sources of demographic data. The Market Audit Survey annually collects information on household assets, liabilities, demographics and financial behavior for approximately 120,000 U.S. households. We used Claritas figures for some of the income data. A note of caution: If you plan to use any market data, make sure you fully understand the methodology used in their model and that you can verify it with other data sources. Also, these data are good for large metropolitan areas, but not as good for smaller areas. Additional sources of data we used to look at income included: Census Current Population Survey data (downloadable off www.census.gov using the Ferret extraction tool), for which we had to combine years to get a valid sample; and California state franchise tax board tax filing data. Using these data, we were able to show that the mean income was increasing faster than the median income, showing that the higher end was pulling up the average.\n\nHousing data\n\nAlthough many things may seem expensive in Silicon Valley, the one driving factor behind most people's struggles is housing. We used California Association of Realtors data and data from the Real Estate Research Board of Northern California to look at trends in housing costs. Depending on how assessments are done in a particular area, property tax data might be useful for this as well. Because Proposition 13 froze California assessment data at sales prices, it's not a useful tool for looking at housing costs. Rent data was harder to find. A few private companies do rent analysis, but they vary depending on how the data are gathered.\n\nHUD Fair Market Rent measures are a good start, although they estimate rents lower than the median for an area. Another source for rent data is RealFacts.\n\nThe RealFacts data inventory has been assembled over a period of years, and provides complete and current information on more than 4,000 apartment complexes in 10 Metropolitan Statistical Areas (MSAs), including California, Oregon, Washington, Nevada, Utah and New Mexico. These tend to be slightly higher because they are for apartment complexes.\n\nIncome data\n\nTo get a handle on how much money insiders in Silicon Valley made, we used a database of SEC corporate insider trading files from Thomson Financial Wealth Identification (formerly CDA). We used this to get an estimate of the number of people with large amounts of wealth.\n\nTo figure out the value of stock options held by employees of select Silicon Valley companies, Institutional Shareholder Services did an analysis based on 10-K filings and several other company factors such as turnover.\n\nAfter analyzing many economic measures for Silicon Valley, we had a starting point. As with any data-based project, the data are the beginning. The bottom line of using any of these types of data sources is to compare the results to other information.\n\nThe large amounts of data that we gathered were not all included in the stories, but we were able to make many of them available online so that individuals could look up wealth, income and other measures for their neighborhoods.", "provenance": { "extracted_by": "anthropic/claude-sonnet-4-6", "extracted_at": "2026-07-05", "verified": true }, "similar": [ { "slug": "1994-november-uplink-update", "score": 0.15 }, { "slug": "1994-september-coming-events", "score": 0.132 }, { "slug": "1994-october-coming-events", "score": 0.13 }, { "slug": "1994-august-coming-events", "score": 0.126 }, { "slug": "1994-september-train-with-best-in-car", "score": 0.121 } ], "html_url": "https://dwillis.github.io/uplink/article/2000-januaryfebruary-cost-of-living.html", "json_url": "https://dwillis.github.io/uplink/article/2000-januaryfebruary-cost-of-living.json" }