Bibliographic record
Abstract
With Silicon Valley employment still well below the 2000 peak, and rising foreclosures nationwide, this article examines whether the area's housing market is vulnerable to a correction. Recent statistics suggest that several factors have helped support home prices, but that there are uncertainties going forward. Although the San Jose MSA has regained less than one-fifth of jobs lost since 2000, there are now strong signs that employment is in recovery, with job growth occurring in diverse sectors. The San Jose MSA's wage and salary jobs now outstrip the area's resident labor force, suggesting that there may be a pent-up demand for housing from commuters in surrounding communities. This demand may be one of several factors that kept the region's housing prices stable immediately following the dot-com bust, and allowed price increases to follow in the absence of full employment recovery. Other factors include low interest rates; a mobile younger workforce, whose departure affected rent more than home prices; and a shift in investment from stocks to homeownership following the dot-com bust. In the 2007 housing market slowdown, the greatest impact nationwide has been at the low end of the market, while Silicon Valley's expensive housing market has a foreclosure rate well below the statewide average. Nevertheless, there is some evidence of softening. The number of home sales has decreased. The same-home sales price index was down slightly first quarter, as was the median price for new homes. The median price of existing homes is rising, but in part because of the drop off in sales at the low end and in part due to sellers taking homes off the market rather than lowering prices. The paper concludes with several possible scenarios for Silicon Valley home prices going forward--a two-tiered market with price increases in some segments and declines in others; price stabilization across home-types; or a significant recession-induced price decline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".