Second District Highlights Bypassing the Bust: The Stability of Upstate New York’s Housing Markets during the Recession
Bibliographic record
Abstract
Over the past decade, the United States has seen real estate activity swing from boom to bust. But upstate New York has been largely insulated from this volatility, with metropolitan areas such as Buffalo, Rochester, and Syracuse even registering home price increases during the recession. An analysis of upstate housing markets over the most recent residential real estate cycle indicates that the region’s relatively low incidence of nonprime mortgages and the better-than-average performance of these loans contributed to this stability. The United States experienced a sizable boom in real estate activity between 1998 and 2006, followed by a sharp contraction. Home prices rose on average more than 8 percent per year between 2000 and 2006—but have been falling more recently at an average annual rate of 4 percent. 1 In states such as California, Arizona, and Florida, the collapse in home prices has been particularly severe. Somewhat surprisingly, however, many parts of the country have not experienced dramatic declines in housing prices, with some regions even registering price increases since the recession began. Upstate New York is one such region. Despite upstate’s long-term weak economic growth and population loss, Buffalo, Rochester, and Syracuse all ranked in the top 10 percent of metro areas in terms of home price appreciation in 2009, with Buffalo ranking sixth overall. In this edition of Second District Highlights, we assess the performance of upstate New York’s housing markets during the most recent residential real estate cycle. We analyze the extent to which the region has been insulated from the boom-bust pattern in housing prices seen in many parts of the country since 2000 and compare the pattern of real estate activity for the region with patterns for U.S. metropolitan areas. We also examine the extent of lending activity in the riskiest segment of the residential mortgage market—“nonprime ” mortgages—and compare the regional and national penetration and performance of these loans.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".