Characteristics of High-Foreclosure Neighborhoods in the Tenth District
Bibliographic record
Abstract
The foreclosure crisis that began in earnest in 2006 continues to shrink the once valuable assets of homeowners, communities, and investors. In the last three years, more than three million households have lost their homes, and as many as 5 million more could lose their homes in the next three years. ; A striking feature of the crisis is the variation in its severity across both time and space. Initially, the foreclosure crisis hit low-income neighborhoods disproportionately. Foreclosures remain concentrated in these neighborhoods. But in recent months, the foreclosure epidemic has spread more deeply into higher-income neighborhoods. What accounts for the evolving pattern of foreclosure rates across neighborhoods, and where might concentrations of foreclosures occur in the future? ; Edmiston analyzes the seven states of the Tenth Federal Reserve District to help shed light on the foreclosure rate pattern and to explore where foreclosure trends are likely to head. His analysis confirms that foreclosure rates have been high in low-income neighborhoods--but only to the extent that subprime mortgages penetrated those neighborhoods. He also finds that the foreclosure crisis is seeping into higher-income neighborhoods--due primarily to unfavorable conditions in local economies and residential real estate markets.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".