Committee on Public Services and Consumer Affairs JOINT PUBLIC OVERSIGHT ROUNDTABLE ON AFFORDABLE HOUSING
Bibliographic record
Abstract
Good morning. My name is Peter Tatian and I am a senior researcher in the Urban Institute’s Metropolitan Housing and Communities Policy Center. I am also director of NeighborhoodInfo DC, an information resource for the District of Columbia.1 I am happy to provide this testimony highlighting data compiled by NeighborhoodInfo DC on housing foreclosures and project-based Section 8 housing in Washington, D.C. Foreclosures First, I will talk about foreclosures. The delinquency rate for all mortgages, and especially subprime or high-cost mortgages, has increased over the past year, and these higher delinquencies have resulted in more foreclosures. Increased delinquency and foreclosure rates are largely the result of resets to adjustable-rate loans that were made with a low “teaser ” rate that was initially affordable to the borrower. Nationally, subprime adjustable-rate mortgages accounted for 7 percent of mortgages outstanding, but 43 percent of all foreclosures initiated in the third quarter of 2007 (Stokes and Mechem 2007). Consistent with national trends, a local increase in subprime lending has been followed by a surge in home foreclosures. In the District of Columbia, subprime lending increased from 3.2 percent of conventional home purchase and refinance mortgage loans in 2002, to 12.5 percent in 2005 (Tatian 2007a). Levels of subprime lending have been highest in Wards 4, 5, 7, and 8, where almost four of every ten home purchase loans was a high-cost loan in 2005 (Tatian 2007b). According to data from the D.C. Recorder of Deeds analyzed by NeighborhoodInfo DC, the number of notices of foreclosure filed against residential property owners in D.C. has almost doubled between 2005 and 2007 (table 1). The highest number of notices filed in 2007 were in Ward 5 (523 notices), Ward 7 1 NeighborhoodInfo DC is a partnership between the Urban Institute and the Washington, D.C., Local Initiatives
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.295 | 0.167 |
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".