The sustainability of low-income homeownership: The incidence of unexpected costs and needed repairs among low-income homebuyers
Bibliographic record
Abstract
Until the recent housing market crisis, the United States was producing first-time, low-income homeowners at an unprecedented rate. In a longitudinal study of low-income renters participating in a multi-site homeownership education program, we examine the ability of low-income homebuyers to pay housing-related costs after home purchase, including maintenance or repairs costs. After less than two years of ownership, we find the sustainability of low-income homeownership in jeopardy for sizeable portion of homebuyers. About half of the more than 350 new homeowners surveyed face unexpected costs, and about a third confront home repairs they cannot afford. More than half carry greater nonhousing debt, and about a quarter were 30 days late or more in debt repayment. The findings raise concerns about the long-term sustainability of low-income homeownership and emphasize the importance of requiring effective prepurchase services and effective and ongoing postpurchase counseling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".