Homeownership Gaps Among Low-Income and Minority Households
Bibliographic record
Abstract
Although homeownership rates currently stand at historically high levels for all segments of the U.S. population, large gaps in homeownership rates remain when comparing various groups of the population. As of the third quarter of 2006, the non-Hispanic White (hereafter, White) homeownership rate was 76 percent while African-American and Hispanic homeownership rates were below 50 percent and the Asian homeownership rate was 60 percent. The homeownership gap between African-American and White households was larger in 2006 than it was in 1990, while the homeownership gap between Hispanics and Whites was only slightly smaller in 2006 than it was in 1990. Households with very low incomes had a homeownership rate that was 37 percentage points below the rate for high-income households. These gaps have changed little over the past 50 years. The primary goal of this study is to synthesize what is known about the determinants of gaps in homeownership rates by income status and racial and ethnic status. We first present a conceptual framework for analyzing the determinants of homeownership. We then review the literature that identifies the relative importance of various contributing factors to observed homeownership gaps, separating the factors into those that are observed and those that are part of an unexplained residual that represents unmeasured factors such as discrimination, lack of information about the homebuying and mortgage financing processes, and omitted socioeconomic variables.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".