Housing Attainment of Interracial Couples in the United States
Bibliographic record
Abstract
Over the past few decades, interracial and interethnic marriages have increased significantly. The rise in intermarriages has been celebrated for their potential to reduce ethnoracial distinctions and ethnoracial inequality. Whether the increase in interracial and interethnic unions can reduce disparities in homeownership across ethnoracial groups is unknown. Using data on the 2008–2021 American Community Survey (ACS), we compare the homeownership rates of Millennials in intermarriages with those of couples in endogamous unions and identify the factors contributing to differences in homeownership rates between interracial and couples in ethnoracial endogamy. Our results show that the homeownership rates of interracial couples fall somewhere between those of endogamous couples belonging to the husband’s or wife’s ethnoracial groups. These differences emerge primarily due to variations in nativity composition, educational attainment, family income, average value of homes, and proportion of rentals. The intermediary homeownership rates of interracial couples suggest that the rise in intermarriages will reduce ethnoracial inequality in homeownership rates. Nonetheless, couples in marriages involving an ethnoracial minority spouse have lower homeownership rates than endogamous White couples. This White/non-White divide in homeownership rates highlights the need for a housing policy that enhances opportunities for homeownership among interracial and endogamous minority couples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".