Housing Affordability: Local and National Perspectives
Bibliographic record
Abstract
This paper presents a new approach to measuring affordable homeownership. Future changes in the homeownership rate will depend on the ability of today’s renters to become homeowners. Our proposed housing affordability for renters index (HARI) focuses on how affordable homeownership is for current renters. We look at the share of renters who reported the same or more income than those who recently purchased a home using a mortgage, in effect measuring how many renters have enough income to purchase a house. For each metropolitan statistical area (MSA), we construct a local area index that compares renters and borrowers in the same MSA and a national index that compares renters nationwide with homeowners in a specific MSA. We rely on the Administrative Data Research Facility to construct these indices. This database, constructed by the Urban Institute, aggregates American Community Survey variables and Home Mortgage Disclosure Act variables to common geographies. The new indices reveal that slightly more than a quarter of current US renters have incomes higher than those who recently became homeowners using a mortgage. The indices also reveal how housing affordability differs over time and across race/ethnicity groups and locations. We demonstrate the value of our new indices by showing that they are predictive of homeownership rates: MSAs that are deemed more affordable by our index have higher homeownership rates.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".