REAL ESTATE ECONOMICS The Other Side of Eight Mile: Suburban Population and Housing Supply
Bibliographic record
Abstract
This article establishes a linkage between decadal changes in suburban pop-ulation and the supply of suburban dwelling units. It then estimates an econo-metric supply-and-demand model for 317 U.S. suburban areas for the 1970s, 1980s, and 1990s using the State of the Cities database. Suburban supply is more elastic than central city supply, with suburban estimates between +1.26 and +1.42. However, separate estimates by geographic region lead to sup-ply elasticities of +0.89 for the northeastern quadrant of the United States and +1.86 for the remainder of the United States. This article addresses issues of population change and housing supply in U.S. suburbs. Central cities often have only limited opportunities for new construc-tion, while surrounding suburbs “beyond Eight Mile Road ” may have consid-erable vacant land to accommodate new employers and new residents.1 This
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".