Long-Term Vacant Housing in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Because housing is durable, the housing supply is slow to adapt to declines in demand. This paper uses long-term vacancy—defined as nonseasonal housing units that have been vacant for an unusually long period of time—to quantify the extent of excess supply in the housing market. I find that long-term vacancy is less than 2 percent of all nonseasonal housing units and accounts for only one quarter of the aggregate increase in nonseasonal vacancy from 2001 to 2011. Thus, at the national level, excess supply is considerably less extensive than indicated by traditional measures of vacancy. However, the stock of long-term vacant housing is concentrated in a small number of neighborhoods that do have appreciably high long-term vacancy rates. Some of these neighborhoods have characteristics suggesting that excess supply is related to overbuilding during the housing boom, while others have characteristics that are symptomatic of persistently weak housing demand.
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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 | 0.001 | 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 | 0.001 |
| 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 it