Paradoxes of American and German Housing Policy
Bibliographic record
Abstract
Chapter 8 highlights the paradoxes of American and German housing policymaking amid surging house prices during the 2010s and early 2020s. American housing programs reinforced demand-led growth but also fueled financial bubbles and economic turmoil. In the post-2008-2009 period, this pattern persisted as policymakers continued stimulating housing-based growth, which simultaneously contributed to skyrocketing house prices, fears of a housing bubble, and an affordability crisis. In contrast, German policymakers retrenched housing programs that once supported the country's export-oriented growth regime by deflating housing costs. Consequently, they deprived themselves of the tools to respond to rapidly rising housing costs and affordability problems of recent years that risked fueling inflation and wage demands detrimental to export competitiveness. The conclusion of this book extends the broader lessons beyond the United States and Germany to such countries as Austria, Canada, the Netherlands, Sweden, and the United Kingdom, illustrating how these countries' different growth regimes channel housing policymaking in different directions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".