What Drives Housing Prices Down? Evidence from an International Panel
Bibliographic record
Abstract
Summary In this study, we suggest an explanation for the low growth rates of real housing prices in Canada and Germany in comparison to other OECD countries over the period 1975-2005. We show that the long-run development of housing markets is determined by real disposable per-capita income, the real long-term interest rate, population growth, and urbanization. The differential development of real housing prices in Canada and Germany is attributed to the fundamentals in these two countries. Canada and Germany are characterized by relatively low average growth rates of real disposable income and relatively high interest rates, resulting in depressed housing prices over a long period of time. Institutional structure accentuates these tendencies. Given the importance of housing wealth for private consumption, our paper aims at drawing the attention of policymakers to the necessity of preventing not only overheating but also overcooling of the housing market that entails lower economic growth rate.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".