Bibliographic record
Abstract
Purpose The aim of this study is to analyse the effect of conventional and unconventional monetary policy shocks on housing price dynamics in Europe (2000–2020). We propose a pan-European comparative analysis at a city market level, contrary to the previous literature. Design/methodology/approach We build a quarterly market dataset for 13 European cities (Paris, Lyon, Marseille, Berlin, Munich, Frankfurt, Amsterdam, Madrid, Barcelona, Seville, London, Birmingham and Manchester). We proceed in two steps. First, we develop a structural VAR (vector autoregression) model. Second, we conduct a forecast error variance decomposition analysis. Findings We show that a contractionary policy rate has a negative influence on house prices with relevant differences. A balance sheet shock displays a heterogeneous effect on housing prices. Globally, we observe that a conventional monetary policy shock explains a larger share of total housing price variance than an unconventional monetary policy shock. Finally, our results report that conventional and unconventional monetary policy shocks have a greater impact in more liberalized credit markets. Originality/value We develop a pan-European analysis of house prices at a market level for a sample of 13 European cities. A parsimonious structural VAR model is used to study the dynamics of conventional and unconventional monetary policies on house prices in major European markets: Paris, Lyon, Marseille, Berlin, Munich, Frankfurt, Amsterdam, Madrid, Barcelona, Seville, London, Birmingham and Manchester. Our results highlighting the relative importance of conventional and unconventional monetary shocks, identify the existence of heterogeneous effects of monetary policies in European city markets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".