Money supply, exchange rates and office market dynamics: comparative evidence from the UK and Germany
Bibliographic record
Abstract
Purpose The aim of this study is to shed light on the relative importance of money supply and exchange rates variations on office markets prices dynamics. Design/methodology/approach Using a parsimonious real estate asset pricing model, the authors focus on the two biggest European office markets; namely the United Kingdom and Germany. The authors use a panel approach based on a robust econometric methodology (GMM with correction errors-in-variables). The authors take into account the variations of exchange rates and money supplies for the most important currencies. Findings The results highlight the impact of money supplies and exchange rates on office prices after the Global Financial Crisis. The authors report that the monetary policies in the UK and in Germany (Euro zone) have had significant influences in the real estate sector after the Global Financial Crisis. However, the authors identified significant differences between British and German office markets for the 2009–2019 period regarding the impact of money supply and exchange rates variations on the office prices dynamics. Practical implications The results highlight the impact of money supplies and exchange rates on office prices after the Global Financial Crisis. The detailed and exclusive database (composed of the main office markets in the United Kingdom and in Germany) allows the authors to identify significant differences and opportunities for investors. Originality/value The authors use a parsimonious model and apply a panel approach based on a robust econometric methodology to analyse the impact of exchange rates and money supply variations on the office prices dynamics. The detailed and exclusive database (composed of the main office markets in the United Kingdom and in Germany) allows the authors to identify significant differences for investors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 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; 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".