How Brexit changed the dynamics of UK commercial real estate: evidence from the roles of domestic and foreign monetary policies
Bibliographic record
Abstract
Purpose The main objective of this article is to investigate the impact of conventional and unconventional monetary policies led by the Bank of England, the ECB and the Fed on the dynamics of British commercial real estate markets between 2000 and 2023, using vector autoregression and structural vector autoregression (SVAR) models. We propose comparative analysis of eight commercial real estate markets, including financial and REIT markets, contrary to the previous literature. Design/methodology/approach We consider the dynamics and returns of eight UK commercial real estate markets on a monthly basis, shedding new light on the monetary policies’ impact over 24 years. We proceed in three steps. First, we develop a structural VAR model. Second, we report the impulse response analysis. Third, we focus on the relative impact of the Brexit. Findings We show that monetary policies have a highly significant impact on eight British commercial real estate sectors, with different implications. Favourable policies from the BoE and Fed have a positive impact on the returns of all asset classes, while the ECB has an investment drain effect. As an illustration of financialization, REITs seem to be a transmission channel for all asset classes. However, Brexit appears to mark a major turning point as the BoE and BCE monetary policy effects drop sharply after 2016. Practical implications These results are valuable for investors, as the central bank’s monetary policies significantly affect the performances of their portfolio. They could also be beneficial to the central banks themselves, as we have entered a new policy cycle in 2024, and its effect on UK real estate remains uncertain. Originality/value A parsimonious structural VAR model is used to study the dynamics of conventional and unconventional monetary policies led by the BoE, the ECB and the Fed on eight UK commercial real estate markets’ returns during 24 years, marked by important breaking points: Office City, Office Mid Town & West End, Office Rest of South East, Office Rest of the UK, Retail South East, Retail Rest of the UK, Industrial South East and Industrial Rest of the UK. Our results, highlighting the relative importance of conventional and unconventional monetary shocks, identify the existence of heterogeneous effects of monetary policies on the different markets, shedding new light on the transmission channel played by the REITs and stocks markets.
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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 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".