Financialisation and participation in the metropolisation dynamics of European-listed property companies
Bibliographic record
Abstract
Purpose This paper aims to explore the relationship between the financialisation dynamics of listed property companies (LPCs) and their participation in the metropolisation dynamics, in ten European countries between 2000 and 2017. The study takes place in a context of globalised real estate markets and modification of traditional urban economics. Design/methodology/approach The measure of financialisation corresponds to a beta increase, in the sense of the capital asset pricing model, and is corroborated by an informativeness index. LPC-owned properties are classified along two spatial segmentations. Panel models are used to analyse the relation between financial and urban hierarchies (through building arbitrages). Findings Financialisation is generally associated with a decrease in the number of assets owned, especially in the Netherlands and the UK, whereas non-financialised companies tend to increase their number of assets, especially in “flight-to-quality” countries such as Germany and Switzerland. In the first case, non-urban spaces and small and medium urban areas are arbitraged in favour of urban cores and metropoles. In the second, investments are reallocated towards hinterlands and the lower segments of the urban hierarchy. Over the study period, the parallelism between the financial hierarchy and the urban hierarchy was reinforced. Spain illustrates the risks of this evolution, whereas Sweden and Belgium present specificities. Originality/value This paper illustrates how LPCs function as transmitting channels in the new spatial and urban organisation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".