Long run relationships between City office rents and the economy in the UK: creating a database for research
Bibliographic record
Abstract
This paper sets out progress during the first eighteen months of doctoral research into the City of London office market. The overall aim of the research is to explore relationships between office rents and the economy in the UK over the last 150 years. To do this, a database of lettings has been created from which a long run index of City office rents can be constructed. With this index, it should then be possible to analyse trends in rents and relationships with their long run determinants. The focus of this paper is on the creation of the rent database. First, it considers the existing secondary sources of long run rental data for the UK. This highlights a lack of information for years prior to 1970 and the need for primary data collection if earlier periods are to be studied. The paper then discusses the selection of the City of London and of the time period chosen for research. After this, it describes how a dataset covering the period 1860-1960 has been assembled using the records of property companies active in the City office market. It is hoped that, if successful, this research will contribute to existing knowledge on the long run characteristics of commercial real estate. In particular, it should add a price dimension (rents) to the existing long run information on stock/supply and investment. Hence, it should enable a more complete picture of the development and performance of commercial real estate through time to be gained. \n
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".