Estimating the value, ownership structure and turnover rate for investible commercial real estate from transaction datasets
Bibliographic record
Abstract
Purpose Commercial real estate (CRE) is a major investment asset. Yet detailed information on the value of investible CRE in different cities is lacking. The authors propose an innovative method to measure the value of investible CRE using transaction datasets. Design/methodology/approach The authors take transaction prices and index them to produce a time series of values for each asset. The sum of the values at each point represents the value of investible CRE at that date. The authors’ method is applied to transaction data for New York, London and Toronto. Findings London had the highest proportions of institutional and foreign ownership, and its turnover was more resilient to the downturn in global CRE following the GFC. The results illustrate the potential of the authors’ method to shed light on the characteristics of investible CRE markets. Research limitations/implications The authors use data from Real Capital Analytics (RCA). This provides good coverage of transactions for investible CRE in the cities that the authors examine, but data from other sources might lead to different estimates. Practical implications Measuring the value and turnover of investible CRE is important for portfolio strategies that account for the size and liquidity of investment markets. Knowledge of these features, and of ownership patterns, provides a better understanding of market operation. Originality/value The authors’ modification of the perpetual inventory technique is simple, novel and practical. The authors propose this approach given the absence of a building-by-building inventory of investible CRE in many markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".