Bibliographic record
Abstract
Purpose The aim of the study is to utilize cointegration techniques and analyze the degree of linkages among four key property types (retail, office, industrial, and residential) of eight major countries throughout North America and Europe. Additionally, the study evaluates whether investors can attain greater diversification benefits by investing across specific property sectors within their own nations in the long‐run. Finally, the study examines whether certain property sectors can be considered the “leader” that drives the remaining sectors over time. Design/methodology/approach Multivariate cointegration tests developed by Johansen and Johansen and Juselius are utilized to evaluate whether long‐run equilibrium relationship(s) exist among the four property sectors. If evidence of cointegration is found, hypothesis tests are implemented to separate out the markets that can be excluded from the cointegrating relationships and to identify the markets that are the sources of the common trends (weakly exogenous), respectively. Findings Long‐run cointegration results indicate that the four property sectors of the USA, Canada, Netherlands, and the UK have fully converged implying limited diversification possibilities. The property sectors of Finland, France, Germany and Sweden, however, have only partially converged. Further analysis reveals that for these four countries, the industrial sectors provide the greatest long‐run diversification benefits. Finally, weak exogeneity tests indicate that for an overwhelming majority of the countries under consideration, the residential sectors are the sources of the common stochastic trends, that “lead” the remaining property types towards the long‐run equilibrium relationships. Practical implications The conclusions from this study should be beneficial to investors, portfolio managers, pension fund managers and other institutional investors in the USA and abroad who are contemplating to invest across property sectors within their own countries in making more informed portfolio allocation decisions. The findings also highlight the importance of implementing time‐series econometric techniques to accurately and appropriately model interactions among property sectors over time. Originality/value This is one of the few studies that utilize modern‐day timeseries techniques to analyze the dynamic interactions among the property sectors of eight major nations throughout North America and Europe. Prior studies, have been limited to modeling interrelationships between the property sectors of the USA and UK, with little attention given to other major real estate 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 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 | 0.000 | 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 | 0.001 |
| 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".