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Record W2889133148 · doi:10.15396/eres2018_168

Commercial Property Cycles and Sub-market Emergence in Selected Canadian Cities.

2018· article· en· W2889133148 on OpenAlexaboutno aff
Neil Dunse, Colin Jones, Terry Brooke

Bibliographic record

Venue25th Annual European Real Estate Society Conference · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsProperty marketInvestment (military)BusinessReal estate investment trustPrimary marketFinanceResidential propertyReal estateIndustrial organizationEconomicsEconomic geographyGeography

Abstract

fetched live from OpenAlex

The research examines office property markets cycles in four Canadian cities - Calgary, Edmonton, Vancouver and Toronto. Each of the four cities has a different economic base and, as a result, have potentially significantly different commercial property development and investment cycles. The analysis examines cycles in annual office building construction in each market on a building by building basis over the past 100 years. The analysis of investment cycles is based on office building sales transactions and quarterly market rent data over the past 20 to 25 years. The four cities are shown to be rarely in the same phase of a development/investment cycle. There are significant differences in the primary office and industrial user groups that shape these markets and affect market cycles. The same major investment firms, pension funds and REITs, seek large office and industrial properties in all four cities as each progress through a complete development and investment cycle. However, each market also has a significant component of regional and local investors. The role of property market cycles in the emergence and changes in office sub-markets is also examined.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.211
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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