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Record W4254380633 · doi:10.53383/100214

International Real Estate Review

2016· article· en· W4254380633 on OpenAlexaff
William Mingyan Cheung, James Chicheong Lei

Bibliographic record

VenueInternational Real Estate Review · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsMcGill University
FundersUniversidade de Macau
KeywordsReal estateReal estate investment trustPrice discoveryDatabase transactionBusinessCapitalization rateFinancial economicsCorporate Real EstateInvestment (military)Cost approachTransaction costEconomicsFinanceMonetary economicsComputer scienceDatabaseFutures contract

Abstract

fetched live from OpenAlex

This study examines whether property transaction affects the price discovery process in real estate markets. Prior literature shows that price discovery generally first takes place in the securitized public real estate investment trust (REIT) market. We conjecture that property transaction provides novel information to the direct real estate market and can change the dynamics between public and private real estate returns. We employ a unique dataset of property transactions to construct "transaction windows¨ and specifically examine the causality between public and private real estate markets around these periods. We form firm-level pairs of public and private price series, and estimate the normalized common factor loadings per Gonzalo and Granger (1995) by using a vector error-correction model. Our findings show that a significant proportion of price discovery happens in the private market instead of the public REIT market. Our results are robust to investments of different property types and different lengths of transaction windows. Overall, the findings in this study imply that property acquisition and disposition provide crucial information to the private real estate market and induce a reverse causality between the public and private 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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.004

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.

Opus teacher head0.038
GPT teacher head0.282
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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
Published2016
Admission routes1
Has abstractyes

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