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Record W2169366334 · doi:10.1108/ijefm-02-2014-0007

A longitudinal study of the impact of the Sydney Olympics on real estate markets

2015· article· en· W2169366334 on OpenAlexaff
Qiang Lu, Yupin Yang

Bibliographic record

VenueInternational Journal of Event and Festival Management · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBiddingReal estateHost (biology)OriginalityPeriod (music)Value (mathematics)BusinessDatabase transactionEconomyAdvertisingEconomic geographyGeographyEconomicsMarketingFinanceSociologyQualitative research

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to examine the impact of the Sydney 2000 Olympic Games on the residential real estate markets of the host city during the bidding, pre-Olympic and post-Olympic periods. Design/methodology/approach – This study uses a difference-in-differences model to analyze the transaction prices for all properties in New South Wales, Australia for the period from 1980 to 2007. Findings – The paper finds that the impact on real estate markets varies across different suburbs in the host city and over time. The real estate markets of host suburbs experience substantially higher growth during the bidding and pre-Olympic periods but not during the post-Olympic period. However, the property prices in non-host suburbs in the host city increase at a higher rate during the pre- and post-Olympic periods but not during the bidding period. Originality/value – This study offers insights into the long-term impact of the Olympic Games on host suburbs and non-host suburbs in the host city during different periods by analyzing a large longitudinal data set over a period of 27 years.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.193

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 science0.0010.000
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 head0.063
GPT teacher head0.392
Teacher spread0.329 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2015
Admission routes1
Has abstractyes

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