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Record W4402669070 · doi:10.1016/j.jhe.2024.102025

Unpacking the relation between media sentiment and house prices: A topic modeling approach

2024· article· en· W4402669070 on OpenAlexafffundabout
Ernest N. Biktimirov, Tatyana Sokolyk, Anteneh Ayanso

Bibliographic record

VenueJournal of Housing Economics · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsUnpackingRelation (database)Computer scienceSociologyData miningLinguistics

Abstract

fetched live from OpenAlex

• General media sentiment relates to house price changes in Canada but not Australia. • Australia and Canada have similar and different topics discussed in housing media. • Sentiments of topics related to economy positively relate to house prices in Canada. • Media sentiment on rental market is positively related to house prices in Australia. • Higher market transparency may lessen relation between sentiment and house prices. This study uses a topic modeling approach to investigate the relation between news media sentiment and house price movements. By examining real estate related articles published in local newspapers of 16 major cities in Canada and Australia, we find that housing media sentiment has significant relation with future house price movements in the Canadian market but not in the Australian market. We identify the specific topics discussed in news media related to the housing market and report differences in their themes and media sentiment's predictive power between Canada and Australia. This analysis presents novel inferences of qualitative and hard-to-quantify information related to the housing market in two different countries.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.056
GPT teacher head0.223
Teacher spread0.167 · 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 designSimulation or modeling
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
Published2024
Admission routes3
Has abstractyes

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