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Record W3201897753 · doi:10.53383/100033

International Real Estate Review

2001· article· en· W3201897753 on OpenAlexaboutno aff
Tien Foo Sing

Bibliographic record

VenueInternational Real Estate Review · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)EconomicsReal estateInflation (cosmology)Stock (firearms)Interest rateSupply and demandMonetary economicsAgricultural economicsFinancial economicsFinanceMacroeconomicsEngineering

Abstract

fetched live from OpenAlex

This study examines economic and market factors that drive the demand, supply, and pricing of condominiums in Singapore using a 2-stage least squares regression methodology. This empirical study covers a sample period of 12 years from 1988 to 2000. The condominium housing demand model showed that GDP growth and the inflation rate had positive relationships with condominium demand one quarter ahead. However, demand for condominiums was negatively related to one-quarter lagged stock price change, two-quarter lagged condominium housing price change, lagged demand in the previous two quarters, and one-quarter lagged household formation. On the supply side, changes in last-quarter condominium housing stock, condominium commencement, the prime lending rate, and current and lagged-quarter labor costs would adversely affect developers?decisions to commence new condominium projects. In the condominium price model, the dummy variable used to test the effects of the government’s anti-speculation policies in May 1996, which increased the supply of residential lands and restricted the loan quantum to a limit of 80% of the housing price, was significant and positive. It implied that the policies were effective in dampening condominium prices by 0.32% per quarter for two consecutive quarters in 4Q1996 and 1Q1997.

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), Insufficient 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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0040.003

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 head0.044
GPT teacher head0.291
Teacher spread0.247 · 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

Citations10
Published2001
Admission routes1
Has abstractyes

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