Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.101 | 0.042 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".