Bibliographic record
Abstract
This paper examines the impact of consumer sentiment on sales price of housing. To account for the roles of market fundamental variables and to figure out additional effect of consumer sentiment, an estimating model is developed by carefully examining demand supply factors affecting housing market. To analyze short-run adjustment process as well as long-run movement of sales price of housing, an error-correction model is estimated using quarterly data for relevant variables. Estimation results show that the long-run movement of sales price of housing is affected by income, interest rate, expected house price appreciation rate, price of land, and degree of housing shortage. Consumer sentiment is also found to significantly and positively affect the long-run movement of sales price of housing. Estimation results also indicates that there exists an error-correction mechanism on sales price of housing. It is found that about 37% of disequilibrium is eliminated in one quarter. Short-run adjustment of sales price of housing is mainly influenced by expected house price appreciation rate and housing shortage as well as consumer sentiment which positively affects the change in sales price of housing. All these results suggest that consumers` psychological factors are inherent in Korean housing market represented by sales price of housing and that housing demand is determined not only by consumers` ability to buy but also by consumers` willingness to buy as argued by Katona(1968).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".