Bibliographic record
Abstract
This study sought to identify the characteristics of price changes between types of houses in Seoul's housing market based on the theory of housing sub-market. To this end, we secured the differentiation of the research through prior research on the housing sub-market, and established time series data from the second quarter of 2006 to the fourth quarter of 2018 for apartment prices, detached and multi-family housing prices and officetel prices in each of the five areas of Seoul using the data of 114 Reps. In addition, the EVIEWS 8.0 program resulted in the following meaningful results through empirical analysis of price fluctuation characteristics by region. First, the results of the Grandeur-In-relationship test showed that there exists a causal relationship in all five areas where apartment prices affect different price types. Second, the results of the shock response analysis and variance decomposition analysis in the five zones showed that the impact of the prices of detached and multi-family homes on apartment prices was high. Third, the results of the shock response analysis and dispersion analysis in the five areas showed that the impact of the apartment price on the officetel price was low. The significance of this study can be found in that it identified the need for various detailed studies of the housing sub-market by conducting a study on the characteristics of price changes by type of housing for the purpose of a detailed understanding of the housing market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.064 |
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; both teacher heads agree on what is shown here.
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".