Analysis on the Structural Change of Chonsei and Monthly Rent Market
Bibliographic record
Abstract
국가적으로 전세난이 심화되어 사회문제로 대두되어 본 연구는 우리나라 주택 전월세 시장 구조변화와 정부의 정책에 대해, 임차인, 임대인, 공인중개인 등 시장참여자 의견을 분석하고자 한다. 전월세 시장 특성 분석을 위해 지역 및 거주 유형별로 약 2,000여명의 설문조사 결과를 바탕으로 임차인, 임대인의 의식변화와 차이를 진단하였고, 임대인, 임차인, 전문가 대상의 조사를 통해 분석된 결과를 바탕으로 주택 전세시장 및 주거 안정화를 위한 정책적 시사점을 도출하였다. 본 연구의 기대효과로는 정책 수요자 및 공급자, 중개인에 대한 의겸 수렴과 반영을 통해 정책에 대한 신뢰도 및 효용도를 제고할 수 있고, 전월세 시장의 주거 안정화를 위한 정책 제안을 할 수 있다. This paper analyzes characteristics and changing phenomenon of Chonsei & monthly rental real estate market based on a survey of participants. The frequency analysis, mean analysis, and cross-table analysis was utilized for this study, and a survey for 2,000 rental market participants was accomplished. The survey result of tenants and land-owners regarding the Chonsei market stability indicates the expansion of public housing supply is needed together with lowering of mortgage related restrictions. The major conclusions include that the expansion of public housing and rental supports for low-income families are needed, due to the population structural change and an aging society. In addition, survey result suggests the tenants require the expansion of public housing supply for the residence stability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".