Predicting the Non-Return of Chonsei Lease Deposits in the Republic of Korea
Bibliographic record
Abstract
Chonsei, a Korean housing lease system, enables landlords to acquire direct housing purchase funds without mortgages and offers tenants a cost-effective rental option. However, public concerns have arisen about potential landlord defaults, causing financial distress for tenants. This study examined the risk of non-return of the Chonsei deposit and developed a default prediction model using Chonsei contract data from the Korea Housing and Urban Guarantee Corporation. Starting with the components from Merton’s bond pricing model, we included variables that reflect contract-specific factors, macroeconomic conditions, and the Korean Chonsei practices. The findings revealed that higher house price volatility, elevated debt-to-house value, and risk-free interest rates positively correlate with non-return risk. Meanwhile, certain factors, such as longer remaining maturity, favorable macroeconomic conditions, and rising market Chonsei price trends, demonstrated negative correlations with non-return risk. Consequently, a logistic regression-based default prediction model, with eight risk factors that predict the deposit non-return, was suggested. By identifying risk factors and predicting the non-return risk of deposits, this study contributes to an informed policy decision in planning and practicing Chonsei contracts in the Korean housing market.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".