Housing affordability, borrowing constraints and tenure choice in Korea
Bibliographic record
Abstract
This paper analyses the linkages between housing affordability and borrowing constraints, and the impact of the latter on housing tenure decisions in Korean cities during the period 2006–2016. The conventional aggregate measures of housing affordability provide a mixed picture. The price to income ratio (PIR) increased in major cities but the home purchase affordability, or repayment affordability improved between 2006 and 2016. Also, the access to mortgage finance increased substantially thanks to the expansion of mortgage supply, low interest rates and lenient macroprudential regulations. We investigate whether and how these changes affected household home purchases using the household-level micro data from the 2006 and 2016 Korea Housing Surveys. We construct the variable indicating the degree of borrowing constraint in terms of wealth each household faces and show that the number of borrowing-constrained households dropped significantly between 2006 and 2016. We then investigate the effect of the wealth constraint by including a measure of the degree of the borrowing constraint as an explanatory variable in the tenure choice equation, together with other key variables such as the cost of owning relative to renting. Estimation results confirm that, as expected, the propensity to home purchases declines if households are judged as wealth-constrained. It was also found that the detrimental effect of the wealth constraint on home ownership increased significantly, particularly for severely constrained households, while the homeownership rates for others improved during our study period. Our findings suggest that affordability measures that do not consider borrowing constraints may not be very helpful in gauging affordability of home purchases at the household level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".