Housing Defect Assessment through Household Scale and General Contractor Level
Bibliographic record
Abstract
Consumer dissatisfaction and damage are increasing worldwide due to the increase in defects caused by the decline in housing quality, and disputes over housing defects are expanding. The number of housing units, a representative standard related to housing quality, is used in Canada, Japan, and Korea. Generally, quality costs increase as the number of housing units increases, and each country’s laws apply stricter management standards. Therefore, the quality is expected to be better as the number of units increases. In 2020, South Korea added a new regulation requiring inspections by a quality inspection team by a public institution only when building housing complexes with more than 300 households. There is a debate about whether this direction of regulation is appropriate. This study examines whether the number of households is being used appropriately as a criterion related to housing quality. It aims to determine whether the limit of 300 households is appropriate for distinguishing housing quality. In addition, since the contractor’s role is vital in housing construction, the contractor’s capabilities and supply–demand relationship were also considered as factors affecting housing quality. The ratio of defect repair costs to construction costs was used as a quality measure for 285 housing complexes in Korea. Generally, the lower the defect repair–construction costs ratio, the better the quality. A comparative study was conducted through a variance analysis on the scale of 300 households and the status of the contractor’s capability, whether they were among the top 10 construction companies with excellent construction performance, and whether a sole contract was made. The results showed that the quality was better in the cases with 300 or more households than in the cases with fewer than 300 households. The quality was better in the cases built by higher-ranking contractors than in those built by other contractors, but there was no difference according to supply-and-demand relationships. The results of the comprehensive analysis indicated that the quality was better when higher-ranking contractors built housing complexes with 300 or more households than when lower-ranking contractors built housing complexes with fewer than 300 households. Therefore, the direction of the Korean regulation requiring quality inspections for housing complexes with more than 300 households is incorrect and should be improved to regulate housing complexes with fewer than 300 households, and of low quality. In addition, the standard of determining housing quality based solely on the number of households should be revised, and the direction should be changed to strengthen quality control and the public supervision of housing built by low-capacity contractors. If the results of this study are utilized with this view in mind, a reasonable system to protect housing consumers will be promoted.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".