| Title |
Assessing Issues of Apartment Housing Defect Cases Using Text Mining |
| Authors |
방홍순(Bang, Hong-Soon) ; 허한결(Heo, Han-Kyul) ; 김옥규(Kim, Ok-kyue) |
| DOI |
https://doi.org/10.5659/JAIK.2022.38.10.293 |
| Keywords |
Text-Mining; An Apartment House; Defect Judgment; Issue; Analysis |
| Abstract |
In response to improper apartment housing maintenance practices, residents can file defect complaints or defect dispute litigations against
construction companies. Legal arguments can then be made to address conflicting claims regarding housing and maintenance standards along
with repair methods. A considerable amount of money and time are typically spent in these legal matters. To address these problems,
conflicting claims and other controversial issues must first be resolved before a defect complaint or defect dispute litigation is issued. In this
study, 61 court rulings were examined to analyze the controversies related to apartment housing construction defects. Keywords were derived
from these cases using a Python program. The derived keywords were classified by work type, location, cause, and type. The main issues
related to work type in housing defect court rulings were tiles, fire doors, paint, trees, and facilities. The data related to these items were
compared with defect repair data. Analysis of the court cases of main work types and defect repair data revealed that various construction
types such as tiles, wallpaper, equipment, and furniture overlapped. Our analysis revealed that if the derived work types are systematically
managed, defect disputes and defect filings could be reduced. |