Determination of the relationship between housing characteristics and housing prices before and after the Kahramanmaraş earthquake using machine learning: A case study of Adana, Turkiye
| dc.authorid | Doğan, Simge / 0000-0001-5085-9540 | |
| dc.authorid | Genç, Levent / 0000-0002-0074-0987 | |
| dc.authorid | Yücebaş, Sait Can / 0000-0002-1030-3545 | |
| dc.authorid | Uşaklı, Metin / 0009-0009-8245-5976 | |
| dc.contributor.author | Doğan, Simge | |
| dc.contributor.author | Genç, Levent | |
| dc.contributor.author | Yücebaş, Sait Can | |
| dc.contributor.author | Uşaklı, Metin | |
| dc.contributor.author | Dumlu, Cengizhan | |
| dc.date.accessioned | 2025-01-27T20:55:52Z | |
| dc.date.available | 2025-01-27T20:55:52Z | |
| dc.date.issued | 2024 | |
| dc.department | Çanakkale Onsekiz Mart Üniversitesi | |
| dc.description.abstract | Earthquakes have a significant impact on the real estate sector. Damage caused by earthquakes leads to an imbalance in the supply and demand for housing, thus temporarily causing stagnation in the real estate sector. Two earthquakes occurred in Pazarcık and Elbistan districts of Kahramanmaraş on February 6, 2023 at 04.17 am with a magnitude of 7.7 and 13.24 am with a magnitude of 7.6. A machine learning based model was created to analyze the change in house prices and the variables affecting the price during the earthquake, which is called “the Disaster of the Century”. After the earthquake, the prices of houses for sale in the central districts of Adana province (Seyhan, Yüreğir, Sarıçam and Çukurova) where there was the least damage were collected from the relevant website with a web scraper. These data were classified as categorical and numerical datasets, and the necessary pre-processing stage for machine learning algorithms was performed. The characteristics that change and are effective in housing preferences before the earthquake (February 2022) and after the earthquake (February 2023) were determined by the decision tree method, which is one of the machine learning algorithms. In this context, it is aimed to determine the housing variables that are effective in before and after-earthquake pricing in the central districts of Adana province. In the study, while 'Building Age and Number of Room’ are effective in determining the price in 2022; 'Housing shape and Facade' feature comes to the fore in 2023. The housing characteristics that affect the price change in two years. The change in housing preference criteria after the earthquake shows that the lifestyle in cities has also changed. According to this change, it requires the development of new approaches in urban design and planning approaches and is expected to be a reference for future studies. | |
| dc.identifier.doi | 10.14744/megaron.2024.22316 | |
| dc.identifier.issn | 1309-6915 | |
| dc.identifier.issue | 2 | |
| dc.identifier.trdizinid | 1282648 | |
| dc.identifier.uri | https://doi.org/10.14744/megaron.2024.22316 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/tr/yayin/detay/1282648 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12428/26217 | |
| dc.identifier.volume | 19 | |
| dc.identifier.wos | WOS:001263111600012 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | TR-Dizin | |
| dc.language.iso | en | |
| dc.publisher | Yildiz Technical Univ, Fac Architecture | |
| dc.relation.ispartof | Megaron | |
| dc.relation.publicationcategory | info:eu-repo/semantics/openAccess | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_WoS_20250125 | |
| dc.subject | Adana | |
| dc.subject | earthquake | |
| dc.subject | housing preference criteria | |
| dc.subject | machine learning | |
| dc.subject | real estate | |
| dc.title | Determination of the relationship between housing characteristics and housing prices before and after the Kahramanmaraş earthquake using machine learning: A case study of Adana, Turkiye | |
| dc.type | Article |
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