Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Çanakkale
Agricultural lands are known not only as agricultural production areas but also as areas with high income expectations as an investment tool. In Turkey, recent fluctuations in economic indicators such as the euro, dollar, and gold, along with increasing investment demand, have caused agricultural la...
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| Format: | Article |
| Language: | English |
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Hasan Eleroğlu
2025-05-01
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| Series: | Turkish Journal of Agriculture: Food Science and Technology |
| Online Access: | https://agrifoodscience.com/index.php/TURJAF/article/view/7379 |
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| author | Simge Doğan Levent Genç Sait Can Yücebaş Metin Uşaklı |
| author_facet | Simge Doğan Levent Genç Sait Can Yücebaş Metin Uşaklı |
| author_sort | Simge Doğan |
| collection | DOAJ |
| description | Agricultural lands are known not only as agricultural production areas but also as areas with high income expectations as an investment tool. In Turkey, recent fluctuations in economic indicators such as the euro, dollar, and gold, along with increasing investment demand, have caused agricultural land prices to rise uncontrollably. Controlling land price increases is important for preventing the misuse of agricultural lands. The sustainable management of agricultural lands and price stability are closely related to the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 and 15, “Sustainable Cities and Communities” and “Life on Land.” In this context, accurately predicting prices is important for minimizing price fluctuations in agricultural lands for investors and landowners and supporting sustainable development. In general, the Multiple Linear Regression (MLR) model is considered one of the effective traditional methods for predicting real estate prices. However, depending on the data, more reliable results can be obtained than with powerful deep learning models such as the Extreme Gradient Boosting (XGBoost) algorithm, which exhibits superior prediction performance. This study aims to compare the MLR and XGBoost algorithms to predict agricultural land prices in villages located in the central district of Çanakkale and to examine daily fluctuations in economic indicators such as the dollar, gold, and euro. The results showed that XGBoost (R2 = 0.66) has an advantage in terms of coefficient of determination values compared to MLR (R2 = 0.01). Accurate price prediction for agricultural lands will help control fluctuations in land prices. Additionally, it will support farmers and investors in making informed decisions for a sustainable agricultural economy. |
| format | Article |
| id | doaj-art-90150831742b448b8229c1ef888244d4 |
| institution | OA Journals |
| issn | 2148-127X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Hasan Eleroğlu |
| record_format | Article |
| series | Turkish Journal of Agriculture: Food Science and Technology |
| spelling | doaj-art-90150831742b448b8229c1ef888244d42025-08-20T02:17:06ZengHasan EleroğluTurkish Journal of Agriculture: Food Science and Technology2148-127X2025-05-011351109111610.24925/turjaf.v13i5.1109-1116.73796080Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of ÇanakkaleSimge Doğan0https://orcid.org/0000-0001-5085-9540Levent Genç1https://orcid.org/0000-0002-0074-0987Sait Can Yücebaş2https://orcid.org/0000-0002-1030-3545Metin Uşaklı3https://orcid.org/0009-0009-8245-5976Çanakkale Onsekiz Mart University, School of Graduate Studies, Department of Real Estate Development, 17000, Çanakkale, TürkiyeÇanakkale Onsekiz Mart University, Faculty of Architecture and Design, Department of Urban and Regional Planning, Land Use and Climate Change Laboratory (LULC-Lab), 17000, Çanakkale, TürkiyeÇanakkale Onsekiz Mart University, Faculty of Engineering, Department of Computer Engineering, 17000, Çanakkale, TürkiyeComputer Agriculture Environmental Planning (ComAgEnPlan) Working Group, 17000, Çanakkale, TürkiyeAgricultural lands are known not only as agricultural production areas but also as areas with high income expectations as an investment tool. In Turkey, recent fluctuations in economic indicators such as the euro, dollar, and gold, along with increasing investment demand, have caused agricultural land prices to rise uncontrollably. Controlling land price increases is important for preventing the misuse of agricultural lands. The sustainable management of agricultural lands and price stability are closely related to the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 and 15, “Sustainable Cities and Communities” and “Life on Land.” In this context, accurately predicting prices is important for minimizing price fluctuations in agricultural lands for investors and landowners and supporting sustainable development. In general, the Multiple Linear Regression (MLR) model is considered one of the effective traditional methods for predicting real estate prices. However, depending on the data, more reliable results can be obtained than with powerful deep learning models such as the Extreme Gradient Boosting (XGBoost) algorithm, which exhibits superior prediction performance. This study aims to compare the MLR and XGBoost algorithms to predict agricultural land prices in villages located in the central district of Çanakkale and to examine daily fluctuations in economic indicators such as the dollar, gold, and euro. The results showed that XGBoost (R2 = 0.66) has an advantage in terms of coefficient of determination values compared to MLR (R2 = 0.01). Accurate price prediction for agricultural lands will help control fluctuations in land prices. Additionally, it will support farmers and investors in making informed decisions for a sustainable agricultural economy.https://agrifoodscience.com/index.php/TURJAF/article/view/7379 |
| spellingShingle | Simge Doğan Levent Genç Sait Can Yücebaş Metin Uşaklı Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Çanakkale Turkish Journal of Agriculture: Food Science and Technology |
| title | Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Çanakkale |
| title_full | Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Çanakkale |
| title_fullStr | Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Çanakkale |
| title_full_unstemmed | Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Çanakkale |
| title_short | Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Çanakkale |
| title_sort | analyzing agricultural land price prediction using linear regression and xgboost machine learning algorithms a case study of canakkale |
| url | https://agrifoodscience.com/index.php/TURJAF/article/view/7379 |
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