用机器学习预测加纳房租，准确率超87%。
Predicting House Rental Prices in Ghana Using Machine Learning
- 用CatBoost等模型分析房源数据，捕捉房价复杂关系。
- 模型R²达0.876，位置、房间数和装修状态是关键因素。
- 适合房产从业者、投资者及城市政策制定者参考。
本研究探讨机器学习模型在预测加纳房屋租金中的有效性，旨在解决住房市场信息不准确、难获取的问题。基于全面的租金挂牌数据，我们训练并评估了多种模型，包括CatBoost、XGBoost和随机森林。其中CatBoost表现最佳，实现R²为0.876，表明其能有效捕捉住房市场的复杂关系。特征重要性分析显示，地理位置特征、卧室数量、卫生间数量及装修状况是影响租金价格的关键因素。研究结果为房地产专业人士、投资者和政策制定者提供了有价值的信息，同时也指出了未来研究方向，如引入时间序列数据和探索区域差异。
原文摘要 · Abstract (English)
This study investigates the efficacy of machine learning models for predicting house rental prices in Ghana, addressing the need for accurate and accessible housing market information. Utilising a comprehensive dataset of rental listings, we trained and evaluated various models, including CatBoost, XGBoost, and Random Forest. CatBoost emerged as the best-performing model, achieving an $R^2$ of 0.876, demonstrating its ability to effectively capture complex relationships within the housing market. Feature importance analysis revealed that location-based features, number of bedrooms, bathrooms, and furnishing status are key drivers of rental prices. Our findings provide valuable insights for stakeholders, including real estate professionals, investors, and policymakers, while also highlighting opportunities for future research, such as incorporating temporal data and exploring regional variations.
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