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spatialeconomic.bsky.social • 81 days ago

What is the paper all about? 🤔

🚨🚨New Article Online🚨🚨

"Determinants of housing rental prices in Seoul: applying explainable AI" by Tae-Young Kim, Eunil Park & Doojin Ryu.

www.tandfonline.com/doi/full/10....

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spatialeconomic.bsky.social•81 days ago

Our study analyzes determinants of housing rental prices in Seoul using explainable AI (XAI). We combine XGBoost and TreeSHAP to identify key factors influencing Jeonse (Lease / Deposit) prices, addressing the limitations of traditional regression models. 1/5

spatialeconomic.bsky.social•81 days ago

XGBoost, a robust machine learning model, outperforms traditional methods by predicting rental prices more accurately. It effectively handles nonlinear relationships and multicollinearity in large datasets. 2/5

spatialeconomic.bsky.social•81 days ago

TreeSHAP interprets XGBoost predictions, offering insights into critical determinants like proximity to public transport, property age, and neighborhood characteristics. It highlights both global trends and local influences. 3/5

spatialeconomic.bsky.social•81 days ago

Findings reveal that machine learning models enhance understanding of housing markets. XGBoost excels in prediction accuracy, while TreeSHAP uncovers variables’ contributions, providing transparent and actionable insights. 4/5

spatialeconomic.bsky.social•81 days ago

Our approach demonstrates the value of XAI in real estate analysis, offering a reliable alternative to traditional methods. This study serves as a cornerstone for understanding and forecasting housing market trends in Seoul. 5/5

spatialeconomic.bsky.social•81 days ago

The summary was kindly provided by Doojin Ryu.

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