Development of a Weighted Average Ensemble Model for Predicting Officially Assessed Land Prices Using Grid Map Data and SHAP

Development of a Weighted Average Ensemble Model for Predicting Officially Assessed Land Prices Using Grid Map Data and SHAP

This study proposes a weighted average ensemble model to predict the Officially Assessed Land Price in Sejong City, South Korea, using 500m <inline-formula> <tex-math notation="LaTeX">$\times 500$ </tex-math></inline-formula>m grid-based spatial data. The model anal...

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Bibliographic Details
Main Authors: Surin Im, Kangmin Kim, Geunhee Lee, Hoi-Jeong Lim
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Grid data
gradient boosting
LightGBM
XGBoost
officially assessed land price
SHAP
Online Access:https://ieeexplore.ieee.org/document/11017662/
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Summary:This study proposes a weighted average ensemble model to predict the Officially Assessed Land Price in Sejong City, South Korea, using 500m <inline-formula> <tex-math notation="LaTeX">$\times 500$ </tex-math></inline-formula>m grid-based spatial data. The model analyzes the impact of key variables through SHAP for improved interpretability. Independent variables were grouped into three categories: building characteristics, population demographics, and accessibility to social infrastructure. To construct the predictive model, three boosting algorithms&#x2014;Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and LightGBM&#x2014;were evaluated. The optimal ensemble model, weighted as GBM:XGBoost:LightGBM =0:9:1, achieved a high predictive performance with R2 of 0.8964. SHAP analysis revealed five major factors influencing land price: floor area ratio, accessibility to fire stations, total male population, number of reinforced concrete buildings, and accessibility to general hospitals. The model&#x2019;s outputs were visualized through a grid-based land price map, revealing elevated land values within Sejong&#x2019;s Multifunctional Administrative City and around key transportation hubs. By employing fine-scale spatial data and enhancing model interpretability through SHAP, this research presents a robust empirical framework that addresses the limitations of traditional administrative-bound analyses. The findings provide valuable insights for data-driven policy formulation and the evaluation of the real estate market.
ISSN:2169-3536

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