Shuohan Jiang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shuohan Jiang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Jing Li
College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
1 shared publication - Wanxin Li
College of Sciences, North China University of Science and Technology, China
1 shared publication - Xiaolin Sun
College of Civil and Architectural Engineering, North China University of Science and Technology, Tangshan 063210, China
1 shared publication - Zezhong Wang
College of Civil and Architectural Engineering, North China University of Science and Technology, Tangshan 063210, China
1 shared publication
Latest Publications
- Open Access
- 3 March 2026
- Published by MDPI AG in Preprints.org
Beyond GWR: A Spatial Clustering and Partitioned Stacking Framework for Capturing Nonlinear Locational Premiums and Submarket Specificity in Real Estate Valuation
Based on a spatial clustering and partitioned stacking ensemble model, this study addresses the limitations of traditional geoweighting regression in capturing nonlinear location premiums and submarket heterogeneity within urban real estate markets. It proposes a two - stage modeling framework: “spatial clustering → partitioned differentiated stacking ensemble.” Using long - term multi - source transaction data for Beijing's secondary housing market, the study divides the market into three spatially heterogeneous submarkets: core, near - suburban, and far - suburban. Stacked ensemble models based on ElasticNet, XGBoost, LightGBM, and Random Forest are constructed within each submarket.Factor analysis extracts interpretable common factors, which are combined with Lasso and SHAP for feature selection and impact mechanism analysis.Results indicate that the zoned stacking model performs exceptionally well across all three submarkets, achieving an R² of 0.916 in the core urban area. Significant nonlinear location premiums exist within the core urban area.The multi - level interpretability framework reveals the differentiated effects of location and scale factors across different submarkets.This study advances from “global modeling” to “spatial zoning + adaptive ensemble,” providing a viable tool for refined valuation and risk management in highly heterogeneous markets.
- 3 March 2026
- Published by MDPI AG in Preprints.org
Open AccessBeyond GWR: A Spatial Clustering and Partitioned Stacking Framework for Capturing Nonlinear Locational Premiums and Submarket Specificity in Real Estate Valuation
Based on a spatial clustering and partitioned stacking ensemble model, this study addresses the limitations of traditional geoweighting regression in capturing nonlinear location premiums and submarket heterogeneity within urban real estate markets. It proposes a two - stage modeling framework: “spatial clustering → partitioned differentiated stacking ensemble.” Using long - term multi - source transaction data for Beijing's secondary housing market, the study divides the market into three spatially heterogeneous submarkets: core, near - suburban, and far - suburban. Stacked ensemble models based on ElasticNet, XGBoost, LightGBM, and Random Forest are constructed within each submarket.Factor analysis extracts interpretable common factors, which are combined with Lasso and SHAP for feature selection and impact mechanism analysis.Results indicate that the zoned stacking model performs exceptionally well across all three submarkets, achieving an R² of 0.916 in the core urban area. Significant nonlinear location premiums exist within the core urban area.The multi - level interpretability framework reveals the differentiated effects of location and scale factors across different submarkets.This study advances from “global modeling” to “spatial zoning + adaptive ensemble,” providing a viable tool for refined valuation and risk management in highly heterogeneous markets.