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Title |
Re-Evaluating the Premium Effect of Urban Green Space |
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Author |
Duogui Yang, Junyi Guo |
| Citation |
Vol. 26 No. 10 pp. 129-131 |
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Abstract |
Urban green space (UGS) delivers ecological and livability benefits and creates housing price premiums. Quantifying UGS related premiums and their spatial heterogeneity is critical for green space layout and refined urban governance. Constrained by linear assumptions and flawed parameter tuning, existing research mostly confirms premium existence rather than quantifying scale dependent premium discrepancies. Using 2591 residential POI samples from Huangdao District (Qingdao), this study applies PSO RF and WKmeans SMOTE hybrid machine learning models for empirical analysis. Results reveal the feature contribution order: greening rate > straight line distance to green space > actual distance to green space. The premium exhibits notable scale heterogeneity: greening rate governs premiums for large scale UGS, whereas accessibility matters more for small scale UGS. Higher green space effect intensity corresponds to larger housing premiums, alongside stronger price dispersion for large scale UGS. The models overcome traditional methodological limits, offering empirical evidence for green space planning and sophisticated real estate regulation. |
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Keywords |
urban green space, housing price premium effect, spatial heterogeneity, machine learning |
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