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Evaluating the Effects of Landscape on Housing Prices in Urban China

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  • Qingyun Du
  • Chao Wu
  • Xinyue Ye
  • Fu Ren
  • Yongjun Lin

Abstract

The rapid urbanisation of China has received growing attention regarding its urban residential environments. In this article, we model the spatial heterogeneity of housing prices and explore the spatial discrepancy of landscape effects on property values in Shenzhen, a large Chinese city. In contrast to previous studies, this paper integrates the official housing transaction records and housing attributes from open data along with field surveys. Then, the results using the hedonic price model (HPM), geographically weighted regression (GWR) without landscape metrics and GWR with landscape metrics are compared. The results show that GWR with landscape metrics outperforms the other two models. In summary, this research provides new insights into landscape metrics in real estate studies and can guide decision‐makers plan and design cities while also providing guidance to regulate and control urban property values based on local conditions.

Suggested Citation

  • Qingyun Du & Chao Wu & Xinyue Ye & Fu Ren & Yongjun Lin, 2018. "Evaluating the Effects of Landscape on Housing Prices in Urban China," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 109(4), pages 525-541, September.
    • Handle: RePEc:bla:tvecsg:v:109:y:2018:i:4:p:525-541
    DOI: 10.1111/tesg.12308
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    2. Sisman, S. & Aydinoglu, A.C., 2022. "Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis," Land Use Policy, Elsevier, vol. 119(C).
    3. Alice Barreca, 2022. "Architectural Quality and the Housing Market: Values of the Late Twentieth Century Built Heritage," Sustainability, MDPI, vol. 14(5), pages 1-24, February.
    4. Wu, Chao & Du, Yihao & Li, Sheng & Liu, Pengyu & Ye, Xinyue, 2022. "Does visual contact with green space impact housing pricesʔ An integrated approach of machine learning and hedonic modeling based on the perception of green space," Land Use Policy, Elsevier, vol. 115(C).
    5. Gružauskas Valentas & Kriščiūnas Andrius & Čalnerytė Dalia & Navickas Valentinas, 2020. "Analytical Method for Correction Coefficient Determination for Applying Comparative Method for Real Estate Valuation," Real Estate Management and Valuation, Sciendo, vol. 28(2), pages 52-62, June.
    6. Sheng Li & Yi Jiang & Shuisong Ke & Ke Nie & Chao Wu, 2021. "Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)," Land, MDPI, vol. 10(5), pages 1-15, May.
    7. Kang, Yuhao & Zhang, Fan & Peng, Wenzhe & Gao, Song & Rao, Jinmeng & Duarte, Fabio & Ratti, Carlo, 2021. "Understanding house price appreciation using multi-source big geo-data and machine learning," Land Use Policy, Elsevier, vol. 111(C).

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