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Operational performance of urban real estate in China: An additive network DEA model

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Listed:
  • Zhang, Hao
  • Nalinrat, Wattanaporn
  • Xiang, Rong
  • Yu, Anyu
  • Gao, Yue

Abstract

The real estate industry encompasses sequential sub-processes in operations, including land acquisition, house construction, and house sales and rentals. Investigating the sub-process structure of real estate operations is essential to demystifying and improving the overall operational performance. This study proposes an additive network DEA model to estimate the process-oriented performance of urban real estate operations and capture hidden sub-process performance. The sequential linear programming method is used to address the model's nonlinearity. We further explore the impact of operational performance on housing prices to identify the main underlying driver of China's booming real estate market. The proposed model is applied to assess the operational performance of Chinese urban real estate markets over the past decade. The empirical findings reveal that: (1) performance losses may stem from weaknesses in the housing construction process, with significant improvement potential in overall operational and sub-process performance in most cities. (2) Enhanced performance in the construction process can fuel short-term housing prices increases during market booms. (3) Higher real estate operational performance may initially raise housing prices but ultimately inhibit them in the long term due to limited market demand. Our proposed method framework proves to be an effective tool for policymakers to design wise operational plans for improving real estate operational performance.

Suggested Citation

  • Zhang, Hao & Nalinrat, Wattanaporn & Xiang, Rong & Yu, Anyu & Gao, Yue, 2026. "Operational performance of urban real estate in China: An additive network DEA model," Socio-Economic Planning Sciences, Elsevier, vol. 104(C).
    • Handle: RePEc:eee:soceps:v:104:y:2026:i:c:s0038012125002630
    DOI: 10.1016/j.seps.2025.102414
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