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Is digital trade affecting city house prices? An artificial intelligence perspective

HuiYing Yang & Xiaohuan Hou

International Journal of Strategic Property Management2026https://doi.org/10.3846/ijspm.2026.26144article
AJG 2ABDC B
Weight
0.50

Abstract

As a new engine of economic growth, digital trade is playing an increasingly pivotal role in shaping urban dynamics, including housing markets. This study investigated how the development of digital trade influenced city-level house prices in China. A digital trade index and an urban AI index were constructed using the entropy-TOPSIS method and text mining techniques, respectively. Empirical results showed that digital trade exerted a significant and robust positive linear effect on urban house prices, with no evidence of a nonlinear relationship. AI significantly strengthened this effect, acting as a positive moderator, while trade openness weakened it. Further heterogeneity analysis revealed that the impact of digital trade was more pronounced in coastal and high-income cities, yet AI integration substantially boosted this effect in non-coastal and low-income cities, suggesting strong potential for digital catch-up in underdeveloped regions. These findings indicated that digital trade, AI adoption, and regional characteristics jointly shape urban housing outcomes. Therefore, beyond advocating for stronger governmental support for digital infrastructure and emerging technologies, this study also highlighted the importance of enhancing AI capability and optimising trade openness strategies to ensure balanced urban development and sustainable real estate growth.

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https://doi.org/https://doi.org/10.3846/ijspm.2026.26144

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@article{huiying2026,
  title        = {{Is digital trade affecting city house prices? An artificial intelligence perspective}},
  author       = {HuiYing Yang & Xiaohuan Hou},
  journal      = {International Journal of Strategic Property Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.3846/ijspm.2026.26144},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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