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The value of retail rents with regression models: a case study of Shanghai

Author

Listed:
  • Jian Liang
  • Mats Wilhelmsson

Abstract

Purpose - The purpose of this paper is to estimate the determinants of the retail space rent in Shanghai. Design/methodology/approach - Hedonic model and spatial regression models are used in the paper. The problem of spatial autocorrelation is tested by Moran's I statistics, and the root mean square error (RMSE) test is performed to find out the best model. Findings - The significant explaining variables are the age, the area of retail space, the distance to the Jing An CBD centre, the type of the retail and the district of the property. A new classification of district in retail research context is suggested in this paper, and it is proved to be better than the districts set up by government to explain the retail rent variation. Originality/value - This paper presents the first empirical study about the retail rental market in Shanghai. The research helps retail property investors and retail tenants deepen their understanding of the retail market in Shanghai. Spatial econometrics techniques are first introduced into the empirical retail rent research to produce a more precise estimation.

Suggested Citation

  • Jian Liang & Mats Wilhelmsson, 2011. "The value of retail rents with regression models: a case study of Shanghai," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 29(6), pages 630-643, September.
    • Handle: RePEc:eme:jpifpp:v:29:y:2011:i:6:p:630-643
    DOI: 10.1108/14635781111171788
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    Citations

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    Cited by:

    1. Qiulin Ke & Michael White, 2015. "Retail rent dynamics in two Chinese cities," Journal of Property Research, Taylor & Francis Journals, vol. 32(4), pages 324-340, December.
    2. Hermansson, Cecilia & Lundgren, Berndt, 2022. "What factors matter in rent negotiations? Differences in views between landlords and retail trade tenants," Working Paper Series 22/9, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    3. Maral Taşcılar & Kerem Yavuz Arslanlı, 2022. "Forecasting commercial real estate indicators under COVID-19 by adopting human activity using social big data," Asia-Pacific Journal of Regional Science, Springer, vol. 6(3), pages 1111-1132, October.

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