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Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective

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Listed:
  • Hang Shen

    (School of Resource and Environment Sciences, Wuhan University, Wuhan 430079, China)

  • Lin Li

    (School of Resource and Environment Sciences, Wuhan University, Wuhan 430079, China
    Institute of Smart Perception and Intelligent Computing, SRES, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • Haihong Zhu

    (School of Resource and Environment Sciences, Wuhan University, Wuhan 430079, China)

  • Yu Liu

    (School of Resource and Environment Sciences, Wuhan University, Wuhan 430079, China
    Institute of Environment and Development, Guangdong Academy of Social Sciences, Guangzhou 510635, China)

  • Zhenwei Luo

    (School of Resource and Environment Sciences, Wuhan University, Wuhan 430079, China)

Abstract

Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the locational characteristics of a house from a comprehensive geographical perspective. Taking four major Chinese cities (Wuhan, Nanjing, Beijing, and Xi’an) as study areas, we compare the proposed method with other commonly used methods, and this paper presents a more precise estimation model for rental house prices. The method proposed in this paper may serve as a useful reference for individuals and enterprises in their transactions relevant to rental houses, and for the government in terms of the policies and positions of public rental housing.

Suggested Citation

  • Hang Shen & Lin Li & Haihong Zhu & Yu Liu & Zhenwei Luo, 2021. "Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective," Land, MDPI, vol. 11(1), pages 1-28, December.
    • Handle: RePEc:gam:jlands:v:11:y:2021:i:1:p:4-:d:707496
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    References listed on IDEAS

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    1. Chihiro Shimizu & Koji Karato & Kiyohiko Nishimura, 2014. "Nonlinearity of housing price structure," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 7(4), pages 459-488, September.
    2. Marcelo Cajias & Sebastian Ertl, 2018. "Spatial effects and non-linearity in hedonic modeling," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 36(1), pages 32-49, February.
    3. Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
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