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Influence Factors and Regression Model of Urban Housing Prices Based on Internet Open Access Data

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Listed:
  • Hao Wu

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Hongzan Jiao

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Yang Yu

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Zhigang Li

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Zhenghong Peng

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Lingbo Liu

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Zheng Zeng

    (Department of School of Arts & Communication, China University of Geosciences, Wuhan 430074, China)

Abstract

With the commercialization of housing and the deepening of urbanization in China, housing prices are having increasing influence on the land market, and thus indirectly affecting urban development. As various spatial features of an urban housing property directly affect its price, the study of this connection has significance for urban planning. The present study uses mainly open internet data of housing prices, supplemented by other data sources, to identify the spatial features of housing prices and the influence factors in a case study city, Wuhan. Methods employed in the study include the hedonic linear regression model, the geographically weighted regression (GWR) model and the artificial neural network (ANN) model, etc. Progress is made in the following two aspects: first, when calculating the influence factors, hierarchical values for accessibility variables of certain public facilities are used instead of simple Euclidean distance and the results shows a better model fit; second, the ANN model shows the best fit in the study, and while the three models all show respective strengths, the combined use of all models offers the possibility of a more comprehensive analysis of the influence factors of housing prices.

Suggested Citation

  • Hao Wu & Hongzan Jiao & Yang Yu & Zhigang Li & Zhenghong Peng & Lingbo Liu & Zheng Zeng, 2018. "Influence Factors and Regression Model of Urban Housing Prices Based on Internet Open Access Data," Sustainability, MDPI, vol. 10(5), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1676-:d:148305
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    3. Lee Whieldon & Huthaifa I. Ashqar, 2022. "Predicting residential property value: a comparison of multiple regression techniques," SN Business & Economics, Springer, vol. 2(11), pages 1-16, November.
    4. Hyunwoo Lim & Minyoung Park, 2019. "Modeling the Spatial Dimensions of Warehouse Rent Determinants: A Case Study of Seoul Metropolitan Area, South Korea," Sustainability, MDPI, vol. 12(1), pages 1-17, December.
    5. Sijia Li & Chao Wu & Yu Lin & Zhengyang Li & Qingyun Du, 2020. "Urban Morphology Promotes Urban Vibrancy from the Spatiotemporal and Synergetic Perspectives: A Case Study Using Multisource Data in Shenzhen, China," Sustainability, MDPI, vol. 12(12), pages 1-24, June.
    6. Hawnaz Magid Abdulla & Muammal Alaaddin Ibrahim, 2023. "The Impact of Urban Spatial Plan on Land Value: An Approach System to Relating Space Syntax Premises to the Land Price," Sustainability, MDPI, vol. 15(9), pages 1-28, April.
    7. Mirosław Bełej & Radosław Cellmer & Michał Głuszak, 2020. "The Impact of Airport Proximity on Single-Family House Prices—Evidence from Poland," Sustainability, MDPI, vol. 12(19), pages 1-26, September.
    8. Pearson, Jonathan & Muldoon-Smith, Kevin & Liu, Henry & Robson, Simon, 2022. "How does the extension of existing transport infrastructure affect land value? A case study of the Tyne and Wear Light Transit Metro system," Land Use Policy, Elsevier, vol. 112(C).
    9. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.
    10. Yuchen Qin & Yikang Zhang & Minfeng Yao & Qiwei Chen, 2023. "How to Measure the Impact of Walking Accessibility of Suburban Rail Station Catchment Areas on the Commercial Premium Benefits of Joint Development," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    11. Liu, Xuan & Tong, De & Huang, Jiangming & Zheng, Wenfeng & Kong, Minghui & Zhou, Guohui, 2022. "What matters in the e-commerce era? Modelling and mapping shop rents in Guangzhou, China," Land Use Policy, Elsevier, vol. 123(C).
    12. Jing Wu & Xirui Chen & Shulin Chen, 2019. "Temporal Characteristics of Waterfronts in Wuhan City and People’s Behavioral Preferences Based on Social Media Data," Sustainability, MDPI, vol. 11(22), pages 1-37, November.
    13. Cyprian Chwiałkowski & Adam Zydroń & Dariusz Kayzer, 2022. "Assessing the Impact of Selected Attributes on Dwelling Prices Using Ordinary Least Squares Regression and Geographically Weighted Regression: A Case Study in Poznań, Poland," Land, MDPI, vol. 12(1), pages 1-20, December.
    14. Talat Munshi, 2020. "Accessibility, Infrastructure Provision and Residential Land Value: Modelling the Relation Using Geographic Weighted Regression in the City of Rajkot, India," Sustainability, MDPI, vol. 12(20), pages 1-16, October.

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