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Application of Hedonic Model to Determine Residential Land Valuation in Suburban Areas: The Case of Hue City, Vietnam

Author

Listed:
  • Thanh Le Huu Ngoc

    (University of Agriculture and Forestry, Hue University, 102 Phung Hung st., Hue City, Thua Thien Hue province, VietNam)

  • Ngu Nguyen Huu

    (University of Agriculture and Forestry, Hue University, 102 Phung Hung st., Hue City, Thua Thien Hue province, VietNam)

Abstract

The paper aims to promote a model for determining residential land prices within a suburb of Hue city, Vietnam to improve the efficiency of land price determination and management. The study conducted surveys of 27 variables of 200 residential land plots in order to run the hedonic model using SPSS 26.0 software. The result developed a model that explained 78% of the change in land prices by incorporating five factors: area of land, width of road attached to the land plot, distance to hospital, ability to generate cash flow and planning information. Meanwhile, the writers also produced a map of residential land prices in the 2022 suburban areas of Hue city, Vietnam. This is a helpful tool in land market consulting and land valuation procedures.

Suggested Citation

  • Thanh Le Huu Ngoc & Ngu Nguyen Huu, 2024. "Application of Hedonic Model to Determine Residential Land Valuation in Suburban Areas: The Case of Hue City, Vietnam," Real Estate Management and Valuation, Sciendo, vol. 32(2), pages 70-79.
    • Handle: RePEc:vrs:remava:v:32:y:2024:i:2:p:70-79:n:1006
    DOI: 10.2478/remav-2024-0016
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    References listed on IDEAS

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    1. Renigier-Biłozor, Malgorzata & Janowski, Artur & d’Amato, Maurizio, 2019. "Automated Valuation Model based on fuzzy and rough set theory for real estate market with insufficient source data," Land Use Policy, Elsevier, vol. 87(C).
    2. Rotimi Boluwatife Abidoye & Albert P. C. Chan, 2017. "Modelling property values in Nigeria using artificial neural network," Journal of Property Research, Taylor & Francis Journals, vol. 34(1), pages 36-53, January.
    3. Bovkir, Rabia & Aydinoglu, Arif Cagdas, 2018. "Providing land value information from geographic data infrastructure by using fuzzy logic analysis approach," Land Use Policy, Elsevier, vol. 78(C), pages 46-60.
    4. Jannet C. Bencure & Nitin K. Tripathi & Hiroyuki Miyazaki & Sarawut Ninsawat & Sohee Minsun Kim, 2019. "Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches," Sustainability, MDPI, vol. 11(13), pages 1-17, July.
    5. Doan, Quang Cuong, 2023. "Determining the optimal land valuation model: A case study of Hanoi, Vietnam," Land Use Policy, Elsevier, vol. 127(C).
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • R39 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other

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