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Sustainable tax system design for use of mass real estate appraisal in land management

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  • Unel, Fatma Bunyan
  • Yalpir, Sukran

Abstract

Sustainable land management aims at land use by protecting nature. Real estate value is one of the important parts of land management, especially at the decision stages. The value associated with a major part of domestic real estate is tax value. The purpose of this study is to develop a tax system that is easy to understand and use as well as compatible with innovations. The sustainability of mass real estate appraisal is provided with legal, physical, locational, neighborhood and economic criteria according to ordinary and extraordinary conditions. The plots reserved for housing in urban areas were selected as the real property type with updates regarding appraised value for taxation. Valuation stages were discussed separately from general to detail, and coefficients were used under the current tax law. Sustainable mass plot appraisal system was designed by providing sustainable cycle economic and extraordinary criteria. The application was carried out in Konya/Lalebahçe Neighborhood and the sustainability of the system was observed. A multiple regression model was created from the samples of 2015 and the real estate value estimation was made with the data of the criteria affecting the value. With the model obtained from the economic criteria of 2005–2015, the economic valuation rate of real estate was found for the years 2016–2021. The results were compared with 10 market sample data for the years 2020–2021. The values estimated by the economic valuation rate of the real estate in 2020 approached the market values with an average of ± 7 % standard deviation and in 2021 with a ± 14 % standard deviation. A sustainable mass appraisal system for taxation was successfully tested and its sustainability was observed.

Suggested Citation

  • Unel, Fatma Bunyan & Yalpir, Sukran, 2023. "Sustainable tax system design for use of mass real estate appraisal in land management," Land Use Policy, Elsevier, vol. 131(C).
    • Handle: RePEc:eee:lauspo:v:131:y:2023:i:c:s0264837723002004
    DOI: 10.1016/j.landusepol.2023.106734
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    2. Abdul Rahman, Mohd Shahril & Awang, Mariah & Jagun, Zainab Toyin, 2024. "Polycrisis: Factors, impacts, and responses in the housing market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
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    4. Sevgen Sibel Canaz & Tanrivermiş Yeşim, 2024. "Comparison of Machine Learning Algorithms for Mass Appraisal of Real Estate Data," Real Estate Management and Valuation, Sciendo, vol. 32(2), pages 100-111.

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