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Land Plots Evaluation for Agriculture and Green Energy Projects: How to Overcome the Conflict Using Mathematics

Author

Listed:
  • Igor Ilin

    (Graduate School of Business Engineering, Peter the Great St. Petersburg State Polytechnic University, 195021 St. Petersburg, Russia)

  • Mikhail Laskin

    (Graduate School of Business Engineering, Peter the Great St. Petersburg State Polytechnic University, 195021 St. Petersburg, Russia
    St. Petersburg Federal Research Center of the Russian Academy of Sciences, 199178 St. Petersburg, Russia)

  • Irina Logacheva

    (State Budgetary Institution of the Leningrad Region “Leningrad Regional Cadastral Appraisal Institution”, 195112 Str. Petersburg, Russia)

  • Askar Sarygulov

    (Center for Fundamental Research, St. Petersburg State University of Economics, 191023 St. Petersburg, Russia)

  • Andrea Tick

    (Keleti Károly Faculty of Business and Management, Óbuda University, 1034 Budapest, Hungary)

Abstract

Seventeen sustainable development goals were formulated to create a harmonious world order for the benefit of different nations and peoples. At the same time, economic practice provides a lot of examples of conflicts of an economic nature between individual sustainable development goals. One of these conflicts is the need for environmental imperatives and economic growth when a massive assessment of land used for crop production and green energy projects is needed. The present paper considers a non-traditional approach to the mass evaluation of land plots on the condition that geographic information systems provide the main source of information, such as the case of land allocation for green energy facilities and evaluation of agricultural plots. The novelty of the proposed approach firstly means the development of a comparative approach, which receives much less attention in the valuation literature than cost and income approaches, as it can give an adequate picture of the current state of the market. The model includes the study of the entire dataset, the selection of model distributions and the construction of estimates based on model distributions. The methodology of multivariate lognormal distribution of factors and prices of analogues is used. The peculiarity of the market evaluation of land plots in such cases is, as a rule, the absence of rank predictors and sufficient number of continuous predictors, which provides a base for the application of a novel approach. The method of express testing of hypotheses about joint normality of logarithms of values of pricing factors and prices is proposed. The market value is estimated as an estimate of the modal value of conditional lognormal price distribution. Secondly, the problem of market valuation is solved in case of the almost complete absence of information about price-forming factors in the areas being assessed, and thirdly, the factors are determined based on geoinformation databases (distance to the nearest large city, regional center, federal highway, large rivers, lakes, and solid waste landfills), which allow for market assessment in the absence of information on pricing factors for land plots, except for the offer price and the plot area. The research was necessitated by the claim to determine on a specific date the cadastral value of agricultural land for the purposes of taxation, corresponding to the market value, in the almost complete absence of information on pricing factors in the assessed areas. The value of land reflects a complex combination of factors, so the use of the proposed mathematical toolkit allows for building a consistent model for the evaluation of land where improvements are absent or have no value in terms of land acquisition purposes.

Suggested Citation

  • Igor Ilin & Mikhail Laskin & Irina Logacheva & Askar Sarygulov & Andrea Tick, 2022. "Land Plots Evaluation for Agriculture and Green Energy Projects: How to Overcome the Conflict Using Mathematics," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4376-:d:978722
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    References listed on IDEAS

    as
    1. Luc Anselin & Nancy Lozano-Gracia, 2009. "Errors in variables and spatial effects in hedonic house price models of ambient air quality," Studies in Empirical Economics, in: Giuseppe Arbia & Badi H. Baltagi (ed.), Spatial Econometrics, pages 5-34, Springer.
    2. Yilmazer, Seckin & Kocaman, Sultan, 2020. "A mass appraisal assessment study using machine learning based on multiple regression and random forest," Land Use Policy, Elsevier, vol. 99(C).
    3. Benson, Earl D & Hansen, Julia L. & Schwartz Jr., Arthur & Smersh, Greg T., 1998. "Pricing Residential Amenities: The Value of a View," The Journal of Real Estate Finance and Economics, Springer, vol. 16(1), pages 55-73, January.
    4. Steven Peterson & Albert B. Flanagan, 2009. "Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal," Journal of Real Estate Research, American Real Estate Society, vol. 31(2), pages 147-164.
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