Assessing Ecological Impacts of Urban Land Valuation: AI and Regression Models for Sustainable Land Management

Authors

  • Yana Volkova

    Department of Geodesy, Land Management and Cadastres, Saint Petersburg State University of Architecture and Civil Engineering, Petersburg 190005, Russia
  • Elena Bykowa

    Department of Land Management and Cadastres, Empress Catherine II Saint Petersburg Mining University, Petersburg 199106, Russia
  • Oksana Pirogova

    Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, Petersburg 195251, Russia
  • Sergey Barykinc

    Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, Petersburg 195251, Russia
  • Dmitriy Rodionov

    Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University, Petersburg 195251, Russia
  • Ilya Sonts

    Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, Petersburg 195251, Russia
  • Angela Mottaeva

    Department Organization of construction and real estate management, NRU MGSU, Moscow 129337, Russia
  • Alexey Mikhaylov

    Financial Faculty, Financial University under the Government of the Russian Federation, Moscow 125167, Russia

    Department of Scientific, Baku Eurasian University, Baku AZ 1073, Republic of Azerbaijan
  • Dmitry Morkovkin

    Financial Faculty, Financial University under the Government of the Russian Federation, Moscow 125167, Russia
  • N. B. A. Yousif

    Department of Sociology, College of Humanities and Science, Ajman University, Ajman P.O.Box: 346, UAE

    Humanities and Social Sciences Research Centre (HSSRC), Ajman University, Ajman P.O.Box: 346, UAE
  • Tomonobu Senjyu

    Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
  • Farooq Ahmed Shah

    Department of Mathematics, COMSATS University Islamabad, Islamabad 45550, Pakistan

DOI:

https://doi.org/10.30564/re.v7i2.9780
Received: 29 April 2025 | Revised: 6 May 2025 | Accepted: 16 May 2025 | Published Online: 7 June 2025

Abstract

The results of mass appraisal in many countries are used as a basis for calculating the amount of real estate tax, therefore, regardless of the methods used to calculate it, the resulting value should be as close as possible to the market value of the real estate to maintain a balance of interests between the state and the rights holders. In practice, this condition is not always met, since, firstly, the quality of market data is often very low, and secondly, some markets are characterized by low activity, which is expressed in a deficit of information on asking prices. The aim of the work is ecological valuation of land use: how regression-based mass appraisal can inform ecological conservation, land degradation, and sustainable land management. Four multiple regression models were constructed for AI generated map of land plots for recreational use in St. Petersburg (Russia) with different volumes of market information (32, 30, 20 and 15 units of market information with four price-forming factors). During the analysis of the quality of the models, it was revealed that the best result is shown by the model built on the maximum sample size, then the model based on 15 analogs, which proves that a larger number of analog objects does not always allow us to achieve better results, since the more analog objects there are.

Keywords:

Land Use Sustainability; Ecological Valuation; Regression Modeling; AI in Ecology, Landscape Conservation

References

[1] Meraj, G., Singh, S.K., Kanga, S., et al. 2022. Modeling on comparison of ecosystem services concepts, tools, methods and their ecological-economic implications: A review. Modeling Earth Systems and Environment. 8(1), 15-34.

[2] Hu, Q.Y., Chen, S.L., 2021. Optimizing the ecological networks based on the supply and demand of ecosystem services in Xiamen-Zhangzhou-Quanzhou region. Journal of Natural Resources. 36(2), 342-355.

[3] Raihan, A., 2023. A review on the integrative approach for economic valuation of forest ecosystem services. Journal of Environmental Science and Economics. 2(3), 1-18.

[4] Wang, B., Zhang, Q., Cui, F., 2021. Scientific research on ecosystem services and human well-being: A bibliometric analysis. Ecological Indicators. 125, 107449.

[5] Tappeiner, U., Leitinger, G., Zariņa, A., et al., 2021. How to consider history in landscape ecology: patterns, processes, and pathways. Landscape Ecology. 36, 2317-2328.

[6] Guo, M., Ma, S., Wang, L. J., et al. 2021. Impacts of future climate change and different management scenarios on water-related ecosystem services: A case study in the Jianghuai ecological economic Zone, China. Ecological Indicators. 127, 107732.

[7] Jin, G., Chen, K., Liao, T., et al., 2021. Measuring ecosystem services based on government intentions for future land use in Hubei Province: Implications for sustainable landscape management. Landscape Ecology. 36, 2025-2042.

[8] Pan, Y., Zhang, B., Wu, Y., et al., 2021. Sustainability assessment of urban ecological-economic systems based on emergy analysis: A case study in Simao, China. Ecological Indicators. 121, 107157.

[9] Vári, Á., Podschun, S.A., Erős, T., et al., 2022. Freshwater systems and ecosystem services: Challenges and chances for cross-fertilization of disciplines. Ambio. 51(1), 135-151.

[10] Tao, J., Lu, Y., Ge, D., et al., 2022. The spatial pattern of agricultural ecosystem services from the production-living-ecology perspective: A case study of the Huaihai Economic Zone, China. Land Use Policy. 122, 106355.

[11] Aleksandrova, T., Nikolaeva, N., Afanasova, A., et al., 2023. Extraction of Low-Dimensional Structures of Noble and Rare Metals from Carbonaceous Ores Using Low-Temperature and Energy Impacts at Succeeding Stages of Raw Material Transformation. Minerals. 13, 84. https://doi.org/10.3390/min13010084

[12] Anisimova I.N., Barinov V.P., Gribovsky S.V., 2004. Taking into account different types of price-forming factors in multivariate regression models of real estate valuation. Assessment Issues. 2, 15-33.

[13] Barinov N.P., 2024. Application of regression analysis in problems of individual and mass valuation of real estate objects. Available online: http://tmpo.su/barinov-n-p-primenenie-regressionnogo-analiza-v-zadachax-opredeleniya-stoimostnyx-xarakteristik-obektov-nedvizhimosti/ (accessed 14 July 2024)

[14] Borst, R.A., 1991. Artificial neural networks: The next modelling/calibration technology for the assessmentcommunity. Property Tax Journal. 10(1), 69–94

[15] Chen, C., Ma, X., Zhang, X., 2024. Empirical Study on Real Estate Mass Appraisal Based on Dynamic Neural Networks. Buildings. 14, 2199. https://doi.org/10.3390/buildings14072199

[16] Common Statistical Errors and Mistakes: Valuation and Reliability. - Free Online Library. Available online: www.thefreelibrary.com/Common+statistical+errors+and+mistakes%3A+valuation+and+reliability.-a0354182486. (accessed 28 July 2024)

[17] Cherepovitsyn, A., Tsvetkova, A., Komendantova, N., 2020. Approaches to Assessing the Strategic Sustainability of High-Risk Offshore Oil and Gas Projects. J. Mar. Sci. Eng. 8, 995. https://doi.org/10.3390/jmse8120995

[18] Clint B., «Why Inferential Statistics Are Inappropriate for Development Studies and How the Same Data Can Be Better Used». Available online: http://papers.ssrn.com/sol3/pa pers.cfm?abstract_id=1775002 (accessed 14 July 2024).

[19] Dell G., 2004. The myth and the reality; the problem and the solution. In: Valuationin sights & perspectives, Chicago. 9(3), 20-44

[20] Doszyń, M., 2022. Might expert knowledge improve econometric real estate mass appraisal?. J Real Estate Finan Econ. 69, 719–740. https://doi.org/10.1007/s11146-022-09891-3

[21] Eckert J.K., Gloudemans R. Almy R., 1990. Property Appraisal and Assessment Administration. International Association of Assessing Officers.

[22] Green, S.B., 1991. How many subjects does it take to do a regression analysis? Multivariate Behavioral Research. 26, 499-510

[23] Gribovsky, S.V., Sivets, S.A., Levykina, I.A., 2014. Mathematical Methods of Estimation of Property Value; Maroseika. Moscow, Russia. 204–223

[24] Hair, J.F., Black, W.C., Babin, B.J., et al., 2018. EMultivariate Data Analysis (8th ed.). United Kingdom: Cengage Learning.

[25] International Association of Assessing Officers., 2024. A criterion for measuring fairness, quality, equity and accuracy. International Association of Assessing Officers. Approved April 2013. Available online: https://www.iaao.org/media/standards/Standard_on_Ratio_Studies.pdf (accessed 28 July 2024)

[26] Jarosz, M., Kutrzyński, M., Lasota, T., 2020. Machine Learning Models for Real Estate Appraisal Constructed Using Spline Trend Functions. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science. 12033. DOI: https://doi.org/10.1007/978-3-030-41964-6_55

[27] Kaminsky A.V., 2016. Cadastral assessment: it is not possible to cooperate or resist. Property relations in the Russian Federation. 2, 10-27.

[28] Katsman V.E., Kosorukova I.V., Rodin A.Yu., 2008. Evaluation activities. Legal, theoretical and mathematical foundations. Book 1;Market DS, Moscow, Russia. 46-111

[29] Korostelev S.P., 2024. On the “Unified federal methodology" of cadastral valuation of "real estate and land”. Available online: http://www.labrate.ru/articles/2017-1_korostelev.pdf (accessed 10 of July 2024)

[30] Korostelev S.P., 2024. On the issues of quality of cadastral valuation in the new “Methodological guidelines on state cadastral valuation”. Available online: https://www.valnet.ru/media/files/m7-316.pdf (accessed 10 July 2024).

[31] Kovyazin V.F., Nguyen T.A., Nguyen T.T., 2023. Monitoring the forest fund lands of Kon Tum province, Vietnam using remote sensing data of Earth. Geodesy and cartography = GeodeziaiKartografia. 84(8), 57-64. (In Russian). DOI: https://doi.org/10.22389/0016-7126-2023-998-8-57-64

[32] Kovyazin, V., Bogdanova, E., 2024. Problems of Formation of Specially Protected Natural Areas in the Arctic Zone of Russia. In O. Pasko & N. Lebedeva (Eds.), Ecological Aspects of Soil and Land Preservation. 328-349. DOI: https://doi.org/10.4018/979-8-3693-3374-7.ch013

[33] Lai, R., 2023. Data Quality Management for Equitable Mass Appraisal during Turbulent Times in Hong Kong. Mass Appraisal Valuation Symposium. 2. Available from: https://researchexchange.iaao.org/mavs/mavs2023/session5/2

[34] Lapinskas, A.A., 2023. Influence of mining rent on the efficiency of using natural potential: the paradox of plenty and its Russian specifics. Journal of Mining Institute. 259, 79-94. DOI: 10.31897/PMI.2023.13 P.82.

[35] Leifer, L.A., 2009. Accuracy of assessment results and limits of appraiser’s liability. Property relations in the Russian Federation. 4(91), 45-78.

[36] Leifer, L.A., 2024. Mass and individual assessment. Accuracy of methods and the cost of errors. Available online: https://www.ocenchik.ru/docsb/2568-massovaya-ocenka-tochnost-metodov.html (accessed 10 July 2024).

[37] Leifer, L.A., 2018. Kraynikova T.V. Optimal number of analogues in conditions of heterogeneity of market data. Property relations in the Russian Federation. 6(201), 44-63.

[38] Lenk, M.M., Worzala, E.M., Silva, A., 1997. High-tech valuation:Should artificial neural networks bypass the human valuer? Journal of Property Valuation and Investment. 15, 8-26.

[39] Marvin L., 2009. Wolverton, An Introduction to Statistics for Appraisers. Chicago: Appraisal Institute.

[40] Massel L., Komendantova N., Massel A., et al., 2024. Resilience of socio-ecological and energy systems: intelligent information technologies for risk assessment of natural and technogenic threats Journal of Infrastructure, Policy and Development. 8, 1-13.

[41] Matrokhina K.V., TrofimetsV.Ya., Mazakov E.B., et al., 2023. Development of the methodology for scenario analysis of investment projects of enterprises of the mineral resource complex. Journal of Mining Institute. 259, 112-124. DOI: https://doi.org/10.31897/PMI.2023.3

[42] Pavlova V. A., Sulin M. A., LepikhinaO.Yu., 2019. The mathematical modelling of the land resources mass evaluation in agriculture. Journal of Physics. Conference Series. 1333, 1-6. DOI: https://doi.org/10.1088/1742-6596/1333/3/032049

[43] Pázman A., 2001. Linearization of nonlinear regression models by smoothing. Tatra Mountains Mathematical Publications. 22.

[44] Pravdina E. A., LepikhinaO.Yu., 2019. Variable accounting of pricing factors at land parcels cadastral valuation (on the example of Saint-Petersburg). Bulletin of the Tomsk polytechnic University. 2(330), 65-74. DOI: https://doi.org/10.18799/24131830/2019/2/94

[45] Raguzin, I.I., Bykova, E.N., Lepikhina, O.Y., 2023. Polygonal Metric Grid Method for Estimating the Cadastral Value of Land Plots. Lomonosov Geography Journal. 3, 92-103. DOI: https://doi.org/10.55959/MSU0579-9414.5.78.3.8

[46] Ravikumar, A.S., 2017. Real estate price prediction using machine learning. Dublin, National College of Ireland.

[47] Report on the Determination of Cadastral Value of Real Estate in the Territory of Saint Petersburg, Volume 3—Determinationof Cadastral Value of Land Parcels (as of 1 January 2021) 1/2022. Available online: https://rosreestr.ru/wps/portal/cc_ib_svedFDGKO?archive_id=23341 (accessed 28 July 2024)

[48] Sachs, L., 1993. StatistischeMethoden. Springer EBooks, Springer Nature。

[49] Samson, W.D., 2002. History of Taxation. In: Lymer, A., Hasseldine, J. (eds) The International Taxation System. Springer, Boston, MA. 21-41. DOI: https://doi.org/10.1007/978-1-4615-1071-0_2

[50] Sawant, R., Jangid, Y., Tiwari, T., 2018. Comprehensive analysis of housing price prediction in pune using multi-featured random forest approach. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA).

[51] Skachkova M., Guryeva O., 2023. Monitoring the state of green spaces in St. Petersburg based on remote sensing data. Ecologyand Industry of Russia. 27(5), 51-57. DOI: https://doi.org/10.18412/1816-0395-2023-5-51-57

[52] Skachkova M.E., Guryeva O.S., 2022. Information support for urban development activities in Russia. Geodesy and cartography. 8, 45-55. DOI: https://doi.org/10.22389/0016-7126-2022-986-8-45-55

[53] Tabachnick, B.G., Fidell, L.S., 2019. Using Multivariate Statistics; California State University: Los Angeles, CA, USA, p. 815.

[54] Wang, D., Li, V.J., 2019. Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review. Sustainability. 11, 7006.

[55] Worzala, E., Lenk, M., Silva, A., 1995. An exploration of neuralnetworks and its application to real estate valuation. Journal of Real Estate Research. 10(2), 185–201. DOI: https://doi.org/10.1080/10835547.1995.12090782

[56] Yasnitsky, L.N., Yasnitsky, V.L., Alekseev, A.O., 2021. The Complex Neural Network Model for Mass Appraisal and Scenario Forecasting of the Urban Real Estate Market Value That Adapts Itself to Space and Time. Complexity. 1–17. DOI: https://doi.org/10.1155/2021/5392170

[57] Zakharov, A. V., Kharlamov, A. V., 2010. Geocoding technique in constructing geographically weighted regression models for mass appraisal under conditions of uncertainty and heterogeneity of initial data. Property relations in the Russian Federation. 7, 76-85.

[58] Zhou, G., Ji, Y., Chen, X., er al., 2018, Artificial Neural Networks and the Mass Appraisal of Real Estate. International Journal of Online and Biomedical Engineering (iJOE). 14(03), 180–187. DOI: https://doi.org/10.3991/ijoe.v14i03.8420

[59] Pervukhin, D., Davardoost, H., Kotov, D., et al., 2023. A sustainable development goals-based mathematical model for selecting oil and gas investment projects under uncertainty and limited resources. Advanced Mathematical Models & Applications. 8(3), 502-528.

[60] Litvinenko, V.S., Petrov, E.I., Vasilevskaya, D.V., et al., 2023. Assessment of the role of the state in the management of mineral resources. Journal of Mining Institute. 259, 95-111. DOI: https://doi.org/10.31897/PMI.2022.100

[61] Khawand, S., 2025. Emerging Strategies for Ecological Conservation: Challenging Traditional Theories and Advancing Sustainable Solutions. Research in Ecology. 7(2), 1–18. DOI: https://doi.org/10.30564/re.v7i2.8815

[62] Raman, E., Sharma, P., Anand, S., er al., 2025. Spatio-Temporal Assessment of Land Use Land Cover Changes Affecting Regional Ecology in Patna Urban Agglomeration (PUA) in Bihar, India During 1990 to 2024. Research in Ecology. 7(1), 1–14. DOI: https://doi.org/10.30564/re.v7i1.7872

[63] Ren, X., Tan, X., Luo, D., et al., 2025. Coupling and Spatial Disparities of Regional Economy and Ecosystem in High-Quality Town Development. Research in Ecology. 7(1), 15–29. DOI: https://doi.org/10.30564/re.v7i1.8118

[64] Dimitriadis, K.A., Koursaros, D., Savva, C.S., 2024. Evaluating the sophisticated digital assets and cryptocurrencies capacities of substituting international currencies in inflationary eras. International Review of Financial Analysis. 96, 103693.

[65] Mikhaylov, A., Yousif, N.B.A., Dincer, H., et al., 2025. Analysis of the knowledge and innovation-based customer expectations for the green crypto assets in investment strategies using artificial intelligence and facial expression-based fuzzy modelling. Quality and Quantity. DOI: https://doi.org/10.1007/s11135-025-02098-7

[66] Kalkavan, H., Yuksel, S., Eti, S., et al., 2025. Strategy Recommendations for Minimizing Economic Inequalities Increased by Renewable Energy Investments Regarding Sustainable Development. Sustainable Energy Technologies and Assessments. 75(3), 104225, DOI: https://doi.org/10.1016/j.seta.2025.104225

[67] Duan, F., Ali, A.B., Jasim, D.J., et al., 2025. Parameters estimation of PV models using a novel hybrid equilibrium optimization algorithm. Energy Exploration & Exploitation. DOI: https://doi.org/10.1177/01445987241311310

[68] Chen, T-H., Chang, H.W., Mikhaylov, A., et al., 2025. Revisit Energy Consumption, Economic Growth and Carbon Dioxide Emissions Links in Transition Countries using a New Developed Quantile on Quantile Approach. Applied Economics. 57(6), 583-599, DOI: https://doi.org/10.1080/00036846.2024.2305611

[69] Mikhaylov, A., Yousif, N.B.A., An, J., 2025. How High Efficiency of the Chilean stock Market Does Impact on Energy Transition? Research Using Deep Seek AI Optimization. Finance: Theory and Practice. 29(1), 181-194. DOI: https://doi.org/10.26794/2587-5671-2025-29-1-181-194

[70] Galala, A., Nazir, U., Ayadi, M., et al., 2025. Predicting simulations of entropy generation with multiple nanoscales towards cylinder/surface engaging non-Fourier law: Energy systems and power generation. Results in Engineering. 105428. DOI: https://doi.org/10.1016/j.rineng.2025.105428

Downloads

How to Cite

Volkova , Y., Bykowa, E., Oksana Pirogova, Sergey Barykinc, Dmitriy Rodionov, Ilya Sonts, Angela Mottaeva, Mikhaylov, A., Dmitry Morkovkin, N. B. A. Yousif, Tomonobu Senjyu, & Farooq Ahmed Shah. (2025). Assessing Ecological Impacts of Urban Land Valuation: AI and Regression Models for Sustainable Land Management. Research in Ecology, 7(2), 192–208. https://doi.org/10.30564/re.v7i2.9780

Issue

Vol. 7, Iss. 2 (June 2025) (in progress)

Article Type

Article (This article belongs to the Special Issue "Innovative application of AI and machine learning in solving ecological problems")

License

Copyright © 2025 Yana Volkova , Elena Bykowa, Oksana Pirogova, Sergey Barykinc, Dmitriy Rodionov, Ilya Sonts, Angela Mottaeva, Alexey Mikhaylov, Dmitry Morkovkin, N. B. A. Yousif, Tomonobu Senjyu, Farooq Ahmed Shah

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC