IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i15p9383-d877118.html
   My bibliography  Save this article

Automation of Negative Infrastructural Externalities Assessment Methods to Determine the Cost of Land Resources Based on the Development of a “Thin Client” Model

Author

Listed:
  • Elena Bykowa

    (Department of Engineering Geodesy, Saint Petersburg Mining University, 21-Line, 2, 199106 St. Petersburg, Russia)

  • Maria Skachkova

    (Department of Engineering Geodesy, Saint Petersburg Mining University, 21-Line, 2, 199106 St. Petersburg, Russia)

  • Ivan Raguzin

    (Department of Engineering Geodesy, Saint Petersburg Mining University, 21-Line, 2, 199106 St. Petersburg, Russia)

  • Irina Dyachkova

    (Department of Engineering Geodesy, Saint Petersburg Mining University, 21-Line, 2, 199106 St. Petersburg, Russia)

  • Maxim Boltov

    (Department of Information Systems and Computer Science, Saint Petersburg Mining University, 21-Line, 2, 199106 St. Petersburg, Russia)

Abstract

This article discusses the need to transform real estate valuation methods. It is associated with the problems of obtaining unreliable results affecting the subsequent adoption of management decisions. As an important element of land plots assessment, the authors define the Negative Infrastructural Externalities arising from the presence of infrastructure and other regime-forming facilities. These externalities represent the loss of title holders due to the encumbrances arising from the use of land plots. The world community (and the authors as part of it) sees one of the transformation methods in the automation of the evaluation process. Therefore, the purpose of this study is to develop a mechanism of automating the Negative Infrastructural Externalities assessment process in the conditions of a non-existent and weak market activity of land relations. Modern trends dictate the saving of hardware, labor and money resources; in this connection, the methods of Negative Infrastructural Externalities assessment are implemented on the basis of the “thin client” technology. The research is based on the following methods: the analytical method is used to perform a critical analysis of the problem area and to substantiate the research topic’s relevance; methods of object-oriented programming and methods of modular programming (Cowan’s axiom of modularity) are used as tools for developing the web application logic, as well as the interaction of its individual elements; the attribute-driven design approach is used in the creation of software architectures. The result of the study is the developed and substantiated architecture of a web application for assessing negative infrastructural external factors in determining the land value, the implemented modular structure of the specified web application and the developed conceptual model of the database. The practical implementation of the listed proposals is made by means of the Python programming language. The advantage of the created automated system is the possibility of multi-disciplinary use of the expert assessment approach when changing the settings.

Suggested Citation

  • Elena Bykowa & Maria Skachkova & Ivan Raguzin & Irina Dyachkova & Maxim Boltov, 2022. "Automation of Negative Infrastructural Externalities Assessment Methods to Determine the Cost of Land Resources Based on the Development of a “Thin Client” Model," Sustainability, MDPI, vol. 14(15), pages 1-29, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9383-:d:877118
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/15/9383/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/15/9383/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yin-Fang Zhang & Shengbao Ji, 2019. "Infrastructure, externalities and regional industrial productivity in China: a spatial econometric approach," Regional Studies, Taylor & Francis Journals, vol. 53(8), pages 1112-1124, August.
    2. Rohan Mark Bennett & Eva-Maria Unger & Christiaan Lemmen & Paula Dijkstra, 2021. "Land Administration Maintenance: A Review of the Persistent Problem and Emerging Fit-for-Purpose Solutions," Land, MDPI, vol. 10(5), pages 1-18, May.
    3. Ihlanfeldt, Keith R., 2007. "The effect of land use regulation on housing and land prices," Journal of Urban Economics, Elsevier, vol. 61(3), pages 420-435, May.
    4. Evert Meijers & Martijn Burger & Edward L. Glaeser & Giacomo A. M. Ponzetto & Yimei Zou, 2016. "Urban networks: Connecting markets, people, and ideas," Papers in Regional Science, Wiley Blackwell, vol. 95(1), pages 17-59, March.
    5. Natale Arcuri & Manuela De Ruggiero & Francesca Salvo & Raffaele Zinno, 2020. "Automated Valuation Methods through the Cost Approach in a BIM and GIS Integration Framework for Smart City Appraisals," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
    6. Najkar, N. & Kohansal, M. R. & Ghorbani, M., 2018. "Estimating Spatial Effects of Transport Infrastructure on Agricultural Output of Iran," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 10(2).
    7. Rainer Schulz & Martin Wersing & Axel Werwatz, 2014. "Automated valuation modelling: a specification exercise," Journal of Property Research, Taylor & Francis Journals, vol. 31(2), pages 131-153, June.
    8. Yunjong Kim & Seungwoo Choi & Mun Yong Yi, 2020. "Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    9. W. Patrick Beaton, 1991. "The Impact of Regional Land-Use Controls on Property Values: The Case of the New Jersey Pinelands," Land Economics, University of Wisconsin Press, vol. 67(2), pages 172-194.
    10. Glaeser, Edward L & Gyourko, Joseph & Saks, Raven, 2005. "Why Is Manhattan So Expensive? Regulation and the Rise in Housing Prices," Journal of Law and Economics, University of Chicago Press, vol. 48(2), pages 331-369, October.
    11. Sebastian Gnat, 2021. "Property Mass Valuation on Small Markets," Land, MDPI, vol. 10(4), pages 1-14, April.
    12. 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.
    13. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    14. Demetriou, Demetris, 2018. "Automating the land valuation process carried out in land consolidation schemes," Land Use Policy, Elsevier, vol. 75(C), pages 21-32.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sisman, S. & Aydinoglu, A.C., 2022. "Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis," Land Use Policy, Elsevier, vol. 119(C).
    2. Han, Wenjing & Zhang, Xiaoling & Zheng, Xian, 2020. "Land use regulation and urban land value: Evidence from China," Land Use Policy, Elsevier, vol. 92(C).
    3. Ritashree Chakrabarti & Junfu Zhang, 2015. "Unaffordable housing and local employment growth: Evidence from California municipalities," Urban Studies, Urban Studies Journal Limited, vol. 52(6), pages 1134-1151, May.
    4. Zhang, Junfu, 2023. "JUE Insight: Measuring the Stringency of Land Use Regulation Using a Shadow Price Approach," Journal of Urban Economics, Elsevier, vol. 133(C).
    5. Tikoudis, Ioannis & Verhoef, Erik T. & van Ommeren, Jos N., 2018. "Second-best urban tolls in a monocentric city with housing market regulations," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 342-359.
    6. Cheshire, Paul, 2009. "Urban land markets and policy failures," LSE Research Online Documents on Economics 30837, London School of Economics and Political Science, LSE Library.
    7. Guillaume POUYANNE & Frederic GASCHET, 2013. "The Effect Of Current And Future Land Use On House Prices," ERSA conference papers ersa13p249, European Regional Science Association.
    8. Molloy, Raven, 2020. "The effect of housing supply regulation on housing affordability: A review," Regional Science and Urban Economics, Elsevier, vol. 80(C).
    9. 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).
    10. Boarnet, Marlon G. & McLaughlin, Ralph B. & Carruthers, John I., 2011. "Does state growth management change the pattern of urban growth? Evidence from Florida," Regional Science and Urban Economics, Elsevier, vol. 41(3), pages 236-252, May.
    11. Elena Bykowa & Maria Hełdak & Julia Sishchuk, 2020. "Cadastral Land Value Modelling Based on Zoning by Prestige: A Case Study of a Resort Town," Sustainability, MDPI, vol. 12(19), pages 1-26, September.
    12. Cheshire, Paul & Hilber, Christian A.L. & Koster, Hans R.A., 2018. "Empty homes, longer commutes: The unintended consequences of more restrictive local planning," Journal of Public Economics, Elsevier, vol. 158(C), pages 126-151.
    13. Cheung, Ron & Ihlanfeldt, Keith & Mayock, Thomas, 2009. "The regulatory tax and house price appreciation in Florida," Journal of Housing Economics, Elsevier, vol. 18(1), pages 34-48, March.
    14. Jyh-Bang Jou & Tan (Charlene) Lee, 2015. "How Do Density Ceiling Controls Affect Housing Prices and Urban Boundaries?," The Journal of Real Estate Finance and Economics, Springer, vol. 50(2), pages 219-241, February.
    15. Raul-Tomas Mora-Garcia & Maria-Francisca Cespedes-Lopez & V. Raul Perez-Sanchez, 2022. "Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times," Land, MDPI, vol. 11(11), pages 1-32, November.
    16. Ritashree Chakrabarti & Junfu Zhang, 2010. "Unaffordable housing and local employment growth," New England Public Policy Center Working Paper 10-3, Federal Reserve Bank of Boston.
    17. Michael Manville & Michael Lens & Paavo Monkkonen, 2022. "Zoning and affordability: A reply to Rodríguez-Pose and Storper," Urban Studies, Urban Studies Journal Limited, vol. 59(1), pages 36-58, January.
    18. Gyourko, Joseph & Molloy, Raven, 2015. "Regulation and Housing Supply," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 1289-1337, Elsevier.
    19. Monkkonen, Paavo & Manville, Michael & Lens, Michael, 2024. "Built out cities? A new approach to measuring land use regulation," Journal of Housing Economics, Elsevier, vol. 63(C).
    20. Sebastian Gnat, 2021. "Property Mass Valuation on Small Markets," Land, MDPI, vol. 10(4), pages 1-14, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9383-:d:877118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.