Decision tree models for technical due diligence in land development

• Autorzy: Waga Marcin , Radziszewska-Zielina Elżbieta , Sroka Bartłomiej

Identyfikatory

DOI

10.24425/bpasts.2025.155027

• Języki publikacji: EN

Abstrakty

EN

In the design of modern urban systems that consider the needs of different resident groups, technical due diligence (TDD) processes play a crucial role. According to current market practice, these processes precede land acquisition and the commencement of construction projects. As part of this process, the feasibility of the planned investment is verified. Already at the preliminary report stage, potential legal, technical, environmental, social, and economic constraints that may prevent the implementation of the project are identified. If no significant barriers are found, a final report is issued. This article presents a methodology for identifying factors that may block the implementation of an investment at the preliminary report stage. It also proposes a supporting model for the investment decision-making process regarding land acquisition, based on decision trees. Based on completed TDD processes, an algorithm was developed that enables the construction of a decision tree, considering various attributes describing the analyzed building plot. The use of decision trees allows for the early identification of key attributes requiring detailed analysis in the initial phase of TDD. This facilitates more efficient preparation of preliminary reports, considering the interests of different resident groups. In the final report stage, decision tree models can serve as a crucial tool to support investors in making decisions regarding the potential land acquisition.

Słowa kluczowe

EN

technical due diligence; building plot; decision tree

PL

audyt nieruchomości; działka budowlana; drzewo decyzyjne

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2025
• Tom: Vol. 73, nr 6, Special Section
• Strony: art. no. e155027
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Waga Marcin

• Doctoral School, Cracow University of Technology, Poland

• https://orcid.org/0009-0002-7691-0978

autor

Radziszewska-Zielina Elżbieta

• Faculty of Civil Engineering, Cracow University of Technology, Poland

• https://orcid.org/0000-0002-3237-4360

autor

Sroka Bartłomiej

• Faculty of Civil Engineering, Cracow University of Technology, Poland

• https://orcid.org/0000-0001-8845-2789

Bibliografia

• [1] E. Radziszewska-Zielina, E. Kania, and G. Śladowski, “Problems in carrying out construction projects in large urban agglomerations on the example of the construction of the varso building complex in Warsaw,” in Advances and Trends in Engineering Sciences and Technologies III – Proceedings of the 3rd International Conference on Engineering Sciences and Technologies, ESaT 2018, 2019, pp. 533–538. doi: 10.1051/matecconf/201711700144.
• [2] I. Sanz-Prieto, L. de-la-Fuente-Valentín, and S. Ríos-Aguilar, “Technical due diligence as a methodology for assessing risks in start-up ecosystems: An advanced approach,” Inf. Process. Manag., vol. 58, no. 5, p. 102617, 2021, doi: 10.1016/j.ipm.2021.102617.
• [3] “Due Diligence Law and Legal Definition,” USLegal, Inc. [Online]. Available: https://definitions.uslegal.com/d/due-diligence/{#}google_vignette (Accessed: Jan. 27, 2024).
• [4] B. Kutera and H. Anysz, “The methodology of technical due diligence report preparation for an office, residential and industrial buildings,” in MATEC Web Conf, vol. 86, 07009, 2016. doi: 10.1051/matecconf/20168607009.
• [5] M. Waga, E. Radziszewska-Zielina, and B. Sroka, “Review of methods for preparing technical due diligence reports for the purchase of commercial real estate,” Przegląd Budowlany, vol. 96, no. 1, pp. 102–106, 2025, doi: 10.5604/01.3001.0055.0060.
• [6] X. Li, S. Yi, A.B. Cundy, and W. Chen, “Sustainable decisionmaking for contaminated site risk management: A decision tree model using machine learning algorithms,” J. Clean. Prod., vol. 371, p. 133612, 2022, doi: 10.1016/j.jclepro.2022.133612.
• [7] Y. Yao, J. Li, X. Zhang, P. Duan, S. Li, and Q. Xu, “Investigation on the expansion of urban construction land use based on the CART-CA Model,” ISPRS Int. J. Geo-Inf., vol. 6, no. 5, p. 149, 2017, doi: 10.3390/ijgi6050149.
• [8] J. Surma, “Hacking Machine Learning: Towards The Comprehensive Taxonomy of Attacks Against Machine Learning Systems,” in ACM Int. Conf. Proc. Ser., 2020, pp. 1–4. doi: 10.1145/3390557.3394126.
• [9] I. Rojek, R. Burduk, and P. Heda, “Ensemble selection in oneversus-one scheme – case study for cutting tools classification,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 1, p. e136044, 2021, doi: 10.24425/bpasts.2021.136044.
• [10] E. Radziszewska-Zielina, “Analysis of the impact of the level of partnering relations on the selected indexes of success of Polish construction enterprises,” Eng. Econ., vol. 21, no. 3, pp. 324–335, 2010.
• [11] K. Galjanić, I. Marović, and T. Hanak, “Performance Measurement Framework for Prediction and Management of Construction Investments,” Sustainability (Switzerland), vol. 15, no. 18, p. 3617, 2023, doi: 10.3390/su151813617.
• [12] E. Radziszewska-Zielina, G. Śladowski, E. Kania, B. Sroka, and B. Szewczyk, “Managing Information Flow in Self-Organising Networks of Communication between Construction Project Participants,” Arch. Civ. Eng., vol. 65, no. 2, pp. 133–148, 2019, doi: 10.2478/ace-2019-0024.
• [13] M. Waga, E. Radziszewska-Zielina, and B. Sroka, “The role of the technical due diligence process at the stage of land acquisition for construction investment,” Struct Environ., vol. 16, pp. 213–221, 2024, doi: 10.30540/sae-2024-020.
• [14] S. Reich, “Technical Due Diligence”, in Real Estate Due Diligence, vol. Part F614, Springer, 2018. doi: 10.1007/978-3-319-62510-2_5.
• [15] “New guidance note: Building surveys and technical due diligence of commercial property,” Struct. Surv., vol. 29, no. 2, pp. 39–41, May 2011, doi: 10.1108/SS.2011.11029BAE.010.
• [16] J. Surma, Business Intelligence: Making Decisions Through Data Analytics. Business Express Press, 2011. doi: 10.4128/9781606491867.
• [17] L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and regression trees. Chapman and Hall/CRC, 2017. doi: 10.1201/9781315139470.
• [18] A. Fattah, M.M. Fouad, P.S. Yu, and T.F. Gharib, “A Decision Tree Classification Model for University Admission System,” Int. J. Adv. Comput. Sci. Appl., vol. 3, no. 10, 2012, doi: 10.14569/ijacsa.2012.031003.
• [19] “Regulation of the Minister of Transport, Construction, and Maritime Economy on the determination of geotechnical conditions for the foundation of building structures,” Dz.U. 2012 poz. 463, Apr. 2012.
• [20] “Regulation of the Minister of the Environment of December 4, 2017, on the criteria for recognizing natural formations, both living and non-living, as natural monuments,” Dz.U.2017 poz.2300, Dec. 2017.
• [21] E. Kania, E. Radziszewska-Zielina, and G. Śladowski, “Communication and information flow in polish construction projects,” Sustainability (Switzerland), vol. 12, no. 21, 2020, doi: 10.3390/su12219182.
• [22] J.R. Quinlan, “Induction of Decision Trees,” Mach Learn, vol. 1, no. 1, pp. 81–106, 1986, doi: 10.1023/A:1022643204877.
• [23] C.E. Shannon, “A Mathematical Theory of Communication,” Bell Syst. Tech. J., vol. 27, no. 3, pp. 379–423, 1948, doi: 10.1002/j.1538-7305.1948.tb01338.x.
• [24] Y. Wang, Y. Li, Y. Song, X. Rong, and S. Zhang, “Improvement of ID3 algorithm based on simplified information entropy and coordination degree,” Algorithms, vol. 10, no. 4, p. 124, 2017, doi: 10.3390/a10040124.
• [25] “Regulation of the Minister of the Environment of September 1, 2016, on the method of assessing soil contamination,” Dz.U. 2016, poz. 1395, 2016.
• [26] “Act of July 23, 2003, on the Protection and Care of Monuments,” Dz.U. 2003 nr 162 poz. 1568, Jul. 2003.
• [27] Knight Frank, “Kraków commercial real estate market – 2025.” [Online]. Available: https://www.knightfrank.com.pl/research/krakow-rynek-nieruchomosci-komercyjnych-2025-11977.aspx (Accessed: Apr. 01, 2025).
• [28] Jones Lang LaSalle, “CEE Investment Market Perspective – 2024.” [Online]. Available: https://www.jll.pl/en/trends-and-insights/research/cee-investment-market(Accessed: Apr. 01, 2025).
• [29] B. Liu and Z. Sun, “Global Economic Market Forecast and Decision System for IoT and Machine Learning,” Mob. Inf. Syst., vol. 2022, p. 8344791, 2022, doi: 10.1155/2022/8344791.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2026).

PL

Numer podzielony. Artykuł z części: Special section on development trends in urban systems.