Cognitive Automation of Real Property Management Processes.

• Autorzy: Dariusz Nowak-Nova
• Języki publikacji: EN

Abstrakty

EN

The article discusses the possibility of using Automated Valuation Models (AVM), en-hanced with technologies such as machine learning algorithms and artificial neural net-works, for cognitive processing in the field of Facility Management. Experiments veri-fying the behaviour of a cognitive inference machine in the processes of operational real property management were described. The focus was to assess the functioning of decision service algorithms induced by automated inference engines which operated in the context of general information about real property. The research results confirm that the cognitive perspective, combined with advanced technologies, brings significant benefits. AVMs supplemented with cognitive technologies are able to dynamically react to varying market conditions and real property characteristics. The key aspect of the cognitive approach is that it eliminates the constraints related to different input states and lack of information, which increases the flexibility and efficiency of the processes of operational real property management. This means that real property managers can make more informed decisions while also saving time and resources. The conclusions drawn from the research confirm the importance of using modern technologies in Facility Management and open up new perspectives for the automation of management processes.(original abstract)

Słowa kluczowe

EN

Real estate management; Pricing models; High-tech

PL

Zarządzanie nieruchomościami; Modele wyceny; Nowe technologie

• Czasopismo: Forum Scientiae Oeconomia
• Rocznik: 2024
• Tom: 12
• Numer: nr 1
• Strony: 118--138

Twórcy

autor

Dariusz Nowak-Nova

• WSB University, Poland

Bibliografia

• 1.Adamskiy, D., Bellotti, A., Dzhamtyrova, R., & Kalnishkan, Y. (2019). Aggregating algorithm for prediction of packs. Machine Learning, 108, 1231-1260.https://doi.org/10.1007/s10994-018-5769-2.
• 2.Bergadano, F., Bertilone, R., Paolotti, D., & Ruffo, G. (2019). Learning real estate automated valuation models from heterogeneous data sources.arXiv preprint arXiv:1909.00704
• 3.Bilgilioğlu, S. S., & Yılmaz, H. M. (2023). Comparison of different machine learning models for mass appraisal of real estate.Survey review,55(388), 32-43. https://doi.org/10.1080/00396265.2021.1996799.
• 4.vom Brocke, J., Recker, J. &Mendling, J. (2010). Value-oriented process modeling: Integrating financial perspectives into business process re-design. Business Process Management Journal, 16(2), 333-356.
• 5.Carranza, J. P., Piumetto, M. A., Lucca, C. M., & Da Silva, E. (2022). Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values.Land use policy,119, 106211. https://doi.org/10.1016/j.landusepol.2022.106211
• 6.Cheng, J. C., Chen, W., Tan, Y., & Wang, M. (2016, July). A BIM-based decision support system framework for predictive maintenance management of building facilities. InProceedings of the 16th International Conference on Computing in Civil and Building Engineering (ICCCBE2016)(pp. 711-718)
• 7.Dimopoulos, T., & Bakas, N. (2019). Sensitivity analysis of machine learning models for the mass appraisal of real estate. Case study of residential units in Nicosia, Cyprus.Remote sensing,11(24), 3047https://doi.org/https://doi.org/10.3390/rs11243047
• 8.Engel, C., Ebel, P. &Leimeister, J. M. (2022). Cognitive automation.Electronic Markets. Springer Science and Business Media Deutschland GmbH, 32(1), 339-350. https://doi.org/10.1007/s12525-021-00519-7
• 9.Es-Soufi, W., Yahia, E., & Roucoules, L. (2016). On the use of Process Mining and Machine Learning to support decision making in systems design. InProduct Lifecycle Management for Digital Transformation of Industries: 13th IFIP WG 5.1 International Conference, PLM 2016, Columbia, SC, USA, July 11-13, 2016, Revised Selected Papers 13(pp. 56-66). Springer International Publishing.https://doi.org/10.1007/978-3-319-54660-5_6
• 10.Gallin, J., Molloy, R., Nielsen, E. R., Smith, P. A., & Sommer, K. (2018). Measuring aggregate housing wealth: New insights from an automated valuation model.Measuring Aggregate Housing Wealth: New Insights from an Automated Valuation Model (September, 2018). FEDS Working Paperm 2018-64, 1-43. https://doi.org/10.17016/FEDS.2018.064
• 11.Garcez, A. D. A., Gori, M., Lamb, L. C., Serafini, L., Spranger, M., & Tran, S. N. (2019). Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning.arXiv preprint arXiv:1905.06088
• 12.Gathani, S., Hulsebos, M., Gale, J., Haas, P. J., & Demiralp, Ç. (2021). Augmenting decision making via interactive what-if analysis.arXiv preprint arXiv:2109.06160.
• 13.Geng, B. &Varshney, P. K. (2023). Human-Machine Collaboration for Smart Decision Making: Current Trends and Future Opportunities, arXiv preprint. http://arxiv.org/abs/2301.07766.
• 14.I.A.A.O. (2018) Standard on Automated Valuation Models (AVMs) -2018. Kansas City, MO.: International Association of Assessing Officers.
• 15.ISO 41011:2017 (2017) ISO 41011:2017 -Facility management -Vo c a b u l a r y.
• 16.Jensen, P. A. (2010). The facilities management value map: A conceptual framework.Facilities, 28(3-4), 175-188. https://doi.org/10.1108/02632771011023131.
• 17.Kara, A., Çağdaş, V., Işıkdağ, Ü., & Turan, B. O. (2018). Towards harmonizing property measurement standards. Journal of Spatial Information Science, 17, 87-119.
• 18.Kaur, S., Gupta, S., Singh, S. K., & Perano, M. (2019). Organizational ambidexterity through global strategic partnerships: A cognitive computing perspective.Technological Forecasting and Social Change,145, 43-54. https://doi.org/10.1016/j.techfore.2019.04.027
• 19.Tudzi, E. P., Gavu, E. K., Ayitey, J. Z., & Boakye-Agyeman, N. A. (2015). Corporate real estate management: a survey of literature. InProceedings of 4th International Conference on Infrastructure Development in Africa (ICIDA)(pp. 720-740).
• 20.Kok, N., Koponen, E.-L. &Martínez-Barbosa, C. A. (2017). Big Data in Real Estate? From Manual Appraisal to Automated Valuation. The Journal of Portfolio Management, 43(6), 202-211. https://doi.org/10.3905/jpm.2017.43.6.202
• 21.Lemaignan, S., Warnier, M., Sisbot, E. A., Clodic, A., & Alami, R. (2017). Artificial cognition for social human-robot interaction: An implementation.Artificial Intelligence,247, 45-69.
• 22.Lorenz, F., Willwersch, J., Cajias, M., & Fuerst, F. (2023). Interpretable machine learning for real estate market analysis.Real estate economics,51(5), 1178-1208. https://doi.org/10.1111/1540-6229.12397
• 23.Malibari, N., Katib, I. &Mehmood, R. (2023). Systematic Review on Reinforcement Learning in the field of Fintech, arXiv preprint arXiv:2305.07466.
• 24.Manko, B. A. (2022). Data Analytics in Real EstateHow Zillow.com uses social media information. International Journal of Teaching and Education, X(1), 27-33.
• 25.Matarneh, S. T., Danso-Amoako, M., Al-Bizri, S., Gaterell, M., & Matarneh, R. (2019). Building information modeling for facilities management: A literature review and future research directions.Journal of Building Engineering,24, 100755.
• 26.Mayer, M., Bourassa, S. C., Hoesli, M., & Scognamiglio, D. (2019). Estimation and updating methods for hedonic valuation.Journal of European Real Estate Research,12(1), 134-150.
• 27.Nene, S. (2017). Deep Learning for Natural Language Processing. International Research Journal of Engineering and Technology, 04(11). www.irjet.net.
• 28.Nowak-Nova, D. (2018). Potencjał kognitywnej robotyzacji zaawansowanych procesów biznesowych. Mit czy rzeczywistość?. Przedsiębiorczość i Zarządzanie, 19(5, cz. 2), 163-176.
• 29.O.M.G.DMN Guide (2019) Decision Model And Notation: Version 1.2, OMG Document.
• 30.O.M.G.WfMC Specification (2000) Workflow Management Facility Specification, V1.2, OMG Document.
• 31.Office of Government Property (2021) Government Functional Standard -GovS 004: Property. http://www.nationalarchives.gov.uk/doc/open-government-licence/.
• 32.Office of Government Property (2022a) Facilities Management Standard FMS 001: Management and Services. http://www.nationalarchives.gov.uk/doc/open-government-licence/.
• 33.Office of Government Property (2022b) Facilities Management Standard FMS 002: Asset Data. http://www.nationalarchives.gov.uk/doc/open-government-licence/.
• 34.Opoku, A. and Lee, J. Y. (2022). The Future of Facilities Management: Managing Facilities for Sustainable Development. Sustainability (Switzerland), 14(3), 1705.https://doi.org/10.3390/su14031705
• 35.Pala, F. and Melzi, E. (2009). Facility Management organizational models. in Open facility management. Milano: IFMA, 85-104.
• 36.Pruszkowski, L. (2012). Facility Management jako innowacyjna koncepcja zarządzania procesami pomocniczymi. Innowacje w zarządzaniu i inżynierii produkcji, 214-225.
• 37.Ray, A., Padmanabhan, B. &Bouayad, L. (2023). Systemic Fairness. arXiv preprint. http://arxiv.org/abs/2304.06901.
• 38.Renigier-Biłozor, M., Chmielewska, A., Walacik, M., Janowski, A., & Lepkova, N. (2021). Genetic algorithm application for real estate market analysis in the uncertainty conditions.Journal of Housing and the Built Environment,36(4), 1629-1670. https://doi.org/10.1007/s10901-020-09815-8
• 39.Rousopoulou, V., Vafeiadis, T., Nizamis, A., Iakovidis, I., Samaras, L., Kirtsoglou, A., Georgiadis, K., Ioannidis,D.& Tzovaras, D. (2022). Cognitive analytics platform with AI solutions for anomaly detection.Computers in Industry,134, 103555. https://doi.org/10.1016/j.compind.2021.103555.
• 40.Sathish, B. &Venkataram, P. (2009). A dynamic authentication scheme for mobile transactions. International Journal of Network Security, 8(1), 59-74.
• 41.Steurer, M., Hill, R. J. &Pfeifer, N. (2021). Metrics for evaluating the performance of machine learning based automated valuation models. Journal of Property Research, 38(2), 99-129. https://doi.org/10.1080/09599916.2020.1858937
• 42.Śliwiński, A.,&Śliwiński, B. (2006).Facility management. CH Beck.
• 43.TEGoVA (2017).EVS 6 Automated Valuation Models (AVMs).
• 44.Tetzlaff, L. M. &Szepannek, G. (2022). mlr3shiny-State-of-the-art machine learning made easy. SoftwareX, 20, 101246.https://doi.org/10.1016/j.softx.2022.101246
• 45.Thomas, M. (2014) Zarządzanie utrzymaniem technicznym i konserwacją budynków. Oficyna Wydawnicza Politechniki Warszawskiej.
• 46.Valier, A. (2020). Who performs better? AVMs vs hedonic models. Journal of Property Investment and Finance, 38(3), 213-225. https://doi.org/10.1108/JPIF-12-2019-0157
• 47.Vathana, D., Patel, R.,&Bargoti, M. (2022). Real Estate Price Prediction Using Machine Learning Algorithm. in Advances in Intelligent Computing and Communication: Proceedings of ICAC 2021. Springer Science and Business Media Deutschland GmbH, 431-440. https://doi.org/10.1007/978-981-19-0825-5_47
• 48.Welck, M., Derdak, I. J.,&Veit, D. (2020). "Understanding Individuals" Perceptions Regarding Cognitive Computing Systems. in International Conference on Information Systems (ICIS) 2020, 1-9.
• 49.Xu, J., Lu, W., Xue, F., & Chen, K. (2019). 'Cognitive facility management': Definition, system architecture, and example scenario.Automation in Construction,107, 102922.
• 50.Xu, J., Lu, W., & Li, L.-H. (2019). Cognitive Facilities Management: Definition And Architecture. in International Conference on Smart Infrastructure and Construction 2019 (ICSIC). ICE Publishing, 115-122. https://doi.org/10.1680/icsic.64669.115

Identyfikatory

DOI

10.23762/FSO_VOL12_NO1_6