ORIGINAL PAPER
Real estate management using a cognitive reasoning machine
 
More details
Hide details
1
Akademia WSB w Dąbrowie Górniczej
 
 
Online publication date: 2023-03-20
 
 
Publication date: 2023-03-20
 
 
NSZ 2023;18(1):29-48
 
KEYWORDS
ABSTRACT
The article presents the possibility of using Automatic Valuation Models (AVMs), extended with technologies of Machine Learning algorithms and Neural Networks, for cognitive processing in the area of Facility Management. Experiments simulating, in the processes of operational management of real estate, of AVMs’s behavior in a cognitive reasoning machine, have been described. The correctness of operation of decision service algorithms, triggered by automated inference engines, has been examined for generalization of information on the property and the planning process using the algorithms. The key findings of the study confirm that the adoption of a cognitive perspective for AVMs and the application of algorithm technology and artificial neural networks in the operational management of real estate, increases the productivity of the processes, and, thus brings benefits the managing entity.
 
REFERENCES (29)
1.
Babu, S., Venkataram, P., 2009. A Dynamic Authentication Scheme for Mobile Transactions, International Journal of Network Security, nr 8(1), s. 59-74.
 
2.
Bilgilioğlu, S.S., Hacı, M.Y., 2021. Comparison of Different Machine Learning Models for Mass Appraisal of Real Estate, Survey Review, November, s. 1-12.
 
3.
Bucoń, R., Tomczak, M., 2018. Decision-Making Model Supporting the Process of Planning Expenditures for Residential Building Renovation, Technological and Economic Development of Economy, nr 24 (3), s. 1200-1214.
 
4.
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, nr 119 (August).
 
5.
Cheng, J.C.P., Chen, W., Tan, Y., Wang, M., 2016. A BIM-Based Decision Support System Framework for Predictive Maintenance Management of Building Facilities, 16th International Conference on Computing in Civil and Building Engineering, Osaka.
 
6.
Gavu, K.E., Tudzi, E.P., Ayitey, J.Z., 2015. Corporate Real Estate Management: A Survey of Literature, Conference: 4th International Conference on Infrastructure Development in Africa (ICIDA), Kumasi, Ghana.
 
7.
I.A.A.O., 2018. Standard on Automated Valuation Models (AVMs) – 2018, Kansas City, MO: International Association of Assessing Officers.
 
8.
IBM, 2023, https://www.ibm.com/developerw... (dostęp: 9.02.2033).
 
9.
ISO 41011:2017, 2017. ISO 41011:2017 – Facility Management – Vocabulary.
 
10.
Kara, A., Çağdaş, V., Işıkdağ, U., Bulent, O.T., 2018. Towards Harmonizing Property Measurement Standards, Journal of Spatial Information Science, nr 17, s. 87-119.
 
11.
Kaur, S., Shivam, G., Sanjay, K.S., Mirko, P., 2019. Organizational Ambidexterity through Global Strategic Partnerships: A Cognitive Computing Perspective, Technological Forecasting and Social Change, nr 145, s. 43-54.
 
12.
Kozicki, B., Mitkow, Sz., Sowa, B., 2021. Prognozowanie w obszarze zakupu nieruchomości w Polsce na 2021 rok w aspekcie bezpieczeństwa ekonomicznego, Nowoczesne Systemy Zarządzania, nr 16 (2), s. 23-37.
 
13.
Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R., 2017. Artificial Cognition for Social Human-Robot Interaction: An Implementation, Artificial Intelligence, vol. 247, June, s. 45-69.
 
14.
Lorenz, F., Willwersch, J., Cajias, M., Fuerst, F., 2022. Interpretable Machine Learning for Real Estate Market Analysis, Real Estate Economics, s. 1-31.
 
15.
Mayer, M., Bourassa, S., Hoesli, M., Scognamiglio, D., 2019. Estimation and Updating Methods for Hedonic Valuation, Journal of Real Estate Research, nr 33 (3), s. 87-349.
 
16.
Nowak-Nova, D., 2018. Potencjał kognitywnej robotyzacji zaawansowanych procesów biznesowych. Mit czy rzeczywistość?, Przedsiębiorczość i Zarządzanie, nr 19 (5, cz. 2), s. 76-163.
 
17.
O.M.G.DMN Guide, 2019. Decision Model and Notation: Version 1.2. OMG Document.
 
18.
O.M.G.WfMC Specification, 2000. Workflow Management Facility Specification, V1.2. OMG Document.
 
19.
Pruszkowski, L., 2012. Facility Management jako innowacyjna koncepcja zarządzania procesami pomocniczymi, Innowacje w Zarządzaniu i Inżynierii Produkcji, nr 16, s. 25-214.
 
20.
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, nr 36 (4), s. 70-1629.
 
21.
Steurer, M., Hill, R.J., Pfeifer, N., 2021. Metrics for Evaluating the Performance of Machine Learning Based Automated Valuation Models, Journal of Property Research, nr 38 (2), s. 99-129.
 
22.
Śliwiński, A., Śliwiński, B., 2006. Facility Management, Warszawa: C.H. Beck.
 
23.
Śmietana, K., 2013. Benchmarking w Zarządzaniu Wartością Nieruchomości Przedsiębiorstw, Finanse, Rynki Finansowe, Ubezpieczenia, nr 64 (1), s. 61-451.
 
24.
TEGoVA, 2017. EVS 6 Automated Valuation Models (AVMs).
 
25.
Thomas, W., 2014. Operations Research Vis-a-vis Management at Arthur D. Little and the Massachusetts Institute of Technology in the 1950s, Business History Review, nr 86, s. 99-122.
 
26.
TIBCO, 2023, https://www.tibco.com/resource... (dostęp: 9.02.2033).
 
27.
vom Brocke, J., Recker, J., Mendling, J., 2010. Value-Oriented Process Modeling: Integrating Financial Perspectives into Business Process Re-Design, Business Process Management Journal, nr 16 (2).
 
28.
Welck, M., Derdak, I.J., Veit, D., 2020. Understanding Individuals Perceptions Regarding Cognitive Computing Systems, International Conference on Information Systems (ICIS), s. 1-9.
 
29.
Xu, J., Weisheng, L., Fan, X., Ke, Ch., 2019. Cognitive Facility Management: Definition, System Architecture, and Example Scenario, Automation in Construction, nr 107(2), s. 1-25.
 
eISSN:2719-860X
ISSN:1896-9380
Journals System - logo
Scroll to top