<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents the results of research on the application of neural networks modelling of municipal real estate market rent rates. The test procedure was based on selected networks trained on the local real estate market data and transformation of the detected dependencies – through established models – to estimate the potential market rent rates of municipal premises. On this basis, the assessment of the adequacy of the actual market rent rates of municipal properties was made. Empirical research was conducted on the local real estate market of the city of Olsztyn in Poland. In order to describe the phenomenon of market rent rates formation an unidirectional three-layer network and a network of radial base was selected. Analyses showed a relatively low degree of convergence of the actual municipal rent rents with potential market rent rates. This degree was strongly varied depending on the type of business ran on the property and its’ social and economic impact. The applied research methodology and the obtained results can be used in order to rationalize municipal property management, including the activation of rental policy.</p></abstract>

This paper presents the results of research on the application of neural networks modelling of municipal real estate market rent rates. The test procedure was based on selected networks trained on the local real estate market data and transformation of the detected dependencies – through established models – to estimate the potential market rent rates of municipal premises. On this basis, the assessment of the adequacy of the actual market rent rates of municipal properties was made. Empirical research was conducted on the local real estate market of the city of Olsztyn in Poland. In order to describe the phenomenon of market rent rates formation an unidirectional three-layer network and a network of radial base was selected. Analyses showed a relatively low degree of convergence of the actual municipal rent rents with potential market rent rates. This degree was strongly varied depending on the type of business ran on the property and its’ social and economic impact. The applied research methodology and the obtained results can be used in order to rationalize municipal property management, including the activation of rental policy.

Neural Networks Modelling of Municipal Real Estate Market Rent Rates

University of Warmia and Mazury in Olsztyn, The Faculty of Geodesy, Geospatial and Civil Engineering, Department of Real Estate Resources, Prawocheńskiego Street 15, 10-724 Olsztyn,

University of Warmia and Mazury in Olsztyn, The Faculty of Geodesy, Geospatial and Civil Engineering, Department of Land Management and Regional Development, Prawocheńskiego Street 15, 10-724 Olsztyn,

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Neural Networks Modelling of Municipal Real Estate Market Rent Rates"}

This paper presents the results of research on the application of neural networks modelling of municipal real estate market rent rates. The test procedure was...

Neural Networks Modelling of Municipal Real Estate Market Rent Rates

Publié en ligne: 04 avr. 2017

Pages: 17 - 28

Reçu: 19 nov. 2015

Accepté: 16 nov. 2016

DOI: https://doi.org/10.1515/foli-2016-0022

Mots clés
municipal real estate stock management, artificial neural networks, property rent market

© 2016 University of Szczecin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Neural Networks Modelling of Municipal Real Estate Market Rent Rates

Publié en ligne: 04 avr. 2017

Pages: 17 - 28

Reçu: 19 nov. 2015

Accepté: 16 nov. 2016

DOI: https://doi.org/10.1515/foli-2016-0022

Mots clésmunicipal real estate stock management, artificial neural networks, property rent market

© 2016 University of Szczecin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
municipal real estate stock management, artificial neural networks, property rent market