ORIGINAL ARTICLE
The use of urban indicators in forecasting a real estate value with the use of deep neural network
1
Faculty of Civil Engineering, Architecture and Environmental Engineering, University of Zielona Góra, 1 Prof. Z. Szafrana street, 65-516, Zielona Góra, Poland
2
Department of Computational Intelligence, Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, 24 Warszawska street, 31-155, Cracow, Poland
Submission date: 2018-08-27
Acceptance date: 2018-12-05
Online publication date: 2018-12-31
Publication date: 2018-12-01
Reports on Geodesy and Geoinformatics 2018;106:25-34
KEYWORDS
planning analysislocal zoning planlocal policydeep learningdeep neural networksmachine learningartificial intelligence
ABSTRACT
Records of municipal planning documents directly affect the land use. In this way, the market price of the land is also shaped. Awareness of the economic and social consequences of adapting specific solutions is the primary argument that should condition the local policy in terms of spatial planning. The research results indicate that the network trained with attributes which do not describe a property value by its price was able to estimate it with acceptable and satisfactory results. The possibility to use artificial multilayer networks in spatial policy decision-making seems well founded. The research results show the relevance of the assumption that using them for modeling can be helpful in selecting the most advantageous variant of planning arrangements in a local law document which determines the land use and development, therefore impacts its value.
REFERENCES (28)
1.
Act (2017). Act of 27 March 2003 on spatial planning and development. Official Journal 2017, item 1073, 1566 as amended.
2.
Act (2018). Act of 21 August 1997 on real estate management. Official Journal 2018, item 121.
3.
Amakdouf, H., El Mallahi, M., Zouhri, A., Tahiri, A., and Qjidaa, H. (2018). Classification and recognition of 3D image of Charlier moments using a multilayer perceptron architecture. Procedia Computer Science, 127:226-235, doi: 10.1016/j.procs.2018.01.118.
4.
Bataineh, M. and Marler, T. (2017). Neural network for regression problems with reduced training sets. Neural Networks, 95:1-9, doi: 10.1016/j.neunet.2017.07.018.
5.
Bazan-Krzywoszanska, A., Mrówczynska, M., and Tront, S. (2019). GIS technology, 3D models and mathematical models as a tool for assessing development capabilities of flood risk land to make arrangements of municipal planning documents. Journal of Ecological Engineering, 20(1):25-33, doi: 10.12911/22998993/93866.
6.
Cymerman, R. (2008). Planowanie przestrzenne dla rzeczoznawców majatkowych, zarzadców oraz posredników w obrocie nieruchomosciami. Educaterra.
7.
Cymerman, R., Grabowski, R., and Gwiazdzinska, M. (1999). Wartosc nieruchomosci jako czynnik kreujacy zagospodarowanie przestrzenne. In Materiały VII Konferencji Naukowej Towarzystwa Naukowego Nieruchomosci nt.„Wartosc nieruchomosci w gospodarowaniu przestrzenia”, page 22. Wydawnictwo ART, Olsztyn.
8.
Ertugrul, Ö. F. (2018). A novel type of activation function in artificial neural networks: Trained activation function. Neural Networks, 99:148-157, doi: 10.1016/j.neunet.2018.01.007.
9.
García, N., Gámez, M., and Alfaro, E. (2008). ANN+ GIS: An automated system for property valuation. Neurocomputing, 71(4-6):733-742, doi: 10.1016/j.neucom.2007.07.031.
10.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press Cambridge.
11.
Hofmann, M. and Klinkenberg, R. (2013). RapidMiner: Data mining use cases and business analytics applications. CRC Press Taylor and Francis Group.
12.
Jasinski, T. and Bochenek, A. (2016). Prognozowanie cen nieruchomosci lokalowych za pomoca sztucznych sieci neuronowych. Studia i Prace WNEiZ US, 45(1):317-327, doi: 10.18276/sip.2016.45/1-25.
13.
Juszczyk, M., Lesniak, A., and Zima, K. (2018). ANN based approach for estimation of construction costs of sports fields. Complexity, 2018, doi: 10.1155/2018/7952434.
14.
Łaguna, T., Łaguna, D., and Lendzin, M. (2004). Identy- fikacja skutków uchwalenia i realizacji miejscowego planu zagospodarowania przestrzennego. In Łaguna, T., editor, Ekonomiczne aspekty gospodarki przestrzennej. Wyd. Ekonomia i Srodowisko, Białystok.
15.
Laskowska, E. (2008). Zmiana przeznaczenia gruntu a jego wartosc. Zeszyty Naukowe Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie. Problemy Rolnictwa Swiatowego, 5(20):42-49.
16.
Lesniak, A. and Juszczyk, M. (2018). Prediction of site overhead costs with the use of artificial neural network based model. Archives of Civil and Mechanical Engineering, 18(3):973- 982, doi: 10.1016/j.acme.2018.01.014.
17.
Liu, X.-s., Zhe, D., and Wang, T.-l. (2011). Real estate appraisal system based on GIS and BP neural network. Transactions of Nonferrous Metals Society of China, 21:s626-s630, doi: 10.1016/S1003-6326(12)61652-5.
18.
Minli, Z. and Yueran, G. (2010). Application of BP neural networks to the real estate valuation. In Proceedings of 2010 International Conference on Construction and Real Estate Management, December 1st-3rd, 2010, Brisbane, Australia.
19.
Mrówczynska, M. (2015). Studium nad doborem metod inteligencji numerycznej do rozwiazywania problemów z geodezji inzynieryjnej. Oficyna Wydawnicza Uniwersytetu Zielonogórskiego.
20.
Mrówczynska, M., Łaczak, A., Bazan-Krzywoszanska, A., and Skiba, M. (2018). Improving energy efficiency with the risk of investment of reference to urban development of Zielona Góra. Tehnicki Vjesnik, 25(3):916-922, doi: 10.17559/TV-20161212120336.
21.
Quinlan, J. R. (1992). Learning with continuous classes. In 5th Australian Joint Conference on Artificial Intelligence, volume 92, pages 343-348. World Scientific.
22.
Regulation (2004). Regulation of the Council of Ministers of 21 September 2004 on the property valuation and appraisal reports. Official Journal 2004, no. 207, item 2109.
23.
Szewranski, S., Kazak, J., Zmuda, R., and Wawer, R. (2017). Indicator-based assessment for soil resource management in the Wrocław Larger Urban Zone of Poland. Polish Journal of Environmental Studies, 26(5):2239-2248, doi: 10.15244/pjoes/70178.
24.
Taffese, W. Z. (2007). Case-based reasoning and neural networks for real estate valuation. In Artificial Intelligence and Applications, pages 98-104. ACTA Press Anaheim.
25.
Wang, X., Yan, Y., Tang, P., Bai, X., and Liu, W. (2018). Revisiting multiple instance neural networks. Pattern Recognition, 74:15-24, doi: 10.1016/j.patcog.2017.08.026.
26.
Xu, Q., Deng, K., Jiang, C., Sun, F., and Huang, X. (2017). Composite quantile regression neural network with applications. Expert Systems with Applications, 76:129-139, doi: 10.1016/j.eswa.2017.01.054.
27.
Yeh, I.-C. and Hsu, T.-K. (2018). Building real estate valuation models with comparative approach through casebased reasoning. Applied Soft Computing, 65:260-271, doi: 10.1016/j.asoc.2018.01.029.
28.
Zhang, Z., Ma, X., and Yang, Y. (2003). Bounds on the number of hidden neurons in three-layer binary neural networks. Neural networks, 16(7):995-1002, doi: 10.1016/S0893- 6080(03)00006-6.
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