Assessment of housing construction dynamics in Russia on the basis of neural network modeling
- DOI
- 10.2991/iscde-19.2019.51How to use a DOI?
- Keywords
- neural networks, Kohonen maps, modeling, digital technology, data processing, housing construction
- Abstract
This article discusses the application of neural network modeling to analyze the dynamics of housing construction in Russia. The analysis of construction for the period of 2015-2018 is carried out. The main economic factors affecting the pace of construction are determined. The article presents a brief overview of the software and tools used for data mining, self-organizing Kohonen maps, and describes the technique of neural network modeling of construction activities. The study is limited to the period of 4 years and the identified several key economic indicators. Statistical information from official sources was used as the initial data for construction of the model. As a result of the study, there was an overall decline in the rate of housing construction during the period under review. The impact of income on the pace of construction is identified. Regions of Russia with high rates of housing construction by 2018 have been named, as well as regions with potential opportunities to increase the volume of housing construction in the near future. Thus, the results of the study are of practical importance and can help in the implementation of the planned plans of housing construction in Russia.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - L.E. Surkova PY - 2019/12 DA - 2019/12 TI - Assessment of housing construction dynamics in Russia on the basis of neural network modeling BT - Proceedings of the International Scientific and Practical Conference on Digital Economy (ISCDE 2019) PB - Atlantis Press SP - 935 EP - 939 SN - 2352-5428 UR - https://doi.org/10.2991/iscde-19.2019.51 DO - 10.2991/iscde-19.2019.51 ID - Surkova2019/12 ER -