Russian housing price forecast based on BP neural Network

Sidi Du; Xiqing Liu; Songze Shi; Guanchen Zhou

doi:10.54691/bcpbm.v38i.3766

Authors

Sidi Du
Xiqing Liu
Songze Shi
Guanchen Zhou

DOI:

https://doi.org/10.54691/bcpbm.v38i.3766

Keywords:

PCA model; BP model; feature importance; price.

Abstract

Contemporarily, Russia's real estate market has huge potential for investors. The purpose of this paper is to investigated the impact of various factors on the real estate market price based on various statistical models. We collected past housing price data from the Internet. Subsequently, PCA model was adopted to reduce the dimension of the data to simplify the calculation. Finally, the BP model is implemented to predict the price changes in the real estate market. After calculation, our forecast data and the reality of housing prices are slightly different, but the forecast of the real estate market is consistent with the reality. In order to further find the influence of various factors on housing price, we also calculated the characteristic importance of different factors and found the most important influencing factor, i.e., the region. These results can provide investors with an idea to analyze the real estate market prices, and help policy makers find the real estate market prices in line with the market rules to avoid the occurrence of bubbles.

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