DOI: 10.36347/sjet.2021.v09i10.001

Pages: 158-163

In recent years, housing prices across the country have been on a slow upward trend, and the real estate market is crisscrossed, and Shenyang is no exception. This paper chooses the Elman neural network that can process dynamic time series information to predict the trend of average housing prices in Shenyang. The article selects the average house price of Shenyang for 72 months from September 2015 to August 2021 in Shenyang, using 7 months as a set of training samples, the first 6 months as input data, and the seventh month as output data .There are 66 groups in total, the first 58 groups are used as training sets, and the last 8 groups are used as test sets. At the same time, the BP neural network and the RBF neural network are used for the same prediction, and the prediction results of the three are compared and analyzed. It was found that both the Elman neural network and the BP neural network are better than the RBF neural network in predicting performance. In terms of error details, since the Elman neural network can better process time series data, compared to the BP neural network, the predicted value of the Elman neural network is closer to the true value, and the predictability is better.