House Price Prediction Using LSTM

TL;DR

This paper compares ARIMA and LSTM models for predicting house prices in Chinese cities, demonstrating that LSTM, especially stateful and stacked variants, significantly improves prediction accuracy in time series data.

Contribution

The paper introduces the application of advanced LSTM architectures for house price prediction, outperforming traditional ARIMA models in accuracy.

Findings

LSTM models outperform ARIMA in prediction accuracy.

Stateful and stacked LSTM networks further improve results.

LSTM models are effective for time series house price forecasting.

Abstract

In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squared Error. The result shows that the LSTM model has excellent properties with respect to predict time series. Also, stateful LSTM networks and stack LSTM networks are employed to further study the improvement of accuracy of the house prediction model.