Housing Price Forecasting Using AI (LSTM)

Forecasting the asset prices in any market is an inseparable and critical part of research on markets. Obtaining a credible prediction of an asset's potential future price provides valuable information for investors. Besides that, being confronted with real estate price shocks and price fluctuations in alternative markets, determining the best time for the investment may come up as a big challenge for any investor. Tracing the trend of housing price changes in Iran shows that the average housing price has had a general trend close to other prices and price indices; but the significant issue in this market is the various growth processes and dynamics, compared to changes in other economic indicators. This dynamic gets more complex when a variety of quantitative and qualitative data from diverse types and from several markets are added to the model. This diversity along with the unstructured, stochastic, and scattered datasets makes the implementation of the model so hard to do. The main goal of this paper is to build an Artificial Intelligence model with the most flexibility in the input part for the data type heterogeneity and the lowest error in the output part as the predicted price. All the implemented models showed a high accuracy on the real data extracted from the housing market.

Artificial Intelligence models have an enormous capacity to get a broad range of any kind of data and process and concurrently compile them to build a valid output. This characteristic of AI is very useful in financial models to increase the accuracy of output. Our model is based on the Recurrent Neural Networks. Due to its capability to preserve past information, the LSTM algorithm was implemented as a time series forecasting model.

In this study, using diverse types of official datasets, such as the Central Bank of Iran, we distinguish the influencing variables in the housing market and then we could predict the average housing price in Tehran. Our findings indicate that the average housing price demonstrates the strongest correlation with gold prices, foreign exchange rates, the Consumer Price Index, and market liquidity levels. Utilizing these indicators, predictions with very high accuracy were obtained.

Among the four different models of this research, the best prediction belonged to the multivariate stacked-LSTM model, which was empowered by the highly correlated macroeconomic variables. The validation of models was done by the MAPE Indicator. Furthermore, all the results confirmed that the LSTM algorithm was highly effective in utilizing over two decades of actual housing data from Tehran to forecast future prices.