Advanced Real Estate Price Forecasting Using Multivariate Long Short-Term Memory Networks

Forecasting real estate prices is critical in making financial decisions, formulating investment strategies, and designing plans for urban development. Predictive models can enable stakeholders to detect the market's future trends and make meaningful decisions. Forecasting methods often fail to extract complex nonlinear patterns from economic indicators, interest rates, population, and regional trends in the real estate market. The paper proposes a multivariate LSTM network for advanced real estate price forecasting. The input features under consideration for the proposed model are longitude, latitude, median age of housing, total rooms, total bedrooms, population, households, median income, median house value, and ocean proximity. The multivariate technique offers a better facility for capturing time dependency and relations between varied variables. Therefore, the model performs well in correctly predicting house price fluctuations. This model was tested on housing prices in different regions and related economic indicators. We evaluate the performance of our model in terms of actual versus predicted prices and residual plots. We used cross-validation. The model proved robust and efficient in pricing predictions under different market conditions. All the standard metrics, including RMSE, MAE, and R-squared values, proved the Multivariate LSTM model is highly effective. However, the promising result would have been bettered further if sentiment analysis was incorporated with the dataset-the same thing that would have also improved the precision of the model.