Machine Learning for Economic Forecasting: Understanding US Housing Market Trends, Stock Predictions, and Consumer Sales Strategies

Abstract

The rapid growth of machine learning (ML) technologies has opened new avenues for economic forecasting, particularly in complex markets such as the U.S. housing market, stock market predictions, and consumer sales strategies. This study explores the application of various machine learning models—including Random Forest (RF), Gradient Boosting (GB), and Long Short-Term Memory (LSTM) networks—to analyze and predict trends in the U.S. housing market, stock market, and consumer sales behaviors. By leveraging historical data from real estate transactions, stock price movements, and consumer behavior metrics, this research evaluates the effectiveness of these models in forecasting economic indicators such as housing price fluctuations, stock market performance, and shifts in consumer sales strategies. The study highlights the strengths and limitations of each model, providing a comparative analysis of their predictive accuracy and interpretability. Results demonstrate that LSTM networks excel in capturing time-series patterns and sequential trends, particularly for stock market predictions and housing market forecasting, while ensemble methods like RF and GB offer valuable insights into feature importance and multivariate relationships in consumer sales strategies. The findings provide significant implications for financial institutions, policymakers, and businesses seeking to enhance their decision-making processes through machine learning-driven insights.