Analysis of Real Estate Predictions Based on Different Models

Abstract

As a matter of facts, real estate price prediction is meaningful for investors. This study conducts a comprehensive analysis of multiple real estate price prediction models, including genetic algorithms based on optimization algorithms, random forests based on machine learning, and recurrent neural networks (RNN) based on deep learning. The aim is to evaluate the strengths and weaknesses of these models in terms of predictive accuracy, explanatory power, and computational efficiency. Based on the analysis, genetic algorithms perform well in improving prediction accuracy and model convergence speed, but are sensitive to parameter selection and suffer from overfitting problems. Random forests improve prediction accuracy by integrating multiple decision trees, but are not interpretable enough and are susceptible to data noise. RNN has advantages in processing time series data, but requires a large amount of data and computing resources due to high computational complexity. Although each model has its limitations, it also has great potential. Through methods such as algorithm optimization, model integration, and feature engineering, the accuracy and reliability of real estate price prediction can be further improved. This study not only provides multi-angle model evaluation for real estate price prediction, but also provides development directions for future research.