Comparative Analysis of Linear and Non-Linear Regression Models in Predicting House Prices in Delta State, Nigeria

International audience This study investigates the comparative performance of linear and non-linear regression models in predicting house prices in Delta State, Nigeria, using a simulated dataset designed to mirror local housing market characteristics. Models evaluated include Ordinary Least Squares Linear Regression, Random Forest, and a feed-forward Neural Network. Performance metrics used are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². The analysis includes sensitivity checks on sample size and noise levels, feature importance analysis for tree-based models, and partial dependence interpretation. Findings indicate that non-linear models—particularly ensemble tree-based methods—tend to outperform linear regression in capturing complex interactions among housing attributes, although neural networks require larger datasets and careful regularization to avoid overfitting. Policy implications and recommendations for data collection, model selection, and future research are discussed, with an emphasis on improving housing information infrastructure in Delta State and Sub-Saharan Africa.