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Housing prices in California: an approach to forecasting in the real estate sector
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Author
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- Keywords:
- Machine Learning, Price Prediction, Real Estate Sector, Random Forest, Linear Regression
- Abstract
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This paper presents a comparative analysis of machine learning techniques applied to price forecasting in the California real estate sector. The models investigated were Multiple Linear Regression, Polynomial Regression, Robust Regression (RANSAC) and Random Forest, each one being evaluated based on statistical metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Coefficient of Determination (R²). The dataset used was obtained from the StatLib repository and contains information on property characteristics, location and socioeconomic profile of the population. The results indicate that, although Random Forest presents better predictive performance, there are signs of overfitting, suggesting that an increase in the number of samples could improve the generalization of the model. On the other hand, the Linear Regression and Polynomial Regression models demonstrated greater stability and generalization capacity, although with a slight loss of accuracy. This study contributes to the understanding of the applicability of these techniques in real estate price modeling and discusses the impacts of sample size on model accuracy.
- Author Biography
- References
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- 2025-03-17
- Section
- OPERATIONAL RESEARCH
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Copyright (c) 2025 Christian Gianelli da Silva (Autor)
This work is licensed under a Creative Commons Attribution 4.0 International License.
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