Real Estate Price Prediction Using Machine Learning Models

Abstract

Accurate real estate price prediction is crucial in today’s market to aid buyers, sellers, and investors in making informed decisions. This study employs machine learning algorithms—specifically Linear Regression, Decision Tree Regression, and Random Forest Regression—to model and predict housing prices based on various influential features. The methodology involves data preprocessing, feature engineering, and model evaluation using standard metrics like R² score and Root Mean Squared Error (RMSE). The models are trained on real-world housing datasets, and results demonstrate the efficiency of ensemble learning over traditional linear approaches. This paper establishes that Random Forest offers the most accurate predictions and is suitable for practical applications in real estate.

Introduction

1. Objective

The study explores automated real estate price prediction using machine learning, aiming to overcome the limitations of traditional methods that rely on human judgment and comparable sales analysis. It compares three regression models:

• Linear Regression

• Decision Tree Regression

• Random Forest Regression

The goal is to assess their performance and suitability for real-world deployment in housing price prediction.

2. Dataset: Boston Housing Dataset

• Total Records: 506

• Features: 13 predictors + 1 target (MEDV, median home value)

• No missing values

Key Features:

• RM: Avg. number of rooms per dwelling

• LSTAT: % of lower-status population

• CHAS: Binary indicator for proximity to Charles River

• Other economic and geographic features like crime rate (CRIM), tax rate (TAX), and accessibility (RAD)

Data Preprocessing:

• Normalized features using Min-Max scaling

• Removed outliers in variables like CRIM, TAX, LSTAT

• CHAS required no encoding (already binary)

3. Related Work

• Linear Regression is easy to interpret but fails with non-linear data (Kumar & Singh, 2020).

• Tree-based models (Zhang & Lee, 2019) like Random Forest handle complex interactions and outperform simpler regressors.

• Deep Learning models (Patel & Mehta, 2021) perform well but need more data and lack interpretability.

• This paper focuses on interpretable, effective models for practical use.

4. Methodology

• Model Training: 80/20 train-test split with 5-fold cross-validation

• Random Forest Tuning: Used GridSearchCV for best parameters (e.g., number of trees, depth)

Models Used:

1. Linear Regression – Simple, interpretable baseline

2. Decision Tree – Captures non-linear patterns but can overfit

3. Random Forest – Ensemble model that improves accuracy and generalization

5. Evaluation Metrics

Two key metrics are used for performance comparison:

• R² Score (Coefficient of Determination):
  Measures how well the model explains variance in the target.

  • Higher R² = better fit

• RMSE (Root Mean Squared Error):
  Indicates average prediction error in $1000s.

  • Lower RMSE = more accurate predictions

Why Both?
R² shows model fit, RMSE shows error size—together, they provide a full performance picture.

Conclusion

This study demonstrates the effectiveness of machine learning algorithms in predicting real estate prices using structured housing data. Using the Boston Housing dataset, we evaluated three regression models: Linear Regression, Decision Tree Regression, and Random Forest Regression. The models were assessed based on their R² Score and RMSE performance on test data. The results indicate that Random Forest Regression outperforms the other models, achieving the highest R² score (0.89) and the lowest RMSE (4.12). Its ensemble nature helps overcome overfitting and improves generalization, making it well-suited for regression tasks with moderately sized datasets. Linear Regression served as a reliable and interpretable baseline, while Decision Tree Regression showed overfitting tendencies. The top features influencing housing prices were the number of rooms (RM), the percentage of lower-status population (LSTAT), and the pupil-teacher ratio (PTRATIO). These insights reaffirm the impact of both physical and socioeconomic attributes on real estate value. Overall, this research highlights how data-driven approaches can augment or replace traditional real estate valuation techniques. When properly trained and validated, machine learning models offer fast, accurate, and scalable solutions for property price estimation.

References

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Copyright

Copyright © 2025 Sairaj Nagula. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.