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Buenos Aires Real Estate Price Prediction

App Screenshot

Introduction

This project aims to predict the prices of apartments in Buenos Aires, Argentina, using a robust machine-learning model. The focus is on properties costing less than $400,000. Accurate price predictions are crucial for various stakeholders, including buyers, sellers, real estate agents, and policymakers. Our objective was to develop a reliable model despite the absence of temporal indicators.

Problem Statement

The goal is to identify significant features that accurately predict apartment prices in Buenos Aires and to achieve a Mean Absolute Error (MAE) of less than 50% compared to a baseline model.

Methodology

We followed a prescriptive methodology to guide our model development:

  1. Data Collection: Scraped 12,000+ apartment listings from real estate websites.
  2. Data Preprocessing: Cleaned the dataset by handling missing values, converting data types, removing duplicates, and normalizing features.
  3. Exploratory Data Analysis (EDA): Conducted to understand feature distributions and relationships.
  4. Feature Engineering: Extensively used to identify the most significant features.
  5. Modeling: Iteratively experimented with various models, including multiple versions of the Ordinary Least Squares (OLS) model.
  6. Handling Heteroskedasticity: Identified and addressed this issue to improve model accuracy.
  7. Final Model: Selected a Gradient Boosting Regressor based on performance, which achieved the best results.

Results

  • Optimal High Leverage Threshold: 0.0003
  • Optimal High Residual Threshold: 3
  • Mean Absolute Error (MAE): $23,809.9879
  • R²: 0.7863
  • MAE Improvement: 60% better than the baseline

The final model successfully handled heteroskedasticity and outperformed previous iterations.

Libraries Used

  • Python: Core language used for development.
  • Pandas: For data manipulation and analysis.
  • NumPy: For numerical operations.
  • Matplotlib: For data visualization.
  • Seaborn: For statistical data visualization.
  • Plotly: For interactive graphs.
  • Dash: For web-based application development.
  • Scikit-learn: For machine learning modeling.
  • Statsmodels: For statistical modeling.

Dependencies

Make sure to install the following dependencies before running the project:

pip install pandas numpy matplotlib seaborn plotly dash scikit-learn statsmodels