Melbourne Airbnb Price Prediction: Application of Ensemble Regression Models in Short-term Rental Pricing

This project builds an ensemble regression model to predict nightly prices based on 6000 Melbourne Airbnb listing data points. It aims to solve landlords' pricing challenges (strong subjectivity, incomplete information, etc.) and provide data-driven solutions for short-term rental pricing. The project covers the complete data science workflow, including feature engineering, model integration, and performance evaluation, with clear business value.

Airbnb landlords face pricing challenges: traditional methods rely on experience, with limitations such as strong subjectivity and information lag. Machine learning can learn the relationship between prices and listing features, geographic location, etc., through historical data analysis, providing accurate pricing recommendations. This project is based on this idea and uses Melbourne listing data to build the model.

The data source is approximately 6000 records of Melbourne Airbnb listings, with features including:

• Basic listing features: type, capacity, number of bedrooms/beds/bathrooms
• Geographic location: region, latitude and longitude, distance to city center, surrounding facilities
• Amenities and services: WiFi, air conditioning, kitchen, safety facilities, etc.
• Host reviews: identity verification, response rate, ratings, number of reviews
• Time factors: seasonality, holidays, advance booking time

Data Preprocessing: Cleaning (missing values, outliers), feature encoding (One-Hot/Label Encoding), feature selection (correlation analysis). Ensemble Models: Uses base learners such as Random Forest, Gradient Boosting, XGBoost/LightGBM, with integration strategies including simple averaging, weighted averaging, and stacking. Evaluation Metrics: MSE, RMSE, MAE, R², MAPE

1. Geographic location is the primary factor: Prices are higher near the CBD, scenic spots, and convenient transportation areas;
2. Listing type and capacity: The price of an entire apartment is higher than that of a private room, and the price increases non-linearly with the number of guests/bedrooms;
3. Amenities configuration: Essential amenities are the foundation, and value-added amenities (parking spaces, swimming pools) bring a premium;
4. Review data: High ratings, more reviews, and Superhost certification enhance pricing advantages;
5. Time factors: Price fluctuations are obvious in summer/holidays/major events (e.g., Australian Open)

1. New listing pricing recommendations: Provide initial price references for first-time landlords;
2. Dynamic pricing optimization: Adjust prices based on model predictions (identify underpricing/overpricing opportunities, seasonal adjustments);
3. Investment decision support: Evaluate potential returns, compare investment opportunities, optimize listing configuration;
4. Market analysis: Platform monitoring of abnormal pricing, supply-demand analysis, trend prediction

Limitations: Insufficient data timeliness, insufficient consideration of external factors (competitor pricing, macroeconomics), differences in personalized needs, difficulty in inferring causal relationships. Improvements: Introduce competitor/event/transportation data; adopt time-series models (ARIMA, Prophet); apply deep learning (neural networks, NLP, graph neural networks); personalized recommendations (user portraits, dynamic discounts); enhance interpretability (SHAP values, counterfactual explanations)

This project demonstrates the application value of machine learning in short-term rental pricing, providing decision support for landlords and platforms. For learners, it is a good practice project (complete workflow, feature engineering, model integration); for the industry, the implications are: emphasizing data-driven approaches, using ML as an auxiliary tool, continuous optimization, and fair pricing. In the future, more intelligent pricing tools will drive industry innovation.