Real Estate - Predicting Future Sale Prices
Historical Sales Data
Past Sales Prices
Sales Volume
Geographical Data
Time series analysis
Market trends
Location-based trends
Neighborhood analysis
Number of transactions
Seasonal trends
Property Features
Physical Attributes
Structural Attributes
Size (square footage)
Number of bedrooms
Number of bathrooms
Age of the property
Renovations and upgrades
Type of property (single-family, condo, townhouse)
Architectural style
Building materials
External Factors
Economic Indicators
Market Conditions
Government Policies
Interest rates
Employment rates
Inflation
Supply and demand dynamics
Competition analysis
Competition analysis
Zoning laws
Subsidies
Data Collection and Processing
Data Sources
Data Cleaning
Data Integration
Real estate databases (e.g., MLS)
Government records
Property registries
Handling missing data
Data normalization
Combining multiple datasets
Ensuring data consistency
Predictive Modeling
Machine Learning Algorithms
Feature Engineering
Model Evaluation
Regression analysis (linear, polynomial)
Decision trees
Random forests
Neural networks
Selecting relevant features
Creating new features
Cross-validation
Performance metrics (RMSE, MAE)
Implementation
Software Tools
Deployment
Programming languages (Python, R)
Libraries (Scikit-learn, TensorFlow)
Data visualization tools (Matplotlib, Seaborn)
Creating APIs
Integrating with real estate platforms