For a house price prediction model in Vertex AI, the features you use will significantly impact the model’s accuracy and reliability. Here’s a breakdown of common and important features to consider:
I. Property Features (Intrinsic Characteristics):
- Size:
- Living Area (Square Footage): Generally one of the most significant positive predictors of price.
- Lot Size (Square Footage or Acres): Larger lots can increase value, especially in suburban or rural areas.
- Total Area (including basement, garage, etc.): Provides a more comprehensive view of the property’s size.
- Number of Rooms: Total count of rooms.
- Number of Bedrooms: A key factor for families.
- Number of Bathrooms (Full and Half): More bathrooms usually increase value.
- Basement Area and Features: Finished vs. unfinished, square footage.
- Garage Size (Number of Cars, Area): A significant amenity for many buyers.
- Number of Fireplaces: Can add to the perceived value and comfort.
- Porch/Deck/Patio Area: Outdoor living spaces.
- Age and Condition:
- Year Built: Newer homes often command higher prices due to modern amenities and lower expected maintenance.
- Year Remodeled: Indicates recent updates and improvements.
- Overall Condition Rating: Subjective rating of the property’s general condition (e.g., excellent, good, fair, poor).
- Overall Quality Rating: Subjective rating of the quality of materials and finish.
- Building Characteristics:
- Building Type: House, townhouse, condo, etc.
- House Style: Ranch, two-story, Victorian, etc.
- Foundation Type: Slab, basement, crawl space.
- Roof Material and Style: Can impact aesthetics and durability.
- Exterior Material: Brick, siding, stucco, etc.
- Heating and Cooling Systems: Type and quality (e.g., central AC, forced air).
- Interior Features:
- Kitchen Quality: Rating of kitchen finishes and appliances.
- Bathroom Quality: Rating of bathroom finishes and fixtures.
- Fireplace Quality: Rating of the fireplace.
- Basement Quality: Rating of the basement finish.
- Number of Stories: Affects layout and perceived size.
- Floor Material: Hardwood, carpet, tile, etc.
II. Location Features (Extrinsic Factors):
- Neighborhood: Different neighborhoods have varying levels of desirability and price points.
- Proximity to Amenities:
- Schools (quality and distance)
- Parks and recreational areas
- Public transportation (bus stops, train stations)
- Shopping centers and restaurants
- Hospitals and healthcare facilities
- Accessibility:
- Distance to major highways and roads
- Walkability and bikeability scores
- Safety and Crime Rates: Lower crime rates generally increase property values.
- Environmental Factors:
- Noise levels (proximity to airports, highways)
- Air quality
- Flood zone status
- Views (scenic views can increase value)
- Local Economy:
- Job market and employment rates
- Income levels in the area
- Property taxes
III. Market Trends (Temporal Factors):
- Time of Sale (Month, Year): Housing prices can fluctuate seasonally and with broader economic cycles.
- Interest Rates: Mortgage rates significantly impact affordability and demand.
- Inflation: Can affect the real value of property.
- Unemployment Rates: Economic stability influences housing demand.
- Housing Inventory: Supply and demand dynamics play a crucial role in pricing.
- Economic Growth: A strong local or national economy can drive up housing prices.
IV. Derived or Engineered Features:
- Price per Square Foot: A normalized measure of value.
- Age of House at Time of Sale: Calculated from ‘Year Built’ and ‘Year Sold’.
- Distance to City Center or Key Locations: Calculated using coordinates.
- Density of Amenities: Number of amenities within a certain radius.
- Interaction Terms: Combining existing features (e.g., square footage * location indicator) to capture non-linear relationships.
- Polynomial Features: Creating higher-order terms of numerical features to model non-linear relationships.
When building your house price prediction model in Vertex AI, consider the following:
- Data Availability: Not all of these features might be available in your dataset.
- Data Quality: Ensure your data is accurate and handle missing values appropriately.
- Feature Selection: Use techniques to identify the most relevant features for your model.
- Feature Engineering: Create new features that might improve predictive power.
- Data Encoding: Convert categorical features into numerical representations that your model can understand.
- Scaling Numerical Features: Normalize or standardize numerical features to prevent features with larger ranges from dominating the model.
By carefully selecting and preparing your features, you can build a more accurate and reliable house price prediction model in Vertex AI. Remember to iterate and experiment with different feature combinations to optimize your model’s performance.