AI Notes

House price prediction model features

For a house price prediction model in , 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 , 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.

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