When comparing properties for valuation, differences in key characteristics must be accounted for to determine value adjustments. HouseCanary’s valuation model is designed to analyze these differences and provide accurate property adjustment values based on real market data. Below, we outline the primary property characteristics our model considers when making adjustments.
Key Property Characteristics in the Adjustment Algorithm
Gross Living Area (GLA)
Our model applies piecewise adjustments based on the size of the home and how additional square footage impacts value in the local market.
Number of Bedrooms
Our model adjusts for variations in bedroom count, recognizing that the value impact of adding a bedroom may differ based on the existing number of bedrooms.
Number of Bathrooms
Our model adjusts for variations in bathroom count, recognizing that the value impact of adding a bathroom may differ based on the existing number of bathrooms.
Lot Size
Larger lots may increase property value, but the degree of impact depends on location and zoning restrictions. Our model adjusts for lot size variations accordingly.
Presence of a Pool
Our adjustment model assesses how the presence of a pool affects value based on regional preferences and market conditions.
Presence of a Basement
Our adjustment model assesses how the presence of a basement affects value based on regional preferences and market conditions.
Property Age
Our model applies piecewise adjustments based on the age of the home and how it impacts value in the local market.
Median Price by Block
Our model accounts for the difference in neighborhood median prices at the Census Block level.
How Adjustments Are Calculated
Our model follows a data-driven approach:
Collects Market Data: Using public records, MLS listings, and other property databases, we gather transaction history and property details.
Applies Statistical Modeling: A piecewise robust multi-variate linear regression model t determines the impact of each characteristic on property value.
Force Directionality: Adjustments are validated to ensure logical consistency (e.g., a larger home should not have a lower value per square foot).
Refines Estimates Iteratively: Any variables that are statistically insignificant or violate directionality, are flagged and excluded from the model.