Evaluating Factor Contributions for Sold Homes

We evaluated the contributions of ten intrinsic and extrinsic factors readily available from website data to individual home sale prices for three major U.S. cities using a P-spline generalized additive model (GAM). We identified the relative significance of each factor by evaluating the change in the adjusted R2 value resulting from its removal from the model. We combined this with information from correlation matrices to identify the added predictive value of a factor. For these three cities, the tests revealed that living area and location (latitude, longitude) had the strongest impact on explained variance, and each factor independently added predictive value. Relative impacts of the other factors were city-dependent. We utilized this information to develop an improved GAM with superior concurvity values. The improved GAM required the use of linear orthogonalization of factors combined with smoothing functions based on tensor products of correlated factors.