With the increasing importance of ESG factors in real estate constructions and prices, we investigate commonly-accepted factors in real estate prices through hedonic models and then apply the select ESG factors of green-home, air-conditioning, accessibility, and waterfront to evaluate their impact and significance in increasing the predictive powers of the models. In particular, we investigate the use of a P-spline generalized additive hedonic model (GAM) for real estate prices in large U.S. cities and contrast their predictive efficiency against commonly-used linear and polynomial-based generalized linear models (GLMs). Using intrinsic and extrinsic factors available from Redfin, we show that the GAM model is capable of describing 84% to 92% of the variance in the expected ln(sales price) based upon 2021 data. In contrast, a strictly-linear GLM accounted for 65% to 78% of the variance, while polynomial-based GLMs accounted for 82% to 88%. As climate change is becoming increasingly important, we utilized the GAM model to examine the significance of environmental factors in two urban centers along the Pacific Northwest. While the results indicate city-dependent differences in the significance of environmental factors, we find that multiple environmental factors were significant with their inclusion increasing the adjusted R2 of the GAM model by slightly less than 1%.