Abstract: A machine learning system and method for estimating real estate property sales prices by combining property attributes, contextual attributes, and crowdsourced user estimates in a closed-loop learning system. The system learns numeric representations for properties and users, and employs a modified weighting mechanism that incorporates property information into the value computation, enabling property-specific adjustment of user estimates based on learned user biases. Unlike standard weighting mechanisms that compute values solely from input source representations, the modified mechanism computes values from a combination of user representations, user price estimates, and property representations, enabling context-dependent corrections through a multi-layer transformation.