Abstract: Resolving a discrepancy between disparate data sources is disclosed. Listing data for an item for sale is received from a plurality of disparate data sources. The listing data is compared with reliable listing data from a reliable data source. It is determined whether there is a discrepancy between the listing data and the reliable listing data. In the event there is a discrepancy between the listing data and the reliable listing data, at least a portion of the listing data is filtered.

Abstract: A system for recommending comprises an interface and a processor. An interface is configured to receive an input. The input is stored and associated with two user identifiers. The processor is configured to make recommendations based at least in part on the input.

Abstract: A system for third party email parsing comprises an input interface and a processor. The input interface is configured to receive an email. The processor is configured to: 1) determine whether the email is from a lead generating system; and 2) in the event that the email is from a lead generating system: a) extract real estate lead information from the email based at least in part on the lead generating system; b) determine whether the real estate lead information is sufficient for a real estate related customer relations management database entry; and c) in the event that the real estate lead information is sufficient for a real estate related customer relations management database entry, add the real estate lead information to a real estate related customer relations management database.

Abstract: A system for updating a database comprises an input interface and a processor. The input interface is configured to receive image data. The processor is configured to determine from the image data one or more attributes of a database entry for real estate property and update the database entry for the real estate property.

Abstract: Estimating a valuation of a target property is disclosed, including receiving user-generated data regarding one or more properties associated with the target property, processing the user-provided data using one or more data models that are configured to process the user-provided data, and combining the output of the one or more data models to obtain an estimated valuation of the target property.

Abstract: Resolving a discrepancy between disparate data sources is disclosed. Listing data for an item for sale is received from a plurality of disparate data sources. The listing data is compared with reliable listing data from a reliable data source. It is determined whether there is a discrepancy between the listing data and the reliable listing data. In the event there is a discrepancy between the listing data and the reliable listing data, at least a portion of the listing data is filtered.

Abstract: A system for rental fraud detection comprises an input interface, a feature vector extractor, a model builder, and a predictor. The input interface is to receive a set of training data, wherein the set of training data comprises a set of rental fraud indications and to receive a set of prediction data. The feature vector extractor is to extract a first training set of feature vectors from the set of training data and to extract a first test feature vector from the set of prediction data. The model builder is to build a first model using the first training set of feature vectors and the rental fraud indications. The predictor is to predict whether the first test feature vector corresponds to fraudulent data using the first model.

Abstract: A system for recommending comprises an interface and a processor. An interface is configured to receive an input. The input is stored and associated with two user identifiers. The processor is configured to make recommendations based at least in part on the input.