Abstract: Computer-based processes are disclosed for mining and analyzing owner association (OA) data associated with a plurality of real estate properties and generating an OA amount model that can be used to estimate OA amounts for a subject property. The OA data may also be analyzed to identify OA ratings, OA recommendations, OA delinquency actions, OA contacts information, OA services information, etc. The disclosed processes can also use the generated OA amount model to identify OA amount trends, and determine valuations and investment metrics for real estate properties.

Abstract: Systems and methods are disclosed that can enable a user to search for real estate properties by specifying search criteria that may exclude personal information. The systems and methods then can subsequently conduct a search for real estate property listings based partly on the specified search criteria and access loans associated with the matched properties. The system and methods then can perform analysis of the loans to identify loan and consumer characteristics and generate an electronic report that includes a representation of the loan and consumer characteristics.

Abstract: Computer-based systems and methods are disclosed for modeling and predicting short sale fraud risks for short sale transactions related to real estate properties. In some embodiments, the systems and methods can predict short sale fraud risks for real estate properties by considering a variety of factors, including AVM analytics characteristics, relationship analysis characteristics, and/or marketing analysis characteristics.

Abstract: A method for scalably calculating statistics associated with action performances includes obtaining action performance data for one or more entities, where the action performance data comprises at least a number of times each of the one or more entities performed an action during one or more time segments, wherein the action satisfies a user specified criteria. The method also includes generating, based on the obtained action performance data, a data structure including one or more values that are organized according to a number of time segments that have elapsed between an action performance and a most recent previous action performance. Systems and machine-readable media are also provided.

Abstract: Disclosed systems and methods can determine predicted rental income, estimated error of the prediction, and a set of comparable rental real estate properties for use in the valuation of a subject real estate property rental value. In one embodiment, the rent prediction system receives rental information about real-estate properties, determines feature characteristics, trains a rent amount prediction model using the feature characteristics, determines a second set of feature characteristics based on the output of the rent amount prediction model, and trains an error prediction model using the determined second set of feature characteristics. Using the trained models, the systems and method may predict a rental value and prediction error for one or more subject properties.

Abstract: Disclosed systems and methods can determine predicted rental income, estimated error of the prediction, and a set of comparable rental real estate properties for use in the valuation of a subject real estate property rental value. In one embodiment, the rent prediction system receives rental information about real-estate properties, determines feature characteristics, trains a rent amount prediction model using the feature characteristics, determines a second set of feature characteristics based on the output of the rent amount prediction model, and trains an error prediction model using the determined second set of feature characteristics. Using the trained models, the systems and method may predict a rental value and prediction error for one or more subject properties.

Abstract: Methods and apparatus, including computer program products, related to entity name translation. In general, a set of data records including one or more names of one or more entities in a source language is received and candidate translations are generated for the names of the data records. The candidate translations are strings of characters in a target language that has a different orthography than an orthography of the source language. A data repository is queried for records matching the candidate translations. A translation of at least one of the candidate translations is selected based on a comparison of a combination of candidate translations and properties of the source record comprising of the publication date, entity name co-occurrence and their frequency with the result of the querying the data repository.

Abstract: One methodology in accordance with the disclosure is a computer-implementable methodology for quantitatively assessing the impact of paid search advertising on the total visits (paid and organic search-based visits) from search engines and utilizing this assessment to improve search engine marketing performance. The methodology relies on the computation of a synergy score for each search engine keyword of interest or coefficients in a synergy equation. Once computed, the score can be used to repeatedly compute the total return on advertising spend (ROAS) and other performance metrics on a go-forward basis without the time lag inherent in computing the synergy score itself. The methodology includes specific computer-implementable methods for improving search engine marketing performance based on the total (paid plus organic) performance metrics.

Abstract: An apparatus having, a communications interface, a memory to store processor-executable instructions, and a processor communicatively coupled to the at least one communications interface and a memory. Upon execution of the processor-executable instructions, the processor may receive user interaction data, that includes content items and conversion items, where a conversion item is a user action that satisfies a predetermined conversion criteria. The apparatus receives conversion data including conversion path data for a plurality of conversion paths, such that each conversion path includes user interaction data prior to and including a conversion event. The apparatus determines a first and second distribution data that includes a metric that is determined based on one or more events associated with the advertisement.

Abstract: The invention is a computer-implementable methodology for quantitatively assessing the impact of paid search advertising on the total visits (paid and organic search-based visits) from search engines and utilizing this assessment to improve search engine marketing performance. The methodology relies on the computation of a synergy score for each search engine keyword of interest or coefficients in a synergy equation. Once computed, the score can be used to repeatedly compute the total return on advertising spend (ROAS) and other performance metrics on a go-forward basis without the time lag inherent in computing the synergy score itself. The invention includes specific computer-implementable methods for improving search engine marketing performance based on the total (paid plus organic) performance metrics.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer-readable media, for obtaining information regarding a plurality of conversion paths that each end with a conversion interaction, wherein each conversion path includes one or more user interactions that include a plurality of dimensional data related to the user interaction. For each conversion path, it is determined if the conversion path includes a particular user interaction, a first position of the particular user interaction, and the number of user interactions of the conversion path. The conversion paths are grouped based upon if the conversion path includes the particular user interaction. For each conversion path group, an average position of the particular user interaction and an average path length based upon the path length of the conversion paths is computed. Data regarding the average position of the particular user interaction and the average path length is provided.

Abstract: Methods and apparatus, including computer program products, for cardholder localization based on transaction data. In general, a determination of one or more possible base locations of a cardholder of a payment card may be initiated. Transactions of the cardholder may be associated with merchant customer base location profiles, where each of the merchant customer base location profiles models a distribution of base locations of customers with which a merchant has had a transaction. The one or more possible cardholder base locations may be derived from the merchant customer base location profiles. The one or more possible cardholder base locations may be used to determine a home or office location of a cardholder where such information is not otherwise available, to determine whether a cardholder has moved from a stated address, to determine if a cardholder has multiple residences, to detect fraud, and the like.

Abstract: A computer-implemented technique for fuzzy matching. This works quickly yet accurately to determine if a given computer-readable record is represented, by exact match or pretty close match, in a large collection of computer-readable records. Further tools may be provided to assess the character of the match.

Abstract: Entity disambiguation resolves which names, words, or phrases in text correspond to distinct persons, organizations, locations, or other entities in the context of an entire corpus. The invention is based largely on language-independent algorithms. Thus, it is applicable not only to unstructured text from arbitrary human languages, but also to semi-structured data, such as citation databases and the disambiguation of named entities mentioned in wire transfer transaction records for the purpose of detecting money-laundering activity. The system uses multiple types of context as evidence for determining whether two mentions correspond to the same entity and it automatically learns the weight of evidence of each context item via corpus statistics. The invention uses multiple search keys to efficiently find pairs of mentions that correspond to the same entity, while skipping billions of unnecessary comparisons, yielding a system with very high throughput that can be applied to truly massive data.

Abstract: Methods and apparatus, including computer program products, related to entity name translation. In general, a set of data records including one or more names of one or more entities in a source language is received and candidate translations are generated for the names of the data records. The candidate translations are strings of characters in a target language that has a different orthography than an orthography of the source language. A data repository is queried for records matching the candidate translations. A translation of at least one of the candidate translations is selected based on a comparison of a combination of candidate translations and properties of the source record with the result of the querying the data repository.

Abstract: Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments.

Abstract: A computer-implemented technique for fuzzy matching. This works quickly yet accurately to determine if a given computer-readable record is represented, by exact match or pretty close match, in a large collection of computer-readable records. Further tools may be provided to assess the character of the match.

Abstract: Methods and apparatus, including computer program products, for cardholder localization based on transaction data. In general, a determination of one or more possible base locations of a cardholder of a payment card may be initiated. Transactions of the cardholder may be associated with merchant customer base location profiles, where each of the merchant customer base location profiles models a distribution of base locations of customers with which a merchant has had a transaction. The one or more possible cardholder base locations may be derived from the merchant customer base location profiles. The one or more possible cardholder base locations may be used to determine a home or office location of a cardholder where such information is not otherwise available, to determine whether a cardholder has moved from a stated address, to determine if a cardholder has multiple residences, to detect fraud, and the like.

Abstract: Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments.

Abstract: Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments, which are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur. Supervised segmentation is applied to merchant vectors to form merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. Consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer.