Abstract: Market based data cleaning for mitigation of idiosyncratic errors in transaction data used for property valuation. The market based data cleaning technique helps to ensure that the most accurate record is retained for a transaction when duplicate records are found, by ensuring that the retained record is the most consistent with other transactions of the same property, the local market trend, and neighborhood market. Algorithms accommodate the adoption of a value as a representative single record for a transaction where multiple records are present for a transaction. Following duplicate removal, market based data cleaning further eliminates erroneous records by eliminating transaction outliers, also preferably based upon the local market trend, along with all of the remaining transactions for each given property.

Abstract: Property valuation that uses multiple transactions in predicting a value for a property. Instead of choosing only one prior transaction, valuation uses multiple transactions for a given property to provide a more accurate mark-to-market value. Preferably, a weighted combination of mark-to-market values provided by an HPI and individual ones of the multiple transaction records provide a predicted value for the given property. Weighting factors recency and transaction type to correct for both sources of potential inaccuracy.

Abstract: Property valuation that uses multiple transactions in predicting a value for a property. Instead of choosing only one prior transaction, valuation uses multiple transactions for a given property to provide a more accurate mark-to-market value. Preferably, a weighted combination of mark-to-market values provided by an HPI and individual ones of the multiple transaction records provide a predicted value for the given property. Weighting factors recency and transaction type to correct for both sources of potential inaccuracy.

Abstract: A repeated transaction index (RTI) for estimation of a home price index (HPI) that controls for systematic bias. Purchase transaction records are used to estimate an HPI for areas large enough to have sufficient transaction data. Bias is removed from non-purchase transaction records using the HPI. All available data is used to estimate a localized HPI that pertains to smaller geographical areas (e.g., zip codes, neighborhoods, etc.) after correction of the biases in the non-purchase transaction records.

Abstract: Confidence scoring entails receipt of a predicted property value for a given property, where the predicted property value is determined based upon application of a property value prediction model. A set of values corresponding to input factors is determined for the property value prediction model, and model parameters are estimated based upon those values. A confidence score for the predicted property value is then calculated using a confidence scoring model and the model parameters. The confidence score is preferably provided as an accuracy probability. This may further be in the form of a probability that the predicted property value has an error within a specified range, such as a specified range expressed in terms of a percentage error.