Abstract: The present disclosure provides a direct current (DC) transformer error detection apparatus for a pulsating harmonic signal, including a DC and pulsating harmonic current output module and an external detected input module, where the DC and pulsating harmonic current output module outputs a DC and a DC superimposed pulsating harmonic current to an internal sampling circuit and a self-calibrated standard resistor array; and the internal sampling circuit converts the input DC and the input DC superimposed pulsating harmonic current into a voltage signal, and sends the voltage signal to an analog-to-digital (AD) sampling and measurement component through a front-end conditioning circuit and a detected input channel. The DC transformer error detection apparatus can complete self-calibration for measurement of the DC and the pulsating harmonic signal on a test site.

Abstract: A method and machine-readable media are provided for estimating property values. An exemplary method includes obtaining a first estimate of a property from a first property valuation database, the first estimate exhibiting price tier effect bias. The method further includes deriving a price tier effect adjustment factor based on a price tier associated with the property and purchase transaction data. The method further includes adjusting the first estimate to reduce the price tier effect bias using price tier effect adjustment factor, thereby providing a second estimate having reduced price tier effect bias.

Abstract: A method and system are provided removing systematic bias from property value predictions obtained using automated property valuation models. The systematic bias is removed by deriving monthly adjustment factors that are location-specific. The monthly adjustment factors are obtained by comparing newly obtained purchase transaction data to model-generated valuations of properties in a base valuation database. The newly obtained purchase transactions may be obtained from newly obtained data on recently completed purchase transactions and loan application data on future purchase transactions. The monthly adjustment factors are applied to the property valuations in the base valuation database to obtain adjusted property values having reduced bias and thus improved accuracy.