Patent US5361201A successfully refreshed from Google Patents.
US5361201A
Expired / Lapsed
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Real estate appraisal using predictive modeling
Assignee: FIRST AMERICAN CORELOGIC HOLIDNGS Inc
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Filing: 1992-10-19
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Granted: 1994-11-01
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Expires: 2012-10-19
Abstract
Abstract An automated real estate appraisal system (100) and method generates estimates of real estate value using a predictive model such as a neural network (908). The predictive model (908) generates these estimates based on learned relationships among variables describing individual property characteristics (905) as well as general neighborhood characteristics at various levels of geographic specificity (906). The system (100) may also output reason codes indicating relative contributions (1009) of various variables to a particular result, and may generate reports (701) describing property valuations, market trend analyses, property conformity information, and recommendations regarding loans based on risk related to a property.
Inventors
- 👤 Allen Jost
- 👤 Jennifer Nelson
- 👤 Krishna Gopinathan
- 👤 Craig Smith
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-01 11:29
CPC Classifications
G; G06; G06Q; G06Q40/00; G06Q40/02
Claims Analysis
Total Claims: 27
Claim 1
Independent Claim
102 words
A computer-implemented process for appraising a real estate property, comprising the steps of: collecting training data; developing a predictive model from the training data; storing the predictive model; obtaining individual property data for the real estate property; generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data to the stored predictive model; developing an error model from the training data; storing the error model; and generating a signal indicative of an error range for the appraised value responsive to application of the individual property data to the stored error model.
Claim 2
Dependent Claim
102 words
A computer-implemented process for appraising a real estate property, comprising the steps of: collecting training data; developing a predictive model from the training data; storing the predictive model; obtaining individual property data for the real estate property; generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data to the stored predictive model; developing an error model from the training data; storing the error model; and generating a signal indicative of an error range for the appraised value responsive to application of the individual property data to the stored error model.
Claim 3
Dependent Claim
14 words
The computer-implemented process of claim 1, wherein the error model comprises a regression model.
Claim 4
Dependent Claim
157 words
A computer-implemented process for appraising a real estate property, comprising the steps of: collecting training data; developing a predictive model from the training data; storing the predictive model; obtaining individual property data for the real estate property; generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data to the stored predictive model; developing a lower percentile error model from the training data; developing an upper percentile error model from the training data; storing the lower percentile error model; storing the upper percentile error model; generating a signal indicative of a lower bound value for the real estate property responsive to application of the obtained individual property data to the stored lower percentile error model; and generating a signal indicative of an upper bound value for the real estate property responsive to application of the obtained individual property data to the stored upper percentile error model.
Claim 5
Dependent Claim
157 words
A computer-implemented process for appraising a real estate property, comprising the steps of: collecting training data; developing a predictive model from the training data; storing the predictive model; obtaining individual property data for the real estate property; generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data to the stored predictive model; developing a lower percentile error model from the training data; developing an upper percentile error model from the training data; storing the lower percentile error model; storing the upper percentile error model; generating a signal indicative of a lower bound value for the real estate property responsive to application of the obtained individual property data to the stored lower percentile error model; and generating a signal indicative of an upper bound value for the real estate property responsive to application of the obtained individual property data to the stored upper percentile error model.
Claim 6
Dependent Claim
28 words
The computer-implemented process of claim 3, wherein: the lower percentile error model is a computer-implemented neural network; and the upper percentile error model is a computer-implemented neural network.
Claim 7
Dependent Claim
97 words
A computer-implemented process for appraising a real estate property, comprising the steps of: obtaining individual property training data describing past real estate sales; aggregating the obtained property training data into area training data sets, each area training data set describing a plurality of sales within a geographic area; developing a predictive model from the training data; storing the predictive model; obtaining individual property data for the real estate property; and generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data to the stored predictive model.
Claim 8
Dependent Claim
97 words
A computer-implemented process for appraising a real estate property, comprising the steps of: obtaining individual property training data describing past real estate sales; aggregating the obtained property training data into area training data sets, each area training data set describing a plurality of sales within a geographic area; developing a predictive model from the training data; storing the predictive model; obtaining individual property data for the real estate property; and generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data to the stored predictive model.
Claim 9
Dependent Claim
36 words
The computer-implemented process of claim 5, wherein the step of aggregating is repeated using successively larger geographic areas until the number of sales within the geographic area over a predetermined time period exceeds a predetermined number.
Claim 10
Dependent Claim
94 words
A computer-implemented process for appraising a real estate property, comprising the steps of: collecting training data; performing the iterative substeps of: applying input data to a predictive model; ranking output data produced thereby responsive to a measure of quality; and adjusting operation of the model responsive to the results of the ranking substep; storing the predictive model; obtaining individual property data for the real estate property; and generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data to the stored predictive model.
Claim 11
Dependent Claim
94 words
A computer-implemented process for appraising a real estate property, comprising the steps of: collecting training data; performing the iterative substeps of: applying input data to a predictive model; ranking output data produced thereby responsive to a measure of quality; and adjusting operation of the model responsive to the results of the ranking substep; storing the predictive model; obtaining individual property data for the real estate property; and generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data to the stored predictive model.
Claim 12
Dependent Claim
75 words
The computer-implemented process of claim 7, wherein the predictive model comprises a computer-implemented neural network having a plurality of interconnected processing elements, each processing element comprising: a plurality of inputs; a plurality of weights, each associated with a corresponding input to generate weighted inputs; combining means, coupled to the weighted inputs, for combining the weighted inputs; and a transfer function, coupled to the combining means, for processing the combined weighted inputs to produce an output.
Claim 13
Dependent Claim
86 words
A computer-implemented process for appraising a real estate property, comprises the steps of: selecting a geographic area surrounding the real estate property; obtaining area data for the geographic area; collecting training data; developing a predictive model from the training data; storing the predictive model; obtaining individual property data for the real estate property; and generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data and the obtained area data to the stored predictive model.
Claim 14
Dependent Claim
86 words
A computer-implemented process for appraising a real estate property, comprises the steps of: selecting a geographic area surrounding the real estate property; obtaining area data for the geographic area; collecting training data; developing a predictive model from the training data; storing the predictive model; obtaining individual property data for the real estate property; and generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data and the obtained area data to the stored predictive model.
Claim 15
Dependent Claim
49 words
The computer-implemented process of claim 9, further comprising the steps of: developing an error model from the training data; storing the error model; and generating a signal indicative of an error range for the appraised value responsive to application of the individual property data to the stored error model.
Claim 16
Dependent Claim
14 words
The computer-implemented process of claim 10, wherein the error model comprises a regression model.
Claim 17
Dependent Claim
104 words
The computer-implemented process of claim 9, further comprising the steps of: developing a lower percentile error model from the training data; developing an upper percentile error model from the training data; storing the lower percentile error model; storing the upper percentile error model; generating a signal indicative of a lower bound value for the real estate property responsive to application of the obtained individual property data to the stored lower percentile error model; and generating a signal indicative of an upper bound value for the real estate property responsive to application of the obtained individual property data to the stored upper percentile error model.
Claim 18
Dependent Claim
28 words
The computer-implemented process of claim 12, wherein: the lower percentile error model is a computer-implemented neural network; and the upper percentile error model is a computer-implemented neural network.
Claim 19
Dependent Claim
112 words
A computer-implemented process for appraising a real estate property, comprising the steps of: collecting training data; developing a predictive model from the training data; storing the predictive model; obtaining individual property data for the real estate property, the individual property data comprising a plurality of elements; generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data to the stored predictive model; and for each element of the individual property data: determining a relative contribution of the element to the appraised value; determining from each relative contribution a reason code value; and generating a signal indicative of the reason code value.
Claim 20
Dependent Claim
112 words
A computer-implemented process for appraising a real estate property, comprising the steps of: collecting training data; developing a predictive model from the training data; storing the predictive model; obtaining individual property data for the real estate property, the individual property data comprising a plurality of elements; generating a signal indicative of an appraised value for the real estate property responsive to application of the obtained individual property data to the stored predictive model; and for each element of the individual property data: determining a relative contribution of the element to the appraised value; determining from each relative contribution a reason code value; and generating a signal indicative of the reason code value.
Claim 21
Independent Claim
177 words
A system for appraising a real estate property, comprising: a predictive model for determining an appraised value for the real estate property; training data input means, coupled to the predictive model, for obtaining training data; training data aggregation means, coupled to the training data input means, for aggregating the training data into training data sets, each training data set describing a plurality of sales within a geographic area; a model development component, coupled to the predictive model, for training the predictive model from the training data; a storage device for storing the trained predictive model; individual property data input means, coupled to the predictive model, for obtaining individual property data and sending the individual property data to the predictive model; area data input means, coupled to the individual property data input means and to the predictive model, for selecting a geographic area surrounding the real estate property, obtaining area data, and sending the area data to the predictive model; and an output device, coupled to the predictive model, for generating a signal indicative of the appraised value.
Claim 22
Independent Claim
177 words
A system for appraising a real estate property, comprising: a predictive model for determining an appraised value for the real estate property; training data input means, coupled to the predictive model, for obtaining training data; training data aggregation means, coupled to the training data input means, for aggregating the training data into training data sets, each training data set describing a plurality of sales within a geographic area; a model development component, coupled to the predictive model, for training the predictive model from the training data; a storage device for storing the trained predictive model; individual property data input means, coupled to the predictive model, for obtaining individual property data and sending the individual property data to the predictive model; area data input means, coupled to the individual property data input means and to the predictive model, for selecting a geographic area surrounding the real estate property, obtaining area data, and sending the area data to the predictive model; and an output device, coupled to the predictive model, for generating a signal indicative of the appraised value.
Claim 23
Dependent Claim
13 words
The system of claim 15, wherein the predictive model comprises a neural network.
Claim 24
Dependent Claim
106 words
The system of claim 15, further comprising: an error model for determining an error range for the appraised value; and wherein: the training data input means is coupled to the error model; the model development component trains the error model from the training data; the storage device stores the trained error model; the individual property data input means is coupled to the error model and sends the individual property data to the error model; the area data input means is coupled to the error model and sends the area data to the error model; and the output device generates a signal indicative of the error range.
Claim 25
Dependent Claim
13 words
The system of claim 17, wherein the error model comprises a regression model.
Claim 26
Dependent Claim
179 words
The system of claim 15, further comprising: a lower percentile error model for determining an lower bound for the appraised value; an upper percentile error model for determining an upper bound for the appraised value; and wherein: the training data input means is coupled to the error model; the model development component trains the lower percentile error model and the upper percentile error model from the training data; the storage device stores the trained lower percentile error model and the trained upper percentile error model; the individual property data input means is coupled to the lower percentile error model and the upper percentile error model, and sends the individual property data to the lower percentile error model and the upper percentile error model; the area data input means is coupled to the lower percentile error model and the upper percentile error model and sends the area data to the lower percentile error model and the upper percentile error model; and the output device generates a signal indicative of the lower bound and the upper bound for the appraised value.
Claim 27
Dependent Claim
25 words
The system of claim 19, wherein: the lower percentile error model comprises a neural network; and the upper percentile error model comprises a neural network.
References Cited (Backward Citations)
US5235673A
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1997-10-31 | 1998-05-02 | 1998-11-01 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2001-10-31 | 2002-05-02 | 2002-11-01 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2005-10-31 | 2006-05-02 | 2006-11-01 | $7700.00 | Window Closed / Lapsed |