What is claimed is:

    1. 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.

    2. The computer-implemented process of claim 1, wherein the error model comprises a regression model.


    3. 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.

    4. 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.


    5. 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;
• 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.

    6. 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.


    7. 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;
• 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;
• 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.

    8. 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.

    9. 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.

    10. The computer-implemented process of claim 9, further comprising the steps of:

• developing an error model from the training data; storing the error model;
• 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.

    11. The computer-implemented process of claim 10, wherein the error model comprises a regression model.


    12. 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;
• enerating 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.

    13. 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.

    14. 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.

    15. 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.

    16. The system of claim 15, wherein the predictive model comprises a neural network.


    17. 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.

    18. The system of claim 17, wherein the error model comprises a regression model.


    19. 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.

    20. 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.

Other References

    1. Borst, Richard A., "Artificial Neural Networks: The Next Modeling/Calibration Technology for the Assessment Community?", Property Tax Journal, vol. 10 n 1, Mar. 1991, pp. 69-94

    2. Carlson, E., "Self-Organizing Features Maps for Appraisal of Land Value of Shore Parcels", Conference: Artificial Neural Networks, vol. 2 Jun. 1991, pp. 1309-1312

    3. Jackson, Howard F. Jr., "Artificial Int. Appl. for the Real Estate and Financial Industries", Real Estate Finance Journal, vol. v3n3, Winter 1988; pp. 83-84

    4. Cannaday, R. E., et al., "Weighting Schemes for Adjustment Grid Methods of Appraisal", Appraisal Review Journal

    5. Bruce, R. W. and Sundell, D. J., "Multiple Regression Analysis: History and Applications in the Appraisal Professionp", The Real Estate Appraiser

    6. Andrews, R. L. and Ferguson, J. T., "Integrating Judgment with a Regression Appraisal", The Real Estate Appraiser and Analyst, Spring 1986

    7. Gain, K. J. "Appraising by Probability Analysis", The Appraisal Journal, Jan. 1990

    8. O'Toole, J., "Appraisal Procedures and Real Estate Lending", The Appraisal Journal, Jan. 1989

    9. Weirick, W. N. and Ingram. F. J., "Functional Form Choice in Applied Real Estate Analysis", The Appraisal Journal, Jan. 1990

    10. Cronan, et al., "Production System Development for Expert Systems Using a Recursive Partitioning Induction Approach: An Application to Mortgage, Commercial, and Consumer Lending", Decision Sciences, vol. 22 (1991)

    11.Collins, et al., "An Application of a Multiple Neural Network Learning System to Emulation of Mortgage Underwriting Judgements"

    12. Carlson, E, "Self-Organizing Feature Maps for Appraisal of Land Value of Shore Parcels", Artificial Neural Networks, 199