Price indexing

• US 20120066022A1
• Filed: 04/04/2011
• Published: 03/15/2012
• Est. Priority Date: 01/05/2007
• Status: Abandoned Application

• Current Assignee: Radar Logic, Inc.
• Original Assignee: Radar Logic, Inc.
• Inventors: Kagarlis, Marios A., Eberlein, Robert L.
• Application Number:
  US13/066,008
• Publication Number:
  US 20120066022A1
• Time in Patent Office: Days
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

Transactions involving assets that share a common characteristic are represented as respective data points associated with values of the assets. The data points include transaction value information. Respective statistical values are determined for the data points with respect to a probability density function that fits a power law parameterization to the data points. A probability function describing the statistical values is evaluated as a measure of confidence that the probability density function of the data points is validly represented by the power law parametrization.

Claims

1. A computer-based method comprising representing transactions involving assets that share a common characteristic, as respective data points associated with values of the assets, the data points including transaction value information, for the data points, determining respective statistical values with respect to a probability density function that fits a power law parameterization to the data points, an devaluating a probability function describing the statistical values as a measure of confidence that the probability density function of the data points is validly represented by the power law parametrization.

2. The method of claim 1 in which the assets comprise real estate.

3. The method of claim 1 in which the data points represent transactions in real estate.

4. The method of claim 1 in which each of the statistical values comprises a Kolmogorov statistic.

5. The method of claim 1 in which the power law parametrization comprises a triple power law parametrization.

6. The method of claim 1 also including computing a maximum Kolmogorov statistic with respect to the data points.

7. The method of claim 1 in which the probability function describing the statistical values comprises a Kolmogorov probability function.

8. The method of claim 1 also including, if the measure of confidence is below a certain threshold, analyzing the dataset for irregular transaction attributes or errors or manipulations.

9. The method of claim 1 in which the data points are associated with real estate transactions occurring on tradable days for metropolitan statistical areas.

10. The method of claim 9 in which a feature of the probability function describing the statistical values is used as a criterion to determine conformance between spectral features of the dataset and the power law parameterization.

11. The method of claim 9 in which the data points are associated with real estate transactions that represent a subset of data points from a parent metropolitan statistical area, selected by user-defined criteria for use in computing a subindex, and a feature of the probability function is used as a criterion to determine conformance between spectral features of the dataset and the power law parametrization.

12. The method of claim 11 also includingbased on the determined conformance, determining whether to compute the subindex based on values associated with the power law parameterization of the metropolitan statistical area.