Brian Abe

US Patent:

20190392461, Dec 26, 2019

Filed:

Sep 6, 2019

Appl. No.:

16/562939

Inventors:

- Santa Monica CA, US
Brian Izumi Abe - Santa Monica CA, US
Oliver Thomas Sidney Strauss - Santa Barbara CA, US

International Classification:

G06Q 30/02

Abstract:

A residual value forecasting system may utilize heterogeneous data, such as used market data, industry-specific data, and non-industry-specific data, from disparate data sources to produce residual value forecasts of an item based on a sophisticated residual value forecasting model particularly configured for agility. The system can dynamically and quickly adapt to change in data inputs and produce custom outputs. The system may determine a baseline value for an item using the used market data, a microeconomic factor using the industry-specific data, and a macroeconomic factor using the non-industry-specific data, as well as adjustments such as locality adjustments and modifications. Given the macroeconomic factor and the microeconomic factor relative to the locality-adjusted value of the item and in view of the competitive sets of similar and/or substitute items in the same industry, the system can generate an accurate forecast residual value of the item at a future time point.

US Patent:

20180033030, Feb 1, 2018

Filed:

Oct 11, 2017

Appl. No.:

15/729719

Inventors:

- Santa Monica CA, US
Brian Izumi Abe - Santa Monica CA, US
Oliver Thomas Sidney Strauss - Santa Barbara CA, US

International Classification:

G06Q 30/02

Abstract:

A residual value forecasting system may utilize heterogeneous data, such as used market data, industry-specific data, and non-industry-specific data, from disparate data sources to produce residual value forecasts of an item based on a sophisticated residual value forecasting model particularly configured for agility. The system can dynamically and quickly adapt to change in data inputs and produce custom outputs. The system may determine a baseline value for an item using the used market data, a microeconomic factor using the industry-specific data, and a macroeconomic factor using the non-industry-specific data, as well as adjustments such as locality adjustments and modifications. Given the macroeconomic factor and the microeconomic factor relative to the locally adjusted value of the item and in view of the competitive sets of similar and/or substitute items in the same industry, the system can generate an accurate forecast residual value of the item at a future time point.