US 12,482,015 B2
Automatic analysis of regional housing markets based on the appreciation or depreciation of individual homes
Stanley B. Humphries, Sammamish, WA (US); Peter Gross, Seattle, WA (US); Svenja Gudell, Redmond, WA (US); and Krishna Rao, Seattle, WA (US)
Assigned to MFTB Holdco, Inc., Seattle, WA (US)
Filed by MFTB Holdco, Inc., Seattle, WA (US)
Filed on Dec. 27, 2023, as Appl. No. 18/397,914.
Application 18/397,914 is a continuation of application No. 16/423,873, filed on May 28, 2019, granted, now 11,861,635.
Claims priority of provisional application 62/821,159, filed on Mar. 20, 2019.
Prior Publication US 2024/0152943 A1, May 9, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/0204 (2023.01); G06Q 30/0283 (2023.01); G06Q 50/16 (2024.01)
CPC G06Q 30/0205 (2013.01) [G06Q 30/0283 (2013.01); G06Q 50/16 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method in a computing system for determining a housing index value for a subject geographic region for a subject period in time, comprising:
for each home in a set of homes comprising substantially all homes within the subject geographic region:
determining a set of home attribute values for a home, each corresponding to a different home attribute among a set of home attributes;
applying a first valuation model for the subject geographic region to the set of home attribute values to generate a first valuation of the home at a beginning of the subject period, wherein the first valuation model is trained on a first training set comprising one or more home sales in the subject geographic region occurring at the beginning of the subject period and corresponding home attribute values;
applying a second valuation model for the subject geographic region to the set of home attribute values to generate a second valuation of the home at an end of the subject period, wherein the second valuation model is trained on a second training set comprising one or more homes sales in the subject geographic region occurring at the end of the subject period and corresponding home attribute values;
in response to determining that the first valuation of the home or the second valuation of the home was generated based on the set of home attribute values being different at the beginning of the subject period and at the end of the subject period: (1) updating either the first training set or the second training set to use the set of home attributes that are identical at both the beginning and the end of the subject period, thereby generating an updated training set, (2) modifying the first valuation model or the second valuation model to use the updated training set, and (3) regenerating either the first valuation of the home or the second valuation of the home, respectively; and
in response to successfully generating both the first valuation and the second valuation, determining an appreciation rate for the home based on the first valuation of the home at the beginning of the subject period and the second valuation of the home at the end of the subject period, and otherwise, removing the home from the set of homes or imputing an estimated value of the home for at least one of the beginning of the subject period and the end of the subject period;
combining appreciation rates determined for a subset of the set of homes to obtain an aggregate appreciation rate for the subject period by determining a weighted average of the appreciation rates determined for the subset of the set of homes, wherein a weight for each home of the subset of the set of homes is proportional to the estimated value of the home at the beginning of the subject period; and combining the aggregate appreciation rate for the subject period with a housing index value for a prior period to obtain the housing index value for the subject geographic region for the subject period.