US 11,861,748 B1
Valuation of homes using geographic regions of varying granularity
Nima Shahbazi, Toronto (CA); Mohamed Chahhou, Fes (MA); Jordan Meyer, Cary, NC (US); and Shize Su, Bellevue, WA (US)
Assigned to MFTB Holdco, Inc., Seattle, WA (US)
Filed by MFTB Holdco, Inc., Seattle, WA (US)
Filed on Jun. 28, 2019, as Appl. No. 16/457,390.
Int. Cl. G06Q 50/16 (2012.01); G06Q 30/0201 (2023.01); G06Q 30/0204 (2023.01)
CPC G06Q 50/16 (2013.01) [G06Q 30/0205 (2013.01); G06Q 30/0206 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method in a computing system for estimating a value of a subject home, comprising:
creating a training set comprising data of sale transactions of a plurality of homes selected from homes in two or more region sizes;
periodically training one or more machine learning models, comprising at least one of a gradient boosting machine, a support vector machine, or a neural network, using the created training set,
wherein independent variables of the one or more machine learning models comprise, for each of a plurality of region sizes:
an independent variable identifying a region of the region size containing the subject home,
one or more independent variables each identifying a neighboring region of the region size that borders the region of the region size containing the subject home, wherein, for a respective region size, the region containing the subject home is non-overlapping with the neighboring region, and
one or more independent variables each determined from an aggregation of values of a home attribute across all homes, in the region of the region size containing the subject home, for which a value of the home attribute is available;
generating, for each region corresponding to each of the plurality of region sizes and based on a latitude/longitude pair associated with the subject home, a single computer-readable geohash encoding value,
wherein the single computer-readable geohash encoding value of a first region size is generated by discarding a least significant digit of the single computer-readable geohash encoding value of a second region size, the first region size being at a next-less-granular level than the second region size;
determining, based on the single computer-readable geohash encoding value, values of the trained one or more machine learning model's independent variables for the subject home;
applying the trained one or more machine learning models to the determined independent variable values to produce an estimate of the value of the subject home.