Automatically determining a current value for a home
AssigneeMFTB Holdco, Inc.
InventorsStanley B. Humphries, Dong Xiang, Kyusik Chung, Jonathan Lee Burstein
Life of this patent
Estimating the term…
Abstract
A facility for valuing a distinguished home located in a distinguished geographic area is described. The facility receives home attributes for the distinguished home. The facility obtains valuation for the distinguished home by applying to the received home attributes evaluation model for homes in the distinguished geographic area that has been trained using selling price and home attribute data from homes recently sold in the distinguished geographic area. The facility reports the obtained valuation for the distinguished home.
Claims20
A computer-implemented method, in a computing system having a memory and a processor, for generating machine learning models to value homes located in a distinguished geographic area, comprising:
- retrieving, by the processor, home sales data for the distinguished geographic area, the home sales data comprising multiple entries, each entry indicating a selling price and a value for one or more home attributes;
- creating, by the processor, one or more machine learning classification trees by:
- for each distinguished classification tree of the one or more classification trees:
- selecting a subset of the multiple entries;
- selecting a subset of the one or more home attributes;
- for each of the selected home attributes, determining a range of values of the selected attribute among the selected entries;
- establishing a root node in the distinguished classification tree representing the range of values of each of the selected attributes;
- and
- for each distinguished node of the tree that has not been identified as a leaf node, determining a greatest information gain resulting from one or more possible splits in the ranges of values represented by the distinguished node;
- when the greatest information gain exceeds an information gain identified for the distinguished node, establishing, for each of two subranges corresponding to the split with the greatest information gain, a child node of the distinguished node;
- and
- when the greatest information gain does not exceed the information gain identified for the distinguished node, identifying the distinguished node as a leaf node and calculating a mean selling price for homes represented by the leaf node.
A computer-readable medium, not constituting transitory signals, whose contents cause a computing system to perform a method for valuing homes located in a distinguished geographic area, the method comprising:
- receiving, over a computer network, home attributes for a distinguished home;
- obtaining, with a processor, a valuation for the distinguished home by applying, to the home attributes, a machine learning model trained at least in part by applying weights to portions of the model based on training items, each training item comprising attributes for a home in the distinguished geographic area and a selling price,
- wherein the valuation model includes:
- (1) a first component for all homes in the distinguished geographic area;
- and
- (2) a second component for a set of most highly-valued homes in the distinguished geographic area;
- and
- reporting the obtained valuation for the distinguished home.
One or more computer memories, not constituting transitory signals, collectively storing:
- a valuation model data structure, comprising multiple portions, capable of producing a valuation of a home when trained using sales information usable to value a home in a distinguished geographic area when applied to home attributes for a distinguished home,
- wherein the valuation model was trained using sales information for a set of homes in the distinguished geographic area, the sales information including home attributes and a selling price for each home in the set of homes, and
- wherein the training included weighting each portion of the portions of the valuation model based upon a level of success of the valuation model in valuing homes using the sales information.
Dependencies are inferred from the claim text.