Automatically determining a current value for a home

1. 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.

2. The computer-implemented method of claim 1 further comprising:

for each selected classification tree of at least one of the multiple classification trees:

for each entry of one or more entries excluded from the selected entries for the selected classification tree:

identifying a leaf node corresponding to the entry based on a match between one or more attribute values of the entry and one or more attribute ranges corresponding to the identified leaf node; and

determining a difference between the mean selling price for homes represented by the identified leaf node and the selling price of the entry; and

scoring the selected classification tree based on the determined one or more differences corresponding to each of the one or more entries.

3. The computer-implemented method of claim 2 further comprising:

receiving attribute values for a distinguished home;

identifying a certain leaf node in each of the multiple classification trees,

wherein each certain leaf node is identified due to at least one attribute value for the distinguished home falling in one or more attribute ranges corresponding to the certain leaf node;

determining the mean selling prices corresponding to each of the certain leaf nodes,

wherein each mean selling price is weighted by the tree score for the classification tree containing the certain leaf node corresponding to that selling price;

averaging the determined weighted mean selling prices; and

reporting the average as an obtained valuation of the distinguished home.

4. The computer-implemented method of claim 3 further comprising:

determining that a value for a particular attribute for the distinguished home is unavailable; and

in response to the determination, imputing a value for the particular home attribute for the distinguished home.

5. The computer-implemented method of claim 4 further comprising:

choosing, as the imputed value for the particular home attribute, a median value of the particular home attribute from among an identified set of homes sold in the distinguished geographic area.

6. The computer-implemented method of claim 3 further comprising:

blending into the obtained valuation an earlier-reported valuation for the distinguished home by generating a weighted average of the obtained valuation and the earlier-reported valuation in which the earlier-reported valuation is more heavily weighted than the obtained valuation.

7. The computer-implemented method of claim 3 further comprising:

blending into the obtained valuation an earlier-reported valuation for the distinguished home by generating a weighted average of the obtained valuation and the earlier-reported valuation in which the obtained valuation is more heavily weighted than the earlier-reported valuation.

8. 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.

9. The computer-readable medium of claim 8 wherein the valuation model is applied by first applying the first component for all homes in the distinguished geographic area, and using the obtained valuation to weight valuations generated for the home.

10. The computer-readable medium of claim 8 , wherein the method further comprises:

determining that a value for a particular home attribute for the distinguished home is unavailable; and

in response to the determination, imputing a value for the particular home attribute for the distinguished home.

11. The computer-readable medium of claim 10 , wherein the method further comprises choosing, as the imputed value for the particular home attribute, a median value of the particular home attribute from among an identified set of homes sold in the distinguished geographic area.

12. The computer-readable medium of claim 8 , wherein the method further comprises blending into the obtained valuation an earlier-reported valuation for the distinguished home by generating a weighted average of the obtained valuation and the earlier-reported valuation in which the earlier-reported valuation is more heavily weighted than the obtained valuation.

13. The computer-readable medium of claim 8 , wherein the method further comprises:

using the model to produce first valuations for a group of homes in a portion of the distinguished geographic area at a first time index;

determine a first average of the first valuations;

use the model to produce second valuations for the group of homes in the portion of the distinguished geographic area at a second later time index;

determine a second average of the second valuations; and

generate an extent and direction of change between the first average and the second average.

14. The computer-readable medium of claim 13 , wherein indications of the extent and direction of change are provided, in a graphical topological representation, in association with a portion of the graphical topological representation of the portion of the distinguished geographic area.

15. The computer-readable medium of claim 8 ,

wherein the model is used to produce valuations for a group of homes in a portion of the distinguished geographic area; and

wherein a map is provided to a user device with indications of the produced valuations, each valuation indication provided in association with graphical representations of a location of the home for which that valuation was produced.

16. 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.

17. The one or more computer memories of claim 16 , further collectively storing instructions for a statistics module that, when executed by one or more processors, cause the one or more processors to:

use the valuation model to produce first valuations for a group of homes in a portion of the distinguished geographic area at a first time index;

determine a first average of the first valuations;

use the valuation model to produce second valuations for the group of homes in the portion of the distinguished geographic area at a second later time index;

determine a second average of the second valuations; and

generate an extent and a direction of change between the first average and the second average.

18. The one or more computer memories of claim 17 , wherein indications of the extent and the direction of change are provided, in a graphical topological representation, in association with a portion of the graphical topological representation that is for the portion of the distinguished geographic area.

19. The one or more computer memories of claim 16 ,

wherein the valuation model is used to produce valuations for a group of homes in a portion of the distinguished geographic area; and

wherein a map is provided to a client device with indications of the produced valuations, each valuation indication provided in association with graphical representations of a location of the home for which that valuation was produced.

20. The one or more computer memories of claim 16 , wherein the stored valuation model data structure comprises:

a first classification tree forest trained on sales information for a first group of homes in the distinguished geographic area; and

a second classification tree forest trained on sales information for a second group of homes in the distinguished geographic area that is a proper subset of the first group of homes,

wherein the second group of homes is selected based on the selling price for each home in the second group of homes being above a threshold for the distinguished geographic area.