Automatically determining a current value for a home

• US 20070185906A1
• Filed: 02/03/2006
• Published: 08/09/2007
• Est. Priority Date: 02/03/2006
• Status: Active Grant

• Current Assignee: MFTB Holdco Incorporated
• Original Assignee: Zillow, Inc. (Zillow Group, Inc.)
• Inventors: Xiang, Dong, Humphries, Stan, Burstein, Jonathan, Chung, Kyusik
• Granted Patent: US 8,676,680 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0278 Product appraisal
• CPC Class Codes: G06Q 50/16 Real estate

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.

Claims

1. A method for valuing a distinguished home located in a distinguished geographic area, comprising:

retrieving home sales data for the distinguished geographic area, the home sales data comprising a plurality of entries each indicating, for a home located in the distinguished geographic area that was recently sold, a selling price and, for each of a plurality of attributes, the value of the attribute for the home;

creating a plurality of classification trees for the distinguished geographic area;

for each of the classification trees;

randomly selecting a proper subset of the plurality of entries;

randomly selecting a proper subset of the plurality of attributes;

for each of the selected attributes, determining the full range of values of the selected attribute among the selected entries;

establishing a root node representing all of the selected entries and the full range of values of each of the selected attributes;

for each node of the tree;

determining the information gain borne by each possible split of each of the ranges of the selected attributes represented by the node to the sales prices of the entries represented by the node;

if the greatest information gain of a possible split exceeds the information gain of the node;

performing the possible split having the greatest information gain to divide the range into two subranges at a point in the attribute range that produces the largest variance between average selling price for the subranges to average selling price for the range;

for each of the two subranges, establishing a child of the node representing the subrange and the homes represented by the node whose attribute values fall into the subrange;

else, identifying the node as a leaf node and calculating a mean selling price for the homes represented by the node;

for each of a proper subset of the plurality of entries that excludes the selected entries;

identifying a leaf node of the classification tree representing attribute ranges containing the entry'"'"'s attributes;

comparing the price of the identified leaf node to the selling price of the entry;

scoring the classification tree based on the extent to which the prices of the identified leaf nodes differed from the corresponding selling prices;

receiving attributes of the distinguished home;

for each of the classification trees, identifying a leaf node of the classification tree representing attribute ranges containing the distinguished home'"'"'s attributes;

determining an average of the price of the identified leaf node in each of the trees that is weighted by the tree'"'"'s score;

reporting the determined average as the value of the distinguished home.

2. A computer-readable medium whose contents cause a computing system to perform a method for valuing a distinguished home located in a distinguished geographic area, the method comprising:

receiving home attributes for the distinguished home;

obtain a valuation for the distinguished home by applying to the received home attributes a classification tree-based valuation model for homes in the distinguished geographic area trained using selling price and home attribute data from homes recently sold in the distinguished geographic area; and

reporting the obtained valuation for the distinguished home.

3. The computer-readable medium of claim 2 wherein the received home attributes are retrieved from a database of public information.

4. The computer-readable medium of claim 2 wherein the received home attributes are inputted by a person knowledgeable about the attributes of the distinguished home.

5. The computer-readable medium of claim 2, wherein the valuation model is a compound model that includes a component for all homes in the distinguished geographic area, as well as a component for the most highly-valued homes in the distinguished geographic area.

6. The computer-readable medium of claim 5 wherein the compound valuation model is applied by first applying the component for all homes in the distinguished geographic area, and using the produced valuation to weight valuations generated for the home by the two components in the reported valuation.

7. The computer-readable medium of claim 2, further comprising:

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

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

8. The computer-readable medium of claim 7, further comprising choosing as the imputed attribute value the median value of the attribute among homes recently sold in the distinguished geographic area.

9. The computer-readable medium of claim 7, further comprising choosing as the imputed attribute value the mode value of the attribute among homes recently sold in the distinguished geographic area.

10. The computer-readable medium of claim 2, the method further comprising, before reporting the obtained valuation for the distinguished home, blending into the obtained valuation an earlier-reported valuation for the distinguished home.

11. The computer-readable medium of claim 10 wherein the blending comprises 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.

12. The computer-readable medium of claim 2 wherein the valuation model is comprised of a first constituent valuation model trained using selling price and home attribute data from homes recently sold in the distinguished geographic area at the highest selling prices and a second constituent valuation model trained using selling price and home attribute data from homes recently sold in the distinguished geographic area at all selling prices, and wherein obtaining a valuation for the distinguished home comprises blending the constituent valuations obtained by applying each of the constituent valuation models to the received home attributes.

13. The computer-readable medium of claim 2 wherein the valuation model is comprised of a first constituent valuation model trained using selling price and home attribute data from homes recently sold in the distinguished geographic area at the highest selling prices and a second constituent valuation model trained using selling price and home attribute data from homes recently sold in the distinguished geographic area at all selling prices,

and wherein obtaining a valuation for the distinguished home comprises:

applying the second constituent model to obtain the second constituent valuation;

if the obtained second constituent valuation is below a threshold percentile among selling prices in the geographic area, using the second constituent valuation as the valuation;

if the second constituent valuation is above the threshold percentile selling price in the geographic area;

applying the first constituent valuation model to obtain a first constituent valuation; and

computing a weighted average of the constituent valuations in which the weighting is based upon the extent to which the second constituent valuation exceeds the threshold percentile selling price in the geographic area to obtain the valuation.

14. A method in a computing system for establishing a valuation model for homes in a distinguished geographic area, comprising:

identifying sales information for homes in the distinguished geographic area sold after a distinguished past date, the sales information including home attributes and selling price for each home;

initializing a valuation model; and

training the initialized valuation model with the identified sales information for at least a portion of the homes in the distinguished geographic area sold after the distinguished past date, such that the trained valuation model values each of these homes at or near its selling price.

15. The method of claim 14 wherein the valuation model is trained using the identified sales information for a first proper subset of homes in the distinguished geographic area sold after the distinguished past date, the method further comprising:

selecting a second proper subset of homes in the distinguished geographic area sold after the distinguished past date that is distinct from the first proper subset; and

using identified sales information for the second proper subset of homes to validate the trained valuation model by determining the extent to which the trained valuation model'"'"'s valuation of the homes of the second proper subset diverges from the selling prices of the homes of the second proper subset.

16. The method of claim 14 wherein the valuation model is a forest of classification trees each

17. The method of claim 16 wherein each of the forest of classification trees is weighted based upon its level of success in valuing homes in the distinguished geographic area that were recently sold other than those used to train the model.

18. The method of claim 14, further comprising:

identifying among the homes in the distinguished geographic area sold after the distinguished past date homes that have undergone unusually rapid appreciation or depreciation; and

excluding the identified homes from the training of the valuation model.

19. The method of claim 18 wherein homes are identified whose change in selling price from second most recent sale to most recent sale lies outside a predetermined percentage range.

20. The method of claim 18, further comprising:

selecting from the homes in the distinguished geographic area sold after the distinguished past date homes whose change in selling price from second most recent sale to most recent sale lies within a first predetermined percentage range;

initializing and training a second valuation model with identified sales information for the selected homes; and

applying the second valuation model to all of the homes in the distinguished geographic area sold after the distinguished past date, and wherein homes are identified whose difference between selling price from second most recent sale to valuation obtained by applying the second valuation model lies outside a second predetermined percentage range.

21. The method of claim 14, further comprising:

identifying a home in the distinguished geographic area sold after the distinguished past date having an attribute for which no value is available; and

in response to the identifying, imputing a value of the attribute for the identified home.

22. The method of claim 19, further comprising choosing as the imputed attribute value the median value of the attribute among homes in the distinguished geographic area sold after the distinguished date.

23. The method of claim 19, further comprising choosing as the imputed attribute value the mode value of the attribute among homes in the distinguished geographic area sold after the distinguished date.

24. The method of claim 14, further comprising:

identifying a home in the distinguished geographic area sold after the distinguished past date for which no selling price is available; and

in response to the identifying, imputing a selling price for the identified home.

25. The method of claim 24, further comprising:

choosing as the imputed selling price the product of a loan amount and an estimated regional home-to-value ratio.

26. One or more computer memories collectively containing a forest of classification trees usable to value homes in a distinguished geographic area, each of the trees of the forest classifying a random subset of homes and their recent selling prices based on values of a random subset of known home attributes, such that the forest may be used to value a home in the distinguished geographic area based upon its home attributes.

27. The computer memories of claim 26 wherein associated with each tree of the forest is a weight indicating the likely level of accuracy of values produced by the tree relative to the other trees.

28. One or more generated data signals collectively conveying, for a distinguished geographic area, information specifying a display including a characterization of the current value of homes in the distinguished geographic area that is based on applying an aggregation function to a current value attributed to substantially every home in the distinguished geographic area based on automatically comparing the attributes of each home to attributes of homes recently sold in the distinguished geographic area.

29. The generated data signals of claim 28 wherein the aggregation function is mean.

30. The generated data signals of claim 28 wherein the aggregation function is median.

31. The generated data signals of claim 28 wherein the aggregation function is maximum.

32. The generated data signals of claim 28 wherein the display specified by the conveyed information further includes an indication of the magnitude of change from an earlier characterization of the value of homes in the distinguished geographic area to the characterization of the current value of homes in the distinguished geographic area.

33. A computer-readable medium whose contents cause a computing system to perform a method for displaying a trend in the overall valuation of homes in a distinguished geographical area, the method comprising:

at a first time;

training a first model that values houses in the distinguished geographical area based on their attributes, using attributes and selling prices for homes in the distinguished geographical area that sold in a first period ending before the first time;

for each of substantially all of the homes in the distinguished geographical area, applying the first model to the home'"'"'s attributes to obtain a valuation of the home; and

applying an aggregation function to the obtained valuations to obtain a first overall valuation of homes in the distinguished geographical area;

at a second time later than the first time;

training a second model that values houses in the distinguished geographical area based on their attributes, using attributes and selling prices for homes in the distinguished geographical area that sold in a second period ending before the second time;

for each of substantially all of the homes in the distinguished geographical area, applying the second model to the home'"'"'s attributes to obtain a valuation of the home; and

applying an aggregation function to the obtained valuations to obtain a second overall valuation of homes in the distinguished geographical; and

generating a display comparing the first and second overall valuations.

34. The computer-readable medium of claim 33 wherein the display shows the first and second overall valuation.

35. The computer-readable medium of claim 33 wherein the display shows the magnitude and direction of change from the first overall valuation and the second overall valuation.

36. The computer-readable medium of claim 33 wherein the display shows the percentage and direction of change from the first overall valuation and the second overall valuation.

37. The computer-readable medium of claim 33 wherein the display shows the annualized percentage and direction of change from the first overall valuation and the second overall valuation.

38. The computer-readable medium of claim 33 wherein the generated display superimposes a numerical comparison of the first and second overall valuations over a visual depiction of the distinguished geographical area.

39. The computer-readable medium of claim 38 wherein the visual depiction is a map.

40. The computer-readable medium of claim 38 wherein the visual depiction is an aerial photograph.