111111
`
`1111111111111111111111111111111111111111111111111111111111111
`US007970674B2
`
`(12) United States Patent
`Cheng et al.
`
`(10) Patent No.:
`(45) Date of Patent:
`
`US 7,970,674 B2
`Jun. 28, 2011
`
`(54) AUTOMATICALLY DETERMINING A
`CURRENT VALUE FORA REAL ESTATE
`PROPERTY, SUCH AS A HOME, THAT IS
`TAILORED TO INPUT FROM A HUMAN
`USER, SUCH AS ITS OWNER
`
`(75)
`
`Inventors: David Cheng, Seattle, WA (US); Stan
`Humphries, Sammamish, WA (US);
`Kyusik Chung, Seattle, WA (US); Dong
`Xiang, Sammamish, WA (US);
`Jonathan Burstein, Seattle, WA (US)
`
`(73) Assignee: Zillow, Inc., Seattle, WA (US)
`
`( *) Notice:
`
`Subject to any disclaimer, the term of this
`patent is extended or adjusted under 35
`U.S.c. 154(b) by 1394 days.
`
`(21) Appl. No.: 11/347,024
`
`(22) Filed:
`
`Feb. 3,2006
`
`(65)
`
`Prior Publication Data
`
`US 2007/0198278 Al
`
`Aug. 23, 2007
`
`(51)
`
`Int. Cl.
`(2006.01)
`G06Q 40110
`(52) U.S. Cl. .......................................... 705/35; 705/313
`(58) Field of Classification Search .................... 705/35,
`705/313
`See application file for complete search history.
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`5,361,201 A
`1111994 Jost et al.
`6,178,406 Bl*
`112001 Cheetham et al ............... 705/10
`6,240,425 Bl
`5/2001 Naughton
`6,609,118 Bl *
`8/2003 Khedkar et al.
`6,915,206 B2
`7/2005 Sasajima
`7,289,965 Bl *
`1012007 Bradley et al ..................... 705/1
`
`705/36 R
`
`.............. 345/632
`
`7,461,265 B2
`7,487,114 B2
`7,567,262 Bl *
`2003/0212565 Al
`2004/0073508 Al
`2005/0108084 Al *
`2005/0154657 Al *
`2007/0124235 Al
`2007/0185727 Al
`2007/0185906 Al
`2008/0077458 Al
`200910043637 Al *
`OTHER PUBLICATIONS
`
`1212008 Ellmore
`212009 Florance et al.
`7/2009 Clemens et al.
`1112003 Badali et al.
`4/2004 Foster et al.
`.......... 705/10
`5/2005 Ramamoorti et al.
`7/2005 Kim et al ........................ 705/30
`5/2007 Chakrabortyet al.
`8/2007 Ma et al.
`8/2007 Humphries et al.
`3/2008 Andersen et al.
`212009 Eder ............................... 705/10
`
`Vladimir Svetnik et ai, Random Forest: A Classification and Regres(cid:173)
`sion Tool for compound Classification and QSARModleing. J. Chern
`Info. Computer Science, 2003, vol. 43, pp. 1947-1958.*
`U.S. Appl. No. 111927,623, filed Oct. 29, 2007, Humphries et al.
`U.S. Appl. No. 111971,758, filed Jan. 9, 2008, Humphries et al.
`Quinlan, Ross J., "C4.5: Programs for Machine Learning," Machine
`Learning, 1993,302 pages, Morgan Kaufmann Publishers, San Fran(cid:173)
`cisco, CA, USA.
`
`(Continued)
`
`Primary Examiner - Kirsten S Apple
`Assistant Examiner - Abdul Basit
`(74) Attorney, Agent, or Firm - Perkins Coie LLP
`
`ABSTRACT
`(57)
`A facility procuring information about a distinguished prop(cid:173)
`erty from its owner that is usable to refine an automatic
`valuation of the distinguished property is described. The
`facility displays information about the distinguished property
`used in the automatic valuation of the distinguished property.
`The facility obtains user input from the owner adjusting at
`least one aspect of information about the distinguished prop(cid:173)
`erty used in the automatic valuation of the distinguished
`property. The facility then displays to the owner a refined
`valuation of the distinguished property that is based on the
`adjustment of the obtained user input.
`
`40 Claims, 21 Drawing Sheets
`
`computer system
`
`100
`
`'--_____ cp-'u 1101
`
`m.mo~lr 102
`
`pe~""O""""'lr103
`
`computer-readable
`
`m"".d,..
`
`Ir104
`
`oe\Wo~cooo"'OOI'05
`
`

`

`US 7,970,674 B2
`Page 2
`
`OTHER PUBLICATIONS
`
`Mobasher, B. "Classification Via Decision Trees in WEKA," DePaul
`University, Computer Science, Telecommunications, and Informa(cid:173)
`tion Systems, ECT 584-Web Data Mining, 2005, http://maya.cs.
`depau!.edu/-ciasses/Ect584/WEKAIciassity.html, 5 pages [internet
`accessed on Dec. 6, 2007].
`Bennett, Kristin P., "Support Vector Machines: Hype or Hallelujah?"
`SIGKDD Explorations, Dec. 2000, pp. 1-12, vo!' 2, issue 2, ACM
`SIGKDD.
`Hill, T and Lewicki, P., "K-Nearest Neighbors," Statistics Methods
`and Applications, 2007, http://www.statsoft.comitextbook/stknn.
`html, [internet accessed on [Dec. 6, 2007].
`Breiman, L., "Random Forests," Machine Learning, 45, pp. 5-32,
`2001, Kluwer Academic Publishers, The Netherlands.
`http://www.ics.uci.edu/-mlearn/databases/housinglhousing.names,
`1 page [accessed Dec. 13,2005].
`StatSofi, Inc., "Classification Trees," http://www.statsofi.comitext(cid:173)
`book/stciatre.html, pp. 1-20, © 1984-2003 [accessed Dec. 13,2005].
`Breiman et a!., "Random Forest," Classification Description, http://
`www.stat.berkeley.edu/users/breiman/RandornForests/cc_home.
`htm, pp. 1-28 [accessed Dec. 13,2005].
`Real-info.com, "What is an AVM," www.real-info.comiproducts_
`avm.asp? Internet Archive Date: Oct. 30, 2005, 5 pages [accessed
`Mar. 21, 2007].
`RealEstateABC.com, see paragraph headed "How do I make the
`estimate more accurate?" www.realestateabc.comihome-values/.
`Internet Archive Dated: Apr. 5, 2006, 4 pages [accessed Mar. 20,
`2007].
`Standard & Poors, "Guidelines for the use of Automated Valuation
`Models for U.K. RMBS Transactions," http://www.rics.orglNR/
`rdonlyres/8Fcdd20c-7FAC-4549-86FB-3930CDOCBC05/01
`StandardandPoorsReportonAVMs.pdf, Published Feb. 20, 2004, 4
`pages.
`
`www.r-project.org, "The R Project for Statistical Computing," http://
`web.archive.org/web/20060 1 02073 515/www.r-project.org/main.
`shtml, 1 page [internet archive date: Jan. 2, 2006].
`"Centre for Mathematical Sciences," Lund University, http://web.
`archive.org/web/20060 10 1 0051 03/http://www.maths.Ith.se/. 1 page
`[internet archive date: Jan. 1, 2006].
`"An
`Introduction
`to
`R,"
`http://web.archive.org/web/
`200601180 50840/http://cran.r-project.org/doc/manuals/R -intro.
`html, pp. 1-105 [internet archive date: Jan. 18, 2006].
`www.cran.r-project.org, "The Comprehensive R Archive Network,"
`http://web.archive. orglweb/2 00 5 0 83 00739131 cran.r -proj ect.orgl
`banner.shtml, pp. 1-2 [internet archive date: Aug. 30, 2005].
`Non-Final Office Action for U.S. App!. No. 111524,048, Mail Date
`Apr. 29, 2009, 10 pages.
`Non-Final Office Action for U.S. App!. No. 111524,047, Mail Date
`Oct. 28, 2009, 12 pages.
`Final Office Action for U.S. App!. No. 111524,048, Mail Date Dec. 8,
`2009, 12 pages.
`Non-Final Office Action for U.S. App!. No. 111927,623, Mail Date
`Dec. 28, 2010, 22 pages.
`Non-Final Office Action for U.S. App!. No. 111347,000, Mail Date
`Apr. 9, 2010, 29 pages.
`Tay et al., "Artificial Intelligence and the Mass Appraisal of Residen(cid:173)
`tial Apartments," Journal of Property Valuation and Investment, Feb.
`1, 1992, 17 pages.
`Meyer, Robert T, "The Learning of Multiattribute Judgment Poli(cid:173)
`cies," The Journal of Consumer Research, vo!' 14, No.2, Sep. 1987,
`pp.155-173.
`Non-Final Office Action for U.S. App!. No. 111347,000, Mail Date
`Oct. 27, 2010, 25 pages.
`* cited by examiner
`
`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 1 of21
`
`US 7,970,674 B2
`
`computer system
`
`100
`
`CPU
`
`101
`
`memory
`
`102
`
`persistent storage
`
`103
`
`com puter -readable
`media drive
`
`104
`
`network connection
`
`105
`
`FIG.l
`
`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 2 of21
`
`US 7,970,674 B2
`
`201
`
`2 02
`
`203
`
`204
`
`205
`
`206
`
`207
`
`begin
`~
`select recent sales for
`geographic area
`~
`for x=1 to n
`
`~
`construct tree x
`
`~
`score tree x
`
`~
`next x
`
`... 1 '.
`
`receive request for valuation
`identifying home
`~
`apply trees, weighted by
`scores, to attributes of home
`identified in request
`
`I
`FIG. 2
`
`

`

`id
`
`address
`
`.. _ .. __ ._-,.
`sq. ft. bedrooms bathrooms floors view year selling price
`
`-_. '.J . - -_ ..• -
`
`._- - _.-
`
`date
`
`1 111 Main St., Hendricks, IL 62012
`
`2 96 Elm St., Hendricks, IL 62014
`
`1850
`
`2220
`
`3 140 Cottontail Rd., Baron, IL 62019
`
`1375
`
`46 Spratt Ln., Baron, IL 62019
`
`5 776 Fir St., Hendricks, IL 62014
`
`1590
`
`2280
`
`6 111 Industry Ave., Fenton IL 62017
`
`1950
`
`7 105 Elm St., Hendricks, IL 62014
`
`2180
`
`4
`
`6
`
`3
`
`2
`
`3
`
`2
`
`5
`
`2
`
`2
`
`1
`
`2
`
`3
`
`2
`
`2
`
`2 no
`
`1953
`
`$132,500
`
`1/3/2005 V
`
`3 no
`
`1965
`
`1 no
`
`1974
`
`1/8/2005 V
`$201,000
`$98,750 1/11/2005v
`
`1 no
`
`1973
`
`$106,500 1/14/20051,1
`
`2 yes 1948
`
`$251,000 1/26/20051,1
`
`1 no
`
`1925
`
`$240,000
`
`3 yes 1940
`
`$230,000
`
`2/4/20051,1
`2/4/2005 v
`
`300
`
`301
`
`302
`
`303
`
`304
`
`305
`
`306
`
`307
`
`308
`
`~
`7Jl
`•
`~
`~
`~
`
`~ =
`
`~
`
`2-
`?
`N
`~CIO
`N
`
`0 ....
`....
`
`8110 MuffetSt., Baron, IL62019
`
`9 156 Elm St., Hendricks, IL 62014
`
`1675
`
`2400
`
`10 142 Cottontail Rd., Baron, IL 62019
`
`1450
`
`11 160 Prospect Bldv., Fenton IL 62017
`
`1952
`
`12 36 Spratt Ln., Baron, IL 62019
`
`13 118 Main St., Hendricks, IL 62012
`
`1475
`
`2140
`
`14 234 Cottontail Rd., Baron, IL 62019
`
`1980
`
`15 677 Fir St., Hendricks, IL 62014
`
`2320
`
`4
`
`6
`
`3
`
`4
`
`4
`
`5
`
`4
`
`5
`
`2
`
`3
`
`1
`
`2
`
`2
`
`2
`
`3
`
`3
`
`1 no
`
`1975
`
`2 yes 1938
`
`$74,900 2/14/20051,1
`$253,500 2115/2005 v
`
`1 no
`
`1966
`
`$102,000 2118/20051,1
`
`1 no
`
`1920
`
`$230,000 2/20/2005 ,-
`
`1 no
`
`1964
`
`$111,000 2120/2005 ,-
`
`2 no
`
`1935
`
`$211,000 2121/2005 ,-
`
`2 yes 1930
`
`$197,900 2/24/2005 ,-
`
`2 yes 1927
`
`$238,000 2128/2005
`
`309
`
`310
`
`311
`
`312
`
`313
`
`314
`
`315
`
`o
`
`FIG. 3
`
`('D
`
`(.H
`
`rFJ
`
`=-('D
`.....
`0 ....
`N ....
`
`d
`rJl
`",-....1
`\C
`-....1
`
`0'1
`-....1
`
`",=
`~ = N
`
`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 4 of21
`
`US 7,970,674 B2
`
`begin
`
`randomly select fraction of
`recent sales and fraction of
`attributes for tree
`
`create root node representing
`all basis sales and fully range
`of each basis attribute
`
`for each node of tree
`
`401
`
`402
`
`403
`
`No
`
`determine mean selling price of
`basis sales represented by
`node
`
`406
`
`create pair of children for node,
`each representing an attribute
`subrange on a different side of
`the selected split point and
`node's full range of other basis
`attributes, and all qualifying
`basis sales
`
`405
`
`407
`
`next node
`
`end
`
`FIG.4A
`
`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 5 of21
`
`US 7,970,674 B2
`
`begin
`
`No
`
`452
`r-----------~~
`return without identifying
`split opportunity
`
`Yes
`determine mean selling price among sales represented by node
`to obtain node mean selling price
`
`sum the squares of the differences between node mean selling
`price and the selling price of each sale represented by the node
`to obtain node overall squared error
`
`divide the overall squared error by the number of sales
`represented by the node - 1 to obtain node variance
`
`for each possible split opportunity in an attribute range
`represented by node
`
`for each side of the possible split opportunity, determine mean
`selling price among sales on that side to obtain split side mean
`selling price
`
`sum the squares of the differences between the selling price of
`each sale represented by the node and the split side mean
`selling price on the same side of the possible split opportunity to
`obtain possible split opportunity squared error
`
`divide the possible split opportunity squared error by the
`number of sales represented by node - 2 to obtain variance for
`possible split opportunit
`
`next possible split opportunity
`
`select possible split opportunity having lowest variance
`
`453
`
`454
`
`455
`
`456
`
`457
`
`458
`
`459
`
`460
`
`461
`
`463
`=>-N_O_~ return without identifying
`split opportunity
`
`FIG.4B
`
`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 6 of21
`
`US 7,970,674 B2
`
`500
`
`tree 1 basis table
`bedrooms view sellin!:) price
`$201,000 ~ 302
`$74,900 ~ 308
`$253,500 ~ 309
`$230,000 ~ 311
`$211,000 ~ 313
`$238,000 ~ 315
`
`id
`
`address
`
`2 96 Elm St., Hendricks, IL 62014
`
`8 110 Muffet St., Baron, IL 62019
`
`9 156 Elm St., Hendricks, IL 62014
`
`11 160 Prospect Bldv., Fenton IL 62017
`
`13 118 Main St., Hendricks, IL 62012
`
`15 677 Fir St., Hendricks, IL 62014
`
`"'-- 321
`
`"'-- 322
`FIG. 5
`
`6 no
`
`4 no
`
`6 yes
`
`4 no
`
`5 no
`
`5 yes
`
`"--- 324 ~ 327 "--- 329
`
`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 7 of21
`
`US 7,970,674 B2
`
`/600
`
`6010
`
`sales = 2, 8, 9, 11, 13, 15
`bedrooms = 1- 00
`view = no-yes
`
`FIG. 6
`
`/700
`
`601
`
`sales = 2, 8, 9, 11, 13, 15
`bedrooms = 1- 00
`view = no-yes
`
`702
`
`sales = 8, 11
`bedrooms = 1-4
`view = no-yes
`valuation = $152,450
`
`sales = 2, 9, 13, 15
`bedrooms = 5- 00
`view = no-yes
`
`704
`
`sales = 2, 13
`bedrooms = 5- 00
`view = no
`valuation = $206,000
`
`FIG. 7
`
`705
`sales = 9, 15
`bedrooms = 5- 00
`view = yes
`valuation = $245,750
`
`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 8 of21
`
`US 7,970,674 B2
`
`801
`
`802
`
`803
`
`804
`
`805
`
`806
`
`begin
`
`identify for scori ng tree recent
`sales not used as a basis for
`constructing tree
`
`for each identified sale
`
`apply tree to attributes of sale
`to obtain value
`
`compare value to selling price
`to determine magnitude of
`error
`
`next identified sale
`
`calculate score inversely
`related to median error
`ma nitude
`
`end
`
`FIG. 8
`
`

`

`id
`
`address
`
`bedrooms view
`
`selling price
`
`valuation
`
`error
`
`~~ - - . - -_ . . . . y .~-.-
`
`1 111 Main St., Hendricks, IL 62012
`
`4 no
`
`$132,500
`
`$152,450
`
`0.1506 ~
`
`3 140 Cottontail Rd., Baron, IL 62019
`
`3 no
`
`$98,750
`
`$152,450
`
`0.5438 ~
`
`4 6 Spratt Ln., Baron, IL 62019
`
`2 no
`
`$106,500
`
`$152,450
`
`0.4315 ~
`
`5 776 Fir St., Hendricks, IL 62014
`
`3 yes
`
`$251,000
`
`$152,450
`
`0.3926 ~
`
`6 111 Industry Ave., Fenton IL 62017
`
`2 no
`
`$240,000
`
`$152,450
`
`0.3648 ~
`
`7 105 Elm St., Hendricks, IL 62014
`
`5 yes
`
`$230,000
`
`$245,750
`
`0.0685 ~
`
`10 142 Cottontail Rd., Baron, IL 62019
`
`3 no
`
`$102,000
`
`$152,450
`
`0.4946~
`
`12 36 Spratt Ln., Baron, IL 62019
`
`4 no
`
`$111,000
`
`$152,450
`
`0.3734~
`
`14 234 Cottontail Rd., Baron, IL 62019
`
`4 yes
`
`$197,900
`
`$152,450
`
`0.2297~
`
`900
`
`301
`
`303
`
`304
`
`305
`
`306
`
`307
`
`310
`
`312
`
`314
`
`....
`
`321
`
`....
`
`322
`
`....
`
`....
`324
`
`....
`
`327
`
`FIG. 9
`
`....
`
`....
`
`911
`912
`329
`951
`Imedian err. I 0.3734r
`
`~
`7Jl
`•
`~
`~
`~
`
`~ = ~
`
`~ = ?
`
`N
`~CIO
`N
`
`0 ....
`....
`
`('D
`
`rFJ =-('D
`.....
`\0
`0 ....
`N ....
`
`d
`rJl
`-....l
`\c
`-....l = 0..,
`~ = N
`
`-....l
`
`

`

`/1000
`
`1001
`21505 SE 2nd St, Sarnmamisll, WA 98074/
`/1002
`ZESTIMATE™: $455,899 (What's this?)
`Value Range: $250,.744 - $752)233
`
`fa Refine value of this honle !D Map comparable homes
`
`- - - - - - \ :10 11
`
`FIG. 10
`
`~
`7Jl
`•
`~
`~
`~
`
`~ = ~
`
`2-
`?
`N
`~CIO
`
`N o ....
`....
`
`('D
`('D
`
`rFJ =(cid:173)
`.....
`.... o
`o ....
`N ....
`
`d
`rJl
`-....l
`\c
`-....l = 0..,
`~ = N
`
`-....l
`
`

`

`u.s. Patent
`
`U.S. Patent
`
`Juo.28,2011
`Jun.28,2011
`
`Sheet 11 of 21
`Sheet110f21
`
`US 7,970,674 B2
`US 7,970,674 B2
`
`
`
`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 12 of 21
`
`US 7,970,674 B2
`
`/ .. ,.·.·.· ... :2(:rO
`
`~ ... /
`
`Sq h::
`Lot::<<1 H:;
`'V·em' twilL
`'l't';:lf' tlpd;}b:~;:j.:.
`
`T;}t.i~~ ~;h)de",;
`
`?
`
`W::·:::i.;·~':~'J;·<:;··y'::'!}::·!}i~!::, .. <:.,;r;:;:',"!:!}H.::::::::::~.~;;~:~;'
`...............
`
`FJG~ 13
`
`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 13 of 21
`
`US 7,970,674 B2
`
`1401
`
`1402
`
`1403
`
`1404
`
`1405
`
`1406
`
`1407
`
`1408
`
`1409
`
`begin
`
`~
`display initial valuation of
`subject home
`~
`solicit updated home attributes
`from owner
`~
`display refined valuation that
`takes into account updated
`attributes
`~
`solicit information about
`improvements to subject home
`from owner
`~
`display refined valuation that
`takes improvements into
`account
`~
`solicit other factors affecting
`value of home from owner
`
`~
`display refined valuation that
`takes into account other factors
`~
`solicit list of similar camps from
`owner
`
`~
`display refined valuation that
`takes into account similar
`comps
`~
`end
`
`FIG. 14
`
`

`

`u.s. Patent
`
`U S Patent
`
`11028,2n.HJ
`Juo.28,2011
`
`12f041tee«M
`Sheet 14 of 21
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`US 7,970,674 B2
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`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 15 of 21
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`US 7,970,674 B2
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`u.s. Patent
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`U S Patent
`
`Juo.28,2011
`Jun.28,2011
`
`Sheet 16 of 21
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`US 7,970,674 B2
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`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 17 of 21
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`US 7,970,674 B2
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`

`

`u.s. Patent
`
`U.S. Patent
`
`Juo.28,2011
`Jun.28,2011
`
`Sheet 18 of 21
`Sheet180f21
`
`US 7,970,674 B2
`US 7,970,674 B2
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`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 19 of 21
`
`US 7,970,674 B2
`
`I
`
`1930~r-______________________________________ ~
`: Overall Summary
`/11920
`Original Zestimate............................ $555,727 I 1931
`Change Horne Fact ..... """."" ......... "". + $ 1,500/11932
`Change Horne Improvements"" .. " ... "" .. + $
`3,300/11933
`Your other estimated values .. """ .... " ... - $
`300/11934
`Change based on cornparable homes ...... + $ 26501
`/11980
`$ 563,177 / I
`NEW REVISED VALUE
`
`FIG.19B
`
`1940~
`r , ------------------------------------------------------~
`I Home Facts Detailed Summary
`/1942
`I, Residence:
`Single Family
`Water "'(none)
`# Bedrooms:
`4 *(3)
`Garage:
`Attached
`"--1941
`# Baths:,
`2.5
`Architechtural style: Colonial
`Sqfi:
`1658
`Construction quality: Good
`lot size (sf): 2356
`Pool:
`No
`Year built:
`1955
`______________________________ ~c-1931
`Total changes to home facts = $1500 7
`
`* (Previous info)
`
`FIG.19C
`
`1950~
`-o-m--e-I-m-p-r-o-v-e-m-e-n-t-S-D--et-a-il-e-d-s-U-t-n-m-a-r-v-------------------------,
`New Roof.. ............................... + $300/ ~~~~
`I Kitchen Remodel ...................... + $3000~1932
`I Total home improvements = $ 3300 7
`
`fH
`
`FIG.19D
`1960~r-____________________________________________________ ~
`Other Values Detailed Summary
`Orchard in back .......... , .. " .. , .... +
`Need new fence .. , .. , ................ -
`=
`Total other values
`
`/1961
`$700
`1962
`$1000~1933
`- $ 300 7
`
`FIG.19E
`
`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 20 of 21
`
`US 7,970,674 B2
`
`./ .... "} S~35
`1970·····.,.....
`·~l-~~·:-o-rr-:~-.~-.:-~-~~i~~H~c-:rt,-:~-'$-.~[~I~~·t-2.~i~-.~-~~S~·,-H--n-~-~~?~j~--------------------------------~
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`.. 71
`. . U •
`
`

`

`u.s. Patent
`
`Juo.28,2011
`
`Sheet 21 of 21
`
`US 7,970,674 B2
`
`id
`
`sq. ft.
`
`lot size
`
`Henderson Coun V recent sales table for linear regression model
`roof type
`selling price
`use code
`bedrooms bathrooms floors year
`
`2000
`
`1
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7
`
`8
`
`1850
`
`4345
`
`2220
`
`6000
`
`1375
`
`3100
`
`1590
`
`4575
`
`2280
`
`7300
`
`1950
`
`6205
`
`2180
`
`7880
`
`1675
`
`3421
`
`4
`
`6
`
`3
`
`2
`
`3
`
`2
`
`5
`
`4
`
`2
`
`2
`
`1
`
`2
`
`3
`
`2
`
`2
`
`2
`
`2 1953
`
`$132,500 shingle
`
`single-family
`
`3 1965
`
`$201,000 shingle
`
`single-family
`
`1 1974
`
`$98,750 tile
`
`single-family
`
`1 1973
`
`$106,500 shingle
`
`single-family
`
`2 1948
`
`$251,000 shingle
`
`single-family
`
`1 1925
`
`$240,000 shingle
`
`single-family
`
`3 1940
`
`$230,000 shake
`
`single-family
`
`1 1975
`
`$74,900 shingle
`
`single-family
`
`2 1938
`
`$253,500 shingle
`
`single-family
`
`2001
`V---.
`v---:
`2002
`2003
`V---.
`v---:
`2004
`v---:
`2005
`2006
`V---.
`v---:
`2007
`V---. 2008
`
`9
`
`10
`
`11
`
`12
`
`13
`
`14
`
`15
`
`16
`
`17
`
`2400
`
`6050
`
`1450
`
`3230
`
`1952
`
`4912
`
`1475
`
`2900
`
`2140
`
`6330
`
`1980
`
`3500
`
`2320
`
`4250
`
`1925
`
`5015
`
`2025
`
`4015
`
`6
`
`3
`
`4
`
`4
`
`5
`
`4
`
`5
`
`4
`
`4
`
`1 1966
`
`$102,000 shingle
`
`single-family
`
`1 1920
`
`$230,000 shingle
`
`single-family
`
`1 1964
`
`$111,000 shingle
`
`single-family
`
`2 1935
`
`$211,000 shingle
`
`single-family
`
`2 1930
`
`$197,900 shingle
`
`single-fam ily
`
`2 1927
`
`$238,000 shake
`
`single-family
`
`2 1949
`
`$179,900 shingle
`
`single-family
`
`2 1959
`
`$229,900 shake
`
`single-family
`
`V-:
`2009
`v--- 2010
`2011
`".-.
`
`".- 2012
`
`2013
`".-.
`
`".- 2014
`
`2015
`".-.
`
`V- 2016
`V- 2017
`
`3
`
`1
`
`2
`
`2
`
`2
`
`3
`
`3
`
`2
`
`2
`
`"---2021\....20;t--2023 "---2024 "---2025\.... "-2027 "-2028 "-2029
`2026
`FIG. 20
`
`"-2030
`
`

`

`US 7,970,674 B2
`
`1
`AUTOMATICALLY DETERMINING A
`CURRENT VALUE FORA REAL ESTATE
`PROPERTY, SUCH AS A HOME, THAT IS
`TAILORED TO INPUT FROM A HUMAN
`USER, SUCH AS ITS OWNER
`
`TECHNICAL FIELD
`
`The described technology is directed to the field of elec(cid:173)
`tronic commerce techniques, and, more particularly, to the
`field of electronic commerce techniques relating to real
`estate.
`
`BACKGROUND
`
`5
`
`2
`FIG. 4A is a flow diagram showing steps typically per(cid:173)
`formed by the facility in order to construct a tree.
`FIG. 4B is a flow diagram showing steps typically per(cid:173)
`formed by the facility in order to determine whether and how
`to split a node of a tree.
`FIG. 5 is a table diagram showing sample contents of a
`basis table containing the basis information selected for the
`tree.
`FIG. 6 is a tree diagram showing a root node corresponding
`10 to the basis table 500.
`FIG. 7 is a tree diagram showing a completed version of the
`sample tree.
`FIG. 8 is a flow diagram showing steps typically performed
`15 by the facility in order to score a tree.
`FIG. 9 is a table diagram showing sample results for scor(cid:173)
`ing a tree.
`FIG. 10 is a display diagram showing detailed information
`about an individual home.
`FIG. 11 is a display diagram showing a map identifying a
`number of homes in the same geographic area.
`FIG. 12 is a display diagram showing a display typically
`presented by the facility containing the attributes of a particu(cid:173)
`larhome.
`FIG. 13 is a display diagram showing a display typically
`presented by the facility to identify possible comparable sales
`on a map.
`FIG. 14 is a flow diagram showing steps typically per(cid:173)
`formed by the facility in order to tailor a valuation of a subject
`home based on information provided by a user such as the
`home's owner.
`FIG. 15 is a display diagram showing a sample display
`typically presented by the facility to display an initial valua(cid:173)
`tion of the subject home and solicit updated home attributes
`from the user.
`FIG. 16 is a display diagram showing a typical display
`presented by the facility to permit the user to describe
`improvements made to the subject home.
`FIG. 17 is a display diagram showing a sample display
`typically presented by the facility to enable the user to
`describe other aspects of the subject home that affect its value.
`FIG. 18 is a display diagram showing a sample display
`presented by the facility in order to enable the user to identifY
`comps regarded by the owner as similar to the subject home.
`FIGS. 19A-19F show a sample display typically presented
`by the facility in order to present an overall revised value for
`the subject home.
`FIG. 20 is a table diagram showing sample contents of
`recent sales information used to construct a linear regression
`valuation model that is based on the attributes whose values
`are available for the user to update in the first step of the
`process of generating a tailored valuation.
`
`In many roles, it can be useful to be able to accurately
`determine the value of real estate properties ("properties"),
`such as residential real estate properties ("homes"). As
`examples, by using accurate values for homes: taxing bodies 20
`can equitably set property tax levels; sellers and their agents
`can optimally set listing prices; and buyers and their agents
`can determine appropriate offer amounts.
`A variety of conventional approaches exist for valuing
`homes. Perhaps the most reliable is, for a home that was very 25
`recently sold, attributing its selling price as its value. Unfor(cid:173)
`tunately, following the sale of a home, its current value can
`quickly diverge from its sale price. Accordingly, the sale price
`approach to valuing a home tends to be accurate for only a
`short period after the sale occurs. For that reason, at any given 30
`time, only a small percentage of homes can be accurately
`valued using the sale price approach.
`Another widely-used conventional approach to valuing
`homes is appraisal, where a professional appraiser deter(cid:173)
`mines a value for a home by comparing some of its attributes 35
`to the attributes of similar nearby homes that have recently
`sold ("comps"). The appraiser arrives at an appraised value by
`subjectively adjusting the sale prices of the comps to reflect
`differences between the attributes of the comps and the
`attributes of the home being appraised. The accuracy of the 40
`appraisal approach can be adversely affected by the subjec(cid:173)
`tivity involved. Also, appraisals can be expensive, can take
`days or weeks to complete, and may require physical access
`to the home by the appraiser.
`While it might be possible to design systems that automati - 45
`cally value homes, such automatic valuations would gener(cid:173)
`ally be performed based upon the contents of a public data(cid:173)
`base, and without input from each home's owner or other
`information not in the public database. In such systems, fail(cid:173)
`ing to consider such information may result in valuations that 50
`are significantly inaccurate in some instances.
`In view of the shortcomings of conventional approaches to
`valuing homes discussed above, a new approach to valuing
`homes that was responsive to owner input, as well as having
`a high level of accuracy, and being inexpensive and conve- 55
`nient, would have significant utility.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIG. 1 is a block diagram showing some of the components
`typically incorporated in at least some of the computer sys(cid:173)
`tems and other devices on which the facility executes.
`FIG. 2 is a flow diagram showing steps typically performed
`by the facility to automatically determine current values for
`homes in a geographic area.
`FIG. 3 is a table diagram showing sample contents of a
`recent sales table.
`
`DETAILED DESCRIPTION
`
`Overview
`A software facility for automatically determining a current
`value for a home or other property that is tailored to input
`from its owner or another user ("the facility") is described.
`60 While the following discussion liberally employs the word
`"home" to refer to the property being valued in other nearby
`properties, those skilled in the art will appreciate that the
`facility may be straightforwardly applied to properties of
`other types. Similarly, while a wide variety of users may use
`65 the facility, including the owner, an agent or other person
`representing the owner, a prospective buyer, an agent or other
`person representing prospective buyer, or another third party.
`
`

`

`US 7,970,674 B2
`
`3
`In some embodiments, the facility uses a web site to receive
`information from a user and display to the user a refined
`valuation for the home that is based upon the infonnation
`provided by the user. In some embodiments, the infonnation
`provided by the user may include additional, corrected, and/
`or updated attributes of the home relative to the attributes
`known by the facility, such as attributes retrieved by the
`facility from a public or private database of home attributes;
`information about improvements to the home; infonnation
`about other factors likely to affect the value of the home, such 10
`as well-kept grounds, historical significance, ground water
`issues, etc.; and information identifying, among recent,
`nearby sales of comparable homes ("comps"), those that the
`user regards as the most similar to the subject home. In some
`embodiments, the facility displays the results of refining its
`valuation in a manner that makes clear how the valuation was
`affected by the different infonnation provided by the user.
`By enabling an user to refine a valuation of his or her home
`based upon infonnation about the home known to the user, the
`facility in many cases makes the valuation more accurate than
`would otherwise be possible, and/or helps the user to more
`fully accept the valuation as appropriate.
`Home Valuation
`In some embodiments, the facility constructs and/or
`applies housing price models each constituting a forest of 25
`classification trees. In some such embodiments, the facility
`uses a data table that identifies, for each of a number of homes
`recently sold in the geographic region to which the forest
`corresponds, attributes of the home and its selling price. For
`each of the trees comprising the forest, the facility randomly
`selects a fraction of homes identified in the table, as well as a
`fraction of the attributes identified in the table. The facility
`uses the selected attributes of the selected homes, together
`with the selling prices of the selected homes, to construct a
`classification tree in which each non-leaf node represents a
`basis for differentiating selected homes based upon one of the
`selected attributes. For example, where number of bedrooms
`is a selected attribute, a non-leaf node may represent the test
`"numberofbedrooms~4." This node defines 2 subtrees in the
`tree: one representing the selected homes having 4 or fewer
`bedrooms, the other representing the selected homes having 5
`or more bedrooms. Each leaf node of the tree represents all of
`the selected homes having attributes matching the ranges of
`attribute values corresponding to the path from the tree's root
`node to the leaf node. The facility assigns each leaf node a
`value corresponding to the mean of the selling prices of the
`selected homes represented by the leaf node.
`In some areas of the conntry, home selling prices are not
`public records, and may be difficult or impossible to obtain.
`Accordingly, in some embodiments, the facility estimates the
`selling price of a home in such an area ba