US 7,970,674 B2
`(10) Patent N0.:
`(12) Unlted States Patent
`
`Cheng et al.
`(45) Date of Patent:
`Jun. 28, 2011
`
`US007970674B2
`
`(54) AUTOMATICALLY DETERNIININGA
`CURRENT VALUE FORA REAL ESTATE
`PROPERTY: SUCH AS A HOME: THAT IS
`TAILORED TO INPUT FROMA HUMAN
`USER, SUCH AS ITS OWNER
`
`(75)
`
`.
`-
`.
`Inventors. gaVIdlChengS,Seattle,.\X}/1A\gUAxS), item
`“I“? “es:
`ammamls a
`(U )=
`KYHSIR Chunga Seattle, WA (US); DOHg
`Xiang, Sammamish, WA (US);
`-
`7
`Jonathan Bursteln, Seattle, WA OJS)
`
`(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(1)) by 1394 days.
`
`
`
`21) Appl. No.2 11/347,024
`
`22)
`
`65)
`
`Filed:
`
`Feb. 3, 2006
`
`Prior Publication Data
`US 2007/0198278 A1
`Aug. 23, 2007
`
`51)
`
`Int. Cl.
`(2006.01)
`G06Q 40/10
`......................... 705/35; 705/313
`52) US. Cl.
`........
`58) Fleld 0f Class1fieat10n Search .................... 705/35,
`.
`.
`.
`705313
`See application file for complete search hlstory.
`_
`References Clted
`US. PATENT DOCUMENTS
`
`56)
`
`11/1994 JOSt “31'
`5,361,201 A
`1/2001 Cheetham et al.
`6,178,406 B1 *
`5/2001 Naughton
`6 740 425 B1
`8/2003 Khedkar et a1.
`6:609:118 B1 *
`7/2005 Sasajima
`6,915,206 B2
`7,289,965 B1 * 10/2007 Bradley et al.
`
`.............. 705/10
`
`............ 705/36 R
`
`.................... 705/1
`
`.............. 345/632
`
`133883 Elmore
`1
`gig/fig 3%
`,
`orance eta .
`,
`,
`7/2009 Clemens et al.
`7,567,262 B1 *
`11/2003 Badali et al.
`2003/0212565 A1
`4/2004 Foster et al.
`2004/0073508 A1
`
`705/10
`5/2005 Ramamoorti et al.
`2005/0108084 A1 *
`7/2005 Kim et a1.
`....................... 705/30
`2005/0154657 A1*
`5/2007 Chakraborty et al.
`2007/0124235 A1
`8/2007 Ma et al.
`2007/0185727 A1
`8/2007 Humphries et al.
`2007/0185906 A1
`3/2008 Andersen et al.
`2008/0077458 A1
`2/2009 Eder
`............................... 705/10
`2009/0043637 A1 *
`OTHER PUBLICATIONS
`
`Vladimir Svetnik et al, Random Forest: A Classification and Regres-
`sion Tool for compound Classification and QSAR Modleing. J. Chem
`Info, Computer Science, 2003, vol. 43, pp, 1947-1958.*
`U.S.Appl. No. 11/927,623, filed Oct. 29, 2007, Humphries et a1.
`US. Appl. No. 11/971,758, filed Jan. 9, 2008, Humphries et a1.
`Quinlan, Ross J., “C45: Programs for Machine Learning,” Machine
`Learning, 1993, 302 pages, Morgan Kaufmann Publishers, San Fran-
`cisco, CA, USA.
`
`(Continued)
`
`Primary Examiner 7 Kirsten S Apple
`Assistant Examiner 7 Abdul Basit
`(74) Attorney, Agent, or Firm 7 Perkins Coie LLP
`
`ABSTRACT
`(57)
`A facility procuring information about a distinguished prop-
`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 ofthe distinguished property.
`The facility obtains user input from the owner adjusting at
`least one aspect of 1nfom1at10n about the d1st1ngu1s11ed prop-
`erty used 1n the automatlc valuation of the d1stmgu1shed
`property. The facility then displays to the owner a refined
`-
`-
`-
`-
`.
`~
`valuatlon of the dlstinguished property that 1s based on the
`adjusrmem 0fthe Obtained user impur-
`
`40 Claims, 21 Drawing Sheets
`
`computer system 100
`
`CPU /— 101
`
`
`
`mBmury /— mz
`
`
`
`persislsm storage
`
`1— 103
`
`105
`
`
`computer-readable
`
`media drive 10‘
`
`
`
`new/01k connection
`
`
`
`TRULIA - EXHIBIT 1001
`
`TRULIA - EXHIBIT 1001
`
`

`

`US 7,970,674 B2
`Page 2
`
`OTHER PUBLICATIONS
`
`Mobasher, B. “Classification Via Decision Trees in WEKA,” DePaul
`University, Computer Science, Telecommunications, and Informa-
`tion Systems, ECT 584-Web Data Mining, 2005, http://maya.cs.
`depaul.edu/~classes/Ect584/WEKMclassifyhtml, 5pages [internet
`accessed on Dec. 6, 2007].
`Bennett, Kristin P., “Support Vector Machines: Hype or Hallelujah?”
`SIGKDD Explorations, Dec. 2000, pp. 1-12, vol. 2. issue 2, ACM
`SIGKDD.
`Hill. T. and Lewicki, P., “K-Nearest Neighbors.” Smtistics Methods
`and Applications, 2007, http://WWW.statsoft.com/textbook/stknn.
`htrnl, [internet accessed on [Dec. 6. 2007].
`Breirnan, L., “Random Forests,” Machine Learning, 45, pp. 5-32,
`2001, Kluwer Academic Publishers, The Netherlands.
`http://Www.ics.uci.edu/~mlearn/databases/housing/housing.names,
`1 page [accessed Dec. 13, 2005].
`StatSoft, Inc., “Classification Trees,” http://WWW.statsoft.com/text-
`booldstclatrehtml, pp. 1-20, © 1984-2003 [accessed Dec. 13,2005].
`Breirnan et al., “Random Forest.” Classification Description, http://
`WWW.stat.berkeley.edu/users/breiman/RandomForests/ccihome.
`htm. pp. l-28 [accessed Dec. 13. 2005].
`Real-info.com, “What is an AVM,” wwwreal-infocom’products,
`avm.asp? Internet Archive Date: Oct. 30, 2005, 5 pages [accessed
`Mar. 21,2007].
`RealEstateABCcom, see paragraph headed “How do I make the
`estimate more accurate?” wwwrealestateabc.com/home-values/,
`Internet Archive Dated: Apr. 5, 2006, 4 pages [accessed Mar. 20,
`2007].
`Standard & Poors, “Guidelines for the use of Automated Valuation
`Models for UK. RMBS Transactions,” http://WWW.rics.org/NIU
`rdonlyres/8chd20c-7FAC-4549-86FB-3930CDOCBC05/0/
`StandardandPoorsReportonAVMs.pdf, Published Feb. 20, 2004, 4
`pages.
`
`wwwr-projectorg, “The R Project for Statistical Computing,” http://
`webarchive.org/Web/20060102073515/Wwv.r-project.org/main.
`shtrnl, 1 page [internet archive date: Jan. 2, 2006].
`“Centre for Mathematical Sciences,” Lund University, http://web.
`archiveorg/web/20060101005103/http://Www.maths.Ith.se/, 1 page
`[internet archive date: Jan. 1, 2006].
`http://Web.archive.org/Web/
`“An
`Introduction
`to
`R,”
`20060118050840/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.org/W%/20050830073913/cran.r-project.org/
`bannershtml, pp. 1-2 [internet archive date: Aug. 30, 2005].
`Non-Final Office Action for US. Appl. No. 11/524,048, Mail Date
`Apr. 29, 2009, 10 pages.
`Non-Final Office Action for US. Appl. No. 11/524,047, Mail Date
`Oct. 28, 2009, 12 pages.
`Final Office Action for US. Appl. No. 11/524,048, Mail Date Dec. 8,
`2009, 12 pages.
`Non-Final Office Action for US. Appl. No. 11/927,623, Mail Date
`Dec. 28, 2010. 22 pages.
`Non-Final Office Action for US. Appl. No. 11/347,000, Mail Date
`Apr. 9, 2010, 29 pages.
`Tay et al., “Artificial Intelligence and the Mass Appraisal of Residen-
`tial Apartments,” Journal of Property Valuation and Investment, Feb.
`1, 1992, 17 pages.
`Meyer, Robert T., “The Learning of Multiattribute Judgment Poli-
`cies,” The Journal ofConsumer Research, vol. 14, No. 2, Sep. 1987,
`pp. 155-173.
`Non-Final Office Action for US. Appl. No. 11/347,000, Mail Date
`Oct. 27, 2010, 25 pages.
`
`* Cited by examiner
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 1 of 21
`
`US 7,970,674 B2
`
`computer system
`
`100
`
`CPU /—
`
`memory /—
`
`persistent storage
`
`network connection
`
`com purer-readable
`media drive
`
`/—
`
`FIG. 1
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 2 of 21
`
`US 7,970,674 B2
`
`201
`
`202
`
`203
`
`204
`
`205
`
`206
`
`207
`
`select recent sales for
`geographic area
`
`
`
`receive request for valuation
`identifying home
`
`
`
`
`
`apply trees, weighted by
`scores, to attributes of home
`identified in request
`
`FIG. 2
`
`

`

`El
`
`address
`
`bedrooms
`
`sellin- rice ”EH
`
`Henderson Coun
`
`recent sales table
`
`1 111 Main St., Hendricks, IL 62012
`
`2 96 Elm St., Hendricks, IL 62014
`
`3 140 Cottontail Rd., Baron, IL 62019
`
`4 6 Spratt Ln., Baron, IL 62019
`
`5 776 Fir St, Hendricks, IL 62014
`
`6 111 Industry Ave, Fenton IL 62017
`
`7 105 Elm St., Hendricks, IL 62014
`
`8 110 Muffet St, Baron, IL 62019
`
`9 156 Elm St., Hendricks, IL 62014
`
`2/28/2005
`
`10 142 Cottontail Rd.. Baron, IL 62019
`
`11 160 Prospect Bldv., Fenton IL 62017
`
`12 36 Spratt Ln., Baron, IL 62019
`
`13 118 Main St., Hendricks,- IL 62012
`
`14 234 Cottontail Rd., Baron, IL 62019
`
`15 677 Fir St., Hendricks, IL 62014
`
`$132,500
`
`1/3/2005
`
`$201,000
`
`1/8/2005
`
`$98,750
`
`1/11/2005
`
`$106,500
`
`1/14/2005
`
`$251,000
`
`1/26/2005
`
`$240,000
`
`2/4/2005
`
`$230,000
`
`2/4/2005
`
`$74,900
`
`2/14/2005
`
`$253,500
`
`2/15/2005
`
`$102,000
`
`2/18/2005
`
`$230,000
`
`2/20/2005
`
`$111,000
`
`2/20/2005
`
`$211,000
`
`2/21/2005
`
`$197,900
`
`2/24/2005
`
`$238,000
`
`300
`
`301
`
`302
`
`303
`
`304
`
`305
`
`306
`
`307
`
`308
`
`309
`
`310
`
`311
`
`312
`
`313
`
`314
`
`315
`
`mm'S'fl
`
`IIOZ‘sz'unr
`
`IZJOE”9‘19
`
`Z8VL9‘0L6‘LSfl
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 4 of 21
`
`US 7,970,674 B2
`
`401
`
`402
`
`403
`
`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
`
`404
`
`node should
`
`
`
`be split
`
`406
`
`determine mean selling price of
`basis sales represented by
`node
`
`
`
`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
`
`407
`
`next node
`
`FIG. 4A
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 5 of 21
`
`US 7,970,674 B2
`
`451
`
`452
`
`node's
`
`
`
`
`population satisfies split
`threshold
`
`No
`
`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
`
`obtain possible split opportunity squared error
`
`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
`
`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
`
`462
`
`45
`
`3
`
`45
`
`4
`
`455
`
`645
`
`457
`
`458
`
`459
`
`460
`
`461
`
`
`
`.
`.
`a
`.
`retursn ‘Ili’tlroucturltiiritnfymg
`P
`pp
`y
`
`463
`
`
`
`
`selected
`ossible split opportunity variance
`
`
`< node variance
`
`No
`
`
`
`Yes
`
`return identifying selecte
`split opportunity
`
`464
`
`FIG. 4B
`
`
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 6 of 21
`
`US 7,970,674 B2
`
`m
`
`address
`
`bedrooms
`
`tree 1 basis table
`sellin orice
`
`2 96 Elm St., Hendricks, IL 62014
`
`$201,000
`
`$238,000
`
`500
`
`302
`
`308
`
`309
`
`311
`
`313
`
`315
`
`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
`
`FIG. 5
`
`$74,900
`
`$253,500
`
`$230,000
`
`$211,000
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 7 of 21
`
`US 7,970,674 B2
`
`/ 600
`
`601
`
`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
`
`bedrooms 5 4
`
`bedrooms > 4
`
`703
`
`sales = 2, 9, 13, 15
`bedrooms = 5- 00
`
`view = no-yes
`
`
`
`
`view = no
`
`704
`
`705
`
`view = yes
`
`713
`
`
`
`
`sales = 2, 13
`sales = 9, 15
`bedrooms = 5-00
`bedrooms = 5-00
`
`
`view = no
`valuation = $206,000
`
`view = yes
`valuation = $245,750
`
`FIG. 7
`
`
`
`
`
`sales = 8. 11
`bedrooms = 1-4
`
`702
`
`
`
`view = no-yes
`valuation = $152,450
`
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 8 of 21
`
`US 7,970,674 B2
`
`801
`
`802
`
`803
`
`804
`
`805
`
`806
`
`identify for scoring 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
`
`mac nitude
`
`FIG. 8
`
`

`

`_— view
`
`tree 1 scorin- table
`
`900
`
`301
`
`303
`
`304
`
`305
`
`306
`
`307
`
`310
`
`312
`
`314
`
`951
`
`
`
`mama'S'fl
`
`:
`f;
`9o
`g
`H
`
`m
`E;
`2.\D
`e
`g
`
`ZSVL9‘0L6‘LSfl
`
`1 111 Main St., Hendricks, IL 62012
`
`$132,500
`
`$152,450
`
`3 140 Cottontail Rd., Baron, IL 62019
`
`$98,750
`
`$152,450
`
`4 6 Spratt Ln., Baron, IL 62019
`_
`.
`5 776 FH SL, Hendricks, IL 62014
`
`6 111 Industry Ave., Fenton IL 62017
`.
`7 105 Elm St., Hendricks, IL 62014
`
`$106,500
`
`$152,450
`
`$251,000
`
`$152,450
`
`$240,000
`
`$152,450
`
`$230,000
`
`$245,750
`
`
`
`10 142 Cottontail Rd., Baron, IL 62019
`
`$102,000
`
`$152,450
`
`12 36 Spratt Ln., Baron, IL 62019
`
`$111,000
`
`$152,450
`
`14 234 Cottontail Rd., Baron, IL 62019
`
`$197,900
`
`$152,450
`
`
`321
`322
`324
`327
`329
`911
`
`912
`
`FIG. 9
`
`

`

`/1000
`
`21505 SE 2nd St, Sammamish, WA 980?4 f 1001
`1m
`/—1002
`ZESTIMATE : $455,899 (What's: this?)
`
`Value Range: $250,?44 — $152,233
`
`Refine value of this home
`:1011
`
`Mag cnmgarable homes
`
`FIG. 10
`
`mm'S'fl
`
`IIOZ‘sz'unr
`
`[IN01was
`
`Z8VL9‘0L6‘LSfl
`
`

`

`
`
`
`
`mama'S'fl
`
`IIOZ‘sz'unr
`
`WWIImus
`
`Z8VL9‘0L6‘LSfl
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 12 of 21
`
`US 7,970,674 B2
`
`
`
`
`”2. 3a.}{:3{‘2
`
`33% 2’3
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 13 of 21
`
`US 7,970,674 B2
`
`1401
`
`1402
`
`1403
`
`1404
`
`1405
`
`1406
`
`1407
`
`1408
`
`1409
`
`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
`
`displa refined valuation that
`takes into account other factors
`
`solicit list of similar comps from
`owner
`
`display refined valuation that
`takes into account similar
`comps
`
`FIG. 14
`
`

`

`
`
`
`‘wzsa'p
`313%- rgmg aim
`
`
`
`1:4
`was ifinggfiififia mig- £223;
`A {.
`
`
`
`Kgfll g? 5‘?
`
`
`
`mama'S'fl
`
`IIOZ‘sz'unr
`
`IZJOVImus
`
`Z8VL9‘0L6‘LSfl
`
`

`

`U S. Patent
`
`Jun. 28, 2011
`
`Sheet 15 of 21
`
`US 7,970,674 B2
`
`_
`
`7‘
`A,
`
`(
`,
`:52
`,
`Hawaii: :2
`,,
`
`mm; mm; asst? mm uggjmfim 1M9 faiiii
`
`J,
`
`

`

`finamy
`
`atfi
`
`Hwm
`
`a“hatmmgan
`
`
`
`
`
`gm.3:2:as.My3:2;:aatam$3ch:3
`
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 17 of 21
`
`US 7,970,674 B2
`
`313mm 1
`
`'
`
`,5.
`
`‘ssxxxxxasss
`
`x3
`
`‘ <\.
`
`FEE! ‘5 3
`
`

`

`U S. Patent
`
`Jun.28,2011
`
`Sheet180f21
`
`US 7,970,674 B2
`
`,u.x
`
`555;
`
`35%
`
`35$
`
`3mm
`
`h
`
`fl”.
`
`nsvw
`
`
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 19 0121
`
`US 7,970,674 B2
`
`
`
`Overall Summary
`i Original Zestimate ............................ $555,127/I_:::?
`} Change Home Fact .............................. +$
`1,5004—1932
`
`1 Change Home Improvements ................. + $
`33004—1933
`
`F Your other estimated valuee ................. - $
`300 I
`1934
`
`Change based on comparable homes ...... +$
`2650/—1980
`‘ NEW REVISED VALUE
`:
`$ 563,177 f
`
`
`FIG. I9B
`
`
`
`/—1942
`Water *(none)
`View:
`Attached
`Garage:
`Architechtural style: Colonial
`Construction quality: Good
`Pool:
`No
`
`
`
`
`
`
`
`
`
`
`
`
`_
`1
`1 Resndente:
`‘; # Bedrooms:
`
`"‘
`:11 Baths:
`
`V Sq ft:
`1 Lot size (sf):
`‘1 Year built:
`
`‘
`.
`Single Family
`4 *(3)
`2.5 X1941
`1658
`2356
`1955
`
`l
`4—1931
`
`1 Total changes to home facts : $1500
`
`=1 (Previous info)
`
`FIG. 19C
`
`
`
`1950\
`
`Home Improvements Detailed Summary
`/—1951
`$300
`New Roof ................................. +
`1952
`[ Kitchen Remodel ...................... + $3000/— 1932
`
`
`FIG. 19D
`
`
`
`1960—\
`3; Other Values Detailed Summary
`1961
`,
`_
`$700/—1962
`‘V Orchard In hack...................... +
`$1000
`1933
`Need newfence ...................... -
`$300
`Totalothervalues
`—
`l
`
`FIG. 19E
`
`

`

`U.S. Patent
`
`Jun.28,2011
`
`Sheet200f21
`
`US 7,970,674 B2
`
` 5
`
`.1711 '
`
`1 191
`
`
`

`

`U.S. Patent
`
`Jun. 28, 2011
`
`Sheet 21 of 21
`
`US 7,970,674 B2
`
`2000
`
`2001
`
`$132500 smngm
`
`flnweJamHy
`
`_
`$201000 smngm
`
`.
`$9&750 um
`
`.
`$10&500 smngm
`
`.
`$251000 smngb
`
`.
`$240000 smngb
`
`$230000 shake
`
`.
`$71900 smngm
`
`_
`$253500 smngb
`
`_
`$102000 smngb
`
`,
`$23Q000 smngb
`
`.
`$111000 smngm
`
`_
`$211000 angb
`
`‘
`$191900 smngb
`
`$23&000 shake
`
`.
`$179900 sMngm
`
`$22&900 shake
`
`fingeéamfly
`
`,
`.
`$nge4amny
`
`.
`.
`sngefamny
`
`.
`_
`gngeiamny
`
`finmefamfly
`
`,
`_
`mnweiamdy
`
`yngeJamfly
`
`7
`.
`NnmeJamny
`
`‘
`.
`$nge4amfly
`
`fingefamuy
`
`,
`wngeJamHy
`
`.
`_
`fingeJamny
`
`.
`_
`yngeJamfly
`
`,
`_
`mngeJamfly
`
`.
`,
`angefamdy
`
`,
`.
`angefiamny
`
`2002
`
`2003
`
`2004
`
`2005
`
`2006
`
`2007
`
`2008
`
`2009
`
`2010
`
`2011
`
`2012
`
`2013
`
`2014
`
`2015
`
`2016
`
`2017
`
`

`

`US 7,970,674 B2
`
`1
`AUTOMATICALLY DETERMINING A
`CURRENT VALUE FOR A 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-
`tronic commerce techniques, and, more particularly, to the
`field of electronic commerce techniques relating to real
`estate.
`
`BACKGROUND
`
`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
`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
`recently sold, attributing its selling price as its value. Unfor-
`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
`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-
`mines a value for a home by comparing some of its attributes
`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
`appraisal approach can be adversely affected by the subjec-
`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-
`cally value homes, such automatic valuations would gener-
`ally be performed based upon the contents of a public data-
`base, and without input from each home’s owner or other
`information not in the public database. In such systems, fail-
`ing to consider such information may result in valuations that
`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-
`nient, would have significant utility.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIG. 1 is a block diagram showing some ofthe components
`typically incorporated in at least some of the computer sys-
`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.
`
`2
`
`FIG. 4A is a flow diagram showing steps typically per-
`formed by the facility in order to construct a tree.
`FIG. 4B is a flow diagram showing steps typically per-
`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.
`
`10
`
`FIG. 6 is a tree diagram showing a root node corresponding
`to the basis table 500.
`
`FIG. 7 is a tree diagram showing a completed version ofthe
`sample tree.
`FIG. 8 is a flow diagram showing steps typically performed
`by the facility in order to score a tree.
`FIG. 9 is a table diagram showing sample results for scor-
`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-
`lar home.
`
`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-
`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-
`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 otheraspects ofthe 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.
`
`
`
`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.
`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
`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.
`
`15
`
`20
`
`25
`
`30
`
`35
`
`40
`
`45
`
`50
`
`55
`
`60
`
`65
`
`

`

`US 7,970,674 B2
`
`3
`
`4
`
`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 information
`provided by the user. In some embodiments, the information
`provided by the user may include additional, corrected, and/
`or updated attributes of the home relative to the attributes
`known by the facil'ty, such as attributes retrieved by the
`facility from a public or private database of home attributes;
`information about improvements to the home; information
`about other factors li <er to affect the value ofthe home, such 10
`as well-kept grounds, historical significance, ground water
`issues, etc.; and ir formation identifying, among recent,
`nearby sales of comparable homes (“comps”), those that the
`user regards as the Ir ost 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
`
`5
`
`15
`
`
`
`affected by the diffe ‘ent information provided by the user.
`By enabling an user to refine a valuation ofhis or her home
`based upon informat'on about the home known to the user, the
`facility in many cases makes the valuation more accurate than 20
`would otherwise be possible, and/or helps the user to more
`fully accept the valuation as appropriate.
`Home Valuation
`
`the facility constructs and’or
`In some embodiments,
`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 ofa number ofhomes
`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 30
`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 35
`basis for differentiating selected homes based upon one ofthe
`selected attributes. For example, where number of bedrooms
`is a selected attribute, a non-leaf node may represent the test
`“number ofbedroomsé4.” This node defines 2 subtrees in the
`
`tree: one representing the selected homes having 4 or fewer 40
`bedrooms, the other representing the selected homes having 5
`or more bedrooms. Each leaf node ofthe 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 45
`value corresponding to the mean of the selling prices of the
`selected homes represented by the leaf node.
`In some areas of the country, home selling prices are not
`public records, and may be difficult or impossible to obtain.
`Accordingly, in some embodiments, the facility estimates the 50
`selling price of a home in such an area based upon loan values
`associated with its sale and an estimated loan-to-value ratio.
`
`In order to weight the trees ofthe forest, the facility further
`scores the usefulness of each tree by applying the tree to
`homes in the table other than the homes that were selected to 55
`
`construct the tree, and, for each such home, comparing the
`value indicated for the home by the classification tree (i .e., the
`value of the root leaf node into which the tree classifies the
`
`home) to its selling price. The closer the values indicated by
`the tree to the selling prices, the higher the score for the tree. 60
`In most cases, it is possible to determine the attributes of a
`home to be valued. For example, they can often be obtained
`from existing tax or sales records maintained by local gov-
`ernments. Altematively, a home’s attributes may be inputted
`by a person familiar with them, such as the owner, a listing 65
`agent, or a person that derives the information from the owner
`or listing agent. In order to determine a value for a home
`
`whose attributes are known, the facility applies all ofthe trees
`ofthe forest to the home, so that each tree indicates a value for
`the home. The facility then calculates an average of these
`values, each weighted by the score for its tree, to obtain a
`value for the home. In various embodiments, the facility
`presents this value to the owner of the home, a prospective
`buyer of the home, a real estate agent, or another person
`interested in the value of the home or the value of a group of
`homes including the home.
`In some embodiments, the facility applies its model to the
`attributes of a large percentage ofhomes in a geographic area
`to obtain and convey an average home value for the homes in
`that area. In some embodiments, the facility periodically
`determines an average home value for the homes in a geo-
`graphic area, and uses them as a basis for determining and
`conveying a home value index for the geographic area.
`Because the approach employed by the facility to deter-
`mine the value of a home does not rely on the home having
`recently been sold, it can be used to accurately value virtually
`any home whose attributes are known or can be determined.
`Further, because this approach does not require the services of
`a professional appraiser, it can typically determine a home’s
`value quickly and inexpensively, in a manner generally free
`from subjective bias.
`FIG. 1 is a block diagram showing some ofthe components
`typically incorporated in at least some of the computer sys-
`tems and other devices on which the facility executes. These
`computer systems and devices 100 may include one or more
`central processing units (“CPUs”) 101 for executing com-
`puter programs; a computer memory 102 for storing pro-
`grams and dataiincluding data structures, database tables,
`other data tables, etciwhile they are being used; a persistent
`storage device 103, such as a hard drive, for persistently
`storing programs and data; a computer-readable media drive
`104, such as a CD-ROM drive, for reading programs and data
`stored on a computer-readable medium; and a network con-
`nection 105 for connecting the computer system to other
`computer systems, such as via the Internet, to exchange pro-
`grams and/or dataiincluding data structures. In various
`embodiments, the facility can be accessed by any suitable
`user interface including Web services calls to suitable APIs.
`While computer systems configured as described above are
`typically used to support the operation of the facility, one of
`ordinary skill in the art will appreciate that the facility may be
`implemented using devices of various types and configura-
`tions, and having various components.
`FIG. 2 is a flow diagram showing steps typically performed
`by the facility to automatically determine current values for
`homes in a geographic area. The facility may perform these
`steps for one or more geographic areas of one or more differ-
`ent granularities, including neighborhood, city, county, state,
`country, etc. These steps may be performed periodically for
`each geographic area, such as daily. In step 201, the facility
`selects recent sales occurring in the geographic area. The
`facility may use sales data obtained from avariety ofpublic or
`private sources.
`FIG. 3 is a table diagram showing sample contents of a
`recent sales table. The recent sales table 300 is made up of
`rows 301-315, each representing a home sale that occurred in
`a recent period of time, such as the preceding 60 days. Each
`row is divided into the following columns: an identifier col-
`umn 321 containing an identifier for the sale; an address
`column 322 containing the address of the soldhome; a square
`foot column 323 containing the floor area of the home; a
`bedrooms column 324 containing the number ofbedrooms in
`the home; a bathrooms column 325 containing the number of
`bathrooms in the home; a floors column 326 containing the
`
`

`

`US 7,970,674 B2
`
`5
`
`6
`
`number of floors in the home; a view column 327 indicating
`whether the home has a view; a year column 328 showing the
`year in which the house was constructed; a selling price
`column 329 containing the selling price at which the home
`was sold; and a date column 330 showing the date on which
`the home was sold. For example, row 301 indicates that sale
`number 1 of the home at 111 Main St., Hendricks, Ill. 62012
`having a floor area of 1850 square feet, 4 bedrooms, 2 bath-
`rooms, 2 floors, no view, built in 1953, was for$l32,500, and
`occurred on Jan. 3, 2005. While the contents of recent sales
`table 300 were included to pose a comprehensible example,
`those skilled in the art will appreciate that the facility can use
`arecent sales table having columns corresponding to different
`and/or a larger number ofattributes, as well as a larger number
`of rows. Attributes that may be used include, for example,
`construction materials, cooling technology, structure type,
`fireplace type, parking structure, driveway, heating technol-
`ogy, swimming pool type, roofing material, occupancy type,
`home design type, view type, view quality, lot size and dimen-
`sions, number of rooms, number of stories, school district,
`longitude and latitude, neighborhood or subdivision,
`tax
`assessment, attic and other storage, etc. For a variety of rea-
`sons, certain values may be omitted from the recent sales
`table. In some embodiments, the facility imputes missing
`values using the median value in the same column for con-
`tinuous variables, or the mode (i.e., most frequent) value for
`categorical values.
`While FIG. 3 and each of the table diagrams discussed
`below show a table whose contents and organization are
`designed to make them more comprehensible by a human
`reader, those skilled in the art will appreciate that actual data
`structures used by the facility to store this information may
`differ from the table shown, in that they, for example, may be
`organized in a different manner; may contain more or less
`information than shown; may be compressed and/or
`encrypted; etc.
`Returning to FIG. 2, in steps 202-205, the facility con-
`structs and scores a number oftrees, such as 100. This number
`is configurable, with larger numbers typically yielding better
`results but requiring the application of greater computing
`resources. In step 203, the facility constructs a tree. In some
`embodiments