THE COMMON THREAD
`IN MARKET DATA SYSTEMS
`
`Richard A. Borst
`President
`Cole-LayerTrumbJe Company
`
`GENERAL TRACK
`Session G i
`Monday - 1:30 P.M.
`
`PAPER PRESENTED AT THE WORLD CONGRESS ON COMPUTER-ASSISTED VALUATION, SPONSORED BY THE
`LINCOLN INSTITUTE OF LAND POLICY, AUGUST 1 - 6, 1982.
`
`LVI
`
`

`

`THE COMMON THREAD
`IN MARKET DATA SYSTEMS
`
`R. A. BaRST
`
`BACKGROUND
`
`Multiple regression
`state agencies, and
`it Is used to establ
`
`analysis (tIRA) is a tool used by many assessment
`private firms in the valuation of real property.
`ish the coefficients a, in an equation of the form
`
`jurisdictions,
`Briefly stated,
`
`I I I I I
`
`ESP
`
`=
`
`o +
`
`l
`
`X
`
`-
`
`a
`
`ty
`
`parcels of Property.
`
`asked.
`
`They include:
`
`sole method of estimating a property's
`other valuation technlques.
`as compa-
`
`7
`
`X1 + B2 X2 + -- + B
`where ESP represts the estimated spll4nn ---
`' pi ¡LC UT a property and the X1 are proper
`characteristics and/or functional
`-
`combinations of property characteristics
`technique is applied to a set of sales with known
`The MRA
`the B, which in turn are used to estimate the selling price of a designated set of
`property characteristics to obtain
`This valuation technique can be used as the
`value or it can be used in
`conjunction with
`rable sales analysis or the cost approach.
`In describing this technique to potential users, a number of questions are frequently
`What data elements are required to make PIRA work?
`What data elements would be needed in my Jurisdiction
`How many terms are there in a model?
`How many models are needed?
`
`I I I I
`
`I
`
`How many sales are needed?
`
`These are pragmatìc questjos
`It is the purpose of this paper to offer answers to
`these questions based on the
`models that were used to value on the order of 725,000 residential parcels of Property
`Company's experience in developing MRA
`on projects in New Hampshire,
`Massachusetts, New York, New Jersey, North Carolina,
`Carolina, and Texas.
`In all, saine 86 models developed by several Individuals in
`valuing eleven
`Jurisdictions were reyied in Preparing the Information provided
`ISouth
`herein.
`TUr er
`Iu1!°huuI
`Later in the paper, results will be given in the forni of property
`Utilized to develop
`regression based value estinîates.
`characteristics
`a simple fact.
`Recognjjo must be given to
`The factors so described can only be a subset of those initially
`I considered.
`The Company utilizes several data collection
`instruments, but for the
`
`

`

`most part, they contain similar Information.
`A typical set of characteristics collected
`is shown in Exhibit 1.
`Exhibit 2 Is a sample property record card showing the coding
`structure and layout of the characteristics of Exhibit I.
`Most of the items are self-
`explanatory; however, it should be mentioned that COU Is an overall rating relating to
`the condition, desirability, and usefulness of a residence.
`It is from this or similar starting point that the observations of this paper are
`
`ma de.
`
`THE VDELrNG PROCESS
`
`To develop tIRA models, the Company either uses backward stepwise regression analysis
`or constrained regression
`analysis, a variation on the backward elimination
`In constrained regression analysis, some subset of the
`technique.
`regressloncoefficients are
`constrained to fall within certain limits for pragmatic valuation reasons.
`reason Is that there may not be sufficient sales information to develop a regression
`One such
`model that takes into account the contribution a detached garage has on the estimated
`value of a residence In a rational manner.
`This and similar problems with other
`property characteristics (pools, patios, decks, carports) are frequently encountered
`The Company's approach to this problem is to specify that
`certaf n data items must be in the model and that their coefficient must fall within
`
`in regression modeling.
`
`certain supportable bounds1.
`
`Assuming that a computer-readable file of sales and property description data has been
`created, the modeling process involves several steps, including:
`Varlab
`transformations
`
`Specification of variables to consider for regression.
`SpecificatIon of coefficient
`
`It shows the variables
`
`constraints.
`Exhibit 3 will serve to Illustrate some of these points.
`considered for regression and shows the constraints placed on certain coefficients.
`Exhibit 4 further describes the variables of Exhibit 3.
`The fïrst variable listed
`is TOTLND which stands for total land value which has been estimated by an indepen-
`dent appraisal method.
`Note that the constraints on the coefficient for TOTIND
`force the coefficient to be brought in with a value of 1.0.
`this, In effect,means
`that the independent land value estimate will be used in developing the total value
`estimate for the parcel.
`The variable AGE*SF stands for age (in years) X square feet
`of living area, where age is a derived variable calculated by subtracting year built
`from the current year.
`The correspoixting model is shown in Exhibit 5.
`It can be seen that of the original 27 factors considered, 24 were brought into or
`forced into the model.
`Where reporting results In this paper, If a factor is in a
`model, constrained or otherwise, it is considered as a valid value predictor.
`A Companion World Congress paper entitled "Use of Constrained Regression Analysis
`by Non-Statisticians" by the author gives more detail an this technique.
`
`(1)
`
`2
`
`

`

`RESULTS
`
`As was previously stated, 86 models used in eleven different jurisdictions were review-
`ed in compiling the results to be reported.
`Exhibit 6 provides a suirriary of general
`information regarding the number of parcels valued, the number of models used, the
`number of terms in a model, and the number of sales used in developing the models.
`this exhibit, several useful observations can be made:
`
`From
`
`In smaller jurisdictions, two to five modéls. tend to be used.
`jurisdictions had 22 and 28 models.
`
`The two largest
`
`The number of sales in a model ranges widely.
`As few as 118 were used.
`Many were
`in the 200 - 400 range, and only one model (author's review) utilized over 1,000
`sales.
`
`The number of sales In a model Is, somewhat smaller than might be expected compared
`to models developed in the 1970's.
`This Is due to the fact that in many juris-
`dictions, the number of sales available In 1980, 1981, and 1982 isa much lower
`percentage of the parcels to be valued.
`
`The number of terms in a model ranged from five to 36, with 20 being a typical
`number.
`
`The model with only five terms in jurisdiction 2 was developed using RCNLD as one
`of the factors.
`Thus, the figure of five terms is artificially low.
`The model
`was developed for large, high quality homes where direct modeling of the usual
`property characteristics proved less satisfactory.
`The model with seven ternis in
`jurisdiction 8 was developed for a group of parcels where the parcel was being
`acquired on speculation (the value was In the land), and the building description
`had less influence than typical on property value.
`
`The quality of a model can be judged by several measures.
`The statistic used most
`frequently by Company model developers is the ratio of standard error of the esti-
`mate to the mean selling price expressed as a percent.
`The author reviewed this
`statistic for jurisdiction 9 in Exhibit 6.
`The ratio ranged from a low of 5.5%
`to a high of 22.5%.
`The median was 9.7%.
`Examining the extremes, the low ratio
`was formed from a model with a $2,095 standard error, and a $37,873 mean selling.
`price.
`This model was developed on newer, modestly priced homes.
`As expected,
`the model should be accurate in this housing category because the market is well
`understood by both buyer and seller.
`The high figure was the result of a standard
`error of $3,659 and a mean selling price of $16,268.
`This model was developed for
`older (1929 average year built), low price housing.
`In absolute terms, the
`standard error Is reasonable.
`It is the older, low cost less homogenous housing
`that traditionally has been the more difficult to value accurately by any means.
`A $3,000 variation from selling price is a high percent if the selling price is
`in the $10.000 - 20,000 range.
`
`The model shown in Exhibit 5 is typical of the models developed in jurisdiction 9.
`Although not shown, the mean selling price for this model is $45,928.
`Using the
`standard error $3,792 as shown, a figure of 8.3% results.
`The point is that with
`models having 16 - 25 terms, a 10% ratio of standard error to mean selling price
`is a reasonable expectation.
`Better performance can be expected for models
`developed for the more homogenous properties.
`Higher ratios can be expected on
`the difficult to value less homogenous properties.
`
`3
`
`Sr
`
`

`

`To describe the corrinon thread in market data, the frequency of use of a descriptor in
`the eleven jurisdictions of the sample group will be used.
`The results are compiled
`in Exhibit 7.
`
`Ifa variable was used in any model in a jurisdiction, it was counted. Thus, Ifa
`variable was used in all jurisdictions, the count will be eleven.
`Also, when a
`variable such as the number of fireplaces Is listed, It could have been in the model
`directly or a derivative term such as the square root of the number of fireplaces may
`have been used.
`No attempt to make a distinction was made because the major idea of
`the paper is to identify data that needs to be considered for collection prior to the
`modeling process.
`Later on, certain frequently encoúntered data transformations will
`be identified as helpful to prospective model developers.
`
`Coninents on Exhibit 7 include the following:
`
`The most frequently used descriptors for valuation purposes are really quite
`logical - sin, number of baths, age, etc.
`
`-
`
`The less frequently used descriptors are utilized In exception or unusual areas.
`They should not be ignored, however.
`For example, view and waterfront factors
`appeared only once, but when they were in the model, their coefficients had a
`large Influence on value.
`
`Neighborhood variables do not show up very fre4uently.
`The Company's procedure
`Is to geographically delineate neighborhoods as a part of the overall valuation
`process.
`It is a manual procedure that is performed prior to modeling by
`experienced appraisers.
`Each parcel is, therefore, identified as belonging to
`a specific neighborhood.
`
`As part of the modeling process, neighborhoods of similar characteristics (e.g.,
`age, size, sèlling price, style) are combined into "valuation areas".
`It Is on
`sales from a valuation area that the regression models are developed.
`Thus,
`neighborhood would tend not to be a variable In the prOcess
`
`With reference to Exhibit 5, It can be seen that the structure of the model is
`quite simple.
`Most variables are straightforward such as the number of full-
`baths and pool area.
`Certain factors are usually weighted by (multiplied by)
`square feet of living area.
`
`In the model shown - grade factor, age, air conditioning, no heat, date of sale,
`and CUti rating are brought Into the model in this fashion.
`An intuitive notion
`of why this is done can be seen in the air conditioning example.
`By forming the
`product AC*SF, where AC is O or 1, the influence of air conditioned area is being
`brought into the model rather than its existence or nonexistence.
`Experience has
`shown that this is a better value predictor than the unweighted term.
`
`In conclusion, there are great similarities In the model structures encountered.
`With
`the wealth of modeling experience, the Company has been able to make the process more
`routine and has begun the transfer of the modeling process to appraisal oriented
`personnel.
`
`4
`
`1*
`
`

`

`EXHIBIT 1
`
`TYPICAL RESIDENTIAL PROPERTY CHARACTERISTICS
`
`Sales Data
`
`Date
`Price
`Source
`Validity
`
`Land Data
`
`Size - Frontage, Depth; Square Feet; Acreage
`Unit Price
`Gross Value
`
`Property Factors
`
`Topography (level ----)
`Utilities (public -.--.)
`Street or Road (paved ---.)
`Fronting Traffic (light ---)
`
`Dwelling Information
`
`Story Height
`Wall Material
`Roof Material
`Building Style
`Age
`Living Accomodations
`Kitchen Remodeled (Y, N)
`Bathroom Remodeled (Y, N)
`Basement Rating (none ---)
`Heating Rating
`Heating Fuel
`Heating System Type
`Attic Rating
`Interior Condition
`Physical Condition (exterior)
`Other Features (brick trim, fireplaces --)
`Ground Floor Area
`Grade Factor
`Cost & Design Factor
`CDU Rating (condition, desirability, usefulness)
`Additions (garages, carports, ---)
`Other Buildings and Yard Items (sheds, pools --)
`
`5
`
`

`

`PROPERTY FACtORS
`
`TOtOG!Apfly
`
`UTIUTICS
`
`STREET 0* ROAD
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`INFLUENCE FACTORS
`
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`
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`
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`
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`
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`
`EXHIBIT 2
`
`AC",
`
`10. FOOT
`
`DELETI iN-131
`
`O ROSIE
`
`N
`
`ACTUAL
`R9UA«
`
`1W&A1 (FIM ACTUALUNI! PACI
`
`DIPl H
`ucrrs
`
`lt FE CT M
`L*U T PA ICE
`
`LAND DATA & OUTATIOHS
`
`- _____I --
`
`MalICE ricto*
`i L,_.t
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`
`SUMMARY OF VALUES
`
`TOTAL VM.UE tAXI) -
`
`TOTAl. VALUE SUILOINGS
`
`TOTAL VALUE LAND t SLDGS.
`
`MEMORANDUM
`
`'1511
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`ItCTION UITNIUIO IV:
`
`P9OCE$SING DATA
`
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`
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`
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`
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`
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`
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`
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`
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`
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`
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`
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`
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`
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`
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`
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`
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`
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`
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`
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`ADDITION CODES
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`
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`
`ADDITIONS
`
`OTHER FEATURES
`
`SUI TOTAL
`
`s GRAbE FACTOR 5h
`
`t CA B FACTOR %
`
`¡ASE VALUE
`
`iMARKET
`
`AOJUSTNENT
`
`'TRUE VALUE
`
`OI&YTY?ECGBES
`
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`
`NIICELI.AJIEØI4 IM?ROVINENTS
`
`GROSS BUILDING SUMMARY
`
`TOTAL VALUE
`
`ID
`
`USE
`
`CORSIRIJCIIGI
`
`EPICI I RENDO
`
`AGI
`
`COU
`
`SIZE
`
`RAIE
`
`¡ASE VALUE
`
`I SEC DETAILED CAID
`2-SEE DITAILEO REPCRT
`
`DATA
`
`iLTCILQ5y
`
`DATI
`
`I
`
`'
`
`cavw&rnpsno LI7I$S tOS
`F'RThE« OrVIrOtis IFA??LICASS
`
`MAREET AOIUSTMENT
`LISIS I FIL
`I ci4psr
`
`TRUE VALLE
`
`TOTAL CROSS VALUEe
`
`I
`
`I
`
`e e
`
`TYPE CODE
`
`CUAN
`
`YEAR
`
`SIZE
`
`t CONO
`
`RATE
`
`lAIE VALUE
`
`NA
`
`MOO EOOEI
`
`TRUE VALUE
`
`

`

`CLUSU'p NUNOCR
`
`'9
`
`C(JNSTRAIUCO M.R.A. PARAMETERS
`
`INDIc
`
`NAME
`
`TYP
`
`LOS
`
`76
`
`
`
`34
`
`II
`I?
`
`le
`
`Ic
`P0
`
`24
`
`2?
`
`3?
`36
`
`52
`53
`
`60
`65
`
`t
`
`?7
`90
`
`li
`
`VAL
`PÄMRJ4S
`Ft&Bflj
`
`FIPILISM
`WßSt,cs
`
`TLA
`ATCflGq
`
`-'
`
`--
`
`CRPOy
`
`OTHoay
`GRFØSF
`
`--L
`
`ELEC
`
`'i
`
`00
`
`o
`
`00
`
`O
`
`00
`
`Ø
`
`0l
`00
`
`00
`0f
`
`0.00
`
`0.00
`0.00
`I 500.00
`0...
`
`3.00
`
`5.0.
`I 00
`0.00
`0.00
`
`o
`
`o
`
`o ..a,I
`
`0.50
`
`1.50
`
`I I £ G 13
`
`4
`
`0:00
`0.00
`0.00
`0.00
`3500.00
`
`0.00
`I 5.00
`
`2.. o
`¡0.00
`10.00
`
`I 00
`0.00
`
`0.00
`1.50
`0.
`
`ì
`
`1.50
`
`EXHIBIT 3
`
`A"
`
`t,
`
`

`

`NAME
`
`TOTLND
`
`G-BVAL
`
`FAMRÌIS
`
`FULBTH
`
`HLFBTH
`
`BSMTRT
`
`FINBSM
`
`WBSTKS
`
`MET FP
`
`GARAGE
`
`lIA
`
`.ATCHGR
`
`OP PCH
`
`CL Pol
`
`DECK
`
`CRPORT
`
`DTCHGR
`
`POOL
`
`OTHOBY
`
`GRFSF
`
`AGE.SF
`
`2-FAM
`
`ELEC
`
`ACSF
`
`EXHIBIT 4
`
`DESCRIPTION
`
`Total land value estimated independently.
`
`Gross building value of buildings appraised independently.
`Usually detached improvements.
`
`Number of family rooms.
`
`Number of full-baths.
`
`Number of half-baths.
`
`Basement rating (1 = none, 5 = full)
`
`Finished basement area.
`
`Woodburnjng fireplace stacks.
`
`Number metal fireplaces.
`
`Built-in garage.
`
`Total living area.
`
`Attached garage area.
`
`Open porch area.
`
`Closed porch area.
`
`Deck area.
`
`Carport area.
`
`Detached garage area.
`
`Pool area.
`
`Other buildings and yard impròvenients (other then garages,
`carports, and pools).
`
`Grade factor X square feet of living area.
`
`Age in years X square feet of living area.
`
`Two family dwelling (0,1).
`
`Electric heat.
`
`Air conditioning (0,1) X square feet of living area.
`
`g
`
`ri
`
`

`

`Exhibit
`
`Continued
`
`NRSF
`
`DS SF
`
`CDUSF
`
`No heat (0,1) X square feet of living area.
`Date of sale (months
`living area.
`
`from reference date) X square feet of
`
`COndition/desirabj].çty/useful
`living area.
`
`rating X square feet of
`
`10
`
`çA
`
`

`

`flZThnhi;4r
`
`-
`
`-
`
`6.550e
`'o0000
`al,
`
`4. 1040
`S S 85
`9.1753
`0. OTÇft
`
`1.soo
`
`IS CPp
`16
`Dtc
`1 ûo
`19
`20
`
`Acr.sr
`
`*
`
`ATCHG
`op PCH
`
`p
`
`n
`i
`
`
`
`45
`
`II
`12
`
`3
`
`23
`24
`
`Ds.sr
`COU*SF
`
`SOURCE
`
`SliM SQUAREr
`
`ANALYSIS
`
`(iF
`
`ÛEGRECS FREFDÛM
`
`V*R1ANC
`MEANSOU
`
`IO
`
`REGRrZSION
`
`R SOIJARFP FOR MODEL
`STD FRR 0F ESTIMATE
`
`3.789608E.Io
`
`0.9021
`3792,2505
`
`14
`
`2 70006F409
`
`MODEL CLUSTER t
`
`19
`
`VARÊA&F
`
`LENTS
`
`coeFFIcIENT
`
`SiD ERR
`
`T VALLL
`
`95
`
`CONFIDENCE INTERni..
`
`ruLe
`
`fINIjSn
`WMS1Ks
`
`-e
`
`4029.2725
`
`fi
`
`-
`
`4.1957
`1500.0000
`
`6
`
`p22.747
`
`1.4to
`
`3.bj
`'ego
`
`-
`
`2.95
`
`2416,69
`
`2
`
`2972.67
`4.4:'
`
`8
`
`I.Iit
`
`S.92
`
`3.974
`
`2
`
`q
`
`0.750
`0.0fl
`
`0.014
`
`1.05
`
`12.10
`1.65
`
`¡3.09
`
`-
`
`-
`
`3.60
`
`7.89
`0,12
`
`-
`
`0,36
`
`.0
`
`EXHIBIT 5
`
`8.76
`
`11.97
`4.03
`
`p
`
`10.66
`0.04
`sg
`0.22
`
`PAR cniip
`
`F TEST
`
`ß.04o on
`o ot;
`0.03
`
`0.11
`
`0.00
`0.04
`O.4
`0.04
`
`0.37
`
`13.019
`2].9fl4644
`0,652
`
`5.Oso
`
`l.iog
`.002
`146.419
`13.35o
`
`-7
`
`171.237
`
`o
`
`

`

`EXHIBIT 6
`
`Geographic
`
`Location
`
`Jurisdiction
`
`Number
`
`Number of
`
`Models
`
`Number o.f
`
`Terms
`
`Range of Sales
`
`per model
`
`Approximate
`
`Number of
`
`Parcels
`
`NEW ENGLANOE
`
`MID-ATLANTIC
`
`SOUTHEAST
`
`SOUTHWEST
`
`3
`
`1
`
`15 -
`is
`
`258 -
`795
`
`5
`
`2
`
`5 -
`20
`
`N/A
`
`3
`
`3
`
`4
`
`4
`
`15 -
`17
`
`.21 -
`36
`
`122 -
`272
`
`249 -
`575
`
`7
`
`5
`
`13 -
`35
`
`N/A
`
`3
`
`6
`
`2
`
`7
`
`3
`
`8
`
`9
`
`22
`
`10
`
`6
`
`11
`
`.28
`
`17 -
`19
`
`20 -
`23
`
`7 -
`28
`
`16 -
`26
`
`11 -
`22
`
`18 -
`23
`
`232 -
`. 284
`
`280 -
`457
`
`106 -
`386
`
`118 -
`476
`
`158 -
`906
`
`127 -
`
`1 740
`
`('Jr
`
`12.000
`
`35,000
`
`10,000
`
`16,000
`
`80,000
`
`9,000
`
`7,500
`
`10,000
`
`96,000
`
`45,000
`
`400,000
`
`

`

`EXHIBIT 7
`
`FREQUENCY OF OCCURRENCE OF MARKET DATA FACTORS
`
`Factor
`
`Date of Sale
`Living Area
`Number of Full-Baths
`Number of Fireplaces (stacks)
`Attached Garage Cipacity
`Total Land Value
`Age
`Detached Garage (capacity/area)
`Open Porch Area
`Enclosed Porch Area
`Number of Half-Baths
`Heating Rating
`Pool Area
`Grade
`COU
`Basement Garage.
`Finished Basement Area
`Deck
`Basement Rating
`Other Buildings and Yard
`Number of Living Units
`Gross Building Value
`Carport
`Neighborhood
`Heating System Type
`Patio
`Rec Room
`Exterior Wall Material
`Number of Family Rooms
`Physical Condition
`Interior Condition
`Metal Fireplace
`Canopy
`Property Factor (traffic)
`Number of Stories
`Attic Rating
`Kitchen Rating
`Unfinished Basement Area (under addition)
`Waterfront (various - lake, canal, etc.)
`View
`Cracked Slab
`Lot Size
`
`.
`
`13
`
`31
`
`-
`
`Frequency
`(maximum fi)
`
`11
`11
`11
`
`il
`il
`10
`10
`10
`10
`lo
`g
`
`9
`9
`9
`8
`8
`7
`
`7
`6
`6
`6
`5
`5
`5
`3
`3
`3
`2
`2
`2
`2
`
`2
`2
`
`1
`
`i
`
`i
`
`