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International Property Tax Institute
International Property Tax InstituteInternational Property Tax Institute
From Paper to “the Cloud”:From Paper to “the Cloud”:Some Considerations on Some Considerations on
Valuation from North AmericaValuation from North America
Jerry (Jerome C.) German, ASAIPTI Director of Education and Research
2
Le “Think Tank” considers …(Using the royal “We”)
Not “where we are” but …Where do we want to be? Need to
be?Where are we in regards to the (EU
or global) competition?What does the future “look like”?What is “the vision” for the real
estate valuation industry? How do we start the journey?
3
DATA: From Paper to “the Cloud”(Largely in place in North America)
Perhaps THE Caveat is …Information “sharing”Open, freely available dataPublic source insures (enforces)
impartiality and uniformityEgalitarian ideal of a level playing
fieldInherent in definition of market value
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A Few “Bleeding Edge” Examples of GIS Driven Valuation Techniques
*(Actual, implemented procedures from 2006 county-wide revaluation)
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A quick perspective on Lucas County, Toledo, Ohio, USA
200,000 parcels (medium size in US)
Staff of 75Modeling group of 4 peopleField appraisers, 20GIS staff of 6 (after map
conversion)Annual budget of $3 million USD
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Construct (specify) valuation modelsthat are:
ReasonableRationalExplainableTo all constituencies
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EstimatedMarketValue
EstimatedMarketValue
EstimatedMarketValue
Location Building Land
Neighborhood * [ Quality *Class
+ Topo ]Sq. FeetLiving area + Baths
Width
* +
Lot size
GeneralQuality
%
=
=
=
Building Buildingquality * Quantity % $( ) ( )
Land LandQuality * Quantity % $
( () )
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Use GIS (Geographic Information Systems) for display and analysis
Perhaps the most important enhancement to (mass) valuation since the evolution of CAMA (Computer Assisted Mass Appraisal)
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How do we quantify . . . ?
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How do you include a spatial component (variable) in a valuation model?
It can take two forms in the model Rather traditional neighborhood
adjustment factor (dummy or categorical variable) refined and verified with spatial analysis
Interpolated location proxy surface variable derived through spatial or geostatistical analysis creating a continuous, no boundary location adjustment factor
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Hard Coded Residential Neighborhoods
Neighborhood Number Table Driven Categorical
or Dummy Variable Location Adjustment
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Response Surface of Location Adjustment Factors Smoothed (Averaged) by Physical Blocks
Red = Adjustment Factor of 1.0 and higher
Blue = Adjustment Factor of
.99 and lower
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Commercial Sales Comparison Location Factor 2-D
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9002
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60039004
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1005
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40027003
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50016002
2003
10012008
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7002
1002
2004
8002
20062005
3001
3001
2007
8002
6001
8001
4002
90029002
3001
3005
9002
APARTMENTS
MajorRds
CiNbhd06
PbbPgiNra
AvgPgiNra
< -2.88 Std. Dev.
-2.88 - -2.63 Std. Dev.
-2.63 - -2.38 Std. Dev.
-2.38 - -2.13 Std. Dev.
-2.13 - -1.88 Std. Dev.
-1.88 - -1.63 Std. Dev.
-1.63 - -1.38 Std. Dev.
-1.38 - -1.13 Std. Dev.
-1.13 - -0.87 Std. Dev.
-0.87 - -0.62 Std. Dev.
-0.62 - -0.37 Std. Dev.
-0.37 - -0.12 Std. Dev.
-0.12 - 0.13 Std. Dev.
0.13 - 0.38 Std. Dev.
0.38 - 0.63 Std. Dev.
0.63 - 0.88 Std. Dev.
0.88 - 1.13 Std. Dev.
1.13 - 1.38 Std. Dev.
1.38 - 1.63 Std. Dev.
1.63 - 1.88 Std. Dev.
1.88 - 2.13 Std. Dev.
2.13 - 2.38 Std. Dev.
2.38 - 2.63 Std. Dev.
2.63 - 2.85 Std. Dev.
RESPONSE SURFACE: RENT per Sq.Ft.
High RentsLow Rents
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9002
1006
60039004
9001
9003
1005
7001
5002
8002
4001
40027003
1003
1004
2001
50016002
2003
10012008
8001
3005
4003
8003
5004 2002
8002
5006
3003
5005
3001
3004
3002
4004
6004
5003
7002
1002
2004
8002
20062005
3001
3001
2007
8002
6001
8001
4002
90029002
3001
3005
9002
RESPONSE SURFACE: EXPENSE RATES
High
ExpensesLow
Expenses
APARTMENTS
MajorRds
CiNbhd06
PbbExp
Avg_Predic
< -2.63 Std. Dev.
-2.63 - -2.38 Std. Dev.
-2.38 - -2.13 Std. Dev.
-2.13 - -1.88 Std. Dev.
-1.88 - -1.63 Std. Dev.
-1.63 - -1.38 Std. Dev.
-1.38 - -1.13 Std. Dev.
-1.13 - -0.87 Std. Dev.
-0.87 - -0.62 Std. Dev.
-0.62 - -0.37 Std. Dev.
-0.37 - -0.12 Std. Dev.
-0.12 - 0.13 Std. Dev.
0.13 - 0.38 Std. Dev.
0.38 - 0.63 Std. Dev.
0.63 - 0.88 Std. Dev.
0.88 - 1.13 Std. Dev.
1.13 - 1.38 Std. Dev.
1.38 - 1.63 Std. Dev.
1.63 - 1.88 Std. Dev.
1.88 - 2.13 Std. Dev.
2.13 - 2.38 Std. Dev.
2.38 - 2.63 Std. Dev.
> 2.63 Std. Dev.
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9002
1006
60039004
9001
9003
1005
8002
1004
5002
4002
7003
2001
5001
6002
2008
8001
3005
80038002
3001
3002
8002
8002
8001
4002
90029002
APARTMENTS
MajorRds
CiNbhd06
PbbCap
Avg_Predic
< -2.13 Std. Dev.
-2.13 - -1.88 Std. Dev.
-1.88 - -1.63 Std. Dev.
-1.63 - -1.38 Std. Dev.
-1.38 - -1.13 Std. Dev.
-1.13 - -0.88 Std. Dev.
-0.88 - -0.63 Std. Dev.
-0.63 - -0.38 Std. Dev.
-0.38 - -0.13 Std. Dev.
-0.13 - 0.12 Std. Dev.
0.12 - 0.37 Std. Dev.
0.37 - 0.62 Std. Dev.
0.62 - 0.88 Std. Dev.
0.88 - 1.13 Std. Dev.
1.13 - 1.38 Std. Dev.
1.38 - 1.63 Std. Dev.
1.63 - 1.88 Std. Dev.
1.88 - 2.13 Std. Dev.
2.13 - 2.38 Std. Dev.
2.38 - 2.63 Std. Dev.
> 2.63 Std. Dev.
RESPONSE SURFACE: CAP RATES
High
Cap RatesLow
Cap Rates
17
PGI PgiNraLoc^1.35*AgeMult^(-.05)*((8000/Nra)^.04)*.45*Nra
VAC CondLv^(-.4) *AgeMult^(-.2)*VacLoc^.7*.43
EXP GradeLv^(-.1)*CondLv^(-.1)*AgeMult^(-.02) *ExpLoc *.96
CAP CondLv^(-.01)*CapLoc^1.05*1.075
CAP VALUE ((Pg*(1-Va))*(1-Ex))/Ca
GRM GradeLv^.3*CondLv^.6*GrmLoc^.85*AgeMult^.1*LogUnits^(-.14)*1.4
GRM VALUE Pgi*Grm
SALECOMP VAL. GradeLv^.5*CondLv^.5*SaleLoc^1.1*AgeMult^.05*35*Gba*((8000/Gba)^.07)
Spatially “Enabled” 2006 APARTMENT MODELS
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The Way Ahead Competition in global markets Driven by information Information stored in (mostly) uniform
databases Uniform data definitions (metadata) Data comes predominately from
property tax systems or cadastres Private industry will leverage data and
systems for economic advantage
International Property Tax Institute
Thank You
IPTI @ www.ipti.org
International Property Tax Institute4950 Yonge Street
Suite 2308Toronto, Ontario, Canada M2N 6K1
(416) 644-2772(416) 644-5152
