1. International Property Tax Institute
From Paper to “the Cloud”:
Some Considerations on
Valuation from North America
Jerry (Jerome C.) German, ASA
IPTI Director of Education and Research
jgerman@ipti.org
International Property Tax Institute

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?
2

3. DATA: From Paper to “the Cloud”
(Largely in place in North America)
 Perhaps THE Caveat is …
 Information “sharing”
 Open, freely available data
 Public source insures (enforces)
impartiality and uniformity
 Egalitarian ideal of a level playing field
 Inherent in definition of market value
3

4. A Few “Bleeding Edge” Examples of
GIS Driven Valuation Techniques
*(Actual, implemented procedures
from 2006 county-wide revaluation)
4

5. A quick perspective on Lucas
County, Toledo, Ohio, USA
 200,000 parcels (medium size in US)
 Staff of 75
 Modeling group of 4 people
 Field appraisers, 20
 GIS staff of 6 (after map conversion)
 Annual budget of $3 million USD
5

6. Construct (specify) valuation models
that are:
 Reasonable
 Rational
 Explainable
 To all constituencies
6

7. Location Building Land
[
Estimated
Market
Value
= ∗ + ]
Estimated
Market
Value
= Neighborhood
* [ ( Quality
Class
*
Sq. Feet
+
Baths
)+(
Living area
Topo
*
Width
+
Lot size
) ]
[( )]
Estimated Building Building
)+ (
General Land Land
Market = Quality quality * Quantity Quality * Quantity
Value % ∗ % $ % $
7

8. 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)
8

9. How do we quantify . . . ?
9

10. 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
10

11. Hard Coded Residential Neighborhoods
Neighborhood Number
Table Driven
Categorical or Dummy
Variable Location
Adjustment
11

12. 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
12

13. Commercial Sales Comparison Location Factor 2-D
OHIO TURNPIKE VILL A
ArrowHead Sales Prices GE
Y
SS
BA
EM TE
RYAN
IN
PO
CK
NDS
Y RY
LE KO
AN
REYNOL DS
INDIAN WOO D
C
M HI
W OO D LA
MID DLESBRO UGH
I 475
TO LL GA TE
H
O
LL
W K
AN
A HA
D
M
TO K
I 475
PIC CADILLY
BR I C K-
OS
YAR D SAL ISBU
RY
AG
D U SS
E
EL
BR
IAR
KIT
ARROWHEAD
WES
F IE
T M EA OX FOR D
DO W
LD
S
I 475
STRAYER
LD LONGBOW
W EAT HER FIE
FO
CO
RD
M AU M
NA
EE W E
S TE R
NT
N
IL LIN O
IS
13

14. RESPONSE SURFACE: RENT per Sq.Ft.
6001
6002 5001
5002
2001
9004 5003
6003
High Rents
6004 5004 2002 1004
5006
2004
Low Rents
5005
2003 2005
2006 1005 1006
1003
4001 2007
9003 1001 1002
4003 2008
7001
70 02
4004
40 02
4002 A P RT M EN TS
A
7003 MajorRds
C Nbh
i d06
3004 PbbPgi Nra
3005 Av gPgiNra
30 05 < -2.88 Std. Dev.
3003 3001 -2.88 - -2.63 Std. Dev.
9001 30 01
3001 -2.63 - -2.38 Std. Dev.
3002 3001 -2.38 - -2.13 Std. Dev.
-2.13 - -1.88 Std. Dev.
8002
-1.88 - -1.63 Std. Dev.
-1.63 - -1.38 Std. Dev.
-1.38 - -1.13 Std. Dev.
8003 -1.13 - -0.87 Std. Dev.
-0.87 - -0.62 Std. Dev.
9002 -0.62 - -0.37 Std. Dev.
8002 8001
8001 -0.37 - -0.12 Std. Dev.
-0.12 - 0.1 Std Dev.
3 .
0.13 - 0.38 Std. Dev.
800 2 0.38 - 0.63 Std. Dev.
0.63 - 0.88 Std. Dev.
8002 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.
9002 90 02
2.13 - 2.38 Std. Dev.
9002
2
2
.38 - 2
.63 - 2
.63 Std. De
.85 Std. De
v.
v.
14

15. RESPONSE SURFACE: EXPENSE RATES
6001
6002 5001
5002
2001
9004 5003
6003
Low 6004 5004
5006
2004
2002 High
1004
5005
Expenses
2003 2005
2006 Expenses
1003
1005 1006
4001 2007
9003 1001 1002
4003 2008
7001
70 02
4004
40 02 APARTM ENTS
4002
7003 MajorRds
C Nbh
i d06
3004 3005 PbbExp
30 05 Av g_P redic
3003 3001 < -2.63 Std. Dev.
9001 30 01 -2.63 - -2.38 Std. Dev.
3001
-2.38 - -2.13 Std. Dev.
3002 3001
-2.13 - -1.88 Std. Dev.
8002
-1.88 - -1.63 Std. Dev.
-1.63 - -1.38 Std. Dev.
-1.38 - -1.13 Std. Dev.
8003
-1.13 - -0.87 Std. Dev.
-0.87 - -0.62 Std. Dev.
9002
8002 8001 -0.62 - -0.37 Std. Dev.
8001 -0.37 - -0.12 Std. Dev.
-0.12 - 0.1 Std Dev.
3 .
0.13 - 0.38 Std. Dev.
800 2
0.38 - 0.63 Std. Dev.
8002 0.63 - 0.88 Std. Dev.
0.88 - 1.13 Std. Dev.
1.13 - 1.38 Std. Dev.
1.38 - 1.63 Std. Dev.
9002 90 02 1.63 - 1.88 Std. Dev.
9002 1.88 - 2.13 Std. Dev.
2.13 - 2.38 Std. Dev.
2.38 - 2.63 Std. Dev. 15
> 2.63 Std Dev.
.

16. RESPONSE SURFACE: CAP RATES
500211 10 11 109 11 12 5001 12 11 1212
11 10 1011 111010 1011 11 11 10 9101011 11 12 12 12121212 1212 12
11 1111 11 11 10 1011 1011 10 1111 12
12
10 1111 11 11 11 10 11 1111 101011 1010 10 1111 11 9 12 12 13 12 1212
11
11 1111 11 11 11 111111 1111 1211
11 11 11 1111 11 12 1212
11 10 11 11 11 1111 11 11 10 11 11 12 12
11
11 10 11 12 11 11 13 1312 12
12
11 11
10
11 11 11 11 1111 11111111 1111 11 11
11 11 12
12
11 11 10 1313
11
11
12 13 1213
11
11 10 11
11
11 910 11 11 11 10 1112 1312 12
11 6002 1111 11 11 11 1111 10 11 11 10 101111 11 10109 11 12 12 12 13 12 13 13
13 121212 2001
11 10
11 10 10 109 10 11 1111 11
11
1010910 10 11 13
10
11
11
11
11 11
11 10 11 910 10 101111111110 9 10 1012 13 1212
13
11
3
12
11
11
13
9004 11 6003 1010 9 1010 11 1011 9 10 1213 14 1 12 1212 12
10 10 1111 999 11 10 10 14 1291312 13 13 14 1313 12 12 12 12
12
10 11 11 11 10 10 9
9
10
12
11 11 12 13
1010 99 10 101111 11 1011 12 131516 1812 12 12 1414 13 13
11
11 11
11
11
11
11
11 1110 10 10 12 12 11 11
10
1004
11
12
10 99 11 11
1110 101111 10 10 10 10 9 10 11 9 10 151414 13 16 11 1213 13 14
High 13
13 3
Low
1
11 10 11 11 111111 11 10 10 9 9 1110 11 17 14 14 1511 13 14 13 11 11
15 12 11
11
11 11 1111111111 1111 11 11 1010109 10 11 11 171619 14 14 12
11
11
11
11 11 11 11
11
16 1513 12 1312 12 11 11 12
11
10
11 11 11 11 1110 16
11 1111 1011 1111 1111 11 11 11 11 11 11 111412 151718 14 1412 1515 13 12 11 11
11 13 11 12 11
Cap Rates
11 11 11 11 11 12 12 1 11 11 11 11 11
11 11 13 15 14 161614 1612 12 13 13 13 1311 12 12 11
11 1
11 11 11 11
Cap Rates
11 11 11 11 11 1111 10 11 1212 11 11 11 11
11
11
11 11
11 11 11 11 12 1212 1414 10 131314 11 10 12 12 11 1111
11 1414 1211 12 12 11 11
11 11 11 11 11 11 11 11 111111 1111 12 14 12 12 12 11 11 11 11 11
11
13
11
12 12 12 1212
11
13 14151413 15 13 13 14 10 1010 10 12 13 12 12 11 1111
11
12
11
11
11
11
11
11 11 11 11 11 11 11 11 11 11
11 1111 12 12 121212 12 1212 11 10 05
11 11 11 11 1314 15 11 14 12 10 12
12
12 12 1006 11
11
11
1414 15 12 11
15
13
11 11
11
11 111111 11 12 11 11 11 11 1111 11 11 1112 1111 1211 12 14 14 16 11 11 11 12 11 11 11 11
11
11 11
11
11 11 11 11 11 11 11 11 11 11 11 13 13 14 12 1211 12 12
1111 12 12 12 14 1414 12 11 11
11
11
11 11 1111 11 1515 15 11
12
11 10 11
9003 11 11 11 1111 11 1110121313 15 1514 14 1313 13 13 12 1212 12 12 12 1111 11 11 11 11
11
11 11
11 11 1210 11 11 1111 111111 11 1111 11
11
11 2008
11
11
11
11
11
11 11 11 1111 11 10
11
11 11 11 11 1012 10 11 11
11 11 1111 11 1011 11 11
11 1111 12 10 10 10 10 11111111 109 10 4002
11 11 11 11 APARTMENTS
11 11 10 11 11 10
11 11 11 10 910 1111 11 11
11 11 11 11 11 11 910 11 11 1111 1111 1212
11 11 7003 11 1 11 11 Major Rds
11 11 11 1 11 11 11 11 11 10 10 10 11 1111 1111 12
11 11 1213 CiNbhd06
11 11 11 11 1111 1111 1111 1111 11 10 1111 10
10 1011 124002
11 11 11 11 11 11 1111 11 11 1111 11 PbbCap
11
11 11 11 11 11 1111 1111 11
12
11
11
1111 10 11 1112
11
11 Avg_P redic
11
11
11 1
11
11 1 11 111111 111210
11
3005
11
9001 11 11111111 11 12
< -2.13 Std. Dev.
11 11 11 11
11
11 11 11
11 1111
11
11
11 11 -2.13 - -1.88 Std. Dev.
11
11
11 11
3001
10
11 11 11 11 -1.88 - -1.63 Std. Dev.
11
11 11 11
11
11
11
11
11
10 1111
10
1111 11 0 11 -1.63 - -1.38 Std. Dev.
11
11 11 1 10 11 11 11 11113002
11
1111 1111 -1.38 - -1.13 Std. Dev.
8002 10 11 1111
11
11
11
11
1111
10
10 -1.13 - -0.88 Std. Dev.
11
9002 11
11 11 10 8003 11 11 11 11
8002 11 -0.88 - -0.63 Std. Dev.
11 11
11
11 11 11 12
11
11
11
11
11
10
-0.63 - -0.38 Std. Dev.
10
10 10 101010 1111
10
11
1010 11 -0.38 - -0.13 Std. Dev.
10 10 10 10 13
11 1010 8001 11 138001
11
11
-0.13 - 0.12 Std. Dev.
10
10 10 12 12
11
10
10
11
11
10
0.12 - 0.37 Std. D ev.
11
10 10 10
10 10 10 10 11 800 2 0.37 - 0.62 Std. D ev.
11
10
11 11
10
10
10 10 118002 0.62 - 0.88 Std. D ev.
10 11
11 10
11 11 11 11 11 10 0.88 - 1.13 Std. D ev.
10
10 10 1111 11 1.13 - 1.38 Std. D ev.
11
10
10
11
10
11 11 1.38 - 1.63 Std. D ev.
10 10 10 11
11
1.63 - 1.88 Std. D ev.
10 90 02
10
10
11
119002 1.88 - 2.13 Std. D ev.
10 1111 2.13 - 2.38 Std. D ev.
2.38 - 2.63 Std. Dev.
> 2.63 Std. Dev. 16

17. Spatially “Enabled” 2006 APARTMENT MODELS
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)
17

18. 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
18

19. Thank You
IPTI @ www.ipti.org
International Property Tax Institute
4950 Yonge Street
Suite 2308
Toronto, Ontario, Canada M2N 6K1
(416) 644-2772
(416) 644-5152
jgerman@ipti.org
International Property Tax Institute