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Wahib_Sprengnetter
Wahib_Sprengnetter
4
Wahib_Sprengnetter
Wahib_Sprengnetter
7
8
Valuation
Confidence rating
෠𝑉 = ത𝑉𝑓𝑎 𝑓𝑥,𝑦 𝑓𝑡
෠𝑉 = ෍ 𝑓(𝑆)𝑃
9
• Dealing with
incomplete
information: one
or more attributes
are missing
• Ensure the
predictive power
of the AVM
• Geographical
density
• Distribution of
data amount
across time
• Correlation
among data: For
example Property
condition, Energy
class and
construction year
tend to be
correlated
Multicollinearity Heterogeneity
Missing valuesOverfitting
Constrained
optimisation:
Ensure the expected
effect of a given
attribute input on the
estimated market value
Data
Transformation:
Categorise, grouping,
apply a nonlinear
function
AccuracyPredictive
power
10
0
10
20
30
40
50
60
70
80
90
100
Hit Rate Performance
Model
A
Model
B
Model
C
AVM
0
10
20
30
40
50
60
70
80
90
100
Hit Rate Performance
0
10
20
30
40
50
60
70
80
90
100
Hit Rate Performance
0
10
20
30
40
50
60
70
80
90
Hit Rate Performance
1111
Low High
Low
Low
High
High
Confidence rating
Availability
Homogeneity
?
AVMAddress
Object Category
/ Type
Living area
Construction
year
Property
standard
Plot area (only
for EFH & MFH)
Garages /
outside parking
space
#Floors / Floor -
Elevator (only
for ETW)
Low Highaccuracy
No input characteristics
Floor area added = 80 sqm
Construction year added = 1990
Property condition added = ‚Gehoben‘
Wahib_Sprengnetter
Wahib_Sprengnetter
Easy access
More transparency
More Leads
Higher Conversion
Wahib_Sprengnetter
REST API
Wahib_Sprengnetter
x
x
x
x
x
x
x
Dr. Wahib Arroum
Chief Data Scientist
Sprengnetter GmbH

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