Sigrid Krogstrup Jensen sijdstdk Cajsa Mølskov cmsdstdk Property that generates a profit for its owner and thus must be valuated and taxed accordingly Business Mixed housing and business ID: 808587
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Slide1
Combining administrative and market data in the development of new commercial real estate indicators
Sigrid
Krogstrup Jensen, sij@dst.dk
Cajsa Mølskov, cms@dst.dk
Slide2Property that generates a profit for its owner, and thus, must be valuated and taxed accordingly
Business
Mixed housing and businessWarehouse and productionPrivate insitutionsSpecialized property
Commercial real estate in Denmark
2
Slide3According to the Danish Dwellings and Buildings register (BBR) a property consists of buildings and units:
Commercial real estate in
Denmark II3
Property
Building 2
Building 1
Unit 4
Unit 1
Unit 2
Unit 3
Unit 5
Slide4Number of commercial properties in Denmark
4
Business
Mixed housing and
business
Production and warehouse
Private institutions
Specialised property
Other
Total
Capital region
844
2.744
1.005
72
82
325
5.072
Sealand region
1.225
514
827
129
268
449
3.412
Southern
Denmark
region
1.822
735
1.171
189
595
1.037
5.549
Central
Jutland
region
1.813
1.054
1.709
130
732
743
6.181
Northern Jutland region
1.044
646
735
119
625
710
3.879
All of Denmark
6.748
5.693
5.447
639
2.302
3.264
24.093
Slide5Administrative data sources and data
5
The Danish FSA
The Danish Buildings and Property Agency
Landsbyggefonden
Rent (net/gross)
Market rent
X
Actual rent
X
X
X
Operating costs (net/gross)
Market operating costs
Actual operating costs
X
X
X
Yield
X
Location
Property ID (BBR)
X
X
X
Address
X
X
Type of property
X
X
X
Type of transaction
Sale
Rent
X
Date
X
X
X
M2
X
X
Value
Price
Valuation
X
Vacancy
Market data sources and data6
The Danish Property Federation
Ejendomstorvet
Lokalebasen
Rent (net/gross)
Market rent
X
X
X
Actual rent
X
X
Operating costs (net/gross)
Market operating costs
X
X
Actual operating costs
X
X
Yield
X
X
Location
Property ID (BBR)
X
Address
X
X
X
Type of property
X
X
X
Type of transaction
Sale
X
X
Rent
X
X
Date
X
X
X
M2
X
X
X
Value
Price
X
X
Valuation
X
Vacancy
X
Data processing7
Slide8InitializationAll raw data is stored in separate
tables
Duplicate records are removedAutomatic validation and calculationsLogical checks are performedRent per m2 is calculated
All corrections and calculations
are
flagged
Initialization and automatic validation and calculations
8
Slide9Why? Validation Enrichment of the data with administrative ID’s
How?
Address, area and property typeAdministrative keysIndicators of quality Automatic match to the BBR9
Slide10Addresses are firstly
validated
using Danish Addresses Web API (DAWA)DAWA uses data from the Danish Address Register (DAR)DAWA has been designed to service IT systems that use addressesOur system uses the ”Address cleaning” process
where an unstructured address is
translated
to a
correct
address
Automatic
match - DAWA
10
Slide11DAWA
assesses
the quality of the returned address ID; A, B, CAutomatic match – DAWA address cleaning11
Unstructured
a
ddress
Query
Valid
a
ddress
ID
BBR
DAWA checks
address
in DAR
Slide12Matches are validated by comparing
the m
2 and the type of property between the BBR and the received dataTypes of matches:Correct matchPreliminary matchMatched but flaggedMissed matchMatch quality:Good match Medium match
Inferior matchAutomatic
match – Match
validation
12
Slide13In the manual treatment there are three possible outcomes:The cause of the failure to match correctly is corrected and the matching process is repeated
The
data cannot be corrected but the match, however, is assesed to be correct and the match is forced through (forced match)The data cannot be corrected, the match is assesed to be incorrect and the match is given up (abandoned match)If there are no match variables available for the observation the match is given up automatically and the observation will not be treated manually.
Manual match and validation
13
Slide14Data source
Time period
No. of unique observations
Match percentages
Administrative data
Unit level
Building level
Property level
No match
The Danish FSA
2012-2018
1.804.875
69,1
1,2
29,4
0,3
The Danish Building and
Property Agency
2017-2018
339
10,3
19,5
68,1
2,1
Landsbyggefonden
2013-2018
575.160
99,6
0,0
0,0
0,4
Market data
The Danish Property Federation
2017-2018
56.345
52,3
19,6
28,1
0,0
Ejendomstorvet
2014-2018
45.860
15,0
16,4
36,6
32,4
Lokalebasen - supply
2014-2018
106.794
14,7
17,5
44,3
23,5
Lokalebasen - realised
2014-2018
4.483
16,8
12,5
25,5
45,2
Preliminary match
results
14
Slide15AdvantagesEasy access to dataRegular
and
consistent data collectionTotal coverageHolds administrative keysDisadvantagesDoes not always cover the target populationCan in some cases only be
used for approximation
Administrative data for CREI-
production
15
Slide16AdvantagesDirectly reflects the
market
In some cases data collection can be ordered to ensure representativityDisadvantagesData is privately owned
and very sensitiveVariables can
be
inconsistent
within
the data
Only
smaller parts of
the population
is coveredData
rarely holds administrative keysMarket data for CREI-production
16
Slide17Commercial real estate indicators of the dynamics of supply and demand:
Commercial
property pricesRental prices on commercial propertyVacancy ratesCommercial property for rent or saleBuilding permits for commercial propertyLending supply and criteriaDemand for new indicators
17
Slide18CPPICommercial property for saleRent per
m
2 on housingIndicators to be substantiated18
Slide19Rental prices on commercial property
Vacancy
ratesCommercial property for rent Indicators to be developed19