Sigrid Krogstrup Jensen, sij@dst.dk Cajsa Mølskov, cms@dst.dk

1. 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

2. Commercial real estate in Denmark • Property that generates a profit for its owner, and thus, must be valuated and taxed accordingly • Business • Mixed housing and business • Warehouse and production • Private insitutions • Specializedproperty

3. Commercial real estate in Denmark II Property Building 2 • According to the Danish Dwellings and Buildings register (BBR) a property consists of buildings and units: Building 1 Unit 2 Unit 4 Unit 5 Unit 3 Unit 1

4. Number of commercial properties in Denmark

5. Administrative data sources and data

6. Market data sources and data

7. Data processing

8. Initialization and automatic validation and calculations • Initialization • All raw data is stored in separate tables • Duplicaterecordsareremoved • Automatic validation and calculations • Logical checks areperformed • Rent per m2 is calculated • All corrections and calculationsare flagged

9. Automatic match to the BBR • Why? • Validation • Enrichment of the data with administrative ID’s • How? • Address, area and property type • Administrative keys • Indicators of quality

10. Automatic match - DAWA • AddressesarefirstlyvalidatedusingDanish Addresses Web API (DAWA) • DAWA uses data from the Danish Address Register (DAR) • DAWA has beendesigned to service IT systems thatuseaddresses • Our system uses the ”Address cleaning” processwhere an unstructuredaddress is translated to a correctaddress

11. Automatic match – DAWA addresscleaning Unstructuredaddress Query DAWA checks address in DAR Valid addressID • DAWA assesses the quality of the returnedaddress ID; A, B, C BBR

12. Automatic match – Match validation • Matches arevalidated by comparing the m2 and the type of propertybetween the BBR and the received data • Types of matches: • Correct match • Preliminary match • Matched but flagged • Missed match • Match quality: • Good match • Medium match • Inferior match

13. Manual match and validation • In 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.

14. Preliminary match results

15. Administrative data for CREI-production • Advantages • Easyaccess to data • Regular and consistent data collection • Total coverage • Holds administrative keys • Disadvantages • Does not always cover the target population • Can in some cases onlybeused for approximation

16. Market data for CREI-production • Advantages • Directlyreflects the market • In some cases data collectioncanbeordered to ensurerepresentativity • Disadvantages • Data is privatelyownedand very sensitive • Variables canbeinconsistentwithin the data • Only smaller parts of the population is covered • Data rarely holds administrative keys

17. Demand for new indicators Commercial real estate indicators of the dynamics of supply and demand: • Commercial property prices • Rental prices on commercial property • Vacancy rates • Commercial property for rent or sale • Building permits for commercial property • Lending supply and criteria

18. Indicators to besubstantiated • CPPI • Commercial property for sale • Rent per m2 on housing

19. Indicators to bedeveloped • Rentalprices on commercialproperty • Vacancy rates • Commercial property for rent