3 datasets found
  1. 4

    Metadata for the dissertation: Improving Commercial Property Price...

    • data.4tu.nl
    Updated Nov 25, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Farley Ishaak (2024). Metadata for the dissertation: Improving Commercial Property Price Statistics [Dataset]. http://doi.org/10.4121/cab0cf0e-668f-46db-82bb-94abe78faeb0.v1
    Explore at:
    Dataset updated
    Nov 25, 2024
    Dataset provided by
    4TU.ResearchData
    Authors
    Farley Ishaak
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2008 - 2023
    Area covered
    Netherlands
    Description

    This metadata document provides details of the data used for the dissertation: “Improving Commercial Property Price Statistics”. The study explores data related and methodological challenges in the construction of price statistics for commercial real estate.


    Short abstract of the dissertation

    Since the financial crisis of 2008, National Statistical Institutes (NSIs) have worked to develop commercial real estate (CRE) indicators for official statistics. These indicators are considered essential in financial stability monitoring and may help contain the consequences of future crises or even prevent future crises. However, progress at NSIs to develop these indicators has been slow due to challenges like low observation numbers and high heterogeneity. This dissertation addresses these challenges by exploring data issues and suggesting methodological improvements.


    The first three studies focus on data challenges regarding share deals and portfolio sales. Both are real estate trading constructions that are specific to CRE. The results show that share deals and portfolio sales significantly differ from the rest of the market. Therefore, under specific circumstances, CRE indicators could benefit from including these trading types. The final two studies focus on methodological challenges regarding index construction methods and the role of sustainability in real estate pricing. The results show that, by combining established techniques, it is possible to construct price indices that meet official statistics’ standards. Furthermore, the results uncover a complex relationship between sustainability and prices: while energy efficiency generally involves price premiums, others aspects like health and environment display a discount for low sustainable properties.


    Overall, this dissertation contributes to the legislative framework that is currently being developed for EU countries to publish official statistics for commercial real estate and adds to the academic discussion by presenting innovative techniques for data analyses and index construction.


    Data sources

    The following data sources were used:

    1. Bussiness Register (Statistics Netherlands)
    2. Transactions linked to the Register of Adresses and Buildings (BAG)
    3. Linking table buildings and companies (Dutch Land Registry Office)
    4. Property Transfer Tax data (Dutch Tax Authorities)
    5. Building sustainability scores (W/E advisors)Commercial real estate transactions (Dutch Land Registry Office)
    6. Commercial real estate transactions (Dutch Land Registry Office)


    Processing methodology

    1. The data is originally stored in an SQL database and is processed with SQL and R code (version 4.2). In the code, the name of the table is tbl_SPE_2_ABR_Bedrijfsinfo. The data is used for deriving company transfers by comparing ownership states of various periods. The first period that an ownership differs of the same company indicates an ownership transfer.
    2. The data is originally stored in an SQL database and is processed with SQL and R code (version 4.2). In the code, the name of the table is tbl_SPE_6_ABR_CompleetMicro. The data is used for calcuting the size of real estate share deals and estimating price developments by applying appropriate filters and counting the output.
    3. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is SPE_KADASTER. The data is used for finding real estate information that corresponds to company transfers by linking the company register (ABR) to the real estate register (BAG).
    4. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is tbl_SPE_3_OVB_Bedrijfsinfo. The data is used for deriving real estate share deals by linking this table (Kadaster) to the real estate register (BAG).
    5. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is duurzaamheid_input_regressie2. The data is used for finding the relationship between sustainabilty measures and real estate transaction prices by linking sustainabilty scores from a consultancy (WE) to transaction prices (Cadastre) and running regression analyses.
    6. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is tbl_OV20_pand. The data is used for 4 purposes (separate studies).
    • (1) Chapter 3: Determining the price effect of portfolio sale by running regression analyses
    • (2) Chapter 4: Developing methods to include portfolio sales in CPPI calcutions by using auxilary data of the real estate properties.
    • (3) Chapter 5: Developing a price index method for small domains by using these data to test the outcomes
    • (4) Chapter 6: Determining the relationship between sustatinability by running regression analyses


    Data restrictions

    As part of the CBS law, sharing micro-data outside of the CBS-environment is prohibited. Furthermore, CBS manages the data, but in some cases other parties are still formal owners of the data. The 2 other parties are The Land Registry Office and WE consultancy. Ownership and intellectual property rights are managed in contracts with both owners. It was agreed upon that the data can only be used for the purpose of the PhD study and that the microdata will never be externally disseminated. The data is still owned by them and the intellectual property rights of the analyses belong to me. An intended use of the microdata should be approved by both Statistics Netherlands and the formal data owner. Because of the above, no data can be publicly shared.


    If one intends to do research on these data, an application for data use can be requested at CBS. CBS will charge costs for anonymising the data and providing a closed environment to work with the data. More information on this can be found at: https://www.cbs.nl/en-gb/our-services/customised-services-microdata/microdata-conducting-your-own-research


    Contact information

    Author: Farley Ishaak

    Statistics Netherlands | Henri Faasdreef 312 | P.O. Box 24500 | 2490 HA The Hague

    TU Delft | Delft University of Technology | Faculty of Architecture and the Built Environment

    Department of Management in the Built Environment | P.O. Box 5043 | 2600 GA Delft

    M +31 6 46307974 | ff.ishaak@cbs.nl | f.f.ishaak@tudelft.nl

  2. g

    Scientific libraries: Offers and use of services in 2023

    • gimi9.com
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Scientific libraries: Offers and use of services in 2023 [Dataset]. https://gimi9.com/dataset/eu_dbs-wb-2023-angeboteundnutzungvondienstleistungen
    Explore at:
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The German Library Statistics (DBS) is the national statistics of the German library system and contains statistical key figures. It includes public libraries, scientific libraries, as well as specialized scientific libraries. More information can be found at DBS. This dataset contains the following information on academic libraries in Bavaria in 2023: Borrowings by total physical units, borrowings, of which: Extensions upon user request, reservations, attendance, requests for information, library visits, 1. ... Virtual visits (visits) input blocked, user training sessions (hours), participants in user training sessions, 1. Calls for e-learning offers from the library, 2. Accepted dissertations of the own university, 3. Accepted dissertations of your own university, of which: Online dissertations, 4. Open access green and gold publications provided on own repositories (accesses in the reporting year), search queries in local online catalogues and discovery systems, search queries in databases and platforms, access to journal titles, full advertisements of journal articles, full advertisements of individual digital documents, 1. Full display of individual digital documents, including: Full ads from commercially distributed e-books, 2. Full display of individual digital documents, including: Full display of individual documents on the institutional repository

  3. d

    Cultural Administration Base

    • data.gov.tw
    json, xls, xml
    Updated May 15, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ministry of Culture (2024). Cultural Administration Base [Dataset]. https://data.gov.tw/en/datasets/6221
    Explore at:
    json, xml, xlsAvailable download formats
    Dataset updated
    May 15, 2024
    Dataset authored and provided by
    Ministry of Culture
    License

    https://data.gov.tw/licensehttps://data.gov.tw/license

    Description

    The Ministry of Culture collects information from various municipal and county cultural bureaus.

  4. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Farley Ishaak (2024). Metadata for the dissertation: Improving Commercial Property Price Statistics [Dataset]. http://doi.org/10.4121/cab0cf0e-668f-46db-82bb-94abe78faeb0.v1

Metadata for the dissertation: Improving Commercial Property Price Statistics

Explore at:
Dataset updated
Nov 25, 2024
Dataset provided by
4TU.ResearchData
Authors
Farley Ishaak
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Time period covered
2008 - 2023
Area covered
Netherlands
Description

This metadata document provides details of the data used for the dissertation: “Improving Commercial Property Price Statistics”. The study explores data related and methodological challenges in the construction of price statistics for commercial real estate.


Short abstract of the dissertation

Since the financial crisis of 2008, National Statistical Institutes (NSIs) have worked to develop commercial real estate (CRE) indicators for official statistics. These indicators are considered essential in financial stability monitoring and may help contain the consequences of future crises or even prevent future crises. However, progress at NSIs to develop these indicators has been slow due to challenges like low observation numbers and high heterogeneity. This dissertation addresses these challenges by exploring data issues and suggesting methodological improvements.


The first three studies focus on data challenges regarding share deals and portfolio sales. Both are real estate trading constructions that are specific to CRE. The results show that share deals and portfolio sales significantly differ from the rest of the market. Therefore, under specific circumstances, CRE indicators could benefit from including these trading types. The final two studies focus on methodological challenges regarding index construction methods and the role of sustainability in real estate pricing. The results show that, by combining established techniques, it is possible to construct price indices that meet official statistics’ standards. Furthermore, the results uncover a complex relationship between sustainability and prices: while energy efficiency generally involves price premiums, others aspects like health and environment display a discount for low sustainable properties.


Overall, this dissertation contributes to the legislative framework that is currently being developed for EU countries to publish official statistics for commercial real estate and adds to the academic discussion by presenting innovative techniques for data analyses and index construction.


Data sources

The following data sources were used:

  1. Bussiness Register (Statistics Netherlands)
  2. Transactions linked to the Register of Adresses and Buildings (BAG)
  3. Linking table buildings and companies (Dutch Land Registry Office)
  4. Property Transfer Tax data (Dutch Tax Authorities)
  5. Building sustainability scores (W/E advisors)Commercial real estate transactions (Dutch Land Registry Office)
  6. Commercial real estate transactions (Dutch Land Registry Office)


Processing methodology

  1. The data is originally stored in an SQL database and is processed with SQL and R code (version 4.2). In the code, the name of the table is tbl_SPE_2_ABR_Bedrijfsinfo. The data is used for deriving company transfers by comparing ownership states of various periods. The first period that an ownership differs of the same company indicates an ownership transfer.
  2. The data is originally stored in an SQL database and is processed with SQL and R code (version 4.2). In the code, the name of the table is tbl_SPE_6_ABR_CompleetMicro. The data is used for calcuting the size of real estate share deals and estimating price developments by applying appropriate filters and counting the output.
  3. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is SPE_KADASTER. The data is used for finding real estate information that corresponds to company transfers by linking the company register (ABR) to the real estate register (BAG).
  4. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is tbl_SPE_3_OVB_Bedrijfsinfo. The data is used for deriving real estate share deals by linking this table (Kadaster) to the real estate register (BAG).
  5. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is duurzaamheid_input_regressie2. The data is used for finding the relationship between sustainabilty measures and real estate transaction prices by linking sustainabilty scores from a consultancy (WE) to transaction prices (Cadastre) and running regression analyses.
  6. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is tbl_OV20_pand. The data is used for 4 purposes (separate studies).
  • (1) Chapter 3: Determining the price effect of portfolio sale by running regression analyses
  • (2) Chapter 4: Developing methods to include portfolio sales in CPPI calcutions by using auxilary data of the real estate properties.
  • (3) Chapter 5: Developing a price index method for small domains by using these data to test the outcomes
  • (4) Chapter 6: Determining the relationship between sustatinability by running regression analyses


Data restrictions

As part of the CBS law, sharing micro-data outside of the CBS-environment is prohibited. Furthermore, CBS manages the data, but in some cases other parties are still formal owners of the data. The 2 other parties are The Land Registry Office and WE consultancy. Ownership and intellectual property rights are managed in contracts with both owners. It was agreed upon that the data can only be used for the purpose of the PhD study and that the microdata will never be externally disseminated. The data is still owned by them and the intellectual property rights of the analyses belong to me. An intended use of the microdata should be approved by both Statistics Netherlands and the formal data owner. Because of the above, no data can be publicly shared.


If one intends to do research on these data, an application for data use can be requested at CBS. CBS will charge costs for anonymising the data and providing a closed environment to work with the data. More information on this can be found at: https://www.cbs.nl/en-gb/our-services/customised-services-microdata/microdata-conducting-your-own-research


Contact information

Author: Farley Ishaak

Statistics Netherlands | Henri Faasdreef 312 | P.O. Box 24500 | 2490 HA The Hague

TU Delft | Delft University of Technology | Faculty of Architecture and the Built Environment

Department of Management in the Built Environment | P.O. Box 5043 | 2600 GA Delft

M +31 6 46307974 | ff.ishaak@cbs.nl | f.f.ishaak@tudelft.nl

Search
Clear search
Close search
Google apps
Main menu