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

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 2019: 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 , searches in local online catalogues and discovery systems, searches in databases, 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

The evolution of a software system can be studied in terms of how various properties as reflected by software metrics change over time. Current models of software evolution have allowed for inferences to be drawn about certain attributes of the software system, for instance, regarding the architecture, complexity and its impact on the development effort. However, an inherent limitation of these models is that they do not provide any direct insight into where growth takes place. In particular, we cannot assess the impact of evolution on the underlying distribution of size and complexity among the various classes. Such an analysis is needed in order to answer questions such as 'do developers tend to evenly distribute complexity as systems get bigger?', and 'do large and complex classes get bigger over time?'. These are questions of more than passing interest since by understanding what typical and successful software evolution looks like, we can identify anomalous situations and take action earlier than might otherwise be possible. Information gained from an analysis of the distribution of growth will also show if there are consistent boundaries within which a software design structure exists. In our study of metric distributions, we focused on 10 different measures that span a range of size and complexity measures. The raw metric data (4 .txt files and 1 .log file in a .zip file measuring ~0.5MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

This file contains raw data base to replicate the statistical analysis conducted for Chapter 3 of my Ph.D dissertation.

This file contains raw data base to replicate the statistical analysis conducted for Chapter 3 of my Ph.D dissertation.

It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. In order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible, but these analysis methods can only be applied after a mature release of the code has been developed. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. We present a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

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