Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/315632 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] AStA Wirtschafts- und Sozialstatistisches Archiv [ISSN:] 1863-8163 [Volume:] 18 [Issue:] 2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2024 [Pages:] 245-278
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
Internet data pose a challenge to the traditional system of official statistics, which relies on more conventional sources such as surveys and registers, not readily adaptable to rapid changes. Expanding this system to include internet data is currently at an experimental stage, exploring these sources’ potentials and benefits. This paper describes a project conducted within the ESSnet Trusted Smart Statistics – Web Intelligence Network framework. It investigates the use of online apartment listings to analyze the rental market. We used web scraping to extract information from two online real estate portals for flats in the city of Berlin. Using this data, we developed a model to predict rental prices per square meter based on the accommodation’s features and location within the city. We detected offers which appear in both portals by means of statistical matching and removed duplicate offers. Missing values were treated by multiple imputation. The prediction model is a semi-parametric approach where the postal districts are used to describe the location effect. Comparisons with microcensus results and the local rent index reveal significant differences between the market of online flat offers and the stock of existing flat contracts. Interested readers will find the commented programming code in the internet supplement.
Schlagwörter: 
Web scraping
Flat offers
Official statistics
Rent indices
Statistical matching
Multiple imputation
Semi-parametric regression
JEL: 
R21
R31
L86
Persistent Identifier der Erstveröffentlichung: 
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Dokumentart: 
Article
Dokumentversion: 
Published Version

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