dc.contributor.author
Meyberg, Camilo
dc.contributor.author
Rendtel, Ulrich
dc.contributor.author
Leerhoff, Holger
dc.date.accessioned
2024-10-29T06:42:51Z
dc.date.available
2024-10-29T06:42:51Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/44014
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-43723
dc.description.abstract
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.
en
dc.format.extent
34 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Web scraping
en
dc.subject
Official statistics
en
dc.subject
Rent indices
en
dc.subject
Statistical matching
en
dc.subject
Multiple imputation
en
dc.subject
Semi-parametric regression
en
dc.subject.ddc
300 Sozialwissenschaften::330 Wirtschaft::330 Wirtschaft
dc.title
Flat rent price prediction in Berlin with web scraping
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.doi
10.1007/s11943-024-00340-6
dcterms.bibliographicCitation.journaltitle
AStA Wirtschafts- und Sozialstatistisches Archiv
dcterms.bibliographicCitation.number
2
dcterms.bibliographicCitation.pagestart
245
dcterms.bibliographicCitation.pageend
278
dcterms.bibliographicCitation.volume
18
dcterms.bibliographicCitation.url
https://doi.org/10.1007/s11943-024-00340-6
refubium.affiliation
Wirtschaftswissenschaft
refubium.affiliation.other
Volkswirtschaftslehre / Institut für Statistik und Ökonometrie

refubium.funding
Springer Nature DEAL
refubium.note.author
Die Publikation wurde aus Open Access Publikationsgeldern der Freien Universität Berlin gefördert.
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
1863-8163