Online housing search dataset: Information flows of real estate platform users

Joep Steegmans; Jonathan de Bruin

doi:10.1016/j.dib.2021.107327

Online housing search dataset: Information flows of real estate platform users

Joep Steegmans^*, Jonathan de Bruin

^*Corresponding author for this work

Applied Econometrics

Leiden University

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

This data article describes user-generated data of Funda.nl, the largest online housing market website of the Netherlands. The data contain the inflow and outflow of hits (mouse clicks, opening of webpages, etc.) at the municipality level. The municipality of the user defines the origin and the municipality of the property that is viewed defines the destination. The data capture real behavior of the platform users. The flow data are based on 1.1 billion hits that are made by the users of the website in the first six months of 2018. The underlying data are collected by Google Analytics, the web analytics tool of Google. Funda utilizes the data for platform stability, security, product development, etc. The proprietary data of Funda are used to generate the information flows between municipalities. In the full sample we have 148,216 information flows between municipalities in the Netherlands, among which 313 zero flows. The data include subsamples for different types of platform users as user search intentions range from serious to fully recreational. The data enable researchers to analyze housing search behavior from a novel perspective. The data are, for instance, relevant for housing market researchers, digital economists, and economic geographers.

Original language	English
Article number	107327
Pages (from-to)	1-9
Journal	Data in Brief
Volume	38
DOIs	https://doi.org/10.1016/j.dib.2021.107327
Publication status	Published - Oct 2021

Bibliographical note

Funding Information:
We want to thank Funda Real Estate B.V. and the Dutch Association of Realtors (NVM) for providing us with the data; we are endebted to Ruben Scholten, Riccardo Pinosio, and Frank Harleman. We also express our gratitude to Utrecht University's Research IT Innovation Program and the Netherlands? Ministry of the Interior and Kingdom Relations (BZK) for their financial support. Google Cloud has provided support through GCP research credits, which allowed us access to the Google Cloud Platform.

Funding Information:
We want to thank Funda Real Estate B.V. and the Dutch Association of Realtors (NVM) for providing us with the data; we are endebted to Ruben Scholten, Riccardo Pinosio, and Frank Harleman. We also express our gratitude to Utrecht University’s Research IT Innovation Program and the Netherlands’ Ministry of the Interior and Kingdom Relations (BZK) for their financial support. Google Cloud has provided support through GCP research credits, which allowed us access to the Google Cloud Platform.

Publisher Copyright:
© 2021 The Author(s)

Keywords

Gravity modeling
Housing market
Online search
Real estate platform
User-generated data

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.dib.2021.107327Licence: CC BY

1-s2.0-S2352340921006119-mainFinal published version, 330 KBLicence: CC BY

Cite this

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title = "Online housing search dataset: Information flows of real estate platform users",

abstract = "This data article describes user-generated data of Funda.nl, the largest online housing market website of the Netherlands. The data contain the inflow and outflow of hits (mouse clicks, opening of webpages, etc.) at the municipality level. The municipality of the user defines the origin and the municipality of the property that is viewed defines the destination. The data capture real behavior of the platform users. The flow data are based on 1.1 billion hits that are made by the users of the website in the first six months of 2018. The underlying data are collected by Google Analytics, the web analytics tool of Google. Funda utilizes the data for platform stability, security, product development, etc. The proprietary data of Funda are used to generate the information flows between municipalities. In the full sample we have 148,216 information flows between municipalities in the Netherlands, among which 313 zero flows. The data include subsamples for different types of platform users as user search intentions range from serious to fully recreational. The data enable researchers to analyze housing search behavior from a novel perspective. The data are, for instance, relevant for housing market researchers, digital economists, and economic geographers.",

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author = "Joep Steegmans and \{de Bruin\}, Jonathan",

note = "Funding Information: We want to thank Funda Real Estate B.V. and the Dutch Association of Realtors (NVM) for providing us with the data; we are endebted to Ruben Scholten, Riccardo Pinosio, and Frank Harleman. We also express our gratitude to Utrecht University's Research IT Innovation Program and the Netherlands? Ministry of the Interior and Kingdom Relations (BZK) for their financial support. Google Cloud has provided support through GCP research credits, which allowed us access to the Google Cloud Platform. Funding Information: We want to thank Funda Real Estate B.V. and the Dutch Association of Realtors (NVM) for providing us with the data; we are endebted to Ruben Scholten, Riccardo Pinosio, and Frank Harleman. We also express our gratitude to Utrecht University{\textquoteright}s Research IT Innovation Program and the Netherlands{\textquoteright} Ministry of the Interior and Kingdom Relations (BZK) for their financial support. Google Cloud has provided support through GCP research credits, which allowed us access to the Google Cloud Platform. Publisher Copyright: {\textcopyright} 2021 The Author(s)",

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doi = "10.1016/j.dib.2021.107327",

language = "English",

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N2 - This data article describes user-generated data of Funda.nl, the largest online housing market website of the Netherlands. The data contain the inflow and outflow of hits (mouse clicks, opening of webpages, etc.) at the municipality level. The municipality of the user defines the origin and the municipality of the property that is viewed defines the destination. The data capture real behavior of the platform users. The flow data are based on 1.1 billion hits that are made by the users of the website in the first six months of 2018. The underlying data are collected by Google Analytics, the web analytics tool of Google. Funda utilizes the data for platform stability, security, product development, etc. The proprietary data of Funda are used to generate the information flows between municipalities. In the full sample we have 148,216 information flows between municipalities in the Netherlands, among which 313 zero flows. The data include subsamples for different types of platform users as user search intentions range from serious to fully recreational. The data enable researchers to analyze housing search behavior from a novel perspective. The data are, for instance, relevant for housing market researchers, digital economists, and economic geographers.

AB - This data article describes user-generated data of Funda.nl, the largest online housing market website of the Netherlands. The data contain the inflow and outflow of hits (mouse clicks, opening of webpages, etc.) at the municipality level. The municipality of the user defines the origin and the municipality of the property that is viewed defines the destination. The data capture real behavior of the platform users. The flow data are based on 1.1 billion hits that are made by the users of the website in the first six months of 2018. The underlying data are collected by Google Analytics, the web analytics tool of Google. Funda utilizes the data for platform stability, security, product development, etc. The proprietary data of Funda are used to generate the information flows between municipalities. In the full sample we have 148,216 information flows between municipalities in the Netherlands, among which 313 zero flows. The data include subsamples for different types of platform users as user search intentions range from serious to fully recreational. The data enable researchers to analyze housing search behavior from a novel perspective. The data are, for instance, relevant for housing market researchers, digital economists, and economic geographers.

KW - Gravity modeling

KW - Housing market

KW - Online search

KW - Real estate platform

KW - User-generated data

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DO - 10.1016/j.dib.2021.107327

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VL - 38

SP - 1

EP - 9

JO - Data in Brief

JF - Data in Brief

M1 - 107327

ER -

Online housing search dataset: Information flows of real estate platform users

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

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