Developments in the residential mortgage market in Germany – what can Google data tell us?
In: Are post-crisis statistical initiatives completed?
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Chauvet, Marcelle & Gabriel, Stuart & Lutz, Chandler, 2016. "Mortgage default risk: New evidence from internet search queries," Journal of Urban Economics, Elsevier, vol. 96(C), pages 91-111.
- Askitas, Nikos & Zimmermann, Klaus F., 2011. "Detecting Mortgage Delinquencies," IZA Discussion Papers 5895, Institute of Labor Economics (IZA).
- McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
- Branislav Saxa, 2014. "Forecasting Mortgages: Internet Search Data as a Proxy for Mortgage Credit Demand," Working Papers 2014/14, Czech National Bank.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
- Nikolaos Askitas & Klaus F. Zimmermann, 2015.
"The internet as a data source for advancement in social sciences,"
International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
- Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The Internet as a Data Source for Advancement in Social Sciences," RatSWD Working Papers 248, German Data Forum (RatSWD).
- Askitas, Nikos & Zimmermann, Klaus F., 2015. "The Internet as a Data Source for Advancement in Social Sciences," IZA Discussion Papers 8899, Institute of Labor Economics (IZA).
- Al-Nasseri, Alya & Menla Ali, Faek & Tucker, Allan, 2021. "Investor sentiment and the dispersion of stock returns: Evidence based on the social network of investors," International Review of Financial Analysis, Elsevier, vol. 78(C).
- Ramya Rajajagadeesan Aroul & Sanjiv Sabherwal & Sergiy Saydometov, 2022. "FEAR Index, city characteristics, and housing returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(1), pages 173-205, March.
- Caporale, Guglielmo Maria & Menla Ali, Faek & Spagnolo, Fabio & Spagnolo, Nicola, 2022.
"Cross-border portfolio flows and news media coverage,"
Journal of International Money and Finance, Elsevier, vol. 126(C).
- Guglielmo Maria Caporale & Faek Menla Ali & Fabio Spagnolo & Nicola Spagnolo, 2020. "Cross-Border Portfolio Flows and News Media Coverage," CESifo Working Paper Series 8112, CESifo.
- Al-Nasseri, Alya & Menla Ali, Faek, 2018. "What does investors' online divergence of opinion tell us about stock returns and trading volume?," Journal of Business Research, Elsevier, vol. 86(C), pages 166-178.
- David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
- Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
- Mioara, POPESCU, 2015. "Construction Of Economic Indicators Using Internet Searches," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 6(1), pages 25-31.
- Damian S. Damianov & Diego Escobari, 2021.
"Getting on and Moving Up the Property Ladder: Real Hedging in the U.S. Housing Market Before and After the Crisis,"
Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(4), pages 1201-1237, December.
- Damianov, Damian S & Escobari, Diego, 2019. "Getting on and moving up the property ladder: Real hedging in the U.S. housing market before and after the crisis," MPRA Paper 92389, University Library of Munich, Germany.
- Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
- Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
- Torsten Schmidt & Simeon Vosen, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers 0382, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
- David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
- Anastasiou, Dimitris & Ballis, Antonis & Drakos, Konstantinos, 2022. "Constructing a positive sentiment index for COVID-19: Evidence from G20 stock markets," International Review of Financial Analysis, Elsevier, vol. 81(C).
- de Pedraza, Pablo & Vollbracht, Ian, 2020. "The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve," GLO Discussion Paper Series 515, Global Labor Organization (GLO).
- Bricongne, Jean-Charles & Meunier, Baptiste & Pouget, Sylvain, 2023.
"Web-scraping housing prices in real-time: The Covid-19 crisis in the UK,"
Journal of Housing Economics, Elsevier, vol. 59(PB).
- Jean-Charles Bricongne & Baptiste Meunier & Sylvain Pouget, 2021. "Web Scraping Housing Prices in Real-time: the Covid-19 Crisis in the UK," Working papers 827, Banque de France.
- Jean-Charles Bricongne & Baptiste Meunier & Sylvain Pouget, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," Post-Print hal-04064185, HAL.
- Jean-Charles Bricongne & Baptiste Meunier & Sylvain Pouget, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," SciencePo Working papers Main hal-04064185, HAL.
- Guzi, Martin & de Pedraza, Pablo, 2013. "A Web Survey Analysis of the Subjective Well-being of Spanish Workers," IZA Discussion Papers 7618, Institute of Labor Economics (IZA).
- Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
- Mihaela Simionescu & Dalia Streimikiene & Wadim Strielkowski, 2020. "What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?," Sustainability, MDPI, vol. 12(3), pages 1-10, January.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bis:bisifc:49-45. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Martin Fessler (email available below). General contact details of provider: https://edirc.repec.org/data/bisssch.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.