Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/203171
Authors: 
Cholodilin, Konstantin A.
Michelsen, Claus
Year of Publication: 
2019
Citation: 
[Journal:] DIW Weekly Report [ISSN:] 2568-7697 [Volume:] 9 [Year:] 2019 [Issue:] 32 [Pages:] 265-273
Abstract: 
Housing prices in many countries have increased significantly over the past years, fueling a fear that speculative price bubbles will return. However, it can be difficult for policymakers to recognize when regulatory interventions in the market are necessary to counteract bubbles. This report shows how modern machine learning methods can be used to forecast speculative price bubbles at an early stage. Early warning models show that many OECD countries have an increased risk of speculative bubbles. In Germany, there are explosive price developments that have decoupled from real estate earnings. However, the forecast model indicates that the risk will decrease somewhat over the coming months at a high level. Unfortunately, the preventative measures in Germany remain insufficient. For example, there is a lack of intervention options involving household debt ceilings, and it is unclear when the Federal Financial Supervisory Authority (BaFin) can begin intervening in the market.
Subjects: 
early warning system
speculative housing price bubble
panel logit,decision tree
random forest
support vector machine
JEL: 
C25
C53
E32
Persistent Identifier of the first edition: 
Document Type: 
Article

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