Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/245368 
Year of Publication: 
2021
Series/Report no.: 
CESifo Working Paper No. 9187
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
We develop a programming algorithm that predicts a balanced-panel mix-adjusted house price index for arbitrary spatial units from repeated cross-sections of geocoded micro data. The algorithm combines parametric and non-parametric estimation techniques to provide a tight local fit where the underlying micro data are abundant and reliable extrapolations where data are sparse. To illustrate the functionality, we generate a panel of German property prices and rents that is unprecedented in its spatial coverage and detail. This novel data set uncovers a battery of stylized facts that motivate further research, e.g. on the density bias of price-to-rent ratios in levels and trends, within and between cities. Our method lends itself to the creation of comparable neighborhood-level qualified rent indices (Mietspiegel) across Germany.
Subjects: 
index
real estate
price
property
rent
JEL: 
R10
Document Type: 
Working Paper
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