Micro-Geographic Property Price and Rent Indices

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CESifo Working Paper ; No. 9187

Classification and Topics

• Classification: Wirtschaft ; General Regional Economics (includes Regional Data)
• Subject: index ; real estate ; price ; property ; rent

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Ahlfeldt, Gabriel ; Heblich, Stephan ; Seidel, Tobias
• Event: Veröffentlichung
• (who): Center for Economic Studies and ifo Institute (CESifo)
• (where): Munich
• (when): 2021

Further information

• Handle: http://hdl.handle.net/10419/245368
• Last update: 10.03.2025, 11:42 AM CET