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Micro-geographic property price and rent indices

Gabriel M. Ahlfeldt, Stephan Heblich and Tobias Seidel. 2021. CEP, Discussion Paper No.1782 July 2021

Viernes 10 de septiembre de 2021, por Carlos San Juan


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 price and rent indices for residential and commercial property.

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