Micro-geographic property price and rent indices

Gabriel Ahlfeldt, Stephan Heblich and Tobias Seidel

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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 price and rent indices for residential and commercial property.

Keywords: index; real estate; price; property; rent (search for similar items in EconPapers)
JEL-codes: R10 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2021-07-20
New Economics Papers: this item is included in nep-geo and nep-ure
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http://eprints.lse.ac.uk/113922/ Open access version. (application/pdf)

Related works:
Journal Article: Micro-geographic property price and rent indices (2023)
Working Paper: Micro-geographic property price and rent indices (2023)
Working Paper: Micro-geographic property price and rent indices (2021)
Working Paper: Micro-Geographic Property Price and Rent Indices (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:113922

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