Micro-geographic property price and rent indices

Datos Bibliográficos

ID21463630
AutoresGabriel M Ahlfeldt (0000-0001-5664-3230, London School of Economics and Political Science, autor de correspondencia), Stephan Heblich (0000-0001-6486-3727), Tobias Seidel (0000-0002-0612-6072, University of Duisburg-Essen)
Año2023
Volumen98
Páginas103836
Fecha de publicación2023-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaRegional Science and Urban Economics (JOURNAL)
Identificadores de la revistaISSN: 0166-0462 • E-ISSN: 1879-2308
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.regsciurbeco.2022.103836
OpenAlexW3197062217
IdiomaEN
Citas recibidas8
Referencias citadas64

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 positive correlation between density and price-to-rent ratios in levels and trends, both within and between cities. Our method lends itself to the creation of comparable neighborhood-level rent indices (Mietspiegel) across Germany

Cartography · Data set · Econometrics · Economic rent · Economics · Geocoding · Geography · Microeconomics · Panel data · Parametric statistics · Spatial econometrics · Statistics · Stylized fact · Computer Science · Housing Market and Economics · Mathematics · Spatial and Panel Data Analysis · Urbanization and City Planning