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Spatiotemporal analysis of German real-estate prices

Author

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  • Philipp Otto

    (European University Viadrina)

  • Wolfgang Schmid

    (European University Viadrina)

Abstract

In this paper, we provide a spatiotemporal examination of German real-estate prices in 412 administrative districts. The price process is spatially autocorrelated and stationary over the considered period from 1995 to 2010. To quantify both spatial and temporal effects of the process, we apply different spatiotemporal models. These models are consistently estimated by the maximum likelihood approach, and they are compared with respect to the impact of shocks on fundamental quantities. Moreover, we interpret the economic importance of our results with respect to migrational issues. We show that the willingness of individuals to move or to commute decreased in Germany during the considered years. Furthermore, we include a detailed interpretation of the so-called ripple effect.

Suggested Citation

  • Philipp Otto & Wolfgang Schmid, 2018. "Spatiotemporal analysis of German real-estate prices," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(1), pages 41-72, January.
    • Handle: RePEc:spr:anresc:v:60:y:2018:i:1:d:10.1007_s00168-016-0789-y
    DOI: 10.1007/s00168-016-0789-y
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    Cited by:

    1. Robert J. Hill & Alicia N. Rambaldi, 2022. "Hedonic Models and House Price Index Numbers," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 413-444, Springer.
    2. Gabriel S. Lee & Stefanie Braun, 2021. "Agglomeration Spillover Effects in German Land and House Prices at the City and County Levels," Working Papers 207, Bavarian Graduate Program in Economics (BGPE).
    3. Markus Hertrich, 2019. "A Novel Housing Price Misalignment Indicator for Germany," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 759-794, November.
    4. I-Chun Tsai, 2018. "The cause and outcomes of the ripple effect: housing prices and transaction volume," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(2), pages 351-373, September.
    5. Alice Barreca, 2022. "Architectural Quality and the Housing Market: Values of the Late Twentieth Century Built Heritage," Sustainability, MDPI, vol. 14(5), pages 1-24, February.
    6. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.

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    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis

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