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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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    References listed on IDEAS

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    1. Pace, R. Kelley & Barry, Ronald & Gilley, Otis W. & Sirmans, C. F., 2000. "A method for spatial-temporal forecasting with an application to real estate prices," International Journal of Forecasting, Elsevier, vol. 16(2), pages 229-246.
    2. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, September.
    3. Bernard Fingleton, 2009. "A generalized method of moments estimator for a spatial model with moving average errors, with application to real estate prices," Studies in Empirical Economics, in: Giuseppe Arbia & Badi H. Baltagi (ed.), Spatial Econometrics, pages 35-57, Springer.
    4. Nafeesa Yunus & J. Hansz & Paul Kennedy, 2012. "Dynamic Interactions Between Private and Public Real Estate Markets: Some International Evidence," The Journal of Real Estate Finance and Economics, Springer, vol. 45(4), pages 1021-1040, November.
    5. Holly, Sean & Pesaran, M. Hashem & Yamagata, Takashi, 2010. "A spatio-temporal model of house prices in the USA," Journal of Econometrics, Elsevier, vol. 158(1), pages 160-173, September.
    6. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    7. Manfred M. Fischer & Daniel A. Griffith, 2008. "Modeling Spatial Autocorrelation In Spatial Interaction Data: An Application To Patent Citation Data In The European Union," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 969-989, December.
    8. Wrenn, Douglas H. & Sam, Abdoul G., 2014. "Geographically and temporally weighted likelihood regression: Exploring the spatiotemporal determinants of land use change," Regional Science and Urban Economics, Elsevier, vol. 44(C), pages 60-74.
    9. Daniel Griffith, 2009. "Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows," Journal of Geographical Systems, Springer, vol. 11(2), pages 117-140, June.
    10. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    11. Green, Richard K., 2002. "Stock prices and house prices in California: new evidence of a wealth effect?," Regional Science and Urban Economics, Elsevier, vol. 32(6), pages 775-783, November.
    12. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
    13. Karl E. Case & Robert J. Shiller, 2003. "Is There a Bubble in the Housing Market?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 34(2), pages 299-362.
    14. Lung-fei Lee & Jihai Yu, 2012. "QML Estimation of Spatial Dynamic Panel Data Models with Time Varying Spatial Weights Matrices," Spatial Economic Analysis, Taylor & Francis Journals, vol. 7(1), pages 31-74, March.
    15. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    16. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    17. Lee, Lung-fei & Yu, Jihai, 2010. "A Spatial Dynamic Panel Data Model With Both Time And Individual Fixed Effects," Econometric Theory, Cambridge University Press, vol. 26(2), pages 564-597, April.
    18. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    19. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    20. C. Sirmans & Barrett Slade, 2012. "National Transaction-based Land Price Indices," The Journal of Real Estate Finance and Economics, Springer, vol. 45(4), pages 829-845, November.
    21. Lee, Lung-fei & Yu, Jihai, 2014. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 180(2), pages 174-197.
    22. Todd Kuethe & Valerien Pede, 2011. "Regional Housing Price Cycles: A Spatio-temporal Analysis Using US State-level Data," Regional Studies, Taylor & Francis Journals, vol. 45(5), pages 563-574.
    23. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    24. Daniel A. Griffith, 2009. "Spatial Autocorrelation in Spatial Interaction," Advances in Spatial Science, in: Aura Reggiani & Peter Nijkamp (ed.), Complexity and Spatial Networks, chapter 0, pages 221-237, Springer.
    25. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    26. Koetter, Michael & Poghosyan, Tigran, 2010. "Real estate prices and bank stability," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1129-1138, June.
    27. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    28. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    29. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    30. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
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    Cited by:

    1. Markus Hertrich, 2019. "A Novel Housing Price Misalignment Indicator for Germany," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 759-794, November.
    2. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    3. 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.
    4. 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).
    5. 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.
    6. 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.

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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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