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Urban house prices: A tale of 48 cities

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  • Kholodilin, Konstantin A.
  • Ulbricht, Dirk

Abstract

In this paper, the authors construct a unique data set of Internet offer prices for flats in 48 large European cities across 24 countries. The data collected between January and May 2012 from 33 websites, are drawn from Internet advertisements of dwellings. Using the resulting sample of more than 1,000,000 announcements, the authors compute the quality-adjusted city-specific house prices. Based on this information, they investigate the determinants of the apartment prices. Four factors are found to be relevant for the dwelling price level using Bayesian Model Averaging: Population density, mortgage per capita, income inequality, and unemployment rate. The results are robust to applying two alternative estimation techniques: OLS and quantile regression. Based on the auhors' estimation results they are able to identify cities where the prices are overvalued. This is a useful indication of a build-up of house price bubbles.

Suggested Citation

  • Kholodilin, Konstantin A. & Ulbricht, Dirk, 2015. "Urban house prices: A tale of 48 cities," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-43.
    • Handle: RePEc:zbw:ifweej:201528
    DOI: 10.5018/economics-ejournal.ja.2015-28
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    Cited by:

    1. Kholodilin, Konstantin A. & Michelsen, Claus & Ulbricht, Dirk, 2018. "Speculative price bubbles in urban housing markets," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 55(4), pages 1957-1983.
    2. Ahfeldt, Gabriel M. & Pietrostefani, Elisabetta, 2017. "The compact city in empirical research: A quantitative literature review," LSE Research Online Documents on Economics 83638, London School of Economics and Political Science, LSE Library.
    3. Konstantin Kholodilin & Irina Koroleva & Darya Kryutchenko, 2022. "Where is the consumer centre? A case of St. Petersburg," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(4), pages 916-938, August.
    4. Jean-Charles Bricongne & Alessandro Turrini & Peter Pontuch, 2019. "Assessing House Prices: Insights from "Houselev", a Dataset of Price Level Estimates," European Economy - Discussion Papers 101, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    5. Konstantin A. Kholodilin & Irina Krylova & Darya Kryutchenko, 2017. "Finding the Consumer Center of St. Petersburg?," HSE Working papers WP BRP 165/EC/2017, National Research University Higher School of Economics.
    6. Konstantin A. Kholodilin & Julien Licheron, 2017. "Macroeconomic Effects of Rental Housing Regulations: The Case of Germany in 1950-2015," Discussion Papers of DIW Berlin 1649, DIW Berlin, German Institute for Economic Research.
    7. Ahlfeldt, Gabriel M. & Pietrostefani, Elisabetta, 2019. "The economic effects of density: A synthesis," Journal of Urban Economics, Elsevier, vol. 111(C), pages 93-107.
    8. Kajuth, Florian, 2020. "The German housing market cycle: Answers to FAQs," Discussion Papers 20/2020, Deutsche Bundesbank.
    9. Stolbov, Mikhail & Shchepeleva, Maria, 2022. "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    10. Kajuth, Florian, 2021. "Land leverage and the housing market: Evidence from Germany1," Journal of Housing Economics, Elsevier, vol. 51(C).
    11. Gabriel M. Ahfeldt & Elisabetta Pietrostefani, 2017. "The Compact City in Empirical Research: A Quantitative Literature Review," SERC Discussion Papers 0215, Centre for Economic Performance, LSE.
    12. Konstantin A. Kholodilin & Irina Krylova & Darya Kryutchenko, 2017. "Where Is the Consumer Center of St. Petersburg?," Discussion Papers of DIW Berlin 1666, DIW Berlin, German Institute for Economic Research.
    13. Jose Torres-Pruñonosa & Pablo García-Estévez & Camilo Prado-Román, 2021. "Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing," Mathematics, MDPI, vol. 9(7), pages 1-16, April.

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    More about this item

    Keywords

    internet advertisements; housing prices; large European cities; fundamentalprices;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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