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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, Kiel Institute for the World Economy (IfW), vol. 9, pages 1-43.
    • Handle: RePEc:zbw:ifweej:201528
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    File URL: http://dx.doi.org/10.5018/economics-ejournal.ja.2015-28
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    References listed on IDEAS

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    1. Mantu Kumar Mahalik & Hrushikesh Mallick, 2011. "What Causes Asset Price Bubble in an Emerging Economy? Some Empirical Evidence in the Housing Sector of India," International Economic Journal, Taylor & Francis Journals, vol. 25(2), pages 215-237.
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    15. Konstantin A. Kholodilin & Claus Michelsen & Dirk Ulbricht, 2014. "Speculative Price Bubbles in Urban Housing Markets in Germany," Discussion Papers of DIW Berlin 1417, DIW Berlin, German Institute for Economic Research.
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    Citations

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    Cited by:

    1. Gabriel Ahlfeldt & Elisabetta Pietrostefani, 2017. "The Economic Effects of Density: A Synthesis," CESifo Working Paper Series 6744, CESifo Group Munich.
    2. Gabriel M. Ahfeldt & Elisabetta Pietrostefani, 2017. "The Compact City in Empirical Research: A Quantitative Literature Review," SERC Discussion Papers 0215, Spatial Economics Research Centre, LSE.
    3. 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.
    4. 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.
    5. 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.
    6. Gabriel M. Ahlfeldt & Elisabetta Pietrostefani, 2017. "The Economic Effects of Density: A Synthesis," SERC Discussion Papers 0210, Spatial Economics Research Centre, LSE.
    7. Ahlfeldt, Gabriel M. & Pietrostefani, Elisabetta, 2017. "The economic effects of density: A synthesis," LSE Research Online Documents on Economics 83628, London School of Economics and Political Science, LSE Library.
    8. 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.

    More about this item

    Keywords

    internet advertisements; housing prices; large European cities; fundamentalprices;

    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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