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Can Internet Ads Serve as an Indicator of Homeownership Rates?

Author

Listed:
  • Konstantin A. Kholodilin
  • Andreas Mense
Abstract
In this paper, we propose an indicator of the homeownership rate based on Internet ads offering the housing for rent and sale. We constructed the HOR estimate using the number of ads in four different markets (flats for rent, flats for sale, houses for rent, and houses for sale). Our HOR indicator was tested using data of German NUTS1 and planning (ROR) regions. The correlation between our estimate of the HOR and the alternative HOR figures varies between 0.834 and 0.874 at NUTS1 level and is 0.761 at the ROR level. All correlation coefficients are statistically significant. Our HOR estimate is particularly highly correlated with the official HOR figures. Thus, it is shown that our Internet-based indices could serve as a good indicator of the homeownership rate in German regions.

Suggested Citation

  • Konstantin A. Kholodilin & Andreas Mense, 2011. "Can Internet Ads Serve as an Indicator of Homeownership Rates?," Discussion Papers of DIW Berlin 1168, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1168
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    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.388082.de/dp1168.pdf
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    References listed on IDEAS

    as
    1. Andrew F. Haughwout & Richard Peach & Joseph Tracy, 2010. "The homeownership gap," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 16(May).
    2. Oliver W. Lerbs & Christian A. Oberst, 2014. "Explaining the Spatial Variation in Homeownership Rates: Results for German Regions," Regional Studies, Taylor & Francis Journals, vol. 48(5), pages 844-865, May.
    3. Glaeser, Edward Ludwig, 2011. "Rethinking the Federal Bias Toward Homeownership," Scholarly Articles 8052149, Harvard Kennedy School of Government.
    4. Brown, W. Mark Hou, Feng Lafrance, Amélie, 2010. "Incomes of Retirement-age and Working-age Canadians: Accounting for Home Ownership," Economic Analysis (EA) Research Paper Series 2010064e, Statistics Canada, Analytical Studies Branch.
    5. Gundi Knies & C. Katharina Spieß, 2007. "Regional Data in the German Socio-Economic Panel Study (SOEP)," Data Documentation 17, DIW Berlin, German Institute for Economic Research.
    6. Kholodilin Konstantin A. & Menz Jan-Oliver & Siliverstovs Boriss, 2010. "What Drives Housing Prices Down? Evidence from an International Panel," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(1), pages 59-76, February.
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    Cited by:

    1. Kholodilin, Konstantin A & Mense, Andreas & Michelsen, Claus, 2017. "The market value of energy efficiency in buildings and the mode of tenure," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 54(14), pages 3218-3238.
    2. Konstantin A. Kholodilin & Andreas Mense, 2012. "Internet-Based Hedonic Indices of Rents and Prices for Flats: Example of Berlin," Discussion Papers of DIW Berlin 1191, DIW Berlin, German Institute for Economic Research.
    3. Thomschke, Lorenz, 2015. "Changes in the distribution of rental prices in Berlin," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 88-100.
    4. Konstantin A. Kholodilin, 2012. "Internet Offer Prices for Flats and Their Determinants: A Cross Section of Large European Cities," Discussion Papers of DIW Berlin 1212, DIW Berlin, German Institute for Economic Research.
    5. Andreas Mense & Konstantin Kholodilin, 2014. "Noise expectations and house prices: the reaction of property prices to an airport expansion," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(3), pages 763-797, May.
    6. 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.

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    More about this item

    Keywords

    Internet ads; homeownership rate; German regions; NUTS; planning regions;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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