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War, Housing Rents, and Free Market: A Case of Berlin's Rental Housing Market during the World War I

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  • Konstantin A. Kholodilin

Abstract

Before the World War I, the urban rental housing market in Germany could be described as a free and competitive market. The government hardly interfered in the relationships between the landlords and ten- ants. The rents were set freely. During the World War I, the market was hit by several violent shocks. The outbreak of the war led initially to a huge outflow of men from cities to the fronts. Towards the end of the war, the cessation of construction as well as an inflow of workers and mustered out of service soldiers produced an acute housing shortage. Using a unique data set of asking rents extracted from the newspaper announcements, we constructed a monthly time series of rents in Berlin over 1909-1917. This variable is employed to measure the effects of demand and supply shocks on different segments of housing: from small dwellings for poor to large apartments for rich. The analysis shows a decline of rents (especially of the cheap dwellings) in the first half of the war, followed by a moderate increase. This stands in marked contrast to a steady and strong increase of the overall price level.

Suggested Citation

  • Konstantin A. Kholodilin, 2015. "War, Housing Rents, and Free Market: A Case of Berlin's Rental Housing Market during the World War I," Discussion Papers of DIW Berlin 1477, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1477
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    File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.503293.de/dp1477.pdf
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    References listed on IDEAS

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    1. Calcagno, Vincent & de Mazancourt, Claire, 2010. "glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i12).
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. Rigobon, Roberto & Stoker, Thomas M., 2009. "Bias From Censored Regressors," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 340-353.
    4. Olsen, Edgar O, 1969. "A Competitive Theory of the Housing Market," American Economic Review, American Economic Association, vol. 59(4), pages 612-622, Part I Se.
    5. Ahlfeldt, Gabriel M. & Wendland, Nicolai, 2011. "Fifty years of urban accessibility: The impact of the urban railway network on the land gradient in Berlin 1890-1936," Regional Science and Urban Economics, Elsevier, vol. 41(2), pages 77-88, March.
    6. Albert Rees & Donald P. Jacobs, 1961. "Real Wages in Manufacturing, 1890-1914," NBER Books, National Bureau of Economic Research, Inc, number rees61-1, September.
    7. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    8. Margo, Robert A., 1996. "The Rental Price of Housing in New York City, 1830–1860," The Journal of Economic History, Cambridge University Press, vol. 56(03), pages 605-625, September.
    9. Konstantin A. Kholodilin & Dirk Ulbricht, 2014. "Mietpreisbremse: Wohnungsmarktregulierung bringt mehr Schaden als Nutzen," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, vol. 81(15), pages 319-327.
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    More about this item

    Keywords

    housing rents; announcements; World War I; Berlin;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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