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House Prices and Rents: Micro Evidence from a Matched Dataset in Central London_x0003_

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  • Philippe Bracke

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Abstract

In this paper I study unit-level data on house prices and rents in Central London. I document the existence of systematic differences in price-rent ratios across property types within the same urban area: bigger properties and properties located in more expensive neighborhoods have higher price-rent ratios. My analysis is based on a unique new dataset: the records of a major Central London real estate agency. The dataset contains information on achieved prices and rents for tens of thousands of properties, as well as detailed descriptions of property characteristics. The period of analysis, 2005 to 2011, covers the last part of the housing boom, the bust of 2008, and the subsequent recovery. In terms of empirical methodology, I use hedonic regressions to estimate average prices and rents within cells of observationally equivalent properties. Since hedonic regressions cannot control for unobserved characteristics, I also run a restricted analysis with properties that are both sold and rented out within 6 months: in this way I am able to measure price-rent ratios directly. In the last part of the paper I discuss potential explanations for the differences in price-rent ratios. One possibility is that gross price-rent ratio disparities hide differences in maintenance costs or vacancy rates. Another possibility, related to the dividend pricing model, is that properties with higher price-rent ratios feature higher expected rent growth or lower risk premia. Contrary to this second view, I find that within Central London the rent growth rates of more expensive properties are not different from those of cheaper properties, but their volatility is significantly higher. This is consistent with a hedging model where higher rent volatility in some housing submarkets pushes people to buy in order to lock in future rents. In order to verify the above mechanisms, I use price and rent indexes derived from the hedonic regressions to estimate the growth and aggregate volatility of prices and rents for different property categories. Using data at the individual property level, I also measure idiosyncratic volatilities by restricting attention to properties that were sold or rented at least twice during the sample period. Since the expectations of agents might differ from the actual historical performance of house prices and rents, I complement my analysis with an expectation survey carried out through the mailing list of the real estate agency that provided the property data.

Suggested Citation

  • Philippe Bracke, 2013. "House Prices and Rents: Micro Evidence from a Matched Dataset in Central London_x0003_," ERSA conference papers ersa13p112, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa13p112
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    File URL: http://www-sre.wu.ac.at/ersa/ersaconfs/ersa13/ERSA2013_paper_00112.pdf
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    References listed on IDEAS

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    Cited by:

    1. Muthoka, Sila, 2015. "Household Demand for Housing in Kenya," MPRA Paper 65469, University Library of Munich, Germany.
    2. Luis Eduardo Quintero, 2014. "Housing Consumption and Prices in a Unified Metropolitan Market with Heterogeneous Preferences," 2014 Papers pqu53, Job Market Papers.
    3. Halket, Jonathan & Pignatti Morano di Custoza, Matteo, 2015. "Homeownership and the scarcity of rentals," Journal of Monetary Economics, Elsevier, vol. 76(C), pages 107-123.

    More about this item

    Keywords

    House prices; housing rents; price index;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • 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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