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Modelling Housing Prices using a Present Value State Space Model

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  • Rambaccussing, Dooruj
Abstract
This paper introduces a State Space approach to explain the dynamics of rent growth, expected returns and Price-Rent ratio in housing markets. According to the present value model, movements in price to rent ratio should be matched by movements in expected returns and expected rent growth. The state space framework assume that both variables follow an autoregression process of order one. The model is applied to the US and UK housing market, which yields series of the latent variables given the behaviour of the Price-Rent ratio. Resampling techniques and bootstrapped likelihood ratios show that expected returns tend to be highly persistent compared to rent growth. The filtered expected returns is considered in a simple predictability of excess returns model with high statistical predictability evidence for the UK. Overall, it is found that the present value model tends to have strong statistical predictability in the UK housing markets.

Suggested Citation

  • Rambaccussing, Dooruj, 2015. "Modelling Housing Prices using a Present Value State Space Model," SIRE Discussion Papers 2015-80, Scottish Institute for Research in Economics (SIRE).
    • Handle: RePEc:edn:sirdps:688
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    File URL: http://hdl.handle.net/10943/688
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    References listed on IDEAS

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    Cited by:

    1. Dooruj McRambaccussing, 2015. "Moment Matching in the Present Value identity, and a New Model," Dundee Discussion Papers in Economics 291, Economic Studies, University of Dundee.

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    More about this item

    Keywords

    price-rent ratio; present value; state space;
    All these keywords.

    JEL classification:

    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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