Long-Run Trends and Cycles in US House Prices

Abstract This paper analyses US nominal house prices at an annual frequency over the period from 1927 to 2022 by means of a very general time series model. This includes both a (linear and non-linear) deterministic and a stochastic component, with the latter allowing for fractional orders of integration at both the long-run and the cyclical frequencies. The results are heterogeneous depending on the model specification and on whether or not the series have been logged. Specifically, a linear model appears to be more appropriate for the logged data whilst a non-linear one appears to be a better fit for the original ones. Further, the order of integration at the zero or long-run frequency is much higher than at the cyclical one. The former is in fact around 1 in all specified models, which implies a high degree of persistence of this component. Finally, the order of integration of the cyclical structure implies that cycles have a periodicity of about 8 years, but it is almost insignificant in all cases.

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Publication Details

Journal
Computational Economics
Published
2025-02-06
DOI
https://doi.org/10.1007/s10614-025-10882-8
Primary Topic
Housing Market and Economics
Type
article
Field-Weighted Citation Impact
0.00

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article

Long-Run Trends and Cycles in US House Prices

Luis A. Gil-Alaña, Guglielmo Maria Caporale
Computational Economics
Housing Market and Economics
article

Long-Run Trends and Cycles in US House Prices

Luis A. Gil-Alaña, Guglielmo Maria Caporale
article en

Abstract

Abstract This paper analyses US nominal house prices at an annual frequency over the period from 1927 to 2022 by means of a very general time series model. This includes both a (linear and non-linear) deterministic and a stochastic component, with the latter allowing for fractional orders of integration at both the long-run and the cyclical frequencies. The results are heterogeneous depending on the model specification and on whether or not the series have been logged. Specifically, a linear model appears to be more appropriate for the logged data whilst a non-linear one appears to be a better fit for the original ones. Further, the order of integration at the zero or long-run frequency is much higher than at the cyclical one. The former is in fact around 1 in all specified models, which implies a high degree of persistence of this component. Finally, the order of integration of the cyclical structure implies that cycles have a periodicity of about 8 years, but it is almost insignificant in all cases.

Computational EconomicsVol. 66(6)
University of London (GB), Universidad Francisco de Vitoria (ES), Brunel University of London (GB), Universidad de Navarra (ES)
Universidad Francisco de Vitoria, Universidad de Navarra, Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España, European Regional Development Fund, Agencia Estatal de Investigación
Openalex Percentile: Top 97%
Housing Market and Economics
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