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Erschienen in: The Journal of Real Estate Finance and Economics 3/2016

01.10.2016

A Dynamic Model of the Housing Market: The Role of Vacancies

verfasst von: Jeffrey Zabel

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 3/2016

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Abstract

While the hedonic property value model and recently developed computable general equilibrium urban models assume the housing market is in equilibrium, recent years have witnessed extreme circumstances such as large changes in housing prices, high levels of mortgage default, and high levels of foreclosure that bring into question this assumption. This highlights the need for a better understanding of the dynamics of the housing market and the mechanisms that drive and sustain periods of disequilibrium. In this analysis, I develop a dynamic model of the housing market where vacancies naturally arise as the error correction mechanism. I estimate this model using annual U.S. panel data at the MSA level for 1990–2011. The results show that when there is excess demand, prices rise when vacancies fall but prices do not fall when there is excess supply and vacancies rise. This is consistent with the belief that prices are sticky downwards and hence prolong housing downturns. On the other hand, when there is excess supply, there is a relatively stronger decline in new housing in response to a rise in vacancies and much less of a new housing reaction when there is excess demand and vacancies fall. Furthermore, when I allow for a structural shift in the housing market brought on by the Great Recession (2006–2011), I find that the housing market became more responsive on both sides – excess supply and demand – during this period.

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Fußnoten
1
See Kuminoff et al. (2010) for an excellent review of computable general equilibrium urban models.
 
2
There are some 5-year files starting in 2009 but not the ones that are needed to calculate vacancy rates at the MSA level.
 
3
I re-estimated the natural vacancy rate using the ACS vacancy data and limiting the data to the same MSAs as in HVS vacancy data. The mean estimate of the natural vacancy rate is 2.31 and the standard deviation is 0.72. This is a lower mean that what was obtained using the HVS vacancy data for the full sample (mean = 2.45). This seems to reflect the fact that the vacancy rates in the ACS data, are on average, slightly lower than those in the HVC data.
 
4
Standard errors reflect the fact that the natural rate is an estimated variable. One might be concerned that these estimates use some of the same data that are included in the housing supply and demand equations. But I estimate the natural vacancy rate using only data from the HVS sample for 1986–1995 and limit the data used to estimate the supply and demand equations to 1996–2011 and I get similar results as when I estimate the natural rate using the full HVS sample. Note that the results are also similar when I use the full HVS sample and the sample limited to 1996–2011 to estimate the supply and demand equations.
 
5
Results are available from the author upon request.
 
6
Regression results are available upon request from the author.
 
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Metadaten
Titel
A Dynamic Model of the Housing Market: The Role of Vacancies
verfasst von
Jeffrey Zabel
Publikationsdatum
01.10.2016
Verlag
Springer US
Erschienen in
The Journal of Real Estate Finance and Economics / Ausgabe 3/2016
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI
https://doi.org/10.1007/s11146-014-9466-z

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