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The Journal of Real Estate Finance and Economics

Erschienen in:

01.07.2016

Long-run Equilibrium Shift and Short-run Dynamics of U.S. Home Price Tiers During the Housing Bubble

verfasst von: Damian S. Damianov, Diego Escobari

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 1/2016

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Abstract

We use vector error correction models to examine the interdependence between the high and the low price tiers during the latest housing market boom and bust. For 118 of the 364 US statistical areas analyzed, the tiered price indexes are bound by a long-run relationship. In general, low tier homes appreciated more than high tier homes in the past two decades. In contrast to previous periods of high volatility, however, low tier homes appreciated more during the boom and lost more value during the bust of the market. We find a shift in the long-run equilibrium during the bubble —the cointegration parameter that ties the tiers together is greater in absolute value during the bubble period compared to the periods of more moderate appreciation and depreciation rates. Moreover, the shift in the long-run equilibrium can be explained by differences in subprime originations across housing markets. We also find that short run price dynamics is driven by momentum in both segments of the market.

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Glaeser et al. (2008) discuss the role of supply elasticity for the severity and duration of housing bubble periods.

Gallin (2006) shows that for the time period 1970-2005 prices and rents are cointegrated. Price-rent ratios tend to predict future changes of both prices and rents whereby the corrective response of prices is greater than that of rents.

See e.g. Mian and Sufi (2009) and Pavlov and Wachter (2011).

Panel augmented Dickey-Fuller unit root tests as proposed by Pesaran (2007) reject the null of a unit root in the first difference both for the low and the high tier at the 1 % significance level.

Augmented Dickey-Fuller unit root test cannot reject the null of a unit root in levels and reject the null of a unit root in first differences at conventional statistical levels for 176 out of the 364 statistical areas.

The data is obtained from the online real estate database Zillow, available at http://www.zillow.com/research/

The statistical areas include both metropolitan and micropolitan statistical areas.

An alternative index that can be used for an analysis of house price dynamics is the repeat sales index constructed by the Office of Federal Housing Enterprise Oversight (OFHEO). This index, however, is limited to houses for which mortgages were provided by Fannie Mae and Freddy Mac thus excluding the subprime segment of the market. For a discussion on the use of indexes in studying house price dynamics see Miao et al. (2011).

The Pesaran (2007) test uses a system of ADF regressions: where the statistic of interest (\(\bar {\textsc z}_{\text {adf}}\)) is distributed standard normal standard normal. It is based on means of individual ADF t-statistic (τ _df) for each SA.

Note that, under the existence of a long-run equilibrium between price tiers, simpler autoregressive (AR) or vector-autoregressive (VAR) specifications of the first-differences of the price tiers would provide biased estimates of the momentum coefficients. An AR would not control for the link between price tiers, while a VAR would be missing the dynamics around the long-run equilibrium.

In general, with n time series, there might exist up to n−1 cointegrating vectors. Johansen’s (1988) approach can be used to estimate these distinct relationships.

Notice that we only work with the 176 SAs that have both tiers integrated of order one, to follow the definition of cointegrating relationship.

This estimation does not take into account shifts. However, later on we estimate a rolling regression that allows for a time-varying β, which is more in line with the evidence of shifts in the cointegrating relationships found in Table 4.

In the context of bubbles, Driffill and Sola (1998) use similar rolling regression approach in a random walk with drift regression to provide evidence of instability and regime-switching. Swanson (1998) uses rolling regressions in vector error correction models similar to the ones we estimate here.

The time of the burst τ _i is obtained as the date in which LowTier \(_{t}^{\textsc {sa}}\) for the corresponding SA reaches its maximum.

The estimation uses a window w = 100. The lines were smoothed using the fitted cubic splines.

One key benefit from separating the dynamics into a long-run and a short-run dynamics is that momentum is a short-run concept while the long-run shift that we find is interpreted as a shift in the permanent components of the price indexes.

The number of observations is smaller in this table as the sample starts in 2007 and was not available for all statistical areas.

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Titel: Long-run Equilibrium Shift and Short-run Dynamics of U.S. Home Price Tiers During the Housing Bubble
verfasst von: Damian S. Damianov
Diego Escobari
Publikationsdatum: 01.07.2016
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 1/2016
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-015-9523-2

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