Metro business cycles

Arias, Maria A.; Gascon, Charles S.; Rapach, David E.

doi:10.1016/j.jue.2016.05.005

Cited by 31 publications

(25 citation statements)

References 29 publications

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“…Returning to the results of our main model, we find much heterogeneity across MSAs in terms of their average housing price growth rates under house-price expansions and under contractions. This finding is consistent with the finding of much heterogeneity of MSA business cycles by Arias et al (2016). We also find that across MSAs higher house-price growth rates in expansion provide little insight into growth rates in contractions.…”

Section: Resultssupporting

confidence: 90%

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House Price Growth Interdependencies and Comovement

Coughlin

Soques

Cohen

2019

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This paper examines house price comovement across U.S. metropolitan areas (MSAs). We develop a Markov-switching framework that includes a spatial similarity element based on distances between MSAs. Our approach allows for house price comovements that occur due to similar timing of downturns across groups or clusters of MSAs. The inclusion of the spatial element improves the model fit compared to a standard endogenous clustering model. We find seven clusters of MSAs, where each cluster experiences idiosyncratic house price downturns, plus one distinct national house price cycle. Notably, only the housing downturn associated with the Great Recession spread across all the MSAs in our sample; all other house price downturns remained contained to a single cluster. We also identify MSA economic and geographic characteristics that correlate with cluster membership. In addition, while prior research has found housing and business cycles to be related closely at the national level, we find very different house price comovement and employment comovement across clusters and across MSAs.

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Section: Resultssupporting

confidence: 90%

“…Another related paper focused on MSAs, is byArias et al (2016). Based on a dynamic factor model, they highlight the heterogeneity of business cycles at the metropolitan level.…”

mentioning

confidence: 99%

House Price Growth Interdependencies and Comovement

Coughlin

Soques

Cohen

2019

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“…A dynamic single factor methodology is used to develop the BCI. This method has been successfully employed to estimate business cycle indices for other regions and seems to be well suited toward doing so for the Petroplex, also (Arias et al, 2016;Fullerton and Subia, 2017).…”

Section: Introductionmentioning

confidence: 99%

Business Cycle Analysis for the Midland-Odessa Petroplex

Downs¹,

Fullerton²

2017

ELet

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A business cycle coincident index (BCI) for the Midland-Odessa region is developed using a dynamic single factor modeling methodology. The model assumes that co-movements of metropolitan economic indicators have a common element that can be summarized as a single underlying and unobservable variable known as the "state of the economy." The model utilizes a Kalman filter smoothing approach which smooths the index across time and across indicators, resulting in index movements that are less pronounced during expansions and recessions. Indicator series used to estimate the MidlandOdessa BCI are: employment, the unemployment rate, real retail sales, and total real wages. The estimated BCI exhibits movements that are correlated with national economic contractions and expansions, movements in oil prices, and an existing Midland-Odessa business cycle index.

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“…In addition, metropolitan areas with larger price booms tended to experience larger price busts. For an examination of the overall performance of metropolitan regions beginning in the early 1990s, seeArias, Gascon, and Rapach (2016).8 Numerous references for this topic exist. Two analyses providing many references areFoote, Gerardi, and Willen (2012) andLevitin and Wachter (2012).…”

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confidence: 99%