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\title{The Contribution of Housing Markets to the Great Recession}
\author{Peter Pedroni\footnote{Department of Economics, Williams College, 24 Hopkins Hall Drive, Williamstown, MA 01267} \\ {\it and} \\
Stephen Sheppard\footnote{Department of Economics, Williams College, 24 Hopkins Hall Drive, Williamstown, MA 01267}  \\ 
\\ 
\\ {\it Please check with authors before quoting}
}

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\begin{document}

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\begin{abstract}

The recession of 2008-2009 was the most severe downturn experienced by the US
since 1981-82 and, by some measures, the most severe since the 1930s. Most
economists and policy makers attribute the recession to the collapse or near
collapse of financial institutions that held large quantities of assets whose
values derived from mortgages on residential property. The values of these
assets dropped precipitously with the collapse of house prices in 2006 and 2007
leading to sharp reductions in investment and lending. Household wealth was also
reduced by the collapse of house prices leading to reductions in household
spending that exacerbated the contraction. 

In this paper, we employ a novel panel-based structurally-identified VAR
approach to examine the evidence for the impact of housing markets on both local
and national unemployment and output during the crisis. We use quarterly data on
house prices, unemployment and output in 384 metro areas. The approach allows us
to identify and isolate shocks originating in the local housing markets, and to
distinguish these from other economics shocks originating at either the local or
national level.  The approach also accounts for heterogeneity in the dynamic
responses to these shocks at the local level, and investigates the relationship
between the size of the local responses and the degree of regulation of housing
supply present at the local level.

Our approach is able to better quantify the extent to which the housing market
contributed to the recent national economic downturn and illustrates the
contribution that is possible when the economies of the regions within the
national economy are used to test and deepen our understanding of aggregate
economic behavior.

\end{abstract}
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\section{Introduction}

The recession that began in December 2007 and officially ended in June of 2009
has been dubbed the ``Great Recession'' in both the popular press and by at least
some academics. It is certainly the most severe recession endured by the US
economy since the recession of 1981-82, and by some measures it is the most
severe economic contraction since the 1930s. 

During the recession itself and during the somewhat tepid recovery that
followed, an analytic narrative has captured the imagination of policy makers,
the popular press and many economists to explain the recession. This narrative
runs roughly as follows: sometime after 2000 or 2001 a housing bubble developed
in the US. The bubble was fueled by excessive and perhaps fraudulent lending by
financial institutions to buyers whose only hope of meeting their repayment
obligations rested on hoped-for capital gains derived from continuation of the
bubble. The financial institutions securitized the resulting mortgages into
assets that were so complex that the risk associated with the assets was
difficult or impossible to determine, and these derivative assets were widely
sold to investors and to other financial organizations who in turn built highly
leveraged operations that assumed continued and secure returns from these
assets. The bubble burst in 2006, and the subsequent decline in house prices led
to increased foreclosures and threatened the stability of major financial
institutions. This instability led to severe restrictions in consumer spending
that had been encouraged by expected capital gains from residential property and
in credit that could no longer be extended to businesses by overleveraged
financial institutions. These restrictions sent the economy into recession
beginning in December 2007.

\begin{figure}[htb]
 \centering
 \includegraphics[width=6.5in,keepaspectratio=true]{./Fig1Shiller.png}
 % Fig1Shiller.png: 1179x834 pixel, 96dpi, 31.18x22.06 cm, bb=0 0 884 625
 \caption{US house prices: an aggregate view of the bubble}
 \label{Fig1Shiller}
\end{figure}

The role of housing in this story is central and the extent of the bubble that
affected the US housing market is illustrated in Figure \ref{Fig1Shiller}, adapted from data in
\citeasnoun{Shiller:2005}. These data or figures similar to this have been widely
reproduced in the popular and financial press and are presented as part of the
evidence supporting the narrative described above. The data support the idea of
``the'' US housing bubble of 2001-2006, and may reinforce an impression that a
graph similar to Figure \ref{Fig1Shiller} describes most US urban areas. This is certainly not
correct, as shown in Figure \ref{Fig2HPI}.

\begin{figure}[htb]
 \centering
 \includegraphics[width=6.5in,keepaspectratio=true]{./Fig2HPI.png}
 % Fig1Shiller.png: 1179x834 pixel, 96dpi, 31.18x22.06 cm, bb=0 0 884 625
 \caption{US house prices: 384 distinct markets}
 \label{Fig2HPI}
\end{figure}


Figure \ref{Fig2HPI} shows the FHFA house price index for 384 US metro areas, normalized to
the same year (1995) as the overall US house price index shown in Figure \ref{Fig1Shiller}. We
see that the actual experience of US housing markets has been highly varied,
with the most extreme areas like Naples, Florida peaking not at 180 percent of
their 1995 levels but at 365 percent. At the same time many metro areas
experienced little or no ``bubble'' in residential house prices. For example
Lafayette, Indiana peaked at 134 percent of 1995 prices at the beginning of
2007, and declined to 133 percent by first quarter of 2010.

It is clear from Figure \ref{Fig2HPI} that the behavior of house prices prior to, during and
after the recession of 2007-09 has exhibited considerable variation. To ignore
this variation is to ignore an important opportunity to learn about the role of
housing in causing the recession and about impacts of policies designed to
prevent future dislocations attributable to the housing or financial markets.

\begin{figure}[htb]
 \centering
 \includegraphics[width=7in,keepaspectratio=true]{./Fig3Map.png}
 % Fig1Shiller.png: 1179x834 pixel, 96dpi, 31.18x22.06 cm, bb=0 0 884 625
 \caption{Spatial structure of housing bubble}
 \label{Fig3Map}
\end{figure}

Once we recognize the variation in actual US metro house price dynamics, it is
natural to inquire whether there is a spatial structure to this variation. Are
cities that experienced more severe house price fluctuations concentrated in
particular regions or are they interspersed with those cities where the
magnitude of house price fluctuations was less severe? Figure \ref{Fig3Map} provides an
initial look at this question.

Figure \ref{Fig3Map} shows the distribution of house price variability in two ways. The
shading of individual metro areas represents the difference between the average
annual rate of growth in house prices from 1995 through the peak of the local
market and the annual rate of decline from the peak of the local market to the
post 2006 trough. The height of the superimposed bars is proportional to the
standard deviation of the metro FHFA all sales house price index from 1995
through the first quarter of 2010. 

The data shown in Figure \ref{Fig3Map} suggest that the most extreme swings in house prices
took place in cities along the Atlantic and Pacific coasts. While possible, it
would be surprising if lending practices in these regions were uniquely
irresponsible or fraudulent. The securitization of mortgages was a national
phenomenon and while it is possible that mortgage lenders away from coastal
states were less likely to be the source of mortgages for creation of derivative
securities it would surely not follow that non-coastal lenders were insulated
from risk or from broader dislocations in national credit markets. 

\citeasnoun{ColemanLaCourLittleVandell:2008} analyze a short panel of data from 20 metro areas and find
no evidence that subprime lending practices were significant determinants of
future house price returns, but do find some other shifts in credit markets that
appear to be linked to the housing market bubble. This sort of co-determination
should not be surprising and poses challenges for testing hypotheses about the
role of housing markets in causing or contributing to the economic decline.

It is possible that there are significant differences between coastal and
non-coastal regions in house price dynamics and/or linkages between the housing
sector and the rest of the economy. There are several possible reasons why such
differences might exist. Differences in the industrial structure of the regions
are clearly present, and these may give rise to different house price dynamics. 

There are also general differences in land use and housing regulation between
the regions. These regulatory regimes can have large impacts on the ability of
housing suppliers to respond to changes in house prices, and such differences in
supply elasticity might offer an explanation. \citeasnoun{GlaeserGyourkoSaiz:2008}
present a model of housing supply and housing bubbles that incorporates
differences in the elasticity of housing supply as an explanation for why some
urban areas may be more prone to housing bubbles. Their examination of the data
provides some support for their model.

Many writers attribute the house price bubble to irrational exuberance (\citeasnoun{Shiller:2005}) 
or to some non-linearity in discounting of rents and the resultant
hyper-sensitivity to interest rates (\citeasnoun{HimmelbergMayerSinai:2005}). While
cultural and other differences between coastal and non-coastal regions in the US
clearly exist, it seems that if such arguments are to provide an explanation of
what the US economy has actually experienced they will probably have to explain
why market participants in coastal cities are more irrational or more non-linear
than their counterparts in the central part of the country.

In this paper we provide an analysis of linkages between the local economy,
house prices and unemployment based on a structural vector auto-regression
(SVAR) analysis of the panel of 384 US metropolitan areas for which FHFA house
price data are available. This approach allows us to examine some of the
possible explanations of how house price swings might have contributed to the
recession of 2007-09, and to explore in greater detail the structure of house
price dynamics in US metro areas.

\section{Empirical methodology}

We use a structural panel VAR methodology as developed in \citeasnoun{Pedroni:2008}.  The
approach enables us to address several challenging aspects of our empirical
analysis.  For example, the approach is able to accommodate the fact that the
true economic shocks of interest are unobserved, and the observed variables are
highly endogenous.  It also accounts for the fact that the economic activity of
different metropolitan areas are interdependent, and that some shocks of
interest originate at the local level while others do not.  Importantly, the
approach accounts for the fact that metropolitan areas are very different from
one another, so that the local responses to both local and national economic
shocks may be very heterogeneous among metropolitan areas.

 The panel structural VAR approach can be viewed as a generalization of time
series based structural VAR approaches such as \citeasnoun{BlanchardQuah:1989} to a
panel data framework.  Analogous to the time series based approach,
identification of the unobserved structural economic shocks is accomplished by a
set of identifying restrictions.  These typically include the assumption that
the true unobserved structural shocks of interest correspond to the forcing
process for the economy being modeled, and therefore are naturally thought of as
orthogonal to one another, and arbitrarily normalizable.  Additionally,
assumptions are typically made regarding the admissible dynamic responses of the
observed variables to the unobserved shocks.  In the case of Blanchard and Quah,
these additional identifying restrictions come in the form of restrictions on
the steady response of certain variables to certain shocks.   Accordingly, the
contemporaneous and short run dynamic responses of all variables are considered
completely endogenous and unrestricted, with only the long run steady state
response of some variables restricted.  

The challenge for panels such as ours is to recognize that individual
metropolitan areas are very different from one another, but also interdependent.
 Toward this end, the panel structural VAR approach allows for complete
heterogeneity in the responses of the metropolitan level variables to the
unobserved shocks.  Confidence intervals for the median responses are then based
on the spatial variation in the estimated responses among metropolitan areas.  
Furthermore, in light of the fact that metropolitan areas are interdependent and
likely responding as a group to common shocks, we also identify the metropolitan
responses to economic shocks originating nationally.   This allows us to
construct confidence intervals that are valid despite the fact that metropolitan
area responses are cross-sectionally dependent.  Finally, this approach allows
us to relate the pattern of estimated dynamic responses among metropolitan areas
and to relate these to observed characteristics of the metro areas.
 
Since we are primarily interested in knowing the consequences of structural
shocks arising in the housing market, we take this structural economic shock to
be any shock that leads to a long run effect on housing price, and refer to it
as a ``house'' shock.  We want to examine the dynamic responses of output and
unemployment to such shocks.  However, we recognize that many other shocks also
impact output and employment, and so we must control for these in a way that
allows for complete endogeneity.   In particular, it will be important to
control for shocks that may have a permanent effect on housing prices, but which
are not fundamentally housing market shocks.   We posit that one such class of
shocks, which we refer to as economic \textit{supply} shocks, can be distinguished from
\textit{housing market} shocks by virtue of the fact that they also permanently alter
local economic activity as measured by metropolitan GDP in the long run. 
Specifically, we assume that while both the supply shocks and housing market
shocks may have a permanent long run effect on housing prices, only the supply
shocks have a permanent effect on output.  Our identification treats these two
classes of shocks as the only shocks that permanently alter housing prices, and
allow both shocks to permanently alter unemployment.  Of course, these are not
the only classes of shocks that might permanently alter unemployment. 
Therefore, we also control for a third class of shocks which are permitted to
have a permanent effect on unemployment, but no long run effect on output or
housing prices.  We refer to these as \textit{other} shocks.
 
To implement this identification scheme while allowing for heterogeneity and
interdependence among metropolitan areas, we take the following steps.  First,
we estimate a dynamic vector autoregression for three variables, namely
metropolitan area output, housing prices and unemployment for each metropolitan
area in our sample.   The lag length for each metro area VAR is chosen by a
standard Akaike Information Criterion, and is allowed to differ among metro
areas.   Next, means for each variable are computed across metropolitan areas
for each time period, and a separate VAR is fitted for these averages.  The
reduced form impulse responses are computed for both the estimated individual
and averaged VARs, and the triangular steady state identification scheme is
applied to each of these in order to extract structural shocks.  The structural
shocks identified from the metropolitan area VAR are composed of both
idiosyncratic local metro specific shocks and common national shocks.  However,
since the structural shocks are orthogonal, these can be further decomposed into
region specific and national shocks by OLS regression of the composite shocks on
the national shocks.  Once these regional and national shocks have been
identified, the structural impulse responses and variance decompositions are
computed.  For more details, we refer readers to \citeasnoun{Pedroni:2008}.

\section{Theoretical Approach}

Our analysis is focused on an urban economy where total output (gross
metropolitan product), real house prices, and unemployment are interdependent
and mutually determined. As noted above, the analysis is based on a dynamic
model in which white noise shocks affect the economy and generate a series of
general equilibrium responses. The supply shocks and the housing shocks are the
primary shocks of interest whose effects we seek to identify.

Our interest is in developing an improved understanding of the metropolitan
economy, including the dynamic structure of its responses to shocks and the
long-run implications of shocks. Our primary motivation is in better
understanding the implications of housing shocks so much of the discussion below
develops with that objective in mind.

\section{The data}

There are 384 metropolitan areas in the US for which FHFA produces a quarterly
all sales house price index. These indices use data from the MSA defined in 2009
by the Office of Management and Budget. Some of the house price indices are
available beginning in the first quarter of 1978, most are available throughout
all of the 1990s and some do not become available until the last few years.
Further details on the mechanism of constructing this index are provided in \citeasnoun{Calhoun:1996}.

We combine the price data with data from the Local Area Unemployment series
provided by the Bureau of Labor Statistics for the same geographic areas. These
unemployment rates are provided monthly and we produce quarterly averages to
match our other data.

Total metropolitan GDP is made available for all metropolitan areas in the US by
the Bureau of Economic Analysis, but only for years after 2000. Moody’s
Analytics has used the BEA methodology to produce a quarterly series of real GDP
estimates for all 384 metro areas, and we utilize these for our measure of total
output.

These are the only variables required for estimation of our SVAR model. In what
follows we will examine the pattern of impacts that is estimated by the
analysis, and relate these impacts to the industrial structure in each city.
Because we will want to use location quotients as an indicator of industrial
structure in each metropolitan area, we use data from the Quarterly Census of
Employment and Wages. Unfortunately, QCEW data for metro areas are based on the
379 areas defined in 2004. We adapt their definitions to match our other data,
but drop the 5 newest metro areas from the data used in our analysis when
examining the impact of industrial structure.

The result is an unbalanced panel of 384 areas for which we have up to 130
complete quarterly observations of real output, house prices and unemployment
for each city beginning with first quarter of 1978 and running through the first
quarter of 2010. These are combined with data identifying each city as coming
from a ``coastal'' state (located on the Atlantic Coast from Maine to Florida, on
the Pacific Coast, and including Nevada and Arizona) or non-coastal state (all
other states). Also included are location quotients for 2-digit NAICS industries
for all cities in the sample based on industrial composition in 2009.

\section{Results}

The structural panel VAR approach described above focuses our attention on three
different types of shock, each of which can be considered as applying only to an
individual metro area or to the national urban system as a whole. To present the
results we focus attention on the variance decompositions (given in the
appendix) and the impulse-response functions for each of the six resulting types
of shock.

The variance decompositions are given in the appendix, and show that the
variance in MSA output is largely explained by shocks to regional supply, with
some impact in first few years from regional house shocks, but this impact is
largely dissipated after 5 years. The variance in local house prices is
predominantly explained by regional supply shocks and regional house shocks. The
variance in unemployment is explained by regional shocks of all three varieties.
While national shocks do have some explanatory power the share of variance
explained is much less than for regional shocks, as would be expected.

\begin{table}[htbp]
\caption{Impulse-response functions for output}
\begin{center}
\begin{tabular}{|c|c|}
\hline 
\textbf{Metro Area Shock} & \textbf{National Shock}  \\ 
 \em & \\
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig4TL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig4TR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig4CL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig4CR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig4BL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig4BR.png} \\ 
\hline 
\end{tabular}
\end{center}
\label{Fig4IR}
\end{table}

Tables \ref{Fig4IR}, \ref{Fig5IR} and \ref{Fig6IR} present results in the form of 
plots of the impulse response functions for metropolitan output,
house prices, and unemployment respectively. These impulse-response functions
are derived from our model estimates, and summarize the dynamic response of the
variables to the shocks that affect the urban system. In the left hand column of
each table are the responses to regional – limited to the metropolitan area –
supply, housing and other shocks. In the right hand column are the responses to
national shocks of the same type. 

For each type of shock and each variable that responds, we present the response
over 20 quarters. The central (blue) line shows the median response of our panel
of cities at each lag from the shock. The upper (green) and lower (black) lines
show the 75th and 25th percentiles of our panel. This provides an indication of
the range of responses to each shock. The vertical scales are adjusted to
accommodate the range of responses to each shock, but show the same variable
within each figure. It is tempting to think of the upper line as identifying the
impulse-response of the ``most responsive'' MSA and the lower line as the dynamic
behavior of the ``least responsive'' but the reader should keep in mind that these
orderings will generally change from quarter to quarter so a particular MSA may
be in the most responsive quartile at one particular lag but closer to the
median one or two lags later.

There are two general observations that can be offered that apply to all three
of the figures:

\begin{itemize}
 \item The regional and the national shocks generate qualitatively similar dynamic
patterns of response
\item The national shocks generate responses that are much smaller than the regional
shocks, with national shock responses ranging from one tenth to one third of the
magnitude of the regional shocks 
\end{itemize}

In the graphs shown in Table \ref{Fig4IR}, the vertical axis presents a response of local output measured in
units, so that the numerical quantities indicated can be interpreted as
percentage changes in metropolitan GDP that occur at various points in time as a
result of the shock. Per our identification restrictions, the impact on local
output of a house or other shock converges to zero as the metropolitan economy
returns to steady state. The response of output to a regional supply shock
converges to about 1.75\% of local GDP for the median metropolitan area, with
most of the impact taking place in the first four quarters.

In the graphs shown in Table \ref{Fig5IR}, the vertical axis presents a response of local house prices
measured in units, so that the numerical quantities indicated can be interpreted
as percentage changes in the house price index for the metropolitan area that
occur over time as a result of the shock. Both house shocks and supply shocks
are seen to generate noticeable changes in metropolitan region house prices,
with the median metropolitan area converging to 1.5\%-2\% increase in house prices
in response to a regional house shock. It is worth noting that there is
considerable heterogeneity across the US urban system in the magnitude of
response to these shocks, with the most responsive quartile of MSAs eventually
realizing more than 4\% increase in house prices as a result of a shock, and the
least responsive quartile experiencing barely 1\%. This heterogeneity in response
will be discussed at greater length below.

\begin{table}[htbp]
\caption{Impulse-response functions for house prices}
\begin{center}
\begin{tabular}{|c|c|}
\hline 
\textbf{Metro Area Shock} & \textbf{National Shock}  \\ 
 \em & \\
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig5TL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig5TR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig5CL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig5CR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig5BL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig5BR.png} \\ 
\hline 
\end{tabular}
\end{center}
\label{Fig5IR}
\end{table}

In the graphs presented in Table \ref{Fig6IR}, the vertical axis presents a response of the local unemployment
rate and the numerical quantities indicated are changes in the this rate for the
metropolitan area that occur at various points in time as a result of the shock.
As might be expected, a supply shock has a negative impact on the MSA
unemployment rate. A regional supply shock that for the median MSA increases
output and MSA house prices by somewhat more than 1.75\% will decrease the MSA
unemployment rate by about 0.425.

The graphs shown in the second row of the table are of particular interest.
They indicate that a house shock, that will increase house prices by somewhat
more than 2\%, will actually reduce the MSA unemployment rate by nearly 0.10
percentage points. Why might this effect arise? At first one might think that
the house shock is stimulating output, and the increase in MSA output increases
labor supply. Review of the data in Table \ref{Fig4IR} above, however, shows that in general this is
not the case. For more than half of the metro areas, a regional house shock
alone has a negative impact on output. As the economy converges towards the
steady-state, the impact of a house shock on output goes to zero. What the
analysis shown in Table \ref{Fig6IR} reveals is that a house shock that increases house
prices in the steady state lowers unemployment for most cities. 

\begin{table}[htbp]
\caption{Impulse-response functions for unemployment}
\begin{center}
\begin{tabular}{|c|c|}
\hline 
\textbf{Metro Area Shock} & \textbf{National Shock}  \\ 
 \em & \\
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig6TL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig6TR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig6CL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig6CR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig6BL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig6BR.png} \\ 
\hline 
\end{tabular}
\end{center}
\label{Fig6IR}
\end{table}

This finding is consistent with a perspective suggested by Oswald but as yet unpublished.
Oswald's hypothesis was in fact inspired by the empirical analysis presented in 
\citeasnoun{HughesMcCormick:1981} and \citeasnoun{HughesMcCormick:1987} that demonstrated
that tenure in council housing would discourage internal migration and potentially retard
labor market adjustment, leading to higher rates of unemployment. The issue has subsequently
been investigated by \citeasnoun{MunchRosholmSvarer:2008}, \citeasnoun{CoulsonFisher:2009} and
others linking local labor market congestion to local housing markets. A shock
that increases house prices will plausibly be associated with easier real estate
transactions and reduced time on the market for houses being sold. This in turn
facilitates geographic labor mobility, reducing local labor market congestion
and reducing unemployment. This finding is important because micro-data studies
of US metropolitan markets have generally found mixed results and at best weak
support for the ``Oswald hypothesis''. The studies however are made difficult
because of endogeneity in labor search decisions and housing tenure decisions.
Under the identification assumptions used in our analysis, the estimates are
robust to general endogeneity and can be expected to more consistently measure
the general equilibrium impacts.

As noted above, there appears to be considerable heterogeneity amongst US
metropolitan areas in the responses of local housing prices to house shocks. The
introductory discussion and the presentation of data in Table \ref{Fig3Map} furthermore
suggests that part of the heterogeneity in house price dynamics may exhibit
spatial structure, with cities located in states along the Atlantic and Pacific
coasts behaving differently from those in the central portion of the US. 

In order to explore this possibility, we divide our panel roughly in half, with
194 MSAs located in Nevada, Arizona, and states along the Atlantic and Pacific
coasts, and 190 located in all other states. We characterize these subsamples as
coastal and non-coastal respectively, recognizing that the division is chosen
because it conveniently divides the sample of US metropolitan areas almost
exactly in half and is suggested by what appear to be important differences in
the magnitude of recently observed fluctuations in house prices.

Tables \ref{Fig7IR}, \ref{Fig8IR} and \ref{Fig9IR} present a graphical comparison of the impulse response functions for
MSA output, house prices and unemployment rates in the two subsamples. For each
figure, the left column presents the response of cities in coastal states and
the right hand column presents the response of cities in non-coastal states. As
in the figures above, the first row shows the impulse-response function for
regional supply shocks, the second row for regional house shocks and the final
row presents the response for other regional shocks.

Table \ref{Fig7IR} immediately presents us with an interesting finding. While the response
of output to a supply shock or to a regional other shock is dynamically and
quantitatively similar, there are clear and significant differences between the
two subsamples in their dynamic response to a regional house shock. A house
shock that increases house prices in both subsamples, and as assumed for
identification has no impact on steady state output in the MSA.

\begin{table}[htbp]
\caption{Comparison of output responses in coastal versus non-coastal regions}
\begin{center}
\begin{tabular}{|c|c|}
\hline 
\textbf{Coastal} & \textbf{Non-coastal}  \\ 
 \em & \\
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig7TL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig7TR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig7CL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig7CR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig7BL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig7BR.png} \\ 
\hline 
\end{tabular}
\end{center}
\label{Fig7IR}
\end{table}

The dynamic response of output to the house shock, however, shows that in the
non-coastal regions MSA output responds negatively or very close to negatively
for 75\% of the urban areas in the subsample. In the coastal sample, by contrast,
more than 25\% of the MSAs exhibit only positive output response to a regional
house shock and half of the cities are exhibiting positive response to a
regional house shock from the fifth quarter onwards. 

Table \ref{Fig8IR} continues the comparison to the response of MSA house prices. Here we
note that the coastal and non-coastal responses to other regional shocks are
dynamically and quantitatively similar. Important differences emerge, however,
when comparing the response of house prices to supply shocks and to house
shocks. The coastal region consistently exhibits greater responsiveness of house
prices to both supply shocks and to house shocks. For the median MSA, a supply
shock that generates about 1.75\% increase in metropolitan GDP in either coastal
or non-coastal regions (first row of Table \ref{Fig7IR}) will generate a 2.5\% increase in
house prices in the coastal region and about a 1.5\% increase in house prices for
the non-coastal region. Similarly, a house shock that generates about a 4\%
steady-state change in house prices in the coastal region will generate slightly
more than 1.5\% change in house prices in the non-coastal region. There is some
factor of housing markets in coastal cities that makes house prices more
responsive to shocks.

\begin{table}[htbp]
\caption{Comparison of house price responses in coastal versus non-coastal regions}
\begin{center}
\begin{tabular}{|c|c|}
\hline 
\textbf{Coastal} & \textbf{Non-coastal}  \\ 
 \em & \\
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig8TL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig8TR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig8CL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig8CR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig8BL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig8BR.png} \\ 
\hline 
\end{tabular}
\end{center}
\label{Fig8IR}
\end{table}

Table \ref{Fig9IR} presents a comparison of responsiveness of unemployment. The dynamic
response and steady state levels of unemployment in response to supply shocks
are very similar in the two regions. Similarly, the response of unemployment to
other regional shocks exhibits very similar patterns in the coastal and the
non-coastal regions.

There are very modest differences between the two regions in how unemployment
responds to house prices shocks. The magnitudes of the responses and the values
to which the MSAs appear to converge in the steady state are similar, as are the
dynamic structure of the responses. Both regions continue to exhibit behavior
consistent with the Oswald hypothesis noted for the combined data above. In the
coastal region it appears that about 75\% of metropolitan areas experience
long-run declines in unemployment in response to a house shock that increases
house prices. In the non-coastal region this share is on the order of 65\%. This
still seems to provide general support for the hypothesis.

\begin{table}[htbp]
\caption{Comparison of unemployment responses in coastal versus non-coastal regions}
\begin{center}
\begin{tabular}{|c|c|}
\hline 
\textbf{Coastal} & \textbf{Non-coastal}  \\ 
 \em & \\
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig9TL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig9TR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig9CL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig9CR.png} \\ 
 \includegraphics[width=3.25in,keepaspectratio=true]{./Fig9BL.png} &  \includegraphics[width=3.25in,keepaspectratio=true]{./Fig9BR.png} \\ 
\hline 
\end{tabular}
\end{center}
\label{Fig9IR}
\end{table}

Next, we turn attention to another possible explanation for the differences in
observed dynamics of US metropolitan economies: differences in industrial
structure. In order to investigate this we focus attention on the variation
across panel members in their fourth quarter responsiveness to various types of
shocks. This must be considered a preliminary check because responses might vary
with respect to a great many factors.

\begin{figure}[htbp]
 \centering
 \includegraphics[width=5.5in,keepaspectratio=true]{./HP2HSFin.png}
 % HP2HSFin.png: 3300x2400 pixel, 600dpi, 13.97x10.16 cm, bb=0 0 396 288
 \caption{Impact of finance and insurance on Q4 response to house shocks}
 \label{FigureHP2HSFin}
\end{figure}

We matched our data and estimated responses to shocks with the QCEW industrial
data on establishments, employment and wages paid. We calculated location
quotients for two-digit NAICS industry groups and examined the relationship
between magnitudes of location quotients and response to shock. In order to
encompass the largest possible group of cities we use location quotients
calculated for establishments because these numbers are not suppressed for
confidentiality reasons in any MSA.

\begin{figure}[htbp]
 \centering
 \includegraphics[width=5.5in,keepaspectratio=true]{./HP2HSRE.png}
 \caption{Impact of real estate and property management on Q4 response to house shocks}
 \label{FigureHP2HSRE}
\end{figure}

The three scatter plots with linear prediction overlay illustrate the point.
Here we see the impact on regional house prices or regional unemployment of
house shocks or supply shocks. In Figure \ref{FigureHP2HSFin} we see that as the location
quotient for finance and insurance establishments increases, the response of
house prices to a house shock tends to decrease. Figure \ref{FigureHP2HSRE} indicates by contrast that as the MSA exhibits greater
concentration of real estate and property management establishments, the
response of house prices to a house shock tends to increase. Finally, Figure \ref{FigureUE2SSConst} reveals that as the
location quotient for construction industry establishments increases, the fourth
quarter response of unemployment to supply shocks increases.

\begin{figure}[htbp]
 \centering
 \includegraphics[width=5.5in,keepaspectratio=true]{./UE2SSConst.png}
  \caption{Impact of construction on Q4 response to supply shocks}
 \label{FigureUE2SSConst}
\end{figure}


\begin{table}[htbp]
\begin{center}
\begin{tabular}{|ll r@{}l r@{}l r@{}l l|}
\hline
 & \multicolumn{ 2}{r}{\textbf{Impacts on $Y$}} & & \multicolumn{ 2}{r}{\textbf{Impacts on $HPI$} } & \multicolumn{ 2}{r}{\textbf{Impacts on $UE$} } &  \em \\ 
 & \textbf{Industry}         & \textbf{$\beta$} & & \textbf{$\beta$}                             &  & \textbf{$\beta$} &                             &  \em \\ 
\hline
\textit{Supply} &  Agriculture  & 0.029 & ** & 0.076 & *** & 0.008 &  & \\ 
\textit{Shock} &  Mining  & 0.044 & *** & -0.005 &  & -0.001 &  & \\ 
 &  Utilities  & -0.005 &  & -0.381 & *** & 0.115 & *** & \\ 
 &  Construction  & 0.067 &  & -0.03 &  & 0.148 & *** & \\ 
 &  Manufact  & 0.079 &  & -0.234 & ** & -0.137 & *** & \\ 
 &  Wholesale  & 0.192 & * & 0.144 &  & -0.243 & *** & \\ 
 &  Retail  & -0.257 & ** & -1.434 & *** & 0.15 & *** & \\ 
 &  Transport  & -0.118 & * & -0.473 & *** & 0.092 & *** & \\ 
 &  Information  & -0.181 &  & -0.12 &  & 0.106 & ** & \\ 
 &  Fin/Ins  & -0.204 &  & -1.078 & *** & 0.01 &  & \\ 
 &  RealEstate  & 0.156 &  & 0.302 & * & 0.111 & ** & \\ 
 &  ProfServ  & 0.064 &  & 0.508 & *** & -0.028 &  & \\ 
 &  Management  & -0.108 & ** & 0.11 &  & -0.028 &  & \\ 
 &  AdminServ  & -0.056 &  & 0.663 & *** & -0.009 &  & \\ 
 &  Education  & -0.151 &  & 0.862 & *** & 0.048 &  & \\ 
 &  Health  & -0.341 & *** & -0.685 & *** & 0.089 & ** & \\ 
 &  Arts/Cult  & -0.112 &  & 0.018 &  & 0.049 &  & \\ 
 &  Hospitality  & -0.307 & ** & -1.012 & *** & 0.149 & *** & \\ 
 &  OtherServ  & 0.045 &  & 0.435 & *** & -0.061 & *** & \\ 
 & \multicolumn{7}{c}{\tiny{*** - significant at 1\%, ** - significant at 5\%, * - significant at 10\% }} & \em \\ 
\hline
\end{tabular}
\end{center}
\caption{Impact of industry structure on response to supply shock}
\label{LQImpactOnSS}
\end{table}

\begin{table}[htbp]
\begin{center}
\begin{tabular}{|ll r@{}l r@{}l r@{}l l|}
\hline
 & \multicolumn{ 2}{r}{\textbf{Impacts on $Y$}} & & \multicolumn{ 2}{r}{\textbf{Impacts on $HPI$} } & \multicolumn{ 2}{r}{\textbf{Impacts on $UE$} } &  \em \\ 
 & \textbf{Industry}         & \textbf{$\beta$} & & \textbf{$\beta$}                             &  & \textbf{$\beta$} &                             &  \em \\ 
\hline
\textit{House} & Agriculture & -0.012 &  & 0.162 & *** & 0.001 &  & \\ 
\textit{Shock} & Mining & -0.005 &  & -0.015 &  & 0.001 &  & \\ 
 & Utilities & 0.029 &  & -0.445 & *** & -0.026 &  & \\ 
 & Construction & 0.154 &  & -0.155 &  & -0.069 & ** & \\ 
 & Manufact & -0.313 & *** & -0.952 & *** & 0.041 & ** & \\ 
 & Wholesale & -0.276 & ** & -1.459 & *** & 0.049 &  & \\ 
 & Retail & -0.072 &  & -2.34 & *** & -0.12 & *** & \\ 
 & Transport & -0.052 &  & -0.732 & *** & -0.038 & * & \\ 
 & Information & -0.075 &  & -0.594 & *** & 0.064 & * & \\ 
 & Fin/Ins & -0.37 & *** & -2.772 & *** & 0.015 &  & \\ 
 & RealEstate & 0.362 & *** & 0.498 & ** & -0.048 &  & \\ 
 & ProfServ & 0.069 &  & 0.191 &  & 0.027 &  & \\ 
 & Management & -0.03 &  & -0.199 & ** & 0.037 & ** & \\ 
 & AdminServ & 0.044 &  & -0.37 & * & 0.047 &  & \\ 
 & Education & -0.231 &  & 0.208 &  & 0.097 & * & \\ 
 & Health & 0.039 &  & -1.025 & *** & 0.01 &  & \\ 
 & Arts/Cult & -0.014 &  & -0.109 &  & 0.029 &  & \\ 
 & Hospitality & 0.071 &  & -1.321 & *** & -0.032 &  & \\ 
 & OtherServ & 0.063 &  & 1.098 & *** & 0.017 &  & \\ 
 & \multicolumn{7}{c}{\tiny{*** - significant at 1\%, ** - significant at 5\%, * - significant at 10\% }} & \em \\ 
\hline
\end{tabular}
\end{center}
\caption{Impact of industry structure on response to housing market shock}
\label{LQImpactOnHS}
\end{table}



\begin{table}[htbp]
\begin{center}
\begin{tabular}{|ll r@{}l r@{}l r@{}l l|}
\hline
 & \multicolumn{ 2}{r}{\textbf{Impacts on $Y$}} & & \multicolumn{ 2}{r}{\textbf{Impacts on $HPI$} } & \multicolumn{ 2}{r}{\textbf{Impacts on $UE$} } &  \em \\ 
 & \textbf{Industry}         & \textbf{$\beta$} & & \textbf{$\beta$}                             &  & \textbf{$\beta$} &                             &  \em \\ 
\hline
\textit{Other} &  Agriculture  & -0.008 &  & -0.011 &  & 0.017 & *** & \\ 
\textit{Shock} &  Mining  & 0.004 &  & 0.019 & *** & 0.01 & *** & \\ 
 &  Utilities  & -0.011 &  & 0.087 & ** & 0.022 &  & \\ 
 &  Construction  & 0.027 &  & 0.333 & *** & -0.111 & *** & \\ 
 &  Manufact  & 0.012 &  & 0.104 & ** & -0.018 &  & \\ 
 &  Wholesale  & 0.131 & ** & 0.165 & ** & -0.088 & *** & \\ 
 &  Retail  & -0.157 & ** & 0.315 & *** & -0.009 &  & \\ 
 &  Transport  & -0.065 & ** & 0.162 & *** & -0.008 &  & \\ 
 &  Information  & 0.212 & *** & 0.014 &  & -0.238 & *** & \\ 
 &  Fin/Ins  & 0.126 & ** & 0.34 & *** & -0.17 & *** & \\ 
 &  RealEstate  & 0.013 &  & 0.2 & ** & -0.072 & ** & \\ 
 &  ProfServ  & 0.075 &  & -0.041 &  & -0.124 & *** & \\ 
 &  Management  & 0.06 & ** & -0.014 &  & -0.05 & *** & \\ 
 &  AdminServ  & 0.09 &  & 0.118 &  & -0.183 & *** & \\ 
 &  Education  & 0.157 & ** & -0.243 & ** & -0.234 & *** & \\ 
 &  Health  & -0.052 &  & 0.075 &  & -0.042 &  & \\ 
 &  Arts/Cult  & 0.032 &  & 0.001 &  & -0.08 & *** & \\ 
 &  Hospitality  & -0.02 &  & 0.063 &  & -0.103 & *** & \\ 
 &  OtherServ  & 0.001 &  & -0.192 & *** & 0.074 & *** & \\ 
 & \multicolumn{7}{c}{\tiny{*** - significant at 1\%, ** - significant at 5\%, * - significant at 10\% }} & \em \\ 
\hline
\end{tabular}
\end{center}
\caption{Impact of industry structure on response to other shock}
\label{LQImpactOnOS}
\end{table}

We can of course move beyond this bivariate analysis and examine the
relationship of location quotients in all of the industries to output, house
price, and unemployment responses to supply, house and other shocks. The results
are summarized in Tables \ref{LQImpactOnSS}, \ref{LQImpactOnHS} and \ref{LQImpactOnOS} .

\section{Interpretation and comments}

Our analysis has presented the implications of a structured panel VAR approach
to understanding house price and output dynamics in US metropolitan areas. We
use quarterly data from all 384 MSAs covering a period that for some cities
extends for 32 years back to the beginning of 1978. We began by observing that
contrary to the impression that might be formed from literature that deals with
``the US housing bubble'' the actual experience of us cities is highly varied.
Some have experienced tremendous increases in house prices followed by
spectacular declines during 2007-2009. Others experienced very modest increases
and have seen only modest declines or even growth in prices during the ``great
recession.'' This pattern of difference in house price dynamics appears to have a
clear regional structure, and its underlying causes are interesting.

Our analysis has established that there are clear differences between cities in
coastal states and cities in non-coastal states. These differences can be
characterized as:

\begin{enumerate}
 \item While steady-state impact on output from house shocks is zero in both
regions, the dynamic path of adjustment is different, with more cities
exhibiting short run positive output response to a house shock in the coastal
region than in the non-coastal region.
\item House prices in the coastal region are generally more responsive to both
supply shocks and house shocks. While the dynamic patterns of house price
response are similar, the magnitude of response is 1.5 to 2 times larger in the
coastal region than in the non-coastal region.
\end{enumerate}

In addition, our analysis has revealed general support in both regions for labor
market linkages with housing markets. A house shock that increases house prices
reduces unemployment in the majority of cities. This does not appear to be a
transitory phenomenon but remains evident five years after the shock has
occurred. This provides general support for the so-called Oswald hypothesis in
US cities.

Finally, we have shown that the short run responsiveness to shocks is dependent
on the industrial structure of the urban area. House price responses and
unemployment responses, in particular, seem to depend significantly on
industrial concentration.

The analysis presented here leaves many questions unanswered. What are the
specific differences between coastal and non-coastal regions that might explain
the clear differences in dynamics that exist? Are there demographic, regulatory,
or other differences that explain why a house shock in the coastal region
generates a much larger house price response? Finally, what structural or other
MSA characteristics influence the strength of linkages between housing and labor
markets? Can we identify the features that determine whether a house shock in a
city is likely to induce a steady-state drop in unemployment?

These and others are questions with important policy implications. It is clear
that the structured panel VAR technique applied in this analysis can be an
important tool for finding the answers.


\pagebreak 
\newpage 


\bibliographystyle{agsm} 
\bibliography{PedroniAndSheppard}

\pagebreak 
\newpage 

\section{Appendix}

\begin{figure}[htb]
 \centering
 \includegraphics[width=7in,keepaspectratio=true]{./Appendix1.png}
 % Appendex1.png: 5100x6600 pixel, 72dpi, 179.92x232.83 cm, bb=0 0 5100 6600
\label{Appendix1}
\end{figure}

\begin{figure}[htb]
 \centering
 \includegraphics[width=7in,keepaspectratio=true]{./Appendix2.png}
 % Appendex1.png: 5100x6600 pixel, 72dpi, 179.92x232.83 cm, bb=0 0 5100 6600
\label{Appendix2}
\end{figure}

\begin{figure}[htb]
 \centering
 \includegraphics[width=7in,keepaspectratio=true]{./Appendix3.png}
 % Appendex1.png: 5100x6600 pixel, 72dpi, 179.92x232.83 cm, bb=0 0 5100 6600
\label{Appendix3}
\end{figure}


\end{document}
