Housing Demand, Regional House Prices and
Consumption
By
Carl J. Liebersohn
B.A., Mathematics and Economics
Amherst College, 2009
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JUNE 2018
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Housing Demand, Regional House Prices and Consumption
By
Carl J. Liebersohn
Submitted to the Sloan School of Management on May 11, 2018 in Partial Fulfillment of the
Requirements for the Degree of Master of Science in Management Science Research
ABSTRACT
This paper provides a new explanation for regional variation in the 2000-2012 housing and
consumption boom and bust. Cities with a greater share of growing industries experienced
larger housing demand shocks, larger house price increases from 2001-2006 and greater price
declines from 2006-2011. Consistent with theory, price effects were stronger in housing-
supply inelastic cities. City-level differences in housing demand are also correlated with
supply elasticity. Controlling for industry, I estimate a durables consumption-house price
elasticity of 0.08 from 2001-2006, 60% smaller than estimates without the controls. Post-
2006, the estimated elasticity is 0.19 and housing prices rather than local conditions explain
consumption changes.
Thesis Supervisor: Jonathan A. Parker
Title: Robert C. Merton (1970) Professor of Finance
2
ACKNOWLEDGEMENTS
Special thanks to my committee members, Antoinette Schoar, Jonathan Parker and Jim
Poterba. Thanks also to Xavier Giroud, David Autor, David Berger, Nittai Bergman, Stijn
Claessens, Ricardo Correa, Leonid Kogan, Emi Nakamura, Albert Saiz, Raven Saks, Amir
Sufi, Bill Wheaton, Asaf Bernstein, Kirill Borusyak, Daniel Green, Greg Howard, Peter Hull,
Stephanie Lo, Rachael Meager, and Tejaswi Velayudhan for helpful comments and
conversations. Thanks to the participants of the MIT finance, public finance, macro and
fourth-year lunches, to participants at the Macro Financial Modeling Group Winter 2016
meeting, and to the members of the Federal Reserve Board International Finance Seminar for
helpful comments. It was supported by the Macro Financial Modeling Group Dissertation
Fellowship. This work was completed in part while the author was a Dissertation Fellow at
the Federal Reserve Board; the views contained herein are those of the author and do not
necessarily reflect those of the Board of Governors of the Federal Reserve System, its
members, or its staff. I am solely responsible for any errors.
3
1 Introduction
The close relationship between housing prices, business cycles, and consumption is ex-
tremely well documented (Mian and Sufi, 2014a; Mian et al., 2013; Leamer, 2007; Guren
et al., 2017). This correlation was particularly salient during the Great Recession, which
followed a crash in the housing market. The size of the housing boom and bust preceding
the Great Recession varied across the United States and cities with the most volatile
housing prices had the most volatile consumption as well. Several papers have used cross-
sectional variation in house price changes to quantify and explain the effect of housing
prices on consumption and other regional variables. While it is tempting to interpret
the observed correlation between housing prices and consumption as causal, the factors
driving the regional differences in the size of the boom and bust may also have had a
direct effect on consumption, motivating the use of instrumental variables strategies that
exploit plausibly exogenous variation in the size of the boom and bust (Mian and Sufi,
2014a; Mian et al., 2013). This paper reevaluates the reasons for heterogeneity in housing
prices from 2001-2011. The regional variation in the housing boom can be attributed to
both housing supply differences and to differences in demand which arise from differential
growth in local industries. This differential growth affected both local housing demand
(which affected house prices) and household consumption.
The most widely-used empirical model of the housing market attributes regional dif-
ferences in the size of the housing boom and bust primarily to differences in the elasticity
of housing supply across regions. While elasticity alone partially explains the regional
variation in housing price growth from 2001-2006, this model also gives rise to empirical
puzzles. Regions with a less elastic housing supply did see greater price appreciation, but
they also had more new construction, even though most housing market models predict
that higher prices are due to less new construction. Moreover, housing supply elasticity
explains only a small fraction of the variation in price appreciation in general and under-
4
predicts the size of the price boom in the Southwest (Nathanson and Zwick, 2012). This
paper shows that part of the reason for these puzzles is that demand shocks due to local
industry exposure had important effects on housing price growth.
The United States experienced steady GDP growth in the early 2000s, but there was
substantial variation in this growth across industries. Therefore, differential composition
of industries translated into variation in income growth. Areas that were more exposed
to declining industries, particularly manufacturing, experienced a much smaller housing
boom and bust. Interacting exposure to growing industries with the measure of housing
price elasticity developed by Saiz (2010), I show that housing prices rose most in areas
where both local employment was in growing industries and housing was inelastically
supplied. As a simple proxy for exposure to slower-growing industries, I consider the
fraction of employment in manufacturing in 1998. Using manufacturing employment
share as a proxy for regional housing demand yields estimates which are consistent with
a simple theoretical framework.
Furthermore, industry exposure is highly correlated with housing supply elasticity,
implying that estimates which do not control for heterogeneous demand they may suffer
from omitted variables bias. Accounting for local industry exposure partially resolves the
housing market puzzles. Areas with larger housing demand shocks had more construction
from 2001-2011 and, when controlling for these shocks, housing supply elasticity no longer
has a statistically significant negative effect on new construction. Moreover, the addition
of industry controls reduces the unexplained "excess" price growth in Southwestern ci-
ties by 0.09 log points - from 0.26 to 0.17 - from 2001-2006 in preferred specifications
(although it does little to explain the relatively larger bust in these areas). Including
industry exposure also improves the R 2 of regression specifications used to explain prices
appreciation from 2001-2006.
A flourishing body of empirical literature uses variation in housing prices over this
period to study how housing prices affect household consumption (Mian et al., 2013;
5
Dynan, 2012; Kaplan et al., 2016). Because regional demand shocks played an important
role in the size of the housing boom and bust, I show that much of the correlation between
housing prices and consumption from 2001-2006 was due to the effects of income growth
on both housing demand and consumption rather than the direct effect of housing prices
on consumption.
I estimate the effect of housing prices on consumption by instrumenting for housing
prices using housing supply elasticity, the method pioneered by Mian and Sufi (2011).
I augment this instrument by controlling for local industry composition. Accounting
for local industry exposure, I find that the estimated effect of housing prices on both
durables and non-durables consumption expenditures from 2001-2006 is reduced by about
65% compared to models which do not include these variables. In several specifications I
cannot statistically reject that the consumption-house price elasticity is zero after adding
industry controls. This is because regions with an elastic housing supply also had more
manufacturing, which both led to relatively lower prices and relatively lower demand for
consumption. Estimates that do not control for manufacturing exposure may conflate
these two effects.
On the other hand, the estimated consumption-housing price elasticity for the 2006-
2011 period is large and statistically distinguishable from zero, even after accounting for
industry exposure. The addition of industry controls either has no effect on or increases
the estimated consumption-house price elasticity. The reason for the difference between
2001-2006 and 2006-2011 estimates is that the sign of the omitted variable bias is positive
from 2001-2006 but negative from 2006-2011. The direct effect of manufacturing exposure
on consumption was negative from 2006-2011, as it was from 2001-2006. At the same
time, the effect of elasticity on housing prices was positive because more elastic areas had
smaller housing busts. Since manufacturing-heavy regions also had an elastic housing
supply, these two effects partially cancel each other out in estimates which do not account
for the correlation between housing supply elasticity and manufacturing. Putting together
6
my results from both time periods, I show that the effect of housing prices on consumption
was small from 2001-2006 but large from 2006-2011 and that this difference is statistically
significant.
A different consumption elasticity with respect to positive and negative house price
shocks is consistent with several existing theories. In the model proposed by Berger et al.
(2015), housing booms change the composition of home-owners by increasing the number
of liquidity-constrained individuals who own homes. Because the typical homeowner is
more liquidity constrained when housing prices start to fall than when they start to
rise, falling prices cause greater changes to consumption than rising prices do. A second
possible reason is that cashflow shocks, rather than changes to the value of home equity,
are important for household default and consumption. The effect of cashflow shocks
on consumption may have been greatest in areas with falling housing prices because
homeowners' monthly mortgage payments were highest in these areas, as these were the
areas with the largest housing booms.
2 Data
The bulk of the estimates are based on panels of state- and city-level statistics described
in this section. Using this panel, I calculate five-year differences of many of the main
variables that comprise the years 2001-2006 (what I call the housing boom) and 2006-
2011 (the bust); results are similar using different years to denote the boom and bust.
My main units of analysis are states and U.S. CBSA / Metropolitan Divisions based on
2013 definitions.
Data on housing prices is from the Federal Housing Finance Administration (FHFA).
The main results of this paper are very similar using county-level housing price measures
from Zillow. Both are aggregated to the CBSA / Metropolitan Division level.
Several empirical results investigate the effects of local shocks on housing demand. As
7
a source of plausibly exogenous variation in local demand, I use two measures of local
exposure to industries which expanded or contracted during the relevant time periods.
The first is a measure of regions' ex ante exposure to industries with large ex post growth.
Measures of this sort are commonly known as "Bartik shocks" (from Bartik, 1991) and are
widely used in the empirical study of regional macro-economics. To create this measure,
I predict income growth for city i by interacting i's exposure to each industry j with
industry j's national payroll growth:
Bartiki = E Exposurei, x Growth
This measure is constructed using 2-digit NAICS industries. I fix the year of exposure
to 1998 - prior to the start of the housing boom - and use growth over 1998-2006 to
create the measure.
A second source of regional demand variation comes from exposure to the manufac-
turing sector. Manufacturing was the largest employer in 1998 (among 2-digit NAICS
industries) and underwent a dramatic decline over the time that coincided with the hou-
sing boom. Much of the variation in the Bartik measure is due to variation in exposure to
manufacturing. Moreover, the relationship between manufacturing exposure and housing
prices is also of interest on its own. Regional labor force exposure data is provided by
County Business Patterns (CBP). I use 1998 as a base year for regional industry expo-
sures. Using 2001 as a base year yields very similar results. I focus on measuring labor
force exposure at the 2-digit NAICS level to ensure that measurements are uniform even
as NAICS definitions changed for more detailed categories.
Exposure to trade shocks provide an alternative source of exogenous demand. Using
a measure from Acemoglu et al. (2014), I find similar but noisier results.
To quantify regional elasticity of housing supply, I use the measure created by Saiz
(2010). This measure was downloaded from Albert Saiz's web site. Saiz (2010) provides se-
8
veral version of the elasticity measure, including measures of legal barriers to construction,
land unavailability, and population size, as well as a composite index that combines all
three. Following the existing literature on consumption, I study both the overall elasti-
city measure and the land unavailability measure, which I convert to 1-unavailability so
that a greater value for either measure corresponds to a more elastic area. Because the
unavailable land measure is less likely to be time-variant than the composite elasticity
measure, previous research has preferred this as an instrument for housing prices during
the boom and bust, as its time invariance may make it less affected by the factors that
directly affected housing prices. Therefore, most of the findings in this paper will use the
land unavailability measure as well.1
County-level housing unit counts are taken from Intercensal Estimates. Housing con-
struction is measured as the log change in total number of housing units, according to
intercensal estimates from the U.S. Census Bureau. Calculating new construction as the
number of permitted units (using HUD data) divided by existing units yields similar
results. Cities with a decline in units of above 0.05 log points are not included.
As additional control variables, I measure regional demographic features using largely
data collected from the 2000 Decennial Census. These demographic controls are per
capita income, the fraction of households with a mortgage, median age, and the fraction
of houses that are owner-occupied. I also measure the fraction of mortgages that were
denied in each region in 1996, which Mian and Sufi (2009) argue is a measure of latent
mortgage demand. Results are similar when using a variety of other control variables
taken from the housing literature, such as those used in Albouy (2015).
Aggregate consumption data is from BEA regional accounts, which includes household
consumption divided into several categories. I subtract housing and furniture from the
overall measure and durables measure in order to study only non-housing consumption.
'Furthermore, Section 4 will present evidence that the composite elasticity measure is correlated with
unmeasured demand to a greater degree than the unavailable land measure, thus validating this approach.
9
Because regional accounts PCE data is only available at the state level, and not at the
CBSA level, estimates using this data have states as their unit of analysis rather than
CBSAs.
Few sources of CBSA-level consumption data have a long history available. In or-
der to proxy for consumption at the CBSA level for the 2001-2006 period, I use retail
employment measured in County Business Patterns, which Mian and Sufi (2014b) show
is closely linked to consumption. Guren et al. (2017) argue that this is a good con-
sumption proxy. For 2006-2011, I aggregate household-level data from Nielsen Homescan
non-durables expenditure that is collected as part of a nationally representative survey of
retail consumption expenditures.
Table 1 shows summary statistics of the main variables.
3 Theoretrical Framework and Motivating Evidence
The size of the housing boom varied greatly by city. Figure 1 shows the path of housing
prices in the 20 largest U.S. cities beginning in 2000. Cities in the American Southwest
and on the coasts, such as Boston, Las Vegas, San Francisco and New York, experienced
the largest booms. But many cities - those at the bottom of the graph - barely had
housing booms at all. These are mostly Midwestern and Southern cities. The line at the
very bottom of the graph represents Dallas, which had the smallest housing boom among
major U.S. cities.
In this section, I describe the benchmark framework that explains variation in hou-
sing prices accommodating both heterogeneous demand shocks and heterogeneous housing
supply elasticities. I derive predictions of this model and relate these predictions to previ-
ous research on the housing market from 2001-2011. I then discuss how these predictions
10
relate to estimates of the house-price consumption elasticity.
I begin with a model of the housing market similar to models in Saiz (2010), Glaeser et
al. (2008) and Davidoff (2013). Suppose that housing is supplied in a spot market. Cities
vary in their elasticity of housing supply #3.2 The quantity of new housing supplied in
city i is qf. This is increasing in housing price shocks pi, which are mediated by housing
supply elasticity. Supply and demand are:
A log (qi) = #iA log (pi). (1)
A log(q') -A log(pi) + A log(yi). (2)
This yields
A log qi A log(yi) (3)/3 + 1
1A log pi A log(y2 ) (4)#j + 1
A large body of recent literature implicitly adopts the assumption that the housing
boom was caused by a national housing demand shock that was either uniform or inde-
pendent of the supply elasticity. Under this assumption, it is possible to to study the
effect of housing prices on various outcomes using the Saiz (2010) supply elasticity as an
instrument for housing prices. Combining this assumption with Equations 4 and 3 yields
several empirical predictions, some of which have been tested by the existing literature.
The first prediction is that more elastic regions should have smaller price increases.
Previous papers, such as Glaeser et al. (2008), have found that this prediction does hold
empirically. I confirm their findings in Table 2, which estimates an empirical analogue to
2I abstract from housing demand elasticity which in reality may also vary by city. Harmon (1988)
reviews research on demand elasticity and estimates a long-run average demand elasticity of approximately
1, justifying this assumption in my model. In my empirical estimates, I show my results are robust to
the inclusion of control variables associated with demand elasticity.
11
Equation 4 under the assumption that demand shocks are independent of housing supply
elasticity:
A log pi = Elasticityi + ei (5)
Using both measures of housing supply elasticity, I find that inelastic cities had larger
booms and larger busts, and these relationships are statistically significant at the 1%
level. However, the small R2 of these specifications indicates that much of the variation
in housing prices remains unexplained.
The second prediction is that elastic regions should have greater quantity growth than
inelastic regions, at least from 2001-2006.3 Davidoff (2014) and Davidoff (2013) show
that this does not hold over periods lasting several decades. I show this for the 2001-
2006 period in Table 3, which again estimates Equation 5, but with quantities (number of
housing units) as the dependent variable rather than prices. The overall rate of permitting
in the more-inelastic and more-elastic half of the country is graphed in Figure 4. It is
negative using both measures from 2001-2006, statistically significantly so for the overall
elasticity measure. Given the large and positive price effects of housing supply using both
measures, this is hard to make sense of these results in the uniform shock framework.
Instead, it implies that city-level housing supply elasticities are likely correlated with
local demand shocks. If areas with an inelastic housing supply had large demand shocks,
then they would have had larger price increases but not necessarily smaller construction
booms.
The estimated association between composite elasticity and new units, shown in Co-
lumn 2, is large enough to reject zero effect at the 1% level, whereas the association
between available land and new units is negative but not so large as to reject zero effect
even at the 10% level. This provides some support for the view that the available land
measure is less endogenous than the composite elasticity measure in the sense that it is
3 Glaeser et al. (2008) show that when prices are following a housing bubble, the theoretical association
between prices and elasticity is of ambiguous sign.
12
less correlated with unobserved demand shocks, making it more appropriate to use as an
instrument for housing prices. By contrast, neither elasticity measure has an association
with new construction that is statistically distinguishable from zero from 2006-2011.
Another possible concern with the assumption of independent demand shocks is that
it gives rise to incorrect predictions about housing prices in specific regions of the United
States. The housing boom was especially large in cities located in the Southwestern U.S.
despite the fact that these cities had a high housing supply elasticity. Table 4 estimates
Equation 5 adding an indicator for cities in Nevada, Arizona and New Mexico. In these
cities, housing prices rose 0.26 log points more than what one would expect from their
elasticity alone during the boom and fell by 0.34 log points more than one would expect
during the bust.
These inconsistencies with the "uniform shock" model provide a motivation for in-
corporating city-specific demand shocks. From Equations 3 and 4, one can make three
key predictions about housing demand shocks. First, anything that raises city payrolls
can raise demand for housing in that city, and therefore lead to more construction and
higher prices. Second, these effects are mediated by housing supply elasticity: The effect
on prices should be greater in cities with an inelastic housing supply and the effect on
quantities should be greater in cities with an elastic housing supply. Third, to explain
why construction is unconditionally negatively correlated with housing supply elasticity,
demand shocks should be greater in inelastic areas.
Local demand shocks also matter for estimates of the consumption-house price elas-
ticity. If demand shocks are correlated with housing supply elasticity elasticity, then the
latter may not be a valid instrument for housing prices, as Davidoff (2014) and Davidoff
(2013) argue. The reason is that local economic conditions which affect housing demand
may also directly affect the outcome variables in question. For example, if local income
shocks are correlated with the housing price elasticity, then the negative correlation be-
tween consumption growth and house price growth may be due to income shocks rather
13
than housing prices per se. Adding demand shocks as control variables may therefore
provide new insight into the reasons for the correlation between housing prices and con-
sumption.
The next section will show that exposure to growing industries, and specifically to the
manufacturing sector, satisfies the predictions for local demand shocks and resolves the
puzzles with the housing market model. It also changes the coefficient estimates for the
housing-price consumption elasticity.
4 Empirical Results
The empirical results are divided into three parts. The first part presents estimates of
the effects of industry exposure on housing prices. Compared to models built on the as-
sumption of independent or uniform demand shocks, a model which explicitly accounts for
local demand explains more of the variation in housing prices from 2001-2006. Second, I
revisit the puzzles discussed in the previous section. Accounting for local housing demand
shocks and their correlation with housing supply elasticity partially solves the puzzles. In
the third section, I provide new estimates of the elasticity of consumption with respect
to housing prices. I do this first by using the Saiz elasticity as an instrument for housing
prices and controlling for local demand shocks.
4.1 Housing Prices
The 2001-2006 housing boom was larger in cities with positive predicted demand shocks -
areas with large and positive Bartik shocks or less manufacturing exposure. Moreover, the
effect of demand on prices was greatest in cities where the housing supply was inelastic,
which allows for a rejection of the null hypothesis that housing supply elasticity alone
explains why some areas had larger housing booms and busts than others. Regional de-
mand shocks and their interaction with supply elasticity have greater explanatory power.
14
This is consistent with the theoretical predictions developed in Section 4. Furthermore,
estimates of the effect of supply elasticity on housing prices and other outcomes that do
not control for demand associated with industry exposure suffer from an omitted variables
bias. This is because supply elasticity is highly correlated with industry exposure, and
industry exposure affects housing prices directly through its effects on housing demand.
Visual evidence for the association between available land and manufacturing exposure
is shown in Figure 2. Table 6 formally confirms the strength of this relationship using
several supply elasticity measures and industry exposure, including the manufacturing
exposure measure, the Bartik shock, and import competition as measured by Acemoglu
et al. (2014). These estimates consistently show that areas with more available land -
those that are easier to build in - had great ex ante exposure to the manufacturing sector
than areas that are harder to build in. Furthermore, Table A3 shows that an association
between housing supply elasticity and manufacturing exposure even holds within-states
across-CBSAs, pointing to a strong fundamental (as opposed to merely regional) link
between land availability and industry location. A natural explanation for this link is
that factories require space, and therefore areas with lots of unavailable land have more
of them.
Table Al shows estimates of the effect of industry exposure on local payrolls. Their
strong correlation with regional payrolls bolsters the argument that they are an omitted
variable that directly affects housing demand, as payrolls are the mediating variable that
affects housing prices in the framework introduced in the previous section. This associa-
tion is not surprising; as discussed by Autor et al. (2013) (and many other papers), greater
exposure to the manufacturing sector is associated with negative labor market outcomes
in the early 2000s. Columns 1 and 3 show this for the boom and bust period respectively.
Columns 2 and 4 quantify the effect of the Bartik shock on regional payrolls. As expected,
the signs are positive and statistically significant for the Bartik shock. Furthermore, be-
cause the same industries continued to grow from 2001-2006 as from 2006-2011, the sign
15
of both shocks during both the boom and the bust.
Visual evidence in 3 shows a strong negative relationship between house price growth
and ex ante manufacturing share. CBSAs with more manufacturing workers had lower
house price growth. The evidence in the figure suggests a somewhat nonlinear effect,
whereby manufacturing concentration is less informative for areas with the greatest house
price growth. Table A2 in the Appendix uses the change in exposure to Chinese imports
as a regional demand shock, a measure created in Acemoglu et al. (2014). Although
the estimates in this table are noisier than those using industry shares, the signs and
directions are consistent with demand shocks.
A prediction of the empirical framework is that the interaction between housing elas-
ticity (or available land) and demand shocks should matter for housing prices. Tables 5
and 7 show regression estimates from specifications of the following form:
AY = a + 31Elasticityi + ,32Exposurej + /33E asticityj x Exposure + ej (6)
where Exposure is either the Bartik shock or manufacturing exposure, Y is either prices
or quantities, and the time period is either 2001-2006 or 2006-2011, respectively the boom
and bust.
Figure 3 shows the strong negative relationship between housing prices and manufac-
turing share at the CBSA level. Cities with more manufacturing experienced relatively
smaller housing booms than cities with more manufacturing. The relationship also ap-
pears to be somewhat nonlinear, with there being little effect on housing prices for cities
with a sufficiently low fraction of the workforce in manufacturing.
Columns 1 and 2 of Table 5 show how the interaction between demand shocks and
elasticity affected housing prices from 2001-2006. In line with theoretical predictions,
regions with more exposure to growing industries - meaning either a higher Bartik shock
or a lower manufacturing share - had the greatest growth in housing prices. However, in
16
areas with more available land, the effect of growing industries on prices was dampened. 4
The signs of the main effects are flipped in Columns 3 and 4 compared to Columns 1
and 2, meaning that areas with greater exposure to industries that grew from 2001-2006
had housing prices that fell in relative terms, rather than rose.
The model in Section 4 does not explain why positive income shocks from 2001-2006
should be associated with falling housing prices from 2006-2011. This result is at odds
with a model in which housing demand comes only from local income shocks. Rather, it
is consistent with the mean reversion that is a general feature of housing markets which
predates the most recent boom-bust cycle. Glaeser and Gyourko (2006) explain mean
reversion in the housing market with a model of slow-moving construction combined with
mean-reverting fundamental shocks. Other authors, such as Shiller (2015), instead argue
that this pattern is essentially behavioral, reflecting extrapolation by investors.
4.2 Housing Market Puzzles
I now show how accounting for housing demand shocks affects the empirical puzzles
discussed in Section 3. Both the housing supply puzzle and the Southwest Cities puzzle
are reduced, but not eliminated, after incorporating industry shares as demand shocks.
This means that while industry exposure is likely an important and typically unmeasured
factor affecting housing demand, it may not be the only one.
The first puzzle is that areas with an inelastic housing supply had more new con-
struction from 2001-2011 than areas with an elastic housing supply. Table 7 jointly
considers the effect of elasticity and industry exposure on construction. The main finding
is that larger demand shocks led to more construction, particularly so from 2001-2006, the
time period that is puzzling from the perspective of elasticity alone. Furthermore, theory
predicts that the interaction between elasticity and demand shocks should be positive, i.e.,
4 A similar result holds when using the composite supply elasticity measure rather than unavailable
land.
17
in areas with a more elastic housing supply, positive shocks increase housing quantities
by more than in areas with a negative housing supply. Estimates on the interaction terms
in Table 7 show a negative affect, however, albiet one that is never precisely estimated
enough to reject a zero (or even positive interaction) at conventional levels of statistical
significance. This is either because the data is simply noisy, or because there are other
omitted variables which continue to affect housing demand even as they are correlated
with supply elasticity. The disappearance of the large and highly significant negative
effect shown in Table 3 is therefore only a partial resolution of this puzzle.
Table 8 shows that the "excessive" growth in Southwestern cities is partly explained
by demand shock associated with the manufacturing sector. Controlling for demand
shocks, the estimated coefficient on the Southwest cities indicator falls to about 0.19
log points from 2001-2006 and to 0.33 log points from 2006-2011 (significant at 1%),
shown in Columns 1 and 2. Additional demographic controls (Columns 3-4 reduce the
estimated effect further), and controls for industry shares chosen using a doubly-robust
post-LASSO procedure yields smaller results still.5 Nonetheless, the estimated coefficient
on the Southwest dummy are still highly statistically significant, indicating that this
puzzle is also only partially solved.
This section has explored the relationship between industry exposure, housing supply
elasticity and housing prices. I have shown that the incorporation of manufacturing
exposure as a demand shock partially resolves both puzzles.
5To do this, first the independent variable (prices) and the treatment variable (the Southwest) indicator
are independently LASSO-regressed on the possible controls, which include all two-digit NAICS industry
shares and the demographic controls. The LASSO implementation used here is the lassoregress stata
package by Wilbur Townsend, which uses cross-validation to select parameters. The final OLS regression
then controls for the union of the control variables selected in both LASSO procedures. The algorithm
is described in further detail in Chernozhukov et al. (2015).
18
4.3 Consumption-House Price Elasticity Estimates
The dramatic rise and fall in housing prices from 2001-2011 has been identified as a
key reason for the equally dramatic rise and fall in household consumption. Theoretical
research in this area has relied on the robust and large relationship between housing
prices and consumption documented by recent empirical work for both periods of rising
and falling prices.6 However, the estimated consumption-house price elasticity changes
when I control for local industry exposure.
Table 9 shows estimates of following OLS equation:
A Log(C) = fALog(P) + ci
where i indexes MSAs and time-differences are taken over 2001-2006 and 2006-2011. I
follow Campbell and Cocco (2007) and Attanasio et al. (2002) in estimating the consump-
tion response to housing prices, rather than housing wealth which Mian et al. (2013) and
Kaplan et al. (2016) use.7
The estimates show a positive correlation between housing prices and the several
categories of consumption during both 2001-2006 and 2006-2011. This table uses a state-
level measure of aggregate consumption from PCE regional accounts as well as CBSA-level
6 What follows is an abbreviated list of the empirical papers in this area which are most closely related
to mine: Mian et al. (2013) show that regions of the U.S. where household debt/income ratios increased
the most between 2002 and 2006 had the greatest declines in housing consumption between 2006 and
2009; this holds even when housing supply elasticity as an instrument for debt/income ratios. Mian
and Sufi (2014a) use a similar technique to study household borrowing and spending from 2002 to 2006.
Kaplan et al. (2016) replicate the findings of Mian et al. (2013) using store-level scanner data. Dynan
(2012) uses household-level data from the Panel Study of Income Dynamics to show that consumption
fell most among highly-leveraged households between 2007 and 2009 even when controlling for overall
changes in household wealth. Closely related to these papers is the finding of Mian and Sufi (2014b)
that falling housing prices affected local regional employment in the non-tradables sector, which is closely
associated with local consumption. This list neglects both relevant empirical research from before the
Great Recession and studies which estimate the effects of housing prices on outcomes other than household
consumption.
7 Berger et al. (2015) convert the housing net worth elasticity estimated by Mian et al. (2013) to a
house price elasticity by multiplying it by the mean ration of housing wealth to total wealth, 0.25-0.33.
Reversing this calculation would convert the house price elasticities estimated here to housing net worth
elasticities.
19
estimates that use retail employment as a proxy for consumption, following Guren et al.
(2017), as well as non-durables consumption from Nielsen Homescan data for the years
2006-2011 only.
A larger set of estimates from NIPA state-level regional accounts data is shown in Ta-
bles 9 of the Appendix, for the years 2001-2006. Consistent with theoretical predictions,
durables consumption has a greater elasticity than non-durables.8 The association bet-
ween housing prices and food and beverages consumption is similar to overall nondurables
consumption.9
The OLS elasticity estimates are comparable to those found in Kaplan et al., 2016.
Table 4 in Kaplan et al., 2016 shows an estimated elasticity of consumption with respect
ot housing prices of 0.12-0.18 over the 2006-2009 period, i.e. the bust; for 2006-2011, I
estimate elasticities of 0.12 (for CBSA retail employment, which proxies for consumption),
0.16 (for NIPA goods less housing) and 0.19 (in Nielsen data). All of the coefficient
estimates are all statistically significant at the 1% level.
Next, I follow Mian and Sufi (2011), Mian et al. (2013) and others in instrumenting
for housing prices using the Saiz (2010) land availability measure. As the previous section
showed, a higher housing price elasticity was associated with a smaller boom and bust.
This IV procedure is a first pass towards purging local demand shocks from estimates
of the consumption-house price elasticity. Controlling for industry mix or manufacturing
exposure as well alleviates many of the concerns with demand shocks that are correlated
with the housing supply elasticity, as shown in the previous section. The specification
8I subtract PCE housing consumption from PCE durables consumption to measure non-housing du-
rables.
9Changes in consumption expenditures are likely driven by both increased real consumption and higher
prices. Stroebel and Vavra (2014) and Kaplan et al. (2016) show rising goods prices are associated with
rising housing costs and that approximately 20% of the increased expenditure is due to rising prices.
Unfortunately, no regional PCE deflators exist at the state level for the time periods under consideration
here, so I do not separately calculate the effects on real consumption and its price.
20
therefore includes controls for these variables: The second state is
/Wonsumption = 01A Housing Prices + / 2DemandShock (7)
and the first stage is
A HousingPrices = 1SaizEIlasticity + 02DemandShock (8)
Results from specifications without any controls are shown in Table 10. Specifications
using additional measures of consumption are in Tables A4 (for 2001-2006) and A5 (for
2006-2011). Consistent with previous papers, I consistently find an effect between 0.1
and 0.25, with the larger estimates for durables and smaller estimates for non-durables.
State-level estimates are more noisily measured but otherwise consistent with CBSA-level
estimates.
Comparing the OLS to the 2SLS estimates, it is clear that the instrumented estimates
are in many cases larger than the OLS estimates. This is true as well in, for example,
Kaplan et al. (2016) and Mian et al. (2013). The reason this is surprising is that the likely
direction of bias in the OLS estimates is upwards: Any unmeasured income or wealth
shock would raise demand for both non-housing consumption and for housing, inducing
a spurious positive correlation between housing prices and other kinds of consumption.
Therefore, one would expect that a valid instrumental variables strategy should reduce
the estimated association between housing prices and consumption.
This fact, combined with the positive observed elasticity between elasticity and industry-
exposure, motivate the addition of industry measures as control variables. Simply adding
the Bartik shock as a control variable, in Table 11, reduces the estimated effect of housing
prices on retail employment from 0.14 to 0.05 from 2001-2006. State-level measures of
goods spending fall from 0.125 to 0.11. Using manufacturing share as a control, and using
21
LASSO to choose industry shares as controls, yields similar results (shown in Tables A6
and A7).
The effects are reversed during the bust period. Because greater industry growth is
associated with greater declines in housing prices during the bust period, the sign of the
omitted variables bias is reversed. Adding the Bartik shock as a control variable actually
increases coefficient estimates from 2006-2011; the estimated effect on retail spending rises
from 0.14 to 0.20 during this time period, and the state-level measure of goods is hardly
affected.
Combining the results from both time periods, I can reject that the elasticity of retail
employment with respect to housing prices is the same from 2001-2006 and 2006-2011.
This asymmetry is not apparent without controls, because the direction of bias varies
between time periods.
The findings in this section have two main implications for empirical research on
the effects of housing prices on consumption and other economic outcomes. The first
implication is that there are good reasons to believe that regional demand shocks are
correlated with the elasticity of housing supply. Therefore, the empirical research should
either control for relevant demand shocks - such as ex ante industry shares - or use
methodologies that do not rely on regional variation alone. In this case, controlling for
industry shocks decreased the estimated effect of housing prices on consumption from
2001-2006 but decreased it from 2006-2011. For other variables, the sign and magnitude
of the bias may be different.
The second implication is that variation in exposure to underlying demand shocks
may be important for understanding heterogeneous outcomes during the boom and bust
periods. This is especially salient when studying the interaction of housing prices and
individual characteristics, such as home-ownership or wealth. If variation in these other
characteristics is correlated with variation in exposure to demand shocks, then it may
be the demand shocks rather than housing prices that explain economic behavior. For
22
example, if manufacturing workers are poorer or more likely to be renters than non-
manufacturing workers, then the effects of manufacturing decline may look like differential
exposure to the housing boom, when in fact it is entirely because of differential industry
exposure.
5 Conclusion
This paper has examined the effects of housing demand shocks on housing prices and
consumption during the boom and bust. My findings indicate that demand shocks due
to industry exposure explain much of the correlation between housing prices, income and
consumption from the years 2001-2011.
From 2001-2006, regions with employment in high-growth industries had greater hou-
sing demand. Housing prices rose by more in these regions, and most of all in regions
where the housing supply was inelastic. Controlling for local demand shocks partially
solves puzzles that arose under previous work which did not control for housing demand
shocks. In particular, an inelastic housing supply is associated with greater construction
over this time because inelastic housing supply is associated with positive job growth.
Controlling for demand shocks also increases the explained fraction of housing price va-
riance and reduces the "excess" housing price growth that occurred in the American
Southwest. Finally, controlling for demand shocks reduces the estimated elasticity of
durable and non-durable consumption with respect to housing prices by about 65%.
From 2006-2011, exposure to high-growth industries was associated with rising payrolls
and rents but falling housing prices. This is consistent with mean reversion following the
2001-2006 boom rather than direct changes in housing demand. Controlling for demand
shocks does not decrease the estimated elasticity of consumption with respect to housing
prices.
23
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25
Table 1: Summary Statistics
CBSA State
2001-2006 2006-2011 2001-2006 2006-2011
Mean St. Dev Mean St. Dev Mean St. Dev Mean St. Dev
Bartik Shock 1.07 0.03 1.07 0.03 1.07 0.02 1.07 0.02
Manuf. Share 0.16 0.08 0.16 0.08 0.15 0.06 0.15 0.06
Elasticity 2.55 1.44 2.55 1.44 2.24 1.03 2.24 1.03
House Price 0.32 0.2 -0.17 0.23 0.37 0.19 -0.23 0.21
Payrolls 0.19 0.1 0.06 0.08 0.21 0.07 0.09 0.07
Permits 0.08 0.05 0.03 0.02 0.07 0.04 0.03 0.01
Retail Emp. 0.06 0.09 -0.07 0.07 0.06 0.06 -0.07 0.04
1-Unavail Land 0.74 0.21 0.74 0.21 0.76 0.15 0.76 0.15
Durables 2947 372 2844 421
Goods 10124 890 11055 1060
Log(Durables) 0.15 0.07 -0.04 0.08
Log(Goods) 0.21 0.05 0.09 0.05
Log(Total C) 0.24 0.03 0.11 0.04
Total C 24897 3343 27787 3932
Count 265 265 51 51
Table 2: House Prices and Elasticity
(1) (2) (3) (4)
CBSA Price CBSA Price State Price State Price
2001-2006 2006-2011 2001-2006 2006-2011
Avail. Land -0.523*** 0.467*** -0.535*** 0.560*
(0.0492) (0.0613) (0.151) (0.218)
Constant 0.704*** -0.513*** 0.770*** -0.656***
(0.0403) (0.0498) (0.116) (0.174)
R2 0.293 0.182 0.207 0.166
N 265 265 48 48
Sources: Saiz (2010), FHFA.
Retail measures retail employment; Goods measure goods consumption.
* p < 0.05, ** p < 0.01, *** p < 0.001
26
Table 3: Supply Elasticity and Housing Units
(1)
Units
2001-2006
(2)
Units
2001-2006
(3)
Units
2006-2011
(4)
Units
2006-2011
Avail. Land -0.0186 -0.00609
(0.0164) (0.0116)
Elasticity -0.00704*** -0.00250
(0.00195) (0.00133)
Constant 0.0949*** 0.0990*** 0.0424*** 0.0442***
(0.0133) (0.00660) (0.00933) (0.00411)
R2 0.00561 0.0384 0.00176 0.0138
N 263 263 265 265
Sources: Saiz (2010), HUD.
Units measured as log change in housing units.
* p < 0.05, ** p < 0.01, *** p < 0.001
27
Table 4: Supply Elasticity and Prices in Southwest States
Avail. Land
Southwest
(1)
Prices
2001-2006
-0.525***
(0.0486)
0.258***
(0.0471)
(3)
Prices
2001-2006
-0.435***
(0.0501)
0.226***
(0.0594)
(4)
Prices
2006-2011
0.387***
(0.0587)
-0.302***
(0.0801)
Income/Cap
Med. Age
Frac Homeowner
Frac Mortgager
Mtg Denials 96
Constant
N
-0.0000167**
(0.00000619)
0.0244***
(0.00572)
-1.158***
(0.204)
0.959***
(0.256)
-0.0622*
(0.0297)
0.697***
(0.0402)
0.340
265
-0.505***
(0.0490)
0.246
265
0.279
(0.260)
0.454
264
0.0000188*
(0.00000814)
-0.0226**
(0.00718)
0.470
(0.266)
-1.606***
(0.337)
0.0678
(0.0350)
0.699*
(0.308)
0.378
264
Sources: Saiz (2010), FHFA.
Southwest indicator equals 1 for Nevada, New Mexico and Arizona.
* p < 0.05, ** p < 0.01, *** P < 0.001
28
(2)
Prices
2006-2011
0.469***
(0.0602)
-0.341***
(0.0883)
Table 5: Housing Prices, Demand Shocks, and Elasticity
(1)
House Prices
2001-2006
3.564*
(1.466)
Avail. Land
(2)
House Prices
2001-2006
(3)
House Prices
2006-2011
-8.640***
(2.027)
(4)
House Prices
2006-2011
Bartik Shock
Avail. Land x Bartik Shock
Elasticity
Elasticity x Bartik Shock
Constant
N
4.760***
(1.033)
-3.548**
(1.313)
-4.694***
(1.158)
0.396
264
Sources: Saiz (2010), CBP, HUD.
* p < 0.05, ** p < 0.01, *** p < 0.001
29
4.425***
(0.546)
-7.032***
(1.471)
8.079***
(1.816)
0.886***
(0.191)
-0.853***
(0.176)
-3.657***
(0.760)
-1.107***
(0.231)
1.067***
(0.214)
3.709***
(0.835)
0.279
264
-4.448***
(0.603)
0.397
264
7.412***
(1.645)
0.239
264
Table 6: Housing Elasticity and Industry Exposure
(1)
Avail. Land
0.639***
(0.150)
(2)
Avail. Land
(3)
Avail. Land
Bartik Shock
Import Exp.
Constant
R2
N
0.639***
(0.0310)
0.0562
264
-2.002***
(0.363)
2.961***
(0.399)
0.0979
264
Sources: Saiz (2010), CBP.
* p < 0.05, ** p < 0.01, *** p < 0.001
30
Manuf.
(4)
Elasticity
5.212***
(1.214)
(5)
Elasticity
(6)
Elasticity
-13.34***
(2.889)
0.0131*
(0.00664)
0.715***
(0.0210)
0.0110
264
1.705***
(0.199)
0.0792
264
0.0539
(0.0469)
2.438***
(0.143)
0.00394
264
17.33***
(3.221)
0.0919
264
Table 7: Housing Units and Elasticity, Controlling for Industry Bartik
(1) (2) (3) (4)
Units Units Units Units
2001-2006 2001-2006 2006-2011 2006-2011
Avail. Land 1.082 0.0737
(0.569) (0.377)
Bartik Shock 1.304** 0.542* 0.399 0.225
(0.442) (0.223) (0.284) (0.126)
Avail. Land x Bartik Shock -0.958 -0.0560
(0.510) (0.341)
Elasticity 0.00751 -0.0453
(0.0618) (0.0353)
Elasticity x Bartik Shock -0.0101 0.0410
(0.0566) (0.0325)
Constant -1.381** -0.510* -0.413 -0.211
(0.494) (0.247) (0.314) (0.138)
R 2  0.139 0.134 0.132 0.132
N 262 262 264 264
Sources: Saiz (2010), CBP, HUD.
* p < 0.05, ** p < 0.01, *** p < 0.001
31
Table 8: Prices in Southwest States and Elasticity, Controlling for Industry Shares
Southwest
(1)
Prices
01-06
0. 188***
(0.0496)
Avail. Land -0.448***
(0.0508)
Controls Ind
R2 0.421
N 264
Sources: Saiz (2010), FHFA,
(2)
Prices
06-11
-0.327***
(0.0895)
(3)
Prices
01-06
0. 166**
(0.0592)
0.467*** -0.386***
(0.0636) (0.0520)
Ind Ind, Demo
0.264 0.521
264 264
CBP, US Census Bureau.
(4)
Prices
06-11
-0.293***
(0.0793)
0.400***
(0.0626)
Ind, Demo
0.398
264
Industry controls are Bartik shock and manufacturing share; Demographic controls from 200 Census.
LASSO-chosen controls are chosen using double-robust LASSO from industry controls and shares of 2-digit
NAICS industries using doubly-robust procedure as described in the text. Southwest indicator equals 1 for
Nevada, New Mexico and Arizona.
* p < 0.05, ** p < 0.01, *** P < 0.001
Table 9: House Prices and Consumption: OLS
House Prices
(1)
CBSA
Retail
01-06
0.245***
(0.0211)
(2)
CBSA
Retail
06-11
0.115***
(0.0170)
Constant -0.0204* -0.0520***
(0.00822) (0.00476)
R2 0.326 0.165
N 264 265
Sources: NIPA regional accounts, FHFA, CBP.
Retail measures retail employment; Goods measure
* p < 0.05, ** p < 0.01, *** p < 0.001
(3)
CBSA
Nielsen
06-11
0. 193***
(0.0476)
0.116***
(0.0166)
0.0414
265
(4)
State
Goods
01-06
0.113***
(0.0221)
-0.0420***
(0.00634)
0.411
51
(5)
State
Goods
06-11
0.160***
(0.0285)
0. 124***
(0.00933)
0.453
51
goods consumption excluding housing.
32
(5)
Prices
01-06
0.113*
(0.0503)
-0.238***
(0.0473)
LASSO
0.619
264
(6)
Prices
06-11
-0.215*
(0.0886)
0.215***
(0.0594)
LASSO
0.572
258
Table 10: House Prices and Consumption: Typical 2SLS
(1)
CBSA
Retail
01-06
(2)
CBSA
Retail
06-11
(3)
CBSA
Nielsen
06-11
(4)
State
Goods
01-06
(5)
State
Goods
06-11
House Prices 0.141** 0.137*** 0.299 0.125 0.182**
(0.0433) (0.0406) (0.153) (0.0812) (0.0707)
Constant 0.0122 -0.0484*** 0.134*** 0.168*** 0.129***
(0.0140) (0.00800) (0.0289) (0.0307) (0.0175)
R2 0.267 0.159 0.0287 0.204 0.444
N 264 265 265 48 48
Sources: NIPA regional accounts, Saiz (2010), FHFA, CBP.
Goods consumption categories from NIPA exclude housing.
* p < 0.05, ** p < 0.01, *** p < 0.001
Table 11: Consumption and Elasticity, Controlling for Industry Bartik
(1) (2) (3) (4) (5)
CBSA CBSA CBSA State State
Retail Retail Nielsen Goods Goods
01-06 06-11 06-11 01-06 06-11
House Prices 0.0453 0.197*** 0.291 0.108 0.179**
(0.0573) (0.0473) (0.177) (0.0783) (0.0693)
Bartik Shock 1.009*** 0.481*** -0.0882 314.2*** -71.39
(0.226) (0.144) (0.554) (66.20) (59.37)
Constant -1.075*** -0.571*** 0.230 0.0457 0.157***
(0.238) (0.155) (0.602) (0.0269) (0.0359)
R2 0.253 0.150 0.0299 0.424 0.457
N 264 264 264 48 48
Sources: NIPA regional accounts, Saiz (2010), FHFA, CBP, CBP.
Retail measures retail employment and Goods measure total goods
from NIPA excluding housing.
* p < 0.05, ** p < 0.01, *** p < 0.001
33
46 Figures
2d02 2d04 2d06
Year
2008 2d10 2d12
Figure 1: Housing Prices in 20 Largest CBSAs
34
7-.
/
C0LO -
C)oC
00C3-
x
a.
C 0)
-
2d00
) .?C
oSao
C A~ 0
0 5bt 1012 00
12
0s
000 0.0
051'0 1'5
Saiz (2010) Elasticity
Figure 2: Elasticity vs Manufacturing Share
U -
Hw
: 2
0)
0)0
0) 0
CD
0 .6 .80
Hou) Price 0 rwh 2 00120
Figure ~ ~ ~ ~ ~ ~ 0e 0: 00120 0os Prc rwhad19 aufcuigEpomnhr
35
2005 2010
Year
Inelastic Cities Elastic Cities
Total Permitting Post-2000, in Above- and Below-Median Elasticity Half of
36
Co
)
00
00
Figure 4:
Country
2015
A Appendix Tables
Table Al: Payroll Growth and Industry Shares
(1)
Payrolls
2001-2006
(2)
Payrolls
2006-2011
(3)
Payrolls
2001-2006
(4)
Payrolls
2006-2011
Manuf. -0.637*** -0.238**
(0.101) (0.0867)
Bartik Shock 1.476*** 0.444
(0.249) (0.226)
Constant 0.297*** -1.441*** 0.100*** -0.431
(0.0171) (0.276) (0.0151) (0.250)
R2 0.226 0.215 0.0471 0.0290
N 264 264 264 264
Sources: Saiz (2010), FHFA, County Business Patterns.
* p < 0.05, ** p < 0.01, *** p < 0.001
37
Table A2: Housing Prices, Import Exposure, and Elasticity
(1) (2) (3) (4)
House Prices House Prices House Prices House Prices
2001-2006 2001-2006 2006-2011 2006-2011
Avail. Land -0.531*** 0.509***
(0.0935) (0.129)
Import Exp. -0.0401 -0.0533** 0.0519 0.0589**
(0.0356) (0.0201) (0.0480) (0.0226)
Avail. Land x Import Exp. 0.0202 -0.0375
(0.0429) (0.0580)
Elasticity -0.0848*** 0.0954***
(0.0201) (0.0220)
Elasticity x Import Exp. 0.00972 -0.0127
(0.00626) (0.00687)
Constant 0.761*** 0.590*** -0.594*** -0.464***
(0.0750) (0.0553) (0.103) (0.0642)
R2 0.324 0.296 0.205 0.253
N 264 264 264 264
Sources: Saiz (2010), CBP, HUD, Acemoglu et al (2016).
Import exposure measures change in exposure to Chinese imports from 1991-2011.
* p < 0.05, ** p < 0.01, *** p < 0.001
Table A3: Housing Elasticity and Industry Shares, with State FEs
(1) (2) (3) (4)
Avail. Land Avail. Land Elasticity Elasticity
Manuf. 0.348* 4.289***
(0.148) (1.117)
Bartik Shock -1.390*** -10.42***
(0.402) (2.791)
Constant 0.686*** 2.282*** 1.855*** 14.10***
(0.0279) (0.443) (0.188) (3.097)
State FE Yes Yes Yes Yes
R2 0.511 0.527 0.549 0.548
N 264 264 264 264
Sources: Saiz (2010) FHFA, CBP.
* p < 0.05, ** p < 0.01, *** p < 0.001
38
Table A4: House Prices and Consumption: Other Goods, 2001-2006
(1)
Total
(2)
Goods
(3)
Durables
(4)
Nondurables
(5)
Vehicles
(6)
Food and Bev
House Prices 0.104* 0.125 0.241* 0.0749 0.397** 0.0920
(0.0505) (0.0812) (0.117) (0.0713) (0.144) (0.0670)
Constant 0.197*** 0.168*** 0.0608 0.213*** -0.156** 0.141***
(0.0192) (0.0307) (0.0417) (0.0280) (0.0507) (0.0252)
R2 0.340 0.204 0.369 0.0968 0.377 0.205
N 48 48 48 48 48 48
Sources: NIPA regional accounts, Saiz (2010), FHFA.
Goods consumption categories from NIPA exclude housing.
* p < 0.05, ** p < 0.01, *** p < 0.001
Table A5: House Prices and Consumption: Other Goods, 2006-2011
(1) (2) (3) (4) (5) (6)
Total Goods Durables Nondurables Vehicles Food and Bev
House Prices 0.163** 0.182** 0.298** 0.130* 0.553*** 0.137
(0.0606) (0.0707) (0.102) (0.0644) (0.135) (0.0710)
Constant 0.146*** 0.129*** 0.0315 0.163*** 0.0322 0.162***
(0.0145) (0.0175) (0.0254) (0.0163) (0.0353) (0.0161)
R 2  0.537 0.444 0.448 0.329 0.463 0.316
N 48 48 48 48 48 48
Sources: NIPA regional accounts, Saiz (2010), FHFA.
Goods consumption categories from NIPA exclude housing.
* p < 0.05, ** p < 0.01, *** p < 0.001
39
Table A6: Consumption and Elasticity, Controlling for Manufacturing Share
(1) (2) (3) (4) (5)
CBSA CBSA CBSA State State
Retail Retail Nielsen Goods Goods
01-06 06-11 06-11 01-06 06-11
House Prices 0.0736 0.183*** 0.305 0.113 0.172*
(0.0527) (0.0452) (0.174) (0.0948) (0.0776)
Manuf -0.394*** -0.196*** -0.0235 -0.140 0.137
(0.0782) (0.0534) (0.241) (0.238) (0.160)
Constant 0.0975*** -0.00917 0.138* 0.193** 0. 105**
(0.0272) (0.0148) (0.0593) (0.0700) (0.0389)
R 2  0.291 0.171 0.0265 0.231 0.473
N 264 264 264 48 48
Sources: NIPA regional accounts, Saiz (2010), FHFA, CBP, CBP, US Census Bureau.
Retail measures retail employment and Goods measure total goods form NIPA excluding housing.
* p < 0.05, ** p < 0.01, *** p < 0.001
Table A7: Consumption and Elasticity, Controlling for Post-LASSO Industry Shares
(1) (2) (3) (4) (5)
CBSA CBSA CBSA State State
Retail Retail Nielsen Goods Goods
01-06 06-11 06-11 01-06 06-11
House Prices 0.0526 0.291* 0.542 0.0790 0.152
(0.107) (0.144) (0.511) (0.0734) (0.101)
LASSO Ctrl Yes Yes Yes Yes Yes
R 2  0.429 0.161 0.0744 0.766 0.678
N 261 261 261 48 48
Sources: NIPA regional accounts, Saiz (2010), FHFA, CBP, CBP, US Census Bureau.
LASSO-Chosen controls are chosen from industry shares and demographics using doubly-robust
post-LASSO as described in the text.
* p < 0.05, ** p < 0.01, *** p < 0.001
40