Robert G. King

English

Research Papers in Economics

FEDERAL RESERVE BANK OF ST. LOUIS REVIEW JULY/AUGUST 2008 367
Commentary
Robert G. King
MONETARY POLICY
From the standpoint of monetary policy, there
are three key questions. First, was the behavior of
house prices and quantities normal or unusual
over the recent period? Second, did easy money
cause a major portion of the rise in house prices
and thus make house price declines a necessary
outcome when monetary policy tightened? Third,
could a regular response to housing—perhaps via
the type of monetary conditions index discussed
by JS—be desirable in smoothing out overall eco-
nomic activity and housing markets themselves?
House Prices
It is important to stress that the second section
of the JS study, about the extent to which move-
ments in house prices are unusual, can be read
in quite different ways.
JS show that movements in interest rates and
output largely explain variation in house prices
if one uses a level (Bayesian) VAR. In this case,
there are two implications for monetary policy.
First, it seems unnecessary to think about poten-
tially including house prices in the state vector
to which monetary policy should respond, since
house prices appear to be well explained by
interest rates and output. Second, there is no
sense in which there is a puzzle in recent years:
House prices just moved with macroeconomic
conditions in a fairly standard manner. From the
standpoint of modern macroeconomic analysis
W
hen monetary historians look
back at this decade, they will
undoubtedly highlight the major
increases in house prices over
the early part of the decade and the sharp declines
of recent years as posing a major challenge for
monetary policy and central banking.
“House Prices and the Stance of Monetary
Policy” by Marek Jarocin ´ski and Frank Smets (JS)
is a valuable early contribution to the understand-
ing of this episode. It is extremely clear in spelling
out and accomplishing its two major objectives:
a retrospective econometric analysis of the role
of housing markets in recent developments and
a consideration of the potential role of house
prices in a monetary conditions index.
For my purposes in this discussion, there are
three important pieces of evidence provided by
JS. Using conditional forecasting methods, the
second section of their paper shows that there
may be an important component of house price
variation that cannot be accounted for by shifts
in output and interest rates or there may not: The
qualification is necessary because the results of
difference and level VAR specifications differ
importantly. Their third section uses an identified
VAR to suggest that loose monetary policy may
have contributed to the continuing increase in
house prices in 2004 and 2005. Their fourth sec-
tion investigates the effect of identified “housing
demand shocks,” with results that I will discuss
further below.
Robert G. King is a professor of economics at Boston University.
Federal Reserve Bank of St. Louis Review, July/August 2008, 90(4), pp. 367-370.
© 2008, The Federal Reserve Bank of St. Louis. The views expressed in this article are those of the author(s) and do not necessarily reflect the
views of the Federal Reserve System, the Board of Governors, or the regional Federal Reserve Banks. Articles may be reprinted, reproduced,
published, distributed, displayed, and transmitted in their entirety if copyright notice, author name(s), and full citation are included. Abstracts,
synopses, and other derivative works may be made only with prior written permission of the Federal Reserve Bank of St. Louis.and modern central bank practice based on simple
rules, this is an attractive reading of the data.
However, JS also show that it is possible to
argue that developments in interest rates and out-
put leave a great deal to be explained if one uses
a first difference VAR. In this sense, there may
be an unusual event in recent years, with house
prices departing from output and interest rate
fundamentals just during this period. Or house
prices may be not too closely related to these
fundamentals most of the time, so that may be a
case for thinking about a separate monetary policy
response to housing. That is, we need to know
whether other periods of house price increases
and decreases look similar to or different from
those of recent years.
Monetary Policy as a Cause of House
Prices
There has been a great deal of public discus-
sion about “easy money” in the house price boom,
and there is some evidence for this view in JS.
By shutting down the identified monetary policy
shock in panels 1 of their Figures 6A (differences
VAR) and 6B (level VAR), they find that house
prices would have been lower without monetary
policy shocks during 2004 and early 2005.
I have three observations on this finding.
First, one would like to know the statistical con-
fidence with which we can make this statement
(my own sense based on work with VARs is that
this might be low). Second, taking the result at
face value, it is important to stress that mone-
tary policy accounts for only a temporary interval
of higher house price increases and little of the
ultimate decline in house prices. Third, the JS
accounting method does not automatically mean
that a shock yields a contribution during this
period, as may be seen by comparing this to the
contribution that JS suggest for a term structure
spread: There is nothing contributed to house
prices by the yield curve. So, the method is poten-
tially informative in this and other contexts.
I think that we do not know the role that mone-
tary policy played in these events, but there is
good reason to be skeptical of the manner in which
“easy money” is used in many public discussions.
In the public eye (that of my neighbors and
my real estate agent in a Boston real estate market
that was a hot one starting in about 2000), there
were two distinct parts to the house price boom.
The first was based on income and wealth: As my
real estate agent said in 2000, people were buying
houses in the face of rapidly increasing house
prices with “real money” from successful eco-
nomic ventures. The second was later: People
were buying houses or refinancing houses, taking
advantage of the increasingly favorable terms
offered by lenders. Using my agent’s terminology
at the later time, this was “easy money.” But lender
terms were sufficiently generous that it is hard
to draw a connection to the Fed: The public defi-
nition of easy money is a statement about lending
terms, not necessarily about monetary policy.
Monetary Policy Response to Housing
An unfortunate aspect of the JS paper is that
the dynamic response to an identified housing
demand shock—that object to which a monetary
policy authority would potentially want to
respond—just doesn’t look plausible to me. The
key features of this shock, as described at the start
of their third section, are that it raises housing
prices; it raises private consumption and national
product; and it has a positive effect on house
investment with a timing that is curious.
From the standpoint of designing a monetary
policy response to the housing sector, this puzzling
pattern of responses makes it problematic to
address my third question (above), which is the
critical one from the standpoint of monetary
policy.
THINKING ABOUT DYNAMIC
RESPONSES IN HOUSING
The analysis of the housing demand shock
requires that we begin to think more carefully
about the nature of housing dynamics. While
macroeconomists use the “time to build” model
of Kydland and Prescott (1980) much less now
than some time ago, housing is surely a setting
in which this model is the benchmark.
King
368 JULY/AUGUST 2008 FEDERAL RESERVE BANK OF ST. LOUIS REVIEWTo sketch how such a model works and the
potential conflict that I see with the impulse
responses for the identified demand shock of JS,
let’s think about a setting in which there is an
unexpected increase in housing demand at a fixed
stock of housing. We would see an increase in
house prices, which in turn would stimulate
housing starts and an interval of higher housing
expenditure. If the housing starts were undertaken
“on spec” by construction companies, then one
would expect increased starts only if the future
house prices were expected to be high enough to
justify construction costs.
JS cite the empirical estimates of Topel and
Rosen (1988) and the simulations of a recent quan-
titative macro model developed by Iacoviello and
Neri (2007) as guidance in terms of the effects of
house prices on residential investment. The
estimates of Topel and Rosen (1988), in particular,
suggest an elasticity in the range of 1.5 to 3.15
for the response of investment two years later to
a permanent change in house prices. And JS argue
that their model captures this level of overall
response, thus supporting the identification of
the housing demand shock. However, in terms
of deciding whether this measure of a housing
demand shock is plausible, I think that we need
more detailed dynamic information.
Suppose that it takes three quarters of a year
to complete a housing construction project and
that the distribution of expenditure is uniform
over the construction project. Then, housing
investment (i) is an equally weighted moving
average of starts (s),
where ʱ is a parameter describing the size of
investment projects. More generally, the time-to-
build model may suggest that the time-path of
investment depends on the distribution of invest-
ment costs over the life of the construction process
and the interaction of optimal “housing starts”
with the anticipated path of house prices.
Suppose further that starts increase perma-
nently at date t = ˄. Then, investment builds up to
a new higher level, with one-third of the increase
taking place in each period. Now, the factors
is s s tt t t =+ +     −− α
1
3
12 ,
generating starts are not permanent, but if there
is a sustained increase in house prices, then this
calculation should capture the early part of the
impulse response.
From the standpoint of this type of model,
then, the dynamics in Figure 3 seem curious.
That is, a housing demand shock raises prices at
a point in time by about 1 percent, by the same
amount by a year later, by perhaps 1/2 percent after
two years, and by nothing after three years. The
investment dynamics are a response of about
0.05 for the first two quarters, then perhaps half
that by year’s end, and zero by six quarters.
A conventional view of the construction
process is that at least a year is a reasonable
horizon overall, with the first quarter devoted to
planning and permits and the last three quarters
involving the bulk of the expenditure. There is
no question that construction is faster now than
it was a couple of decades ago. But before accept-
ing the identification of the housing demand
shock, one would like to see that dynamics are
consistent with estimates of the distribution of
quarterly construction costs.
Housing Permits, Starts, and Investment
Housing permits have long been used as a
leading indicator (included in the Conference
Board’s series of leading economic indicators),
as have housing starts. Both of these series have
been historically treated as noisy ones, but also
containing useful information about future eco-
nomic activity. Figure 1 of this commentary shows
why, starting in 1987 as do JS. The reader’s eye is
drawn naturally to the most recent part of the
period, where housing permits and starts (monthly
data) move prior to investment (quarterly data).
If there is a persistent decline in housing starts,
caused by a negative housing demand shock, then
there will be a persistent decline in investment
in any time-to-build model, but it will take time
for the full effect to build up. From this stand-
point, the near-term forecasts for housing invest-
ment are not too rosy.
The identification of housing demand shocks
would benefit from using indicators of permits
and starts. Such empirical work, expanding on
King
FEDERAL RESERVE BANK OF ST. LOUIS REVIEW JULY/AUGUST 2008 369the study of JS, could lead to dynamic responses
for investment flows in response to identified
housing demand shocks that are more in line with
the structural characteristics of housing market
investment. In turn, this would provide a more
secure basis for analysis of the monetary policy
response to housing.
CONCLUSION
The events of the last few years will certainly
stimulate much additional research on the nature
of housing and mortgage markets, as well as their
implications for monetary policy. The analysis
of Jarocin ´ski and Smets highlights a series of
important questions about these linkages, as well
as providing some interesting early empirical
evidence.
REFERENCES
Iacoviello, Matteo and Neri, Stefano. “The Role of
Housing Collateral in an Estimated Two-Sector
Model of the U.S. Economy.” Working Papers in
Economics No. 659, Boston College Department of
Economics, 2007.
Jarocin ´ski, Marek and Smets, Frank. “House Prices
and the Stance of Monetary Policy.” Federal
Reserve Bank of St. Louis Review, July/August
2008, 90(4), pp. 339-65.
Kydland, Finn and Prescott, Edward C. “Time To
Build and Aggregate Fluctuations.” Econometrica,
November 1982, 50(6), pp. 1345-70.
Topel, Robert. H. and Rosen, Sherwin. “Housing
Investment in the United States.” Journal of
Political Economy, August 1988, 96(4), pp. 718-40.
King
370 JULY/AUGUST 2008 FEDERAL RESERVE BANK OF ST. LOUIS REVIEW
500
1,000
1,500
2,000
2,500
1987 1992 1997 2002 2007 2012
Thousands of Units
190
320
450
580
710
HOUST (left axis)
PERMIT (left axis)
PRFIC1 (right axis)
Billions of Chained 2000 $
Figure 1
Housing Starts and Permits and Residential Fixed Investment
NOTE: HOUST is housing starts: total: new privately owned housing units started; PERMIT is new private housing units authorized by
building permit; and PRFIC1 is real private residential fixed investment, 1 decimal. Shaded areas indicate U.S. recessions as determined
by the National Bureau of Economic Research.
SOURCE: Federal Reserve Bank of St. Louis: research.stlouisfed.org.

House Prices and the Stance of Monetary Policy.” Federal Reserve Bank of St. Louis Review,

Housing Investment in the United States.” Journal of Political Economy,

The Role of Housing Collateral in an Estimated Two-Sector Model of the U.S. Economy.” Working Papers

Time To Build and Aggregate Fluctuations.” Econometrica,

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