Martin Feldstein

English

Research Papers in Economics

351
Mr. Lazear: I understand, John, the mechanism that you are talk-
ing about. It is certainly a very interesting one. It is a little surprising, 
though, in the context of a life-cycle model or permanent-income 
hypothesis, that we think as credit markets get better and our ability 
to smooth consumption over time gets better that these effects are 
actually going to become more pronounced, so you are going to see 
more volatility from the housing wealth effect. 
I guess the question I would have in mind is, If I think about this 
in terms of total consumption, as Sydney Ludvigson was thinking 
about it as well, how do those effects offset this? For example, labor 
earnings would be the obvious thing that would come to mind in this 
context. You see a downturn in housing. That would be correlated 
probably with downturns in labor earnings. The question is, If I am 
looking at this in the aggregate and trying to understand how these 
things are correlated over the cycle, do I have to think about more or 
less smoothing over time?
Mr. Visco: I liked the paper very much. Perhaps the only short-
coming of the paper is that it is based on time series data, and there-
fore you are unable to distinguish households according to whether 
they are homeowners or renters. This is, however, a very important 
characteristic of your model. In fact, you state that the net result 
General Discussion:   
Housing, Credit and   
Consumer Expenditure
Chair: Martin Feldstein352  Chair: Martin Feldstein
might be zero exactly because the renters may compensate any pos-
sible effect of homeowners. 
You also said that in Italy there are large differences compared to 
countries where financial markets are much more developed. I think, 
however, that recent changes in financial markets have substantially 
reduced these differences.
We also have evidence that the distinction between homeowners 
and renters is extremely important in Italy. In a recent Bank of Italy 
study, we examined the behavior of more than 50,000 households 
over a period of more than 10 years. We obtained a relatively large 
effect for homeowners, with a propensity to consume out of hous-
ing wealth more or less around the figure you have—3.5 percent. 
However, there is a negative effect, exactly as you were suggesting, 
for renters.
My question is whether there is any possibility to use large cross-
section or panel data for other countries. Certainly in the United 
States, there must be data that allow you to measure the different 
impacts of changes in house prices on these two different, very im-
portant sets of consumers. 
Mr. Makin: A question for Sydney Ludvigson: You just finished your 
presentation by talking about housing wealth effects and consumption, 
then conditioned it by saying, “Unless there are spillover effects.”
Could you expand on that a little bit? And, secondly, a little bit 
more detail on the covariance terms in the nonhousing consumer 
durables table would be interesting. 
Mr. Blinder: One question for each person, and these are real 
questions. I don’t have answers in mind for each.
For John: As I understand the construction of the credit conditions 
index in the United Kingdom, and even more so in the United States 
where it comes from the Senior Loan Officer Opinion Survey, the 
question arises whether the index is actually reflecting mostly institu-
tional legal changes, which you emphasized a lot in the narrative, or 
market behavior for a given institutional legal structure.General Discussion  353
The question for Sydney relates back to an earlier discussion, es-
pecially of Bob Shiller’s paper. Is there a way we can distinguish be-
tween the hypothesis that it is risk premia that are shrinking versus 
expectations that are soaring, which was the Shiller hypothesis? 
Mr. Muellbauer: To Ed Lazear: His question is a very interesting 
question, which is quite similar, in some ways, to a brief discussion I 
had with Bill White. Bill White said, “There is going to be a payback. 
Increased access to credit means you borrow more. If you borrow 
more, you have to pay it back eventually.”
In a way, part of the answer to that you can see in Chart 3 of the 
paper. In Chart 3 of the paper, we plot the long-run effect on the 
consumption-to-income ratio of the two financial assets. The long 
line that has a declining trend is net liquid assets. That is, net liquid 
assets minus debt divided by non-property income. The chart shows 
the contribution of that to the long-run consumption-to-income ra-
tio. So, you can see there is a very big offset from financial liberaliza-
tion because people have acquired a lot more debt. So, net liquid 
assets are now negative, compared with the early days. That is offset, 
to a large extent, by the financial market-credit market liberalization. 
That answers both Bill White and you, to some degree at least. 
Turning to Ignazio Visco’s question. I am very much in favor, of 
course, of using good panel data where those data exist. We are plan-
ning ourselves to return to the U.K. micro evidence to see whether 
we can produce more robust estimates than the two studies that are 
listed so far. 
As far as Italy is concerned, my worry is whether the omission of in-
come changes may not have biased the collateral effects or the wealth 
effects of the estimates, but I very much support that style of work. 
On Alan Blinder’s question on the endogeneity of the credit con-
ditions index: In the Bank of England paper, if you look at it (it is 
rather a long paper), we really do everything possible to prevent that 
credit conditions index from being endogenous. We controlled for 
many variables. We have examined sample selection effects. It has 
been very careful work to exclude that possibility, with a lot of atten-
tion to various measures of risk to try to eliminate the correlation. 354  Chair: Martin Feldstein
For the U.S. credit conditions index that we have, it is pretty pro-
visional so far. This is work with John Duca. I think your point is 
absolutely right. There is a bit of endogeneity in there still, which we 
soon have to eliminate. If we use the HP-filtered version of it, it gets 
rid of a lot of the wriggles. We get very robust results, very similar 
results. I don’t think the overall shape of the thing is going to be very 
different. But you are right. One has to be very careful not to confuse 
short-run endogenous variations due to other things with the evolu-
tion of the supply function for credit.
Ms.  Ludvigson:  John  Makin  asked  about  the  spillover  effects. 
What I was referring to there was that it looks like from the em-
pirical analyses that I have seen—including John Muellbauer’s own 
work—that other fundamental determinants of consumer spending 
are more important than are fluctuations in housing wealth. Vari-
ables such as the unemployment rate and income seem to be very 
important in determining contemporaneous consumption fluctua-
tions. The empirical relationships we have been discussing today are 
marginal relationships; that is, they show the effect on consumer 
spending of housing wealth or credit market conditions, controlling 
for income and income expectations. In thinking about the overall 
change, we want to take into account the possibility that changes in 
housing wealth might influence these other fundamentals, which in 
turn influence spending.
The covariance terms you asked about combine several other as-
pects of the model we investigated. If you look at your footnote, you 
see an expression, which is an approximate equilibrium condition 
in the model. These covariance terms refer to covariance between 
surprises in consumer spending and some of the other terms on the 
right-hand side of that expression. We can talk about that more at 
length, if you like, afterward. 
Alan Blinder had a question. How do we distinguish a hypothesis 
of risk premia versus expectations soaring? If you don’t mind, I’ll 
give a rather roundabout answer to this question. This is not an easy 
question to answer. In the discussion, I had said if we accept for the 
moment that housing price-dividend ratios will continue to fluctuate General Discussion  355
within their old historical ranges, then one of two things must hap-
pen. However, it is possible that these ratios could remain above their 
pre-1998 historical norms for a very long period of time.
I’ve done some work with Jessica Wachter of Wharton (not to be 
confused with Susan Wachter, who is her mother and who presented 
a paper earlier) and Martin Lettau, where we analyzed in a rational as-
set pricing model what the effects of the so-called Great Moderation 
were on risk premia in the equity market. We calibrated the model, 
fed in estimates of the changing volatility of fundamentals—mainly 
consumption—and found you could explain a large fraction of the 
sustained run-up in equity values relative to earnings and dividends. 
They still remain above their historical norms prior to the mid-1990s 
via this decline in macroeconomic risk essentially. 
That is a model-based way of thinking about whether changes in 
asset market valuation ratios may be attributable to risk premia. With 
housing wealth, it strikes me that the situation is more complicated 
because houses effectually pay a dividend equal to the service flow 
from the durable consumption stock. 
It is possible that fluctuations in housing dividends provide simply 
a hedge against rent risk, which is a point made by Todd Sinai and 
Nicholas Souleles. So, if you think housing consumption has become 
less volatile, as it surely has with the Great Moderation, this might 
have dampened volatility in house prices and might have increased 
the demand for renting relative to buying. At the same time, hous-
ing consumption capitalizes the value of the living conditions in the 
general environment in which a home is purchased. You can imagine 
that a decline in the volatility of those services might instead lead to 
an increase in the demand for housing assets.
So, you can look at calibrated models to see whether rational mod-
els, which attribute fluctuations in valuation ratios to movements in 
risk premia, fit the data. 
If you want to go model-free, then maybe surveys would be helpful. 
However, surveys have their own limitations. For example, in this work 
with Jessica and Martin, there is a transition period, as individuals learn 356  Chair: Martin Feldstein
about the shift to a new lower-volatility regime. If you ask people in a 
survey about their expectations for prices going forward, you have to 
be careful about where you are in that transition period. 
Are you at the beginning of the transition to a new persistently 
higher valuation ratio? In that case, you would rationally expect 
higher prices, and that would be perfectly consistent with a decline 
in risk premia. 
Mr. Mayer: I have two comments for Sydney. The first is about the 
first couple of graphs, which show the price-dividend ratio is affected 
a lot by long-term interest rates. That comparison converging back 
to its long-term average would depend on real interest rates moving 
back up again. The role of interest rates in the price-dividend process 
matters a lot. It is hard to think about the price-dividend ratio with-
out putting in the real interest rate term, which is part of why I am a 
fan of thinking about the user cost. 
The second is that measuring dividends on owner-occupied prop-
erties is enormously difficult to do because you don’t really know 
how homeowners value that dividend. Some people just multiply the   
interest rate times the price. Again, the user cost model provides a   
better framework. Similarly, within the user cost model, Alan Blinder’s 
comment is exactly right. In principle, there is no way of differentiating 
between risk and expected appreciation. Those literally are exactly the 
same effect on the price-dividend ratio. That is a difficult problem.
In terms of estimating the risk premium, it is enormously important 
to use cross-sectional data because the volatility of house prices across 
different markets varies enormously. You could look at markets like 
Houston, where there is very little volatility, and markets like New 
York, where there is a lot. That could help in thinking about how to 
incorporate volatility in these models. 
Mr. Sinai: This is a quick technical question for John Muellbauer. 
I didn’t see it, and maybe I missed the method of estimation for the 
time series consumption regressions in the United Kingdom and the 
United States and the issue of simultaneity and whether you have a 
lag-dependent variable in each of those. Answering the question of General Discussion  357
how you estimated the equations will help answer for me how I take 
these numbers.
Mr. Smets: I would like to refer to some work of two colleagues 
of mine, Alessandro Calza and Livio Stracca, together with Tom-
maso Monacelli of Bocconi University. They document that the 
correlation between consumption and house prices differs across 
countries and that those differences are correlated with the flexibil-
ity of the mortgage market and associated features like loan-to-val-
ue ratios. They then show that one can replicate such a correlation 
in a general equilibrium model with constrained borrowers and 
lenders. So, in economies with higher loan-to-value ratios, you 
get a higher correlation between consumption and house prices. 
The crucial factor is that, although the loan-to-value ratios are 
higher, borrowers are still credit-constrained. This is an issue for 
the microdata. In reality, you also have borrowers who are not credit-
constrained, and for those, naturally, you would think that Lazear’s 
intuition would hold and the correlation would be less. In order to 
replicate a positive correlation between consumption and house prices, 
it seems that most borrowers—or a large fraction of borrowers—are 
actually credit-constrained. So, when there are shocks that move house 
prices, they do respond by changing their consumption.
Mr. Muellbauer: I forgot to reply to Sydney earlier, which was a 
mistake. Let me take up a couple of points that Sydney made. She 
says that Chart 2 results in the United States suggest that the U.S. 
credit conditions index has relatively low explanatory power. 
That’s not correct. The reason is that, in Chart 2 in these U.S. 
results, we include a stochastic trend. That stochastic trend incor-
porates the intercept shift in credit conditions, as well as other long-
term factors like the rise in income and equality, changes in the Social 
Security system, and demographic changes and things of that sort.
In other words, when she says that the difference in fits between col-
umns 1 and 2 is not very large, that doesn’t mean very much because 
the intercept shift that is due to credit conditions is all there in the 
stochastic trend. We cannot identify which bit of the stochastic trend 358  Chair: Martin Feldstein
is due to the intercept shift in the credit conditions index. That’s the 
first point.
The second point really is that any empirical work from 1952 
to 1998 that doesn’t incorporate that very important institutional 
changes were taking place in the United States in that period seems 
to me pretty suspect. I am really not at all sure what these fitted re-
lationships mean. 
Ms. Ludvigson: Yes, Chris Mayer made a comment about the hous-
ing price-dividend ratio being affected by long-term interest rates. I 
totally agree with that. In fact, that is an important part of why the 
risk premium is so forecastable, I believe, because when a high-priced 
dividend ratio is forecasting lower housing returns, at the same time, 
it is forecasting higher short-term rates, and the premium itself is even 
more forecastable. That may be one reason we find relatively modest 
effects of movements in these forecastable changes in housing returns 
on consumption—because there are offsetting effects there.
In terms of measuring dividends, it is hard to do. I would welcome 
your suggestions on how to do that better and also on using the 
cross-sectional data to think about movements in risk premia. 

Housing, credit and consumer expenditure: general discussion

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Housing, credit and consumer expenditure: general discussion

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