U.S. Housing Prices: Is There a Bubble?

U.S. Housing Prices: Is There a Bubble?

Order Code RL31918
CRS Report for Congress
Received through the CRS Web
U.S. Housing Prices: Is There a Bubble?
Updated September 7, 2006
Marc Labonte
Specialist in Macroeconomics
Government and Finance Division
Congressional Research Service ˜ The Library of Congress

U.S. Housing Prices: Is There a Bubble?
Summary
U.S. housing prices increased by 10.1% in the past year and a total of 56.5% in
the past five years. Although large, these increases look small in comparison to the
behavior of house prices in certain parts of the country: prices in six states have more
than doubled in the past five years. Recalling the behavior of the stock market in late
1990s, some analysts fear that the recent appreciation in housing prices points to a
bubble, or a rise in house prices driven by “irrational exuberance” that cannot be
explained by fundamentals. (“Fundamental” explanations for a rise in housing prices
include falling interest rates, inflation, and rising incomes.) Recent changes in a few
economic factors suggest reasons why house prices could be rising without a bubble
being present in parts of the country. But when the data are examined at a regional
level, the large price increases in East Coast states and the West have recently led to
a sharp decline in housing affordability that is consistent with a bubble. In the
second quarter of 2006, prices continued to rise, but much more slowly than they had
been. Sharp declines in housing sales and starts this year suggest that the boom may
be coming to an end; policymakers are anxious to see if the next phase will be a
decline or leveling off in prices.
The problem with bubbles is that they cannot be identified with any confidence.
If bubbles could be accurately identified, they would never develop in the first place
because people would respond to the emergence of a bubble by selling the asset to
avoid future losses, thereby eliminating the bubble. Indeed, economists who believe
in the rationality and efficiency of the marketplace use this logic to argue that bubbles
can never exist. Even if the rise in housing prices cannot be explained by the factors
identified in this report, it is possible that other unidentified “fundamentals” are
driving prices up, rather than a bubble.
If housing prices were being driven by a bubble, there is a chance that they could
suddenly collapse, with adverse effects on the U.S. economy. Residential
investment, which rose 9% annually in 2003-2005, fell by 9.8% in 2006:2. It remains
to be seen if this decline is the beginning of a broader trend. A decline in housing
wealth could also depress consumption, thereby depressing aggregate spending in the
short run. A sudden collapse in housing prices could also affect the health of the
financial sector if financial institutions are not adequately safeguarded. All of these
possibilities give Congress a cause for concern, yet effective policy responses to a
bubble are difficult. If house prices were to decline in some regions, it would not be
the first time this occurred. The report examines previous price declines in
California, New England, and Texas. Encouragingly, those declines were much
smaller than the prior increase in prices.
Even if there is not a bubble, it is still possible that house prices could fall in the
near future. For example, interest rates are likely to continue to rise in the next few
years, placing downward pressure on prices, all else equal. But from a
macroeconomic perspective, a fall in house prices is an independent economic
concern only if caused by a bubble. For instance, if interest rates rose sharply
because of stronger economic growth, a resulting fall in housing prices would not be
a cause of concern for the economy as a whole.

Contents
The Recent Behavior of Housing Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Determinants of Housing Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Econometric Evidence of a Bubble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Previous House Price Increases and Declines . . . . . . . . . . . . . . . . . . . . . . . 15
About Bubbles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Research on Housing Bubbles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Macroeconomic Effects of a Bubble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Effects on Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Effects on the Housing Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Effects on the Financial Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
The Household Debt Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Policy Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Technical Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
List of Figures
Figure 1: States With Price Appreciation Above 60% in the Last
Five Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Figure 2: Housing Starts, 1987-2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Figure 3: Interest Rate-Loan Value Combinations for a Constant $1,200
Monthly Payment on a 30-Year Loan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Figure 4: Real Monthly Payment for 30-Year Mortgage vs. Real Disposable
Income (National) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Figure 5: Monthly Payment for 30-Year Mortgage vs. Disposable
Income (California) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Figure 6. Actual vs. Forecasted Housing Prices . . . . . . . . . . . . . . . . . . . . . . . . . 14
Figure 7. California Housing Bust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Figure 8. Texas Housing Crash . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Figure 9. New England Housing Bust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
List of Tables
Table 1. Percentage Increase in Housing Prices by Region . . . . . . . . . . . . . . . . . 3
Table 2. Percentage Increase in House Prices in Top 10 States . . . . . . . . . . . . . . 4
Table 4. Historical Housing Busts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Table 5. Description of Forecasting Models and Results . . . . . . . . . . . . . . . . . . 35
Table 6. Regression Results Underlying The Forecasts . . . . . . . . . . . . . . . . . . . 37

U.S. Housing Prices: Is There a Bubble?
U.S. housing prices increased by 10.1% in the past year and a total of 56.5% in
the past five years.1 These increases easily outstripped the general increase in prices
over this period, so that there has been a large real increase in house prices.2 House
prices began appreciating rapidly in the third quarter of 1997, and accelerated even
further from the fourth quarter of 2003 to the first quarter of 2006. In eight of those
nine quarters, growth was above an annualized rate of 8%. This is the only time
since 1980 that prices have risen more rapidly than 8% for two or more quarters in
a row. In the second quarter of 2006, price appreciation slowed sharply to an
annualized rate of less than 5%. It remains to be seen if this marks the beginning of
the end of the housing boom. Deterioration in leading indicators such as housing
sales and housing starts indicate that may be the case.
Although the increase in national house prices is large, these increases look
small in comparison to the behavior of house prices in certain regions of the country:
in six states (DC, FL, CA, HI, NV, and MD), prices have more than doubled in the
past five years. Early in the housing boom, the large price increases were mostly in
California, Florida, and the Northeast. Lately, the boom has spread to most of the
West and Mid-Atlantic. For example, prices in Arizona increased by 24.1% in the
past year — fastest in the nation — and 96.7% in the past five years.
There could be two forces driving up house prices. First, housing demand could
be increasing faster than supply because something has changed to make housing
more desirable than previously. For example, people could be wealthier and decide
to spend some of that wealth on housing, mortgage rates and costs could have fallen,
or a larger proportion of the population could be of home-buying age. These are
examples of changes in the economic “fundamentals” that determine house prices.
But there is also another possible explanation for why house prices have risen
so rapidly. Recalling the behavior of the stock market in late 1990s, some analysts
fear that the recent appreciation in housing prices points to a price bubble, or a rise
in house prices that cannot be explained by fundamentals. Instead, prices could be
driven by what Alan Greenspan called “irrational exuberance.” If housing prices
were being driven by a bubble, there is a chance that they could suddenly collapse,
with adverse effects on the U.S. economy.
This report first describes recent developments in housing prices and factors that
influence housing demand on a regional and national basis. Second, it uses statistical
1 The author would like to thank Steven Maguire and Pamela Jackson for their assistance.
2 Overall prices increased by 3.3% in the past year and a total of 13.3% in the past five years
as measured in the GDP accounts (measured by the consumer price index, the comparable
numbers are 3.8% in the past year and 13.6% in five years).

CRS-2
techniques to determine whether historical supply and demand relationships can
explain the recent price increase. Third, it examines previous regional price declines.
Fourth, it discusses bubbles and research on housing bubbles. Finally, the report
describes the implications of a housing bubble for public policy, and what policy
options would be available to respond to a bubble.
The Recent Behavior of Housing Prices
A careful look at the data suggests that if there was a turning point in the recent
behavior of national housing prices, it was in the third quarter of 1997.3 Before then,
prices consistently rose by less than 1% per quarter (4% on an annualized basis) in
the 1990s. From this quarter onward, prices consistently rose by more than 1% per
quarter. Thus, this report will focus on the period from the third quarter of 1997
onward as the possible bubble period. (The current boom accelerated further from
the fourth quarter of 2003 to the first quarter of 2006, when prices consistently rose
more than 2% a quarter.) From 1975, when the index was first published, through
the second quarter of 1997, house prices rose on average by 5.3% a year in nominal
terms. Since then, they have increased by an average of 7.8% a year. But the
contrast in price increases between the two periods is greater after adjusting for
inflation. Over those periods, inflation rose by an average of 4.3% in the earlier
period and 2.4% in the latter period. This suggests that in real terms, house prices
increased by about 1% a year before the third quarter of 1997 and almost 5.5% a year
since then.4
The first fact to glean from the housing data is that housing markets are local,
and there is a wide diversity in recent price behavior from market to market. As seen
in Table 1, the increase in house prices in the Pacific region was more than three
3 This report focuses on the house price index published by the Office of Federal Housing
Enterprise Oversight (OFHEO). It is a repeat sales index of single family homes so that it
is measuring the appreciation in price of a given house from the first time it was sold to the
second time it was sold. This type of index solves some problems, but causes others. Since
it is based only on repeat sales, the change in prices from one period to another is measuring
a comparable good (unless additions or renovations were made to the house between sales).
However, the index is not a constant quality index since the houses added to the index over
time may be of different quality than the houses previously included in the index. Thus, the
index is not a pure measure of house price inflation since the index captures changes in
quality (with a lag). The index only records houses purchased with conforming mortgages,
so it excludes houses at the high and low end of the market. It only measures houses that
were actually sold, which may have a different value than other houses. For discussions of
the repeated sales housing index, see Jesse Abraham, “New Evidence on House Prices from
Freddie Mac Repeat Sales,” AREUEA Journal, vol. 19, no. 3, Fall 1991, p. 333; Karl Case
and Robert Shiller, “Prices of Single Family Homes Since 1970: New Indexes for Four
Cities,” New England Economic Review, Sept. 1987, p. 45; Ferdinand Wang and Peter Zorn,
“Estimating House Price Growth With Repeat Sales Data: What’s the Aim of the Game?”
Journal of Housing Economics, vol. 6 no. 2, June 1997, p. 93.
4 This inflation adjustment somewhat understates the real appreciation rate of housing prices
if the quality of the housing stock increases over time since new houses enter the house price
index, upon which the data in this report are based, with a lag.

CRS-3
times greater than in the East South Central, East North Central, or West South
Central regions over the past five years. Regional differences in house price
appreciation are to be expected given the regional differences in employment growth,
income growth, population growth, land availability, desirability, and so on. But
whether the regional differences in price appreciation can be fully explained by these
factors will be investigated below.
Table 1. Percentage Increase in Housing Prices by Region
Census Division
1 Year
5 Years
Since 1980
National
10.1
56.5
298.9
Pacific
14.1
94.1
484.6
Mountain
14.1
55.5
268.6
South Atlantic
13.7
69.0
313.9
Middle Atlantic
11.0
70.5
427.8
West South Central
8.0
27.3
117.6
East South Central
7.8
27.8
177.9
New England
5.7
62.0
528.9
West North Central
4.7
34.7
199.1
East North Central
4.0
27.0
217.2
Source: Office of Federal Housing Enterprise Oversight, House Price Index, September 2006.
Note: Price increases are not adjusted for inflation.
Figure 1 and Table 2 demonstrate that the growth in housing prices is even
more geographically concentrated than data for the nine census regions would
suggest. In 2005, 7 of the 10 states with the greatest house price appreciation were
in the West.5 This marks a geographical shift: five years ago, 7 of the top 10 states
with the greatest appreciation were on the East Coast; today, many of those states are
growing more slowly than the national average. Only 4 states were among the top
10 states over both the last year and the last five years. Over the past five years, the
rate of price increase in the top nine states was more than one and a half times the
national average. During that time, all of the metropolitan areas with the fastest
rising prices were in California and Florida. Alan Greenspan has referred to these
localized price spikes as “froth” in the housing market.6
5 Goldman Sachs recently estimated that house prices relative to income were as high in San
Francisco today as they were in Tokyo before the real estate collapse of the early 1990s. Jan
Hazitus, “U.S. House Prices: Lessons from Abroad,” U.S. Economic Analyst, July 22, 2005,
p. 4.
6 Testimony of Chairman Alan Greenspan before the Joint Economic Committee, 109th
Congress, June 9, 2005.

CRS-4
Figure 1: States With Price Appreciation Above 60% in the Last Five
Years
NH
61.0%
ME
VT
61.7%
WA
66.0%
NY
60.2%
72.8%
OR
CT
63.8%
63.0%
RI
NV
94.0%
104.8%
DE
NJ
CA
70.8%
85.0%
111.9%
DC
AZ
120.0%
96.7%
MD
VA
102.7%
83.4%
HI
111.2%
FL
112.6%
Source: Map created by CRS based on OFHEO data.
Table 2. Percentage Increase in House Prices in Top 10 States
State
1 Year
State
5 Years
Arizona
24.1
District of Columbia
120.0
Florida
21.3
Florida
112.6
Idaho
20.1
California
111.9
Oregon
19.5
Hawaii
111.2
Hawaii
18.1
Nevada
104.8
Washington
17.4
Maryland
102.7
Maryland
16.2
Arizona
96.7
District of Columbia
15.9
Rhode Island
94.0
New Mexico
15.5
New Jersey
85.0
Utah
15.2
Virginia
83.4
Memorandum: National Average
10.1
Memorandum: National Average
56.5
Source: Office of Federal Housing Enterprise Oversight, House Price Index, September 2006.
Note: Price increases are not adjusted for inflation.

CRS-5
The sharp increase in prices experienced by the United States in recent years is
not unique. Many other countries saw nominal price increases from 1997 to 2005
that were even larger than in the United States, including France (87%), Australia
(114%), Spain (145%), and Britain (154%). (However, Germany and Japan, who
have experienced weak economic growth during that time, saw nominal prices fall.7)
To an extent, this is unsurprising since the recent decline in interest rates has been
widespread among developed countries, and the countries listed above experienced
relatively robust economic growth as well. But, if anything, these countries are more
at risk of bubbles than the United States. Historically, housing prices have been
significantly less volatile in the United States than in many foreign countries.8
Housing markets in Britain and Australia cooled down in 2005.
Determinants of Housing Prices
While the increases described above sound impressive, until we consider what
has happened to the factors that determine housing prices, we can make no
judgement as to whether these increases are excessive or compatible with changes
in economic fundamentals. Housing prices will be determined by both supply and
demand. On the supply side, market efficiency suggests that house prices should
reflect the marginal cost of building an additional house. When the demand for
housing increases, price (and profit) increases, and builders respond by building more
houses until the price is driven back down to marginal cost. Since building a house
is time consuming and changes in demand may be difficult to spot, there may be a
lag between the rise in house prices and the increase in supply that drives the price
back down.9 However, over a long enough time horizon, the profit incentive ensures
that prices would always be driven back down to marginal cost.10
A cursory glance at the national data confirms this hypothesis, as seen in Figure
2. Nationally, single-unit housing starts have increased since 1997, but perhaps less
than one would expect given the increase in prices. Since 2002, housing starts have
7 Data from “The Global Housing Boom,” The Economist, June 18, 2005, p. 66. See also
Jon Hilsenrath and Patrick Barta, “Amid Low Rates, Home Prices Rise Across the Global
Village,” Wall Street Journal, June 16, 2005, p. A1.
8 Claudio Borio and Patrick McGuire, “Twin Peaks in Equity and Housing Prices?,” BIS
Quarterly Review, Bank for International Settlements, March 2004, p. 79.
9 Notice that the behavior of prices in the presence of lags in supply changes is similar to the
rise and collapse of a bubble: price rises at first with a change in demand, and then
subsequently falls as supply adjusts. Thus, observed price changes are not sufficient proof
of a housing bubble.
10 The importance of lags is confirmed in Robert Topel and Sherwin Rosen, “Housing
Investment in the United States,” Journal of Political Economy, vol. 96, n. 4, August 1988.
They find that the supply price elasticity rises from 1.68 after one quarter to 2.76 after eight
quarters for a permanent increase in price. A temporary increase in price has a lower supply
elasticity.

CRS-6
risen more rapidly.11 On a regional basis, however, the West and South are the only
regions that have shown a strong increase in housing starts recently. In the Northeast,
although prices have risen significantly, housing starts are much lower than in the rest
of the country and did not shown any significant acceleration until 2004 — supply
was not responding to changes in demand to drive the price back down. This is
puzzling, and suggests that other factors besides a time lag may be suppressing
supply in that region. What might those other factors be?
Figure 2: Housing Starts, 1987-2005
1800
1600
1400
1200
1000
800
600
400
200
01987
1991
1995
1999
2003
South
West
Midwest
Source: U.S. Census Bureau
Note: Figure displays single-unit privately owned housing starts.
On the supply side of the market, marginal cost may or may not be steady over
time. It will change if the technology surrounding the building process or the price
of inputs such as timber, land, and labor changes. In particular, the price of land
would be expected to be affected by the availability of land.12 A decrease in supply
would be more important in densely populated areas that are growing rapidly, and
relatively unimportant in sparsely populated areas. This suggests one fundamental
reason prices might have risen more quickly in the densely populated northeast and
California than in the rest of the country. More indirect factors can also influence
11 While concern about bubbles usually focuses on the demand side of the market, it is
possible that irrational exuberance could also affect the supply side of the market, causing
overbuilding that eventually leaves prices below marginal cost after the bubble bursts.
12 Davis and Heathcote estimate that the supply of land used for housing increased at an
annual average of 0.6% from 1970 to 2003, and land accounts for 38% of housing value.
See Morris Davis and Jonathon Heathcote, “The Price and Quantity of Residential Land in
the United States,” Board of Governors of the Federal Reserve, Finance and Economics
Discussion Series 2004-37, 2004.

CRS-7
cost on the supply side. For example, the implementation of zoning regulations and
“impact fees” to limit new construction would increase the cost of new housing.
Since zoning is determined at the local level, it is difficult to tell how much it is
influencing national prices.13
Housing is somewhat unusual in that it is a good that can be used for either
consumption (i.e., to live in) or investment (to rent out or hold to resell at a profit).
Since the OFHEO house price index measures only single family homes, most of the
houses measured serve primarily a consumption role (although a small proportion of
single family homes are rented out). For the sake of simplicity, let us consider only
factors related to consumption that would influence housing demand. This way we
can neglect factors such as the rate of return on alternative assets and expectations
of future changes in house prices. Unless an owner has the freedom to move from
market to market, it is reasonable to assume (when there is no bubble) that the typical
owner is not motivated by these factors for any given market overall in the short run
since he must rent or buy in the same market even if he wished to take advantage of
profit opportunities.
There is evidence suggesting that the demand for housing has increased recently.
For example, after staying flat throughout the late 1980s and early 1990s, the home-
ownership rate began rising in 1995, and continued rising through 2005. Many
factors may have influenced the demand for housing, and while it is not possible to
measure all of them, a few of them are obvious and easily quantifiable.
First, nominal price changes due to inflation should not have any effect on
demand. All else equal, increases in inflation will be translated into higher housing
prices one for one.14 As noted above, after adjusting for inflation, real house prices
increased by only about 1% a year from 1975 through the second quarter of 1997, but
they have increased by nearly 5% a year since.
Second, as incomes and wealth rise, people may desire to spend some of the
increase on housing. As the income available to spend on housing increases, houses
would be built or renovated with more amenities, causing their price to rise.15 Thus,
13 See Bengte Evenson, “Understanding House Price Volatility,” Illinois State University
Working Paper, 2002. An attempt to measure the impact of regulation on housing supply
and house prices can be found in Raven Saks, “Job Creation and Employment Growth:
Constraints on Employment Growth in Metropolitan Areas,” Harvard University Joint
Center for Housing Studies, Working Paper W04-10, Dec. 2004; and Edward Glaeser and
Joseph Gyourko, “The Impact of Zoning and Housing Affordability,”Harvard Institute of
Economic Research, discussion paper 1948, March 2002.
14 Inflation does reduce demand in the face of liquidity constraints because of the way
mortgages are set up. Since traditional mortgages have level payments over the term of the
mortgage, payments at the beginning of the mortgage are larger in real terms than payments
at the end of the mortgage. This means that initial mortgage payments will be higher
relative to income when inflation is high.
15 If there were a simple way to keep house quality constant in the data, it might be easier
to determine whether there is a bubble. Some other price indices try to control for changes
(continued...)

CRS-8
increases in house prices caused by greater wealth and income would not be
indicative of a bubble, nor would they be indicative of an increase in price above
marginal cost. Higher incomes would also be expected to cause some people to
prefer home ownership to living with family or roommates. Over the long term,
changes in income explain real changes in house prices very well.
Third, since most house sales are financed through a mortgage, mortgage rates
can change the cost of home ownership even when the price of a house is constant.
Figure 3 demonstrates the relationship between interest rates, housing prices, and
monthly mortgage payments. Anytime mortgage interest rates go up, the cost of
carrying a mortgage rises and demand falls; when mortgage rates fall, the cost of
carrying a mortgage falls and demand rises. Assuming the supply of housing is fixed
in the very short run, house prices can rise and fall dramatically as interest rates
change.16 (There should only be a one time — not continuing — price adjustment
to a one time change in interest rates.) If a homeowner’s desired mortgage payment
is held constant at $1,200 per month on a 30-year mortgage, the homeowner could
borrow $200,000 when interest rates are 6%, but only $180,000 for the same
mortgage payment when interest rates are 7%. In reality, the link between house
prices and mortgage rates will be weaker since the rates on some mortgages are
adjustable and low-cost opportunities for refinancing exist. Mortgage rates fell
sharply from 2000 to 2003. However, they have risen slightly since then. Thus,
mortgage rates cannot explain the rapid price appreciation that has occurred since
2003, when price increases have been most rapid.
15 (...continued)
in attributes directly, but this is mathematically difficult and raises questions about which
attributes should be included, and how accurately they can be priced.
16 Interest rates also have an effect on the supply side of the market that should not be
neglected. When interest rates fall, the cost of capital falls for firms. This would reduce the
marginal cost of building a house and induce more houses to be built in the long run.

CRS-9
Figure 3: Interest Rate-Loan Value Combinations for a Constant
$1,200 Monthly Payment on a 30-Year Loan
260000
240000
220000
lue
a 200000
loan v
180000
160000
140000
4%
5%
6%
7%
8%
9%
interest rate
Source: CRS calculations.
Other factors that might influence housing demand include demographics (i.e.,
more people in the age groups that have high home-ownership rates), expanded
access to mortgage markets, tax changes, a relative change in the cost of home
ownership versus renting, and so on. Some analysts are concerned that looser
lending standards are contributing to the rise in housing demand. It is claimed that
increasingly mortgages have higher loan-to-value ratios (reportedly, a fourth of
borrowers made no down payment in 2004),17 and are adjustable rather than fixed
interest rates. In addition, mortgages that allow borrowers to take on more debt, such
as interest-only loans and mortgages that give the borrower an “option” of how much
they wish to pay each month, allowing them to increase the loan principal, have
emerged and are growing in popularity. The Economist reports that 60% of new
mortgages in California in 2005 were interest only or negative amortization,
compared with 8% in 2002.18 BusinessWeek reports that option ARMs accounted for
12% of all new loans in the first five months of 2006, and 80% of borrowers were
making only the minimum payment.19 Some fear these loans would become
unmanageable for borrowers if interest rates rise or house prices decline; if so, they
might be considered both causes (because the riskier borrowing allows borrowers to
bid up prices) and effects (because borrowers are taking on more risk in the belief
17 “Risky Mortgage Business,” New York Times, July 6, 2005.
18 “The Global Housing Boom,” The Economist, June 18, 2005, p. 66.
19 Mara Der Hovanesian, “Nightmare Mortgages,”Business week, vol. 4000, no. 70, Sept.
11, 2006.

CRS-10
that rapid appreciation will continue) of a housing bubble. Evidence of looser
standards is more anecdotal than concrete at this point, however.
Figure 4 makes some simple assumptions for illustrative purposes. It compares
the behavior of the house price index (hpi) to changes in income, inflation, and
interest rates over the past five years.20 If movements in these variables can explain
the movement in house prices, a housing bubble can probably be ruled out without
even considering the full array of fundamentals that influence house prices.21 If these
variables cannot explain the increase in housing prices since 1997, then there may be
a bubble, although we cannot rule out the possibility that neglected factors are driving
supply and demand. As seen in Figure 4, from 1997 to 2005, the increase in real
monthly mortgage costs (which captures changes in house prices and the effects of
changes in mortgage rates) mostly kept pace or were exceeded by increases in real
disposable income. In other words, although house prices were rising rapidly,
affordability was not deteriorating because of falling mortgage rates. This does not
offer evidence that there is a housing price bubble in the nation as a whole during that
period. However, both house prices and mortgage costs have risen rapidly since
2003. As a result, the increase in monthly payments has greatly outpaced income
gains since 2005, which may provide evidence that a bubble has emerged. In the six
quarters ending in 2006:2, real mortgage payments have increased 14%, while real
per capita disposable income has increased 1% at annualized rates.
20 Similarly, Rosen derives a mortgage-servicing index and shows that the cost of servicing
a mortgage has not risen significantly through 2004. Richard Rosen, “Explaining Recent
Changes in Home Prices,” Chicago Fed Letter, no. 216, July 2005.
21 A full discussion of bubbles appears later in the report. To clarify the following
discussion, the reader may wish to read that section first.

CRS-11
Figure 4: Real Monthly Payment for 30-Year Mortgage vs. Real
Disposable Income (National)
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
1997 1998 1999 2001 2002 2003 2004 2006
real
HPI implied
disposable
real monthly
income per
payment
capita
Source: CRS calculations based on data from Bureau of Economic Analysis, Office of Federal
Housing Enterprise Oversight.
Note: For comparison, all data in the figure were transformed into index numbers.
National data mask wide disparities in the regional behavior of house prices, as
illustrated in Tables 1-3, which suggests that some areas have no bubble while other
areas have larger bubbles than an analysis based on national data would indicate.
Although increases in house prices have been significantly greater in certain regions
than the country as a whole, this by itself is not evidence of a bubble since it is
possible that fundamental economic characteristics such as income and population
have increased just as rapidly in these areas.22 To estimate whether there are regional
bubbles at present, the same analysis that was done for the nation as a whole can be
conducted locally. Figure 5 compares the rise of mortgage costs in one of the fastest
appreciating states in the country, California, against the rise of state income and
population.23 Figure 5 suggests that a potential bubble in California started earlier
22 Population effects are included in Figure 5 but not Figure 4 because population is likely
to be a more important factor in densely populated areas than the nation as a whole.
Population growth is accounted for in Figure 5 in the disposable income measure.
23 Of course, when analyzing local regions, it should be noted that not all housing demand
is driven by local factors. For example, housing demand in vacation communities will be
(continued...)

CRS-12
and is much larger. Since 1997, per capita income has risen 41% in California, while
hypothetical mortgage payments have risen 134%.
Figure 5: Monthly Payment for 30-Year Mortgage vs. Disposable
Income (California)
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.81997 1999 2001 2003 2005
disposable
hpi implied
per capita
income
monthly
disposable
(incl. pop.
payment
income
growth)
Source: CRS calculations based on data from Bureau of Economic Analysis, Office of Federal
Housing Enterprise Oversight.
Note: For comparison, all data in the figure were transformed into index numbers. Data are not
adjusted for inflation.
Thus far, this report has discussed reasons why prices might have risen in the
recent past in the absence of a bubble. By the same token, it should be stressed that
even in the absence of a bubble, prices could decline in the future if the economic
fundamentals determining supply and demand change. On the supply side, if supply
has responded sluggishly to favorable changes in demand in the past five years, that
effect could not be expected to last much longer.24 Once new housing is put in place,
23 (...continued)
determined in part by out-of-staters purchasing second homes.
24 One factor that keeps prices from falling significantly is the tendency for sellers to pull
their house off the market when prices begin to fall. There is a high correlation between
(continued...)

CRS-13
it would place downward pressure on prices. On the demand side, if, say, a recession
caused income and non-housing wealth to decline, it could cause prices to
temporarily decline, all else equal. (On the other hand, the poor behavior of the stock
market after 2000 could have caused people to shift more of their wealth into housing
if it is viewed as a “safe haven,” temporarily increasing housing demand.25) And the
future behavior of interest rates could cause a decline in housing prices. In June
2003, nominal mortgage rates reached their lowest point in the past three decades,
due to expansionary monetary policy that lowered overnight interest rates to 1% (the
lowest nominal rate since 1954.) Now that economic conditions have improved, the
Fed has begun to tighten monetary policy, and mortgage rates have increased
somewhat as well. (Economists are surprised, however, that mortgage rates have
risen so much less than short-term rates so far.) If this trend continues, it would raise
the cost of housing to borrowers and reduce demand, thereby putting downward
pressure on housing prices.
Econometric Evidence of a Bubble
Another method for determining whether a bubble is present in housing prices
is to statistically estimate the historical relationship between house prices and
variables that affect housing supply and demand before 1997:3.26 Those relationships
can then be used to forecast what house prices would have been since 1997:3 if the
historical relationships had held constant. If the forecast is similar to the actual
behavior of house prices in the past five years, then a bubble can be ruled out; if the
forecasted appreciation is significantly lower, a bubble may be present. A forecast
cannot definitively prove the presence of a bubble, however, since demand and
supply relationships may have changed so that the historical relationship is no longer
accurate. The forecast may also fail to predict actual events because it is flawed,
either because the wrong mathematical function is used to relate the factors to one
another or because important supply and demand determinants are omitted from the
model. For example, potentially important factors such as demographic composition,
construction costs, and the costs of renting are omitted from the model for technical
reasons.
Figure 6 compares actual housing prices to the forecast results generated by five
different models which are described in a technical appendix. All of the models
except for Model 5 predicted that housing prices would rise considerably more
24 (...continued)
house prices and sales historically. This “downward stickiness” in prices is likely to make
any actual decline in prices smaller than an economic model predicts.
25 In an international study covering the past three decades, Borio and McGuire find that
peaks in equity markets occur on average two years before peaks in housing markets,
suggesting that a fall in the stock market may temporarily boost housing markets, but only
temporarily. Claudio Borio and Patrick McGuire, “Twin Peaks in Equity and Housing
Prices?,” BIS Quarterly Review, Bank for International Settlements, Mar. 2004, p. 79.
26 This approach contrasts to the forecasts made using theoretical relationships in Figures
4-5. For example, instead of positing that inflation raises house prices one for one,
regressions can be used to estimate historically exactly how much of an increase in inflation
is passed through to house prices.

CRS-14
slowly over the past five years than they actually have. Models 1-4 predict housing
prices that were 12.7-22.9% lower than actual prices at the end of 2002, and actual
housing prices are outside the 95% confidence interval in each case. For reasons
discussed in the appendix, while Model 5 does the best job tracking actual prices, it
is the least likely to distinguish between a bubble and house price increases that are
driven by fundamentals. As expected, all five models did predict some house price
appreciation over the last five years — if there is a bubble, it is considerably smaller
than the overall increase in house prices that has been experienced.
Before taking these forecasts as evidence of a national bubble, one should
remember the regional variation present in the national data. The amount of
appreciation forecasted in Models 1 and 4 is only a little less than the appreciation
that occurred in the country outside of the northeast, mid-Atlantic, and Pacific
regions. Unfortunately, similar forecasts were not possible at a local level due to a
paucity of quality data. In any case, interpretation of a forecast based on historical
data would have been ambiguous in key local markets since a bubble may have been
present there during the 1980s (see the next section).
Figure 6. Actual vs. Forecasted Housing Prices
Source: CRS calculations.
Note: See appendix for details.

CRS-15
Previous House Price Increases and Declines
In determining the likelihood of a bubble today, it useful to ask whether there
have been bubbles (departures in housing prices from their fundamental value) in the
past.27 Since bubbles must be transient by definition, a price increase in the past
cannot be identified as a bubble after the fact unless it was followed by a price
decline (although a subsequent price decline is not sufficient evidence that a bubble
has occurred since prices can also decline for fundamental reasons).
Housing prices have never fallen in nominal terms on a national basis for more
than one quarter, and in those cases the decrease was more than reversed in the next
quarter. However, house prices did fall in real terms on a nationwide basis in the
early 1980s. From the second quarter of 1980 to the fourth quarter of 1983, house
prices rose 15.6%, whereas overall inflation, as measured by the GDP deflator, rose
by 26.6%. This trend seems easily explained by the change in fundamentals during
that period: the economy in 1980-1982 featured historically high real interest rates
and the worst economic recession in the post-war period. During this period,
nominal mortgage rates peaked above 18%, the unemployment rate reached double
digits, and real per capita income rose by a cumulative 5.1%. Although there have
not been large nominal declines in housing prices, the housing market has been
highly cyclical, with little appreciation in the early 1980s and early 1990s, and
significant appreciation in the late 1980s and 1990s.28
There have been historical examples of sharp nominal drops in local housing
markets, suggesting that if there was a bubble in some local markets at present, it
would not be unprecedented. A recent Federal Reserve study identified 17 states that
experienced significant declines in housing prices in the 1980s or 1990s. All 17 of
the price declines were linked to four specific economic shocks — the farm and rust
belt decline of the early 1980s, the drop in energy prices in the mid-1980s (which
negatively affected energy-producing states), the downturn in New England in the
late 1980s to early 1990s, and the downturn in California and Hawaii in the early
1990s (which the author links to the reduction in defense spending and Japanese
financial crisis). Interestingly, most of the states affected by the first two shocks did
not experience a preceding housing boom and thus cannot be categorized as bubbles.
Likewise, the author identified several other housing booms in the 1980s and early
1990s that never resulted in housing declines, underlining the point that not all
booms are bubbles.29
Figures 7, 8, and 9 focus on three cases, California, Texas, and New England,
where a sharp prolonged increase in house prices was followed by a significant and
27 A full discussion of bubbles appears later in the report. To clarify the following
discussion, the reader may wish to read that section first.
28 Interestingly, this cyclical pattern has occurred internationally as well, as demonstrated
in Peter Englund and Yannis Ionnaides, “House Price Dynamics: An International Empirical
Perspective,” Journal of Housing Economics, vol. 6, no. 2, June 1997, p. 119.
29 David Wheelcock, “What Happens to Banks When House Prices Fall?,” Federal Reserve
Bank of St. Louis, Review, vol. 86, no. 5, Sep. 2006, p. 413.

CRS-16
prolonged nominal decline in house prices.30 In California, after rising about 75%
in four years, nominal prices fell by 13.3% from the fourth quarter of 1990 to the first
quarter of 1995. In Texas, after rising about 25% in five years (with most of the
increase in the first two years), nominal prices fell by 14.4% from the first quarter of
1986 to the fourth quarter of 1988. In New England, after rising about 170% in six
years, nominal prices fell by 12.9% from the first quarter of 1990 to the first quarter
of 1995. In all three of the cases, although the trough of housing prices took several
years to be reached, most of the decline occurred in a relatively short time. In Texas,
prices were 12.8% down from their peak by the fourth quarter of 1987; in New
England, prices were down 10.6% by the third quarter of 1991. The pattern was a
little different in California, where most of the decline occurred toward the end of the
housing bust, rather than the beginning: prices fell 12.3% from the second quarter of
1992 to the first quarter of 1995. All three areas took several years after the bust had
ended to reach their previous peak, as seen in the figures. A hopeful sign for today
is that in each case, even after the crash much of the prior appreciation was not
reversed.
It would be difficult to explain these price increases and subsequent declines —
which are quite large in real terms — by macroeconomic factors alone. In each of
these cases, while there were periods of rising (and falling) interest rates within each
downswing, the episode as a whole could not be characterized as a period of rising
interest rates. However, in each of the three cases the local economy was
experiencing a recession, although in each case the housing bust exceeded the length
of the recession. In Texas, the state economy shrank 0.5% in 1987; in California, the
economy shrank 1.9% from 1992 to 1993; and in New England, the economy shrank
4.6% from 1990 to 1991. In California and New England, a simple comparison of
house prices and per capita disposable income suggests the pattern of house prices
in the 1980s fits a bubble. During the boom, house price increases exceeded income
gains. When house prices crashed, they were brought back into line with nominal
income (which helps explain why house prices did not decline as much as they had
previously risen). The Texas experience looks least like a classic bubble. There, the
pattern is different: house prices never exceeded income during the boom, which was
considerably smaller than the California and New England booms, and never caught
up to income gains after the housing crash.
30 There was also a smaller boom and bust cycle in New Jersey. Prices in New Jersey rose
in nominal terms by 123.7% between the first quarter of 1983 and the fourth quarter of
1989. They then fell by 7.8% in nominal terms through the third quarter of 1991.

CRS-17
Figure 7. California Housing Bust
240
220
200
180
160
Index (1980=100)
140
1201985 1987 1989 1991 1993 1995 1997
Nominal House Prices
Nominal Disposable Income Per Capita
Figure 8. Texas Housing Crash
240
220
200
180
160
Index (1980=100) 140
120
1001981 1983 1985 1987 1989 1991 1993 1995
Nominal House Prices
Nominal Disposable Income Per Capita
Source: Office of Federal Housing Enterprise Oversight, Bureau of Economic Analysis.

CRS-18
Figure 9. New England Housing Bust
300
280
260
240
100) 220
200
(1980= 180
160
Index
140
120
1001982 1984 1986 1988 1990 1992 1994 1996
Nominal House Price Index
Nominal Disposable Income Per Capita
Source: Office of Federal Housing Enterprise Oversight, Bureau of Economic Analysis.
About Bubbles
A bubble is said to exist when a price increases for a reason unattributable to
changes in the underlying supply and demand determinants of that object. The
problem with bubbles is that they cannot be identified with any confidence since
supply and demand determinants change over time, often unpredictably. If bubbles
could be accurately identified, they would never develop in the first place because
people would respond to the emergence of a bubble by selling the asset to avoid
future losses, thereby eliminating the bubble. Indeed, some economists who believe
markets are always rational and efficient use this logic to argue that bubbles can
never exist.31 Even if the rise in housing prices cannot be explained by the factors
identified in this report, the possibility that other unidentified “fundamentals”are
driving prices up, rather than a bubble, cannot be ruled out. For a policymaker to
identify a bubble requires some special insight into the functioning of a market that
all of the highly knowledgeable and specialized participants in that market lack.
31 For example, see Jean Tirole, “On the Possibility of Speculation Under Rational
Expectations,” Econometrica, Sept. 1982. The view that financial markets are rational and
efficient is the assumption underpinning mainstream financial theory, often referred to as
“efficient market theory.” For a recent defense of efficient market theory, see Burton
Malkiel, “The Efficient Market Hypothesis and Its Critics,” Journal of Economic
Perspectives, vol. 17, no. 1, winter 2003.

CRS-19
Although the recent behavior of the stock market lends strong support in favor
of the existence of bubbles, there are reasons to believe that bubbles are less likely
in housing markets than stock markets. Basically, it is the intangible nature of certain
assets that makes their pricing difficult and opens the possibility of a bubble forming.
For example, corporate equities are difficult to price because their price should equal
the expected future profitability of a company discounted to the present. Since
nobody knows how profitable a corporation will be in the future, the price of its
equity is subjective and imprecise. If enough market participants become
“irrationally exuberant,” a bubble can emerge. Houses are easier to price accurately
because they are more tangible. Each house has an observable number of rooms,
windows, fireplaces, and so on, and can be compared to other houses with similar
attributes. In many areas, buyers should also be able to anticipate that large enough
price increases will induce increases in supply that will push prices back down.
Still, there are intangible attributes to any given house (for example, tastes
change over time) that make pricing less than certain and open the possibility for a
bubble. These intangibles can be thought of as a bundle of services attached to the
house that include schools, entertainment, transportation, and so on. Since housing
is viewed by the owner as both a consumption good and an investment (and for those
buyers who do not live in the house, it is only an investment), the expected price of
the house in the future should be a factor in determining the value of the house today.
And the expected price of the house is uncertain since future interest rates, income,
inflation, and so on are uncertain. These factors may be reasonably predictable on a
national level — minimizing the potential for a national bubble — but they are highly
unpredictable at a local level, making a localized bubble possible. Any particular
local economy could boom in the future, and any given neighborhood could be the
next hot place to live. In land-scarce or otherwise constrained areas, supply cannot
easily be increased to push prices back downward. If these predictions come to pass,
an increase in price is justified. But if enough home buyers irrationally over-weigh
the probability of a certain neighborhood or even metropolitan area booming, a
bubble could emerge. This may be particularly likely to occur if their neighborhood
or city has boomed in recent years and buyers project that trend forward indefinitely.
Another difference between housing markets and stock markets is that there are
high transaction costs — financial and time — to buying or selling a home. This
means that buying or selling solely in response to mispricing is less likely to occur.
Furthermore, the only individuals who can take advantage of mispricing are those
who are not living in their homes or are free to move to non-bubble areas, which may
be unlikely because of professional, family, or community ties. Whether high
transaction costs make bubbles more or less likely is unclear. They reduce the
opportunity for “rational” traders to correct the mistakes of others, as economic
theory would suggest, but also reduce the opportunity for transactions motived solely
by short-term profit. And another factor that may make it more difficult for
“rational” traders to eliminate a bubble in the housing market than financial markets
is the fact that few methods exist in housing markets analogous to selling a stock
short.32
32 Investors sell stocks short by selling a borrowed stock that they believe is overpriced in
(continued...)

CRS-20
There has been recent anecdotal evidence in some local housing markets at the
peak of the boom that is sometimes identified as symptomatic of a bubble, including
prices selling for above list price, homes selling within days of listing, multiple bids
for a house, and buyers forgoing standard services, such as a home inspection, that
would delay a sale. While an examination of these phenomena is beyond the scope
of this report, it is unclear whether this behavior should be associated with a bubble
or not. It is behavior that suggests that buyers consider housing to be underpriced,
regardless of whether or not their reasoning is rational. It is somewhat surprising that
this behavior ever occurs since sellers can observe comparable recent transactions
and set their own price accordingly so that excess demand is eliminated (although
some have suggested that sellers sometimes intentionally underprice in order to
induce buyers to bid against one another). In 2006, the inventory of houses for sale
rose sharply, suggesting that this behavior has probably subsided.
Economists do not assume that prices are efficient because everyone is rational
all the time. Rather, economists assume that efficient pricing occurs because people
do not make systematic mistakes, and because enough people are correct that they
can take advantage of others’ mistakes until prices move back to their efficient point.
For example, if an investor realized there was a stock market bubble that would burst
soon, he could make large profits by selling stocks short. (Of course, those who
realize that there is a bubble may instead try to profit from the bubble by pushing
prices higher and selling before the bubble bursts.33) It may even be possible for the
actions of different people making different errors to cancel each other out, leaving
prices at the same level as if nobody had made a mistake.34 Thus for a bubble to
emerge and persist, the following criteria would have to occur: most people are
making a mistake which is not quickly corrected; most mistakes have a systematic
bias in the same direction; and those who realize that a mistake has occurred do not
or cannot take actions to profit from it that would reduce the bubble.
The efficient market hypothesis is not without its detractors in the economics
profession. A group known as behavioral economists have been trying to use
evidence of non-rational behavior which is well-documented in psychological
research to explain economic phenomena. Some of their efforts have been directed
to explaining how bubbles can form.35 A subset of this research has examined
housing bubbles, which is reviewed in the next section.
32 (...continued)
the anticipation that they will be able to buy back the stock in the future at a lower price,
earning a profit on the difference. Obviously, there is no direct way to sell a borrowed
house and then buy it back in the future.
33 Economic models tend to rule this behavior out since it is too risky that the bubble will
burst before the trade has been made. See Jean Tirole, “On the Possibility of Speculation
Under Rational Expectations,” Econometrica, Sept. 1982.
34 Eugene Fama, “Market Efficiency, Long-term Returns, and Behavioral Finance,” Journal
of Financial Economics, vol. 49, 1998, p. 283.
35 See the Journal of Economic Perspectives, vol. 17, n. 1, Winter 2003 for a symposium on
behavioral finance. A good non-technical discussion of psychological explanations of stock
market bubbles is the subject of Robert Shiller, Irrational Exuberance (Princeton, NJ:
Princeton University Press, 2000).

CRS-21
Research on Housing Bubbles
During the last housing boom and bust in the late 1980s, economists Karl Case
and Robert Shiller wrote a series of papers on whether the behavior of housing prices
in certain markets constituted a bubble. In two papers, the authors presented
evidence that housing markets are not efficient.36 In an efficient market, one could
not predict future house prices based on past housing prices since all existing
information should already be incorporated into the price, yet the authors demonstrate
that past housing prices have a statistically significant effect on future housing
prices.37 They also show that the rate of return on housing was far higher than other
assets during most of the 1970s and 1980s. This suggests that housing was
undervalued, rather than valued at its efficient price.
During the housing boom of the late 1980s, they presented evidence of a
housing bubble based on survey data.38 In 2003, they repeated this survey and found
similar results.39 They tried to ascertain whether people’s attitudes toward housing
prices reflected a rational response to changes in economic fundamentals or
“irrational exuberance.” They pointed to evidence that it was the latter. For example,
in the booming markets of San Francisco, Los Angeles, and insignificant,
homeowners expected prices to rise by at least 13% a year for the next 10 years,
which is significantly higher than the long run historical return on housing or any
other risk-adjusted asset.
Higgins and Osler look at the price rises and declines experienced by some
regions from the late 1980s and early 1990s, and attempt to distinguish how much
of the price change was due to economic fundamentals and how much was due to
non-fundamentals.40 Their measures of fundamentals include income, employment,
construction costs, interest growth, and expected appreciation. They estimate that the
10% real price decline experienced in New England, Mid-Atlantic, Pacific,
Mountain, and East South Central regions can be entirely explained by non-
fundamentals, which they measure through changes in affordability and the
regression residuals. They see this as evidence that the preceding price increase may
have been a bubble, although they cannot rule out that it was caused by unidentified
fundamentals. They estimate that the bursting of this potential bubble subsequently
36 Karl Case and Robert Shiller, “Forecasting Prices and Excess Returns in the Housing
Market ,” AREUEA Journal, vol. 18, n. 3, 1990, p. 263; Karl Case and Robert Shiller, “The
Efficiency of the Market for Single Family Homes,” American Economic Review, vol. 79,
no. 1, Mar. 1989, p. 125.
37 This result is confirmed in Zhong-guo Zhou, “Forecasting Sales and Price for Existing
Single-Family Homes: A VAR Model With Error Correction,” Journal of Real Estate
Research, vol. 14, no. ½, 1997, p. 155.
38 Karl Case and Robert Shiller, “The Behavior of Home Buyers in Boom and Post-Boom
Markets,” New England Economic Review, Nov./Dec. 1988, p. 29.
39 Karl Case and Robert Shiller, “Is There a Bubble in the Housing Market?,” Brookings
Papers on Economic Activity 2, 2003, p. 299.
40 Matthew Higgins and Carol Osler, Asset Market Hangovers and Economic Growth,
Federal Reserve Bank of New York, Research Paper no. 9801, Jan. 1998.

CRS-22
reduced housing investment by five percentage points in the affected regions. Some
of their measures of non-fundamentals, including overbuilding and credit availability,
are statistically insignificant.
Abraham and Hendershott offer evidence that the large boom and bust patterns
of housing prices experienced in the Northeast and West at times cannot be explained
by changes in supply and demand fundamentals and are best explained as bubbles.
For northeast cities, they estimated a “50% gap in 1988 between actual and
equilibrium prices” and a 15%-20% gap in western cities.41
More recently, Green attempted to determine whether a bubble existed in the
Santa Clara County, California housing market.42 He hypothesizes that the behavior
of the stock market is a major determinant of housing prices in Santa Clara, due to
its location in Silicon Valley. He uses the historical relationship between the stock
market and housing prices to forecast whether the recent increase in the stock market
can explain the large increase in housing prices. His results are questionable,
however, since he relies so heavily on the stock market explanation, and does not
consider more traditional explanations such as income. Further, his stock market
model suggests that housing prices should have risen even more than they did in the
late 1990s. And since his study was published, local house prices have stopped rising
rapidly, but have not fallen with the stock market as his research would predict.
In two recent reports, Zandi and Youngblood define local housing price
increases as bubbles if prices are more than one standard deviation above their
estimated equilibrium value, based on their respective criteria. Youngblood finds
evidence of bubbles in 45 metropolitan areas and Zandi finds evidence of bubbles in
41 metropolitan areas.43
In an international study covering the past three decades, Borio and McGuire
find that peaks in equity markets occur on average two years before peaks in housing
markets, suggesting that a fall in the stock market may temporarily boost housing
markets, but only temporarily. They show that the peak in equity prices are better
predictors of housing price peaks than interest rates, GDP growth, or unemployment.
The applicability of their results to U.S. data is limited, however, by the fact that U.S.
housing prices are significantly less volatile than prices in many foreign countries.44
41 Jesse Abraham and Patric Hendershott, “Bubbles in Metropolitan Housing Markets,”
Journal of Housing Research, vol. 7, no. 2, 1996, p. 191.
42 Richard Green, “Can We Explain the Santa Clara Housing Market?”, Housing Policy
Debate, vol. 13, no. 2, 2002, p. 351.
43 Michael Youngblood, “Is There A Bubble in Housing?”, GMAC-RFC Securities Report,
March 2003; Mark Zandi, “House Price Bubbles,” Regional Financial Review,
Economy.com, Aug. 2002.
44 Claudio Borio and Patrick McGuire, “Twin Peaks in Equity and Housing Prices?,” BIS
Quarterly Review, Bank for International Settlements, Mar. 2004, p. 79.

CRS-23
Leamer argues that a house should be valued using a price-earnings ratio
analogous to a popular stock valuation method.45 For a house, the future implicit rent
less maintenance discounted to the present are the house’s future earnings and should
therefore determine the price of the house. If present rents are correlated with future
rents, today’s “earnings” should give an indication of the house’s fundamental value.
Houses with a high P/E ratio are indicative of a bubble. A major drawback to this
analysis is that it does not control for mortgage rates. When rates fall, the cost of
financing a house relative to paying rent falls, yet the P/E ratio would remain
unchanged. In addition, accurately measuring the P/E ratio empirically is difficult.
Implicit rents — present and future — and the proper discount rate cannot be easily
estimated. Historical earnings are only a good guide to future earnings if supply and
demand fundamentals are constant over time. To measure earnings, Leamer uses the
rental data from the CPI index, which is problematic for two reasons. First, the CPI
data is reported as an index, not in dollars, so only historical trends can be observed.
A numerical P/E ratio value that could identify bubbles directly cannot be calculated.
Second, the CPI keeps quality constant in its measurement, so comparisons between
the CPI and house price measures such as the OFHEO house price index or median
house prices are akin to comparing apples to oranges. McCarthy and Peach argue
that the Census Bureau’s constant-quality index is more appropriate for measuring
P/E ratios, and after using this ratio and adjusting for the fall in interest rates there
is little evidence of a bubble.46
McCarthy and Peach then construct and estimate a structural model, and
estimate that the “equilibrium” price that houses should currently be, based on supply
and demand factors, is higher than current market values. Thus, they find little
evidence of a bubble. The fall in interest rates is the main determinant of the recent
rise in equilibrium prices in their model.
Macroeconomic Effects of a Bubble
The primary reason why policymakers may be concerned about a housing
bubble would be if it had an effect on the wider economy — particularly after it
bursts. A bubble could deflate slowly via a long period of stagnant prices or
suddenly. It is the latter scenario that threatens macroeconomic stability. There are
several channels through which the economy could be affected: a reduction in
housing wealth could lead to a reduction in consumption, lower housing prices could
lead to a slowdown in the construction industry, lower housing prices could lead to
problems in the financial system, and lower housing prices could cause personal debt
burdens to become unsustainable. Each effect is discussed below.
Effects on Consumption. Many analysts have speculated that the recent
rise in housing prices is having a positive “wealth effect” on personal consumption.
45 Edward Leamer, “Bubble Trouble? Your Home Has A P/E Ratio Too,” UCLA Anderson
Forecast Report, June 2002 and update June 2003. See also John Krainer, “House Price
Bubbles,” FRBSF Economic Letter 2003-06, Mar. 7, 2003.
46 Jonathan McCarthy and Richard Peach, “Are Home Prices the Next ‘Bubble’?” FRBNY
Economic Policy Review, Dec. 2004.

CRS-24
Since consumption has been the strongest sector of a weak economy, they reason that
a decline in house prices could push the economy back into recession.47 Viewing
housing as an asset from a life-cycle saving perspective, an increase in the value of
that asset would increase an individual’s potential lifetime consumption, assuming
the asset would be liquidated at some point. The life-cycle theory suggests that the
individual would wish to spread the consumption derived from the future income
from the sale of the asset evenly over his lifetime beginning immediately. Since the
consumption derived from the increased wealth is being spread over a lifetime, the
increase in consumption in any given year would be very small. One study estimated
that households increase consumption by 0.08% for every 1% increase in housing
wealth, which was about three times larger than the authors’ estimate of a stock
market wealth effect.48 Another study estimated that households increased
consumption by 0.03%-0.09% for every 1% increase in housing wealth, compared
to a 0%-0.07% increase in consumption for a 1% increase in stock market wealth.49
Still, if housing wealth increased sharply, as it has in some parts of the country
recently, even small wealth effects can add up to large effects on the macroeconomy.
While this analysis has much to recommend it, there are some offsetting factors
that could diminish the importance of a wealth effect. First, the proponents of the
wealth effect tend to focus on the gains to the sellers, but the housing market is made
up of both buyers and sellers. When prices increase, sellers are wealthier and can
increase their lifetime consumption. But this is exactly offset by buyers, whose non-
housing wealth falls when house prices go up (e.g., they will now have to borrow
more to purchase the house and devote more of their income to repaying their
mortgage). Thus, a change in housing prices causes an income transfer from buyers
to sellers, which reduces the lifetime consumption of buyers by as much as the
lifetime consumption of sellers is raised.50
Second, housing is a highly illiquid asset with large transaction costs.
Therefore, it is more difficult to realize a housing capital gain than it is for other
assets. However, a homeowner could increase his consumption in other ways. He
could either save less out of other income than previously planned or he could take
out a home equity loan. When considering the effect of home equity loans on
consumption, however, we should be careful to limit it to loans used for
consumption, not other forms of saving, which could include paying down other
forms of debt or using the capital to renovate or upgrade the house (although much
47 For example, see “Going Through the Roof — House Prices,” The Economist, Mar. 30,
2002, p. 77.
48 John Benjamin, Peter Chinloy, and G. Donald Jud, “Real Estate Wealth Versus Financial
Wealth in Consumption,” mimeo, July 2002. The results are statistically significant at the
1% level.
49 Karl Case, John Quigley, and Robert Shiller, Comparing Wealth Effects: The Stock
Market Versus the Housing Market, National Bureau of Economic Research, Working Paper
8606, Nov. 2001. The wealth effect for housing is statistically significant at the 1% level;
the wealth effect for equities is statistically insignificant in some regressions.
50 In the long run, there can be no net wealth effect because consumption is determined by
the economy’s productive capacity, and a change in the price of an existing asset does not
alter the economy’s productive capacity.

CRS-25
of this investment would be classified as consumption in the GDP accounts).
Personal saving rates have indeed fallen in recent years and are now slightly negative,
which suggests that this channel of the wealth effect has more or less been exhausted.
Third, there is another, more direct, channel through which a rise in housing
wealth influences consumption: by reducing disposable or after-tax income. Most
counties or municipalities levy a property tax as a percentage of the house’s value so
that payment rises when a house’s assessed value rises. This factor partially offsets
any positive wealth effect.
Fourth, the life-cycle model makes several specific assumptions in determining
whether or not consumption would be affected. The appreciation must be
unexpected, since an expected appreciation would already have been incorporated
into the individual’s saving and consumption plans. The asset cannot be held until
death. It must at some point be liquidated and consumed, which is a problematic
assumption with owner-occupied housing since the homeowner still needs to live
somewhere if the asset is liquidated. To increase consumption upon the liquidization
of a primary residence, the owner must sell and move to a lower cost region or
residence. (This is not the case for houses that are rented or secondary residences.)
The riskiness surrounding the appreciation would also be an important determinant
of how much consumption would increase when housing wealth increased. Some
economists have argued that the wealth effect from housing is greater than the wealth
effect from the stock market because gains in housing prices are less likely to be
suddenly reversed. Nevertheless, if a home owner believed that the appreciation in
his house was caused by a bubble, he should be hesitant to increase consumption
since the bubble might burst at any second. Applying these principles to the state of
housing markets today, it suggests that in markets with little appreciation,
consumption would not be greatly altered since the appreciation was probably
expected, and in markets with rapid appreciation, home owners may feel hesitant to
spend that wealth because of their uncertainty about whether or not the appreciation
represents a bubble that may soon burst. If prices did subsequently fall but remained
above pre-boom levels, this hesitancy may prevent consumption from falling
significantly.
Finally, although aggregate demand may increase along with housing prices
through the consumption channel, it is important to distinguish between the
macroeconomic effect of an independent change in house prices and a change in
house prices that is the side effect of another policy change. For an example of the
latter, consider a change in monetary policy that lowers interest rates. This would be
expected to increase investment and consumption in the economy. One of the
channels through which a monetary easing would increase consumption would be the
wealth effect from the increase in housing prices. But the change in house prices in
this example has no independent effect on the economy since house prices only rose
as a result of interest rates declining. While it is fair to talk of independent changes
in housing prices as affecting overall GDP, the change in GDP that results from
another policy change should be attributed to the policy change itself, not to the
change in housing prices that results from the policy change. In this example, it is
more accurate to describe this change in consumption as resulting from the decline
in interest rates rather than the rise in house prices.

CRS-26
This distinction between the macroeconomic effects of an independent rise in
housing prices and a rise that results from another policy change has an important
implication for analyzing the macroeconomic effects of a potential housing bubble.
It suggests that movements in housing prices should only be a specific concern of
macroeconomic policymaking if they are caused by a bubble that is independent of
economic fundamentals. To understand why, it is useful to consider again the
interest rate example. Most economists would agree that the objective of monetary
policy is to keep the growth of output and inflation stable. Sometimes higher interest
rates are necessary to slow the growth of aggregate demand to meet this objective.
If house prices fell as a result of higher interest rates, this would reduce consumption
spending through the wealth effect channel. Since slower aggregate demand growth
was the intention of the monetary policy tightening, this would be of no concern to
policymakers. On the other hand, if a housing bubble suddenly burst, policymakers
would likely be concerned since the bursting of a bubble will shock aggregate
demand and move it away from the growth rate policymakers had targeted.
In general, whether a decline in consumption as a result of the bursting of a
housing bubble were a concern to the macroeconomy would depend largely on the
state of the economy at the time the bubble was burst. If economic activity were
robust, a decline in consumption, which is equivalent to a rise in saving, could be
translated into a rise in capital investment fairly rapidly, and this would be beneficial
to the economy in the long run. A moderate decline in consumption is really only
problematic if the economy is operating below full potential, in which case it could
lead to more underutilized resources in the economy.
Effects on the Housing Industry. On the supply side of the economy, a
decline in housing prices would have a direct effect on the housing industry.
Keeping construction costs constant, lower housing prices would lead to lower
revenues and profits for the housing industry. As a result, fewer new houses would
be built and the output of the housing industry would decline. Again, in evaluating
this decline, it is useful to differentiate between house price declines caused by a
change in fundamentals and the bursting of a bubble. If the decline in housing output
were caused by the bursting of a bubble, it would have negative consequences for the
macroeconomy. Alternatively, a decline in the output of the housing sector caused
by an increase in interest rates cannot be judged to be good or bad except in the
context of the overall state of the economy. Interest-sensitive industries benefit most
when interest rates are lowered because the economy is below full employment, and
bear the brunt of an increase in interest rates because the economy is above full
employment.
For this reason, the residential investment sector has historically been one of the
more volatile sectors of the economy, as shown in Table 4. Recently, it has been one
of the fastest growing sectors of the economy, growing an average of 9% annually
in 2003-2005. The Economist estimates that “over two-fifths of all private-sector
jobs created since 2001 have been in housing-related sectors, such as construction,
real estate, and mortgage brokering.”51 In the past three decades, the sector has
undergone two busts, both of which coincided with periods of rising interest rates.
51 “The Global Housing Boom,” The Economist, June 18, 2005, p. 66.

CRS-27
From 1980 to 1982, residential investment shrank by a cumulative 40.6% in real
terms, while GDP grew by 0.2%. From 1988 to 1991, residential investment shrank
by 23.9%, while GDP grew by 9.2%. It is useful to note that the latter bust preceded
the recession, which did not begin until July 1990. The period of monetary
tightening preceding that recession spanned from 1988 to 1989, as measured by the
federal funds rate. Beginning in the fourth quarter of 2005, residential investment
has declined. For the first two quarters, the decline was negligible, but in the second
quarter of 2006, it declined by 9.8%. It remains to be seen whether this decline
marks the beginning of a trend or if it is only a blip.
Table 4. Historical Housing Busts
Change in Residential
Federal Funds
GDP Growth
Investment
Rate
1980-1982
-40.6%
0.2%
Rose from 9.0% in
7/80 to 19.1% in 6/81
1988-1991
-23.9%
9.2%
Rose from 6.6% in
2/88 to 9.9% in 3/89
Source: Bureau of Economic Analysis; Federal Reserve.
Note: Percent changes are cumulative total.
Effects on the Financial Sector. Besides the overall effects on investment
spending and consumption, a housing bubble could harm the financial sector. Since
efficient financial intermediation is vital to a healthy macroeconomy, if the bursting
of a bubble caused widespread harm to the financial sector, the overall economy
could suffer. There are a number of reasons that a bursting bubble’s effect on the
financial system could be limited, however. A change in the value of a house has no
direct effect on the value of a loan. Thus, a bursting bubble would only be harmful
to the financial system if homeowners responded by defaulting on existing loans.52
While this strategy could be profitable in theory when the value of a mortgage
exceeds the value of the home, in reality it seems unlikely given that houses are not
solely investments to most homeowners and that people wish to maintain a good
credit history. For the value of the mortgage to exceed the value of the house, the
loan would have to have a high loan-to-value ratio (a loan made fairly recently and
probably to a first time homeowner). The data confirm that widespread default is
rare: in the last recession, foreclosure rates only rose from 0.27% in 1988 to 0.34%
in 1991, while delinquency rates rose from 4.79% to 5.03% during that period.
Studies have found that the loan-to-value ratio is an important predictor of default,
52 The collapse of a bubble could also reduce housing sales, since some people take their
homes off of the market rather than lower the asking price. This would require depository
institutions to shift from mortgage lending to other types of lending or investments. While
this would not be expected to greatly affect the overall profitability of the banking sector,
some institutions might find the shift in lending difficult, particularly if they are small and
heavily reliant on mortgage lending.

CRS-28
but that the effect is small.53 One study estimated that “an expected net equity of
negative 10% was predicted, under normal circumstances, to cause less than a 5%
likelihood of default.”54 There was a spike in default rates in Massachusetts that
exceeded the national average, however, following the real estate bust of the early
1990s.55
An increase in the default rate could be harmful to three types of financial
institutions: depository institutions when they keep the mortgage as an asset,
mortgage insurers, and investors who purchase mortgages on the secondary market.
The largest investors in secondary markets are the government sponsored enterprises
(GSEs), Fannie Mae and Freddie Mac. On average, real estate secured lending
makes up about one third of a depository institution’s assets. For savings institutions,
more than one half of total assets are real estate secured.
If the bubble were localized, the chance of harm to the overall financial system
would be reduced. National institutions such as large banks and the GSEs should be
diversified enough from local risk that they should not be seriously harmed by the
bursting of a local bubble. Small, local institutions, particularly savings institutions
would be more vulnerable to the bursting of a local bubble, but a significant number
of these institutions would need to become insolvent before the overall financial
sector was detrimentally affected. On the other hand, one should keep in mind that
the regions that may be experiencing bubbles (California and the northeast) make up
a large fraction of the national housing market.
The Household Debt Channel. Another point of macroeconomic concern
raised in relation to the possible housing bubble is its effect on household debt.
Commentators have argued that the housing bubble has led to households taking out
too much home equity debt, and when the bubble bursts, consumers will be forced
to retrench, causing a recession.56 Data from the Fed reveal that home equity (second
mortgage) debt has indeed risen rapidly in the past few years: home equity loans have
risen from $402 billion in 1999 to $911 billion in the first quarter of 2005. While
home equity loans are often described as supporting consumption expenditures, this
is not necessarily the case. They may also be used to pay off other higher interest
debt or for investment, which could be physical (e.g., home improvements), financial,
53 For a literature review, see Roberto Quercia and Michael Stegman, “Residential Mortgage
Default: A Review of the Literature,” Journal of Housing Research, vol. 3, no. 2, 1992, p.
341.
54 Kerry Vandell and Thomas Thibodeau, “Estimation of Mortgage Defaults Using
Disaggregate Loan History Data,” AREUEA Journal, fall 1985, p. 314.
55 For a history of the effect of previous housing busts on financial institutions, see David
Wheelcock, “What Happens to Banks When House Prices Fall?,” Federal Reserve Bank of
St. Louis, Review, vol. 86, no. 5, Sep. 2006, p. 413.
56 For example, see Dean Baker, “The Run-Up in Home Prices: Is It Real or Is It Another
Bubble?”, Center for Economic and Policy Research, Aug. 2002.

CRS-29
or human (e.g., educational spending).57 Thus, it is only the portion of home equity
loans used for consumption that decreases the nation’s saving.
Although concerned analysts often point to the ratio of total consumer credit to
disposable personal income, which reached 118% in 2005, a better measure of the
debt burden is debt payments as a percentage of income. This figure equaled 14%
in 2005, up from 12% in the late 1990s. Mortgage payments as a percentage of
income have risen less, to 10.9% in 2005. Thus, although total debt increased, the
decline in interest rates meant that the burden of debt declined, which suggests that
consumers are rationally responding to the incentive of lower interest rates, rather
than a bubble.
To the extent that the interest on household debt is adjustable, an increase in
interest rates would increase the burden of servicing that debt, perhaps to
unsustainable levels. Nearly two-thirds of new mortgages in the second half of 2004
were estimated to be adjustable rate,58 although the percentage of total outstanding
mortgages with adjustable rates is much lower (17% in 2003).59 If consumer debt
levels became unsustainable, this could lead to a decline in consumption
expenditures, and possibly a short-term decline in aggregate spending. This may be
more of a cause, then an effect of the housing bubble, however. Greater borrowing
in response to lower interest rates may be partially fueling the rise in house prices,
and the growth in both borrowing and house prices could reverse when rates rise.
A housing bubble, on the other hand, would have little effect on a household’s
ability to service its debt. The only channel between household debt and a housing
bubble comes from the fact that when a household takes out a home equity loan, it
decreases the equity in the home. This increases the probability that a decline in
housing prices would cause the value of the house to fall below the value of the
outstanding debt, making default a profitable strategy. As was discussed previously,
most people do not seem to default on mortgages solely for profit motives, so this
effect may be limited. The bursting of a housing bubble could also reduce new home
equity lending since it would reduce the equity that homeowners could use for
collateral, and to the extent that home equity lending is used for consumption,
aggregate demand could be reduced through that channel.
Another housing bubble concern that has been raised relates to mortgage
refinancing. Many households have recently taken advantage of the low interest rate
atmosphere to refinance their mortgages on more favorable terms, and this could
boost consumption since it frees up disposable income that was previously devoted
to debt service. Goldman Sachs estimates that cash-out refinancing rose sharply to
57 Although home improvements would conceptually be identified as an investment since
it increases the value of housing assets, some types of home improvements would actually
be counted as consumption in the GDP accounts. The same is true of education: while it
adds to “human capital,” it is counted as consumption or government spending in the GDP
accounts.
58 National Association of Realtors, Real Estate Outlook, June 2005, p. 4.
59 Jan Hazitus, “Daily Financial Market Comment,” Goldman Sachs newsletter, Mar. 15,
2005.

CRS-30
an average of $150 billion to 200 billion from 2001 to 2004.60 As with home equity
loans, only a portion of refinancing directly supports consumption spending. And to
an even greater extent than home equity loans, mortgage refinancing activity will be
more dependent on interest rates than housing prices. Thus, it is unlikely that
refinancing activity would be influenced by a housing bubble.
Policy Options
Some analysts have argued that when a bubble emerges, the Fed should raise
interest rates to attempt to prick it before it gets any bigger. Policy responses to a
potential bubble are always problematic because of the inherent difficulty in
differentiating a bubble from price increases motivated by changes in fundamentals.
For this reason, the Fed has continued to prefer to limit its policy responses to
stabilizing inflation and output, and to worry about bubbles only when there is
concrete evidence that they are affecting inflation or output.61 Critics argue that this
strategy amounts to “too little, too late,” because by the time a bubble has deflated,
the damage to the economy has already been done.
Furthermore, housing bubbles are unlikely to be a concern of macroeconomic
stabilization policy when the bubbles are localized. Although the bursting of a
localized bubble could have a negative effect on a local economy, it is unlikely to
have spillover effects on the nation as a whole. Stabilization policy is focused on the
national economy only, and could not be accurately aimed at local markets if desired.
Monetary policy must be exclusively national since financial markets are national:
any attempt to change interest rates in one region would lead capital to flow in or out
of that region until interest rates returned to the national average. Fiscal policy could
be theoretically directed toward a specific region, although its efficacy is limited
when one considers that goods markets are also highly integrated on a national level.
On the other hand, the markets that are candidates for a bubble are large enough that
they comprise a significant fraction of the nation’s total housing wealth. For
example, the Wall Street Journal reported that the 22 metropolitan areas with the
fastest growing house prices comprised 35% of the nation’s housing wealth.62 Thus,
the potential bursting of local housing bubbles could have ramifications for the
overall economy.
If stabilization policy cannot be used to effectively offset the macroeconomic
effects of a housing bubble, can a bubble be eliminated directly through the use of
public policy? Policy tools could be used to reduce or suppress housing demand,
such as a tightening of lending standards. As discussed above, a growing share of
mortgages are non-traditional in this housing boom, and many of these mortgages,
such as the “interest only ARMs” and “option ARMs,” have the potential to result
60 Jan Hatzius, “U.S. Economic Analyst,” Goldman Sachs newsletter, Apr. 15, 2005.
61 See, for example, Ben Bernanke, “Asset Price ‘Bubbles’ and Monetary Policy,” speech
before the National Association of Business Economics, New York, NY, Oct. 15, 2002.
62 Greg IP, “Booming Local Housing Markets Weigh Heavily on Overall Sector,” Wall
Street Journal, June 20, 2005, p. 1.

CRS-31
in rapid debt accumulation in ways that may not be fully understood by borrowers.
These mortgages may have allowed some borrowers to purchase houses that they
cannot really afford, on the belief that their property would become affordable once
prices have risen further. Obviously, if a bubble were to burst, this strategy would
dangerously backfire. This could potentially lead to a vicious cycle if these owners
were forced to sell at “fire sale” prices, thereby pushing down the value of all
properties.
But again, the problem with policies to reduce demand is the uncertainty
concerning whether the price increase is being driven by fundamentals or a bubble.
If price increase were being driven by fundamentals, policy changes to suppress
demand could be seen as needlessly punishing the housing industry. Since housing
bubbles are more likely to be local than national, if policymakers decided to use
public policy to suppress housing demand, policies that could be beneficial in bubble
areas would be damaging in areas that have experienced little price appreciation. On
the other hand, higher priced markets may be more affected at the margin by a
tightening of lending standards.
Price controls are another policy tool to prevent a bubble from forming, but
economists are nearly unanimous in their belief that in competitive markets with
many buyers and sellers, such as the housing market, price controls do more harm
than good. Price controls do not eliminate excess demand — even if there is a
bubble — they shift excess demand into other areas. They would create large
incentives to shift higher costs into forms that would not be covered by the price
controls. Furthermore, they would eliminate the incentive to the supply side of the
market to increase the housing stock, which is the only long-term solution to bringing
prices back down when demand has increased for fundamental reasons.
Policy changes could attempt to remove impediments to increasing supply,
which some economists claim have contributed to rising prices; however, these
impediments mostly fall under the jurisdiction of local government. The government
could also attempt to reduce prices by increasing the housing supply directly through
an increase in public housing investment. Yet the goal of public housing has
traditionally been poverty reduction, whereas a bubble could affect all income levels,
or even higher-income housing exclusively. Furthermore, a bubble is a temporary
phenomenon that will be reversed, whereas increasing public housing is a long-term
response that involves a permanent change in supply.
Finally, some argue that “speculators” are responsible for bubbles, and policies
to curb speculation could eliminate bubbles. In practical terms, it would be dubious
and burdensome for the government to attempt to differentiate between speculative
behavior and normal investment or consumption. Furthermore, although theory is
ambiguous, if anything, it seems more likely that speculators would prevent or reduce
bubbles than cause them. If we define speculators as individuals attempting to profit
from pricing mismatches, then we would expect to see them disinvest from areas that
are overpriced, and the process of disinvestment would help deflate the bubble before
it became serious. If speculation is defined as short-term holdings, then these are
already discouraged by unfavorable capital gains tax treatment.

CRS-32
Conclusion
While the increase in U.S. housing prices since 1997 has been considerable, a
reasonable argument can be made that until recently the increase can be explained by
changes in the economic fundamentals that determine housing supply and demand.
Increases in income and declines in interest rates through 2003 increased housing
demand and placed upward pressure on prices. Since 2004, rapid increases in
housing prices have gone hand in hand with small increases in interest rates, causing
mortgage affordability to deteriorate rapidly, which is consistent with (but not proof
of) a bubble.
But national data mask significant regional differences. In much of the South
and Midwest, the increase in housing prices since 1997 has been modest and
unsurprising. By contrast, East Coast states and the West have experienced
extremely rapid house price appreciation during that time, significantly more than can
be explained by inflation, interest rates, and income alone. Recently, New England,
where prices boomed earliest, has seen price appreciation rates fall below the national
average, while prices in parts of the West, where rapid appreciation started later,
grew fastest in the nation. Bubbles, by their very nature, can never be identified
beforehand with confidence. But the possibility of bubbles currently existing in
many local markets, and perhaps even regions as a whole, is significant. These
regions comprise enough of the national economy that the potential bursting of a
housing bubble could have economy-wide ramifications.
Nationally, the U.S. has seen prolonged periods of stagnant house prices, but
never falling prices. Bubble or not, macroeconomic stability would be threatened
only if prices were to fall suddenly. While nominal house prices have never fallen
nationwide, regional price declines have occurred in the past. On the bright side,
these price declines were far smaller than the preceding ascent. This would diminish
the macroeconomic consequences of the bubble’s unwinding. The historical record
suggests that rising interest rates alone may not be enough to cause a housing crash
since the real estate busts of the recent past were all characterized by severe local
recessions.
Deflating bubbles are not the only source of price declines. Even if there is no
housing bubble in the nation as a whole, supply and demand factors could change in
the near future in such a way that downward pressure was exerted on prices. The
most likely source is a further increase in mortgage rates as the economic expansion
progresses, which would raise the cost of financing a home. Increasing supply, in
response to higher prices, is another plausible fundamental factor that could drive
prices down.
But if prices were to fall, it would be an independent cause for concern to
policymakers only if the fall were due to the deflation of a bubble. In that case,
residential investment could fall significantly, and consumption expenditures could
decline because of a negative wealth effect, although the decline would be only a
small fraction of the decline in wealth. (There are several reasons to believe that the
wealth effect has been exaggerated, however. For instance, the most direct link
between house prices and consumption is negative: higher property tax assessments

CRS-33
reduce after-tax income.) Profits in the housing finance sector could decline,
depending on what happens to default rates. Household debt would be unlikely to
become troublesome since it depends more on interest rates than house prices.
If house prices fell because of an external factor, such as an increase in interest
rates, there would be similar effects on residential investment, consumption, the
financial sector, and there would be a greater effect on household debt. But these
effects should be attributed to the source of the price decline, not the decline itself.
For that reason, the price decline could not be characterized as good or bad without
analyzing why the external factor had changed. For example, one could not
characterize an increase in interest rates as negative if it were caused by a booming
economy, even though it would place downward pressure on housing prices, all else
equal.
The appropriate policy response in the face of a potential bubble is problematic.
Although the bursting of a bubble may be harmful to the economy, all of the policy
options have their drawbacks. The consensus view is that macroeconomic policy is
best focused on stabilizing aggregate output and inflation, and giving special
attention to other issues like bubbles necessarily detracts from those other goals.
Tightening lending standards is a potential regulatory response given the role that
non-traditional mortgages have played in the recent boom. But the main problem
with a policy response to a bubble is identifying the bubble with confidence. That
requires policymakers to have some special insight into the functioning of a market
that all of the highly knowledgeable and specialized participants in that market lack.
Supply and demand determinants change unpredictably over time, so there is never
a fail-safe method to identify what the “right” price should be. Given this
uncertainty, microeconomic policy responses are problematic because policy options
that could effectively counteract a bubble could be quite harmful to the market if the
price rise is attributable to fundamentals. In any case, given that housing bubbles are
more likely local than national phenomena, the policy options available to the federal
government could potentially harm regions that have seen little price appreciation.

CRS-34
Technical Appendix
This appendix presents technical details and description of the forecast results
presented in Figure 6 of the report. Table 5 summarizes the characteristics of all
five models and compares the difference between actual housing prices at the end of
2002 and the forecast. Model 1 uses the ordinary least squares (OLS) regression
method to explain house prices in terms of the most fundamental variables one would
expect to influence housing prices: inflation, real income per capita, real mortgage
rates, seasonal dummy variables, and housing starts. As seen in Table 6, all of the
variables were statistically significant at the 1% level except for interest rates. In
other words, the model predicts that each variable will have an effect on house prices
different from zero in 99 out of 100 samples. Model 2 uses the same methods, but
expands the explanatory variables to include real non-housing net wealth (total net
wealth omitting housing assets and mortgage debt), population, and real tax payments
per capita (since taxes reduce the disposable income available to spend on housing).
All of the variables were statistically significant at the 1% or 5% level (including
interest rates) except for wealth, which was significant at the 10% level. Model 3
adds a linear time trend to the variables. This helps to reduce spurious correlation
between data sets that are highly correlated for reasons other than causation. This
may explain why the parameters on variables that had the wrong sign in Model 2
such as population and tax payments now have the correct sign. Adding a time trend
can also be thought of as a way to compensate for omitted variables. All of the
variables in Model 3 except taxes were statistically significant at the 1% level.

CRS-35
Table 5. Description of Forecasting Models and Results
M

odel 1
M

odel 2
M
odel 3
Model 4
Model 5
Core and
x
x
x
x
x
Seasonal
Variables
Expanded
x
x
x
x
Variables
Time Trend
x
Distributed
x
Lag Model
Auto-
x
regression
% Difference
12.7%
22.4%
22.9%
16.0%
0%
from Actual
in 2002:4
Source: Author’s calculations based on data from Bureau of Labor Statistics, Bureau of Economic
Analysis, Census Bureau, Federal Reserve, Freddie Mac.
Notes: Data are quarterly from 1975:2 to 2002:4. All data except mortgage rates were logged before
being used. Real data was created by deflating nominal data by the GDP deflator.
Core variables: Inflation, Real Income per Capita, Real Mortgage Rates, Housing Starts, Seasonal
Dummy Variables.
Expanded variables: Real Non-Housing Net Wealth per Capita, Population, Real Tax Payments Per
Capita
It is possible that housing prices respond to changes in supply and demand with
a time lag. This could occur because expectations change slowly, for example. To
compensate for this possibility, Model 4 uses a distributed lag model that allows each
explanatory variable in the current quarter and previous four quarters to affect
housing prices. All of the variables were statistically significant at the 1% or 5%
level except for wealth. Another way to compensate for sluggish price adjustment
is to let previous house prices influence current prices. This is done in Model 5 using
an autoregressive method, allowing house prices over the four previous quarters to
affect current house prices and also allowing current values of the core explanatory
variables affect current house prices. This method allows for the possibility that
house prices themselves adjust sluggishly, or can be thought of as a way to capture
the lagged effects of omitted variables. In this model, only lagged housing prices,
housing starts, and population were statistically significant, and several variables had
the wrong signs. This indicates that the past values of housing prices are a better
predictor of current prices than current supply and demand determinants. But it
should be noted that this model would attribute any changes in house prices due to
lags in the explanatory variables to lags in house prices. This suggests that Model
4 might be the best model for our purposes.

CRS-36
Table 6 presents the regression results, listing the beta coefficients for each
variable and the standard error beneath it in parentheses. For variables that were
logged, the beta coefficients can be roughly interpreted as percentage change. For
example, Model 1 predicts that a 1% increase in real income would lead to a 0.918%
increase in housing prices. Because their coefficients are so much larger than the
other variables, income, inflation, and population are essentially driving the results
in Models 1-4. Since income growth and inflation has been low in the last two years,
none of the models predict the large increase in house prices during that period.
Variables such as mortgage rates that one would expect to explain recent house price
appreciation had a much smaller effect on house prices historically than one would
expect.
There are many shortcomings to the models that suggest their results are far
from definitive. Although the root mean square error (a measure of overall goodness
of fit) is very high and most variables are highly significant, this is not unusual for
time-series results and does not necessarily indicate that the results are reliable. On
the contrary, there is not sufficient variation between the variables to yield reliable
estimates. Although statistically significant, the estimated effect of many variables
was negligible. In some models, the coefficient for population and tax payments had
the wrong sign.63 A major shortcoming with the regressions is the fact that the
explanatory variables were not truly independent of one another as OLS requires for
unbiased estimation. Income, inflation, and interest rates are all interrelated variables
that do not meet this criteria. The model also assumes that housing starts cause
changes in house prices, whereas in reality causation runs in both directions. While
an increase in housing starts push down prices, as modeled in the forecasts, causation
runs in the other direction as well — higher house prices leads to an increase in
housing starts. The simple modeling used here does not capture this other effect and
leaves the meaning of the results ambiguous.
Since the results from Model 5 yield such different results, and it is the model
with the lowest root mean square error, it is useful to focus on its proper
interpretation. By allowing for past house prices to influence current prices, it is the
only model that does not explain house prices exclusively by supply and demand
fundamentals. The lagged effect of past housing prices used in Model 5 has a much
larger effect on current prices than any of the other explanatory variables. This leads
the forecast to be much closer to actual results in the past five years, but it also makes
the forecast the least useful for identifying a bubble because supply and demand
variables have such a small role in predicting prices. In other words, if there were a
bubble present last year, rather than identify it, Model 5 would predict it to continue
this year.
63 Whether taxes should have had a negative effect on house prices in these particular
regressions is open to debate. Higher taxes reduce the disposable income available to
individuals to spending on housing, reducing demand, all else equal, and suggesting a
negative effect on house prices. But all else may not be equal in these regressions. For
example, if taxes are raised to pay for government services that increase house values, such
as public safety, the effect on house prices is now ambiguous.

CRS-37
Table 6. Regression Results Underlying The Forecasts
(Dependent Variable = House Price Index)
Independent
Model 1
Model 2
Model 3
Model 4
Model 5
Variable
Intercept
-7.271c
1.166
-46.958c
-1.185
-29.272c
(1.192)
(1.129)
(8.115)
(1.365)
(7.036)
Real Income
0.918c
0.665c
1.646c
0.875c
0.096
per Capita
(0.156)
(0.127)
(0.213)
(0.154)
(0.095)
Inflation
0.791c
1.172c
1.403c
0.934c
-0.594
(0.082)
(0.067)
(0.081)
(0.076)
(0.340)
Real
-0.003
-0.010c
-0.010c
-0.005b
0.000
Mortgage
(0.002)
(0.006)
(0.002)
(0.002)
(0.000)
Rates
Housing
-0.055c
-0.034b
-0.040c
0.051c
0.015a
Starts
(0.018)
(0.011)
(0.012)
(0.013)
(0.007)
Real Non-
0.123a
0.395c
0.049
0.055
Housing Net
(0.067)
(0.092)
(0.084)
(0.040)
Wealth per
Capita
Population
-1.990c
4.949c
-1.803c
6.409c
(0.214)
(1.167)
(0.260)
(1.510)
Real Taxes
0.239c
-0.057
0.314c
-0.021
per Capita
(0.047)
(0.072)
(0.061)
(0.026)
Time Trend
-0.026c
(0.004)
House Price
1.346c
Index, Lag 1
(0.110)
House Price
-0.043
Index, Lag 2
(0.1948)
House Price
-0.049
Index, Lag 3
(0.194)
House Price
-0.266a
Index, Lag 4
(0.111)
Root Mean
0.057
0.115
0.110
0.067
0.007
Square Error
Source: Author’s calculations based on data from Bureau of Labor Statistics, Bureau of Economic
Analysis, Census Bureau, Federal Reserve, Freddie Mac.
Notes: Data are quarterly from 1975:2-2002:4. All data except mortgage rates were logged before
being used. Real data was created by deflating nominal data by the GDP deflator. Seasonal dummy
variables are also included in each model.
a. Statistically significant at 10% level.
b. Statistically significant at 5% level.
c. Statistically significant at 1% level.