House Price Cycles and the Real Economy

DENIZ IGANIMF – RESEARCH

LIME WORKSHOP BRUSSELS - DECEMBER 8, 2012

House Price Cycles and the Real Economy

Disclaimer: Views expressed in the presentation and during the talk are those of the presenter and should not be ascribed to the IMF.

Background Work

The Changing Housing Cycle and the Implications for Monetary Policy – Chapter 3 in WEO April 2008

Housing, Credit, and Real Activity Cycles: Characteristics and Comovement – JHE 2011 (revision of Three Cycles: Housing, Credit, and Real Activity, IMF WP 09/231)

Global Housing Cycles – IMF WP forthcoming

Early Warning Exercise – conducted twice a year

Outline

Research questionsRelevant literature (briefly)Preview of resultsMethodology and data (briefly)ResultsConclusions and policy implications

Research Questions

How similar are the cycles: in duration, amplitude, etc.?

How do the cycles relate to the interest rate changes?

Are the cycles synchronized, or does one lead the others?

How important are global versus local factors?

What are the policy implications?

Literature—Domestic Context

Bank credit, house prices and aggregate demand tend to move in tandem

Financial accelerator effects through external finance premium and collateral prices result in procyclicality of bank credit Bernanke and Gertler, 1989; Kiyotaki and Moore, 1997

Liquidity and leverage of banks also contribute Adrian and Shin (2008), Berger and Bouwman (2008)

Liquidity of housing wealth and availability of housing financing can also alter the relationship through consumption and investment

Literature—International Context

The degree of synchronization in real and financial cycles increased Kose, Prasad and Terrones, 2003; Imbs, 2004; Otrok and

Terrones, 2005

International comovement in house prices may reflect comovement of interest rates Especially with debt-financed ownership and

adjustable-rate mortgages

Global liquidity may have also contributed Belke and Orth, 2008

Literature—Monetary Policy

Transmission through the credit channel and the housing market

Monetary policy, credit and asset prices Benign neglect: Monetary policy is not an effective

tool for targeting asset prices, so better not to prick the bubble but mop up afterwards (Bernanke, Gertler, and Gilchrist, 1999)

Leaning against the wind: Monetary policy needs to play a more proactive role to prevent financial bubbles (Borio and Lowe, 2002, and Borio, 2006)

Contribution of This Paper

Multidirectional links Goodhart and Hofman (2008)

Focus on output, bank credit, house prices (as well as residential investment) and interest rates

Descriptive approach GDFM as opposed to VAR

Data properties Unit root tests Filtering as opposed to differencing

Different horizons Short to medium term (6 to 16 quarters) Long term (16 to 32 quarters)

Preview of Results

House price cycles lead credit and business cycles over the long run

Interest rates tend to lag other cycles at all horizons

Country cycles are largely driven by global factors the role of which has increased over time,

especially for credit and business cyclesU.S. cycles tend to lead other countries’

respective cycles

Generalized Dynamic Factor Model

Identifies a common component using a large number of series

Builds on the traditional factor models Sargent and Sims (1977) and Geweke (1977)

By allowing for serial correlation and weakly cross-sectional correlation of idiosyncratic components Chamberlain (1983) and Chamberlain and Rothschild

(1983)Recent examples

Giannone, Reichlin, and Sala (2002) Forni and others (2005) Eickmeier (2007)

(Approximate) GDFM

where Yt is a (N x T) vector stochastic stationary process with zero mean and

unit variance and Xt and Ξt are (N x T) vectors of common and idiosyncratic

components, respectively. Xt can be written as:

where Ft is a (r x T) vector of common factors and C is a (N x r) matrix of factor

loadings.The model has N>>T and r<<N.The common factors assumed to follow an AR(1) process:

with B and (r x r) matrix and ut a (r x T) vector of residuals.

t t tY X

t t tY CF

1 ,t t tF BF u

Measures of Comovement

The dynamic correlation varies between -1 and 1. Formally,

where the numerator is the cospectrum between y1 and y2 processes at frequency

λ and S (y1) and S (y2) are the spectral density functions of the processes at

frequency λ defined over –π and π.

Coherence is intrinsically related to dynamic correlation given by:

Coherence is symmetric and a real number between 0 and 1.

The phase angle between processes helps identify the lead-lag relationship

where q (y1 y2) is the quadrature spectrum. Only when K (y1y2) ≠0, the phase angle converges in distribution to a normal random variable.

Lead-Lag Relationship

Panel Data

18 industrial countries 1981 Q1 to 2006 Q4 1,283 series Real activity indicators

Consumption Investment, including residential International trade Confidence indicators Portfolio and FDI flows

Financial variables Credit to the private sector and other monetary aggregates Short-term and long-term interest rates House prices and stock prices

Balance sheet data Household savings and assets (in particular, housing stock) Capital stock of business sector

Unit Root Tests

ERS (Elliott, Rothenberg and Stock, 1996) Generalized least squares More powerful than standard Dickey-Fuller test

KPSS (Kwiatowski, Phillips, Schmidt and Shin, 1992) Cross check Stationarity as H0 instead of unit root

Constant and deterministic trend

Lags chosen based on the Schwarz information criterion

Results of Unit Root Tests

House prices are sometimes I(2) France, Ireland, the Netherlands, New Zealand,

Sweden and the United States

Credit series are also I(2) in some cases Japan and Spain

Over-differencing versus under-differencing

Unit Root Tests (Details)

ERS KPSS

Decision in Cases of

Conflicting Evidence ERS KPSS






Conflicting Evidence

Australia I(1) I(1) I(1) I(1) I(0) I(1) I(1)2

I(1) I(1)

Austria I(1) I(1) I(1) I(2) I(2)1, 5

I(1) I(1) I(0) I(1) I(1)1, 5

Belgium I(0) I(1) I(1)2

I(1) I(1) I(1) I(1) I(1) I(1)

Canada I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1)

Denmark I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1)Finland I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(2) I(2)France I(1) I(1) I(2) I(2) I(1) I(1) I(2) I(2)

Germany I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(2) I(2)1, 5

Ireland I(1) I(2) I(1)1

I(1) I(2) I(2)1, 5

I(1) I(1) I(1) I(1)

Italy I(1) I(1) I(0) I(2) I(0)1

I(1) I(1) I(1) I(1)

Japan I(1) I(2) I(1)1

I(1) I(1) I(1) I(1) I(2) I(2)

Netherlands I(1) I(1) I(1) I(2) I(2)1, 5

I(0) I(1) I(1)1

I(1) I(1)

New Zealand I(1) I(1) I(1) I(2) I(2)1, 5

I(0) I(1) I(1)2

I(1) I(1)

Norway I(1) I(1) I(1) I(2) I(2)1, 5

I(1) I(1) I(0) I(2) I(2)5

Spain I(1) I(1) I(1) I(1) I(1) I(1) I(2) I(2)

Switzerland I(1) I(1) I(1) I(1) I(1) I(2) I(1)4

I(1) I(2) I(2)1

United Kingdom I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1)1, 5

United States I(1) I(1) I(2) I(2) I(1) I(1) I(1) I(1)

4. The KPSS test barely passes the confidence level. In addition, observation of the series suggests that it contains a unit root.5. When the ERS test on first differences does reject the I(1) null, a relatively higher error of type I is assumed for KPSS test (1 percent).

Output House Prices CreditResidential Investment

Source: Authors' estimates.

Table 1. Unit Root Tests

Notes: The table reports the results of two unit root tests: ERS (Elliott, Rothenberg, and Stock, 1996) and KPSS (Kwiatkowski, Phillips, Schmidt, and Shin, 1992). All tests were done including a constant and a trend. The number of lags was chosen using the Schwarz criterion and ensuring that no serial correlation is left in the residuals. Highlighting in the table identifies the cases where the two tests rendered conflticting results. In such cases, unit root tests were also done excluding a constant and trend, and graphical evidence was examined particularly closely. Output is the real GDP as calculated by the IMF's International Financial Statistics , house prices are expressed in real terms by deflating the nominal house price indices by CPI, credit is bank credit to the private sector deflated by CPI.1. The charts do suggest that stationarity is only achieved at the specified differenced series.2. The ERS test barely passes the confidence level. 3. Schwarz criterion suggests taking 3 lags and there is no SC either with 1 or 2 lags. Three lags suggest I(1).

Band-Pass Filter

Corbae and Ouliaris (2006) ideal band-pass filter

Passes through the components of time series with periodic fluctuations between 6 and 32 quarters (in line with widely used minor-major cycle lengths)

Consistent

No finite sampling error

Not subject to end-point problems

Advantages of Filtering over Differencing

Figure 1 - United States: Spectra of Real GDP Filtered(Y axis: spectrum; X axis: periodicities in quarters)

0

0,2

0,4

0,6

0,8

1

1,2

1,4

0

0,00005

0,0001

0,00015

0,0002

0,00025

GDP Ideal Band Pass, Corbae-Ouliaris Filter GDP First Differenced (RHS)

Wrong data transformation may introduce a downward bias in the degree of economic integration and an upward bias in the efficiency of uncoordinated macroeconomic policies.

Differenced Series Filtered Series

Average 0.07 0.31

Maximum 0.18 0.70

Minimum 0.01 0.01

Standard deviation 0.04 0.17

Coefficient of variation 0.62 0.55

Variance share exceeding 20 percent 0.03 0.68


Table 2. Variance Shares

Notes: The table reports the variance shares of common components for two alternative ways of data treatment - first differencing and filtering using the ideal band pass filter. The series included in this illustrative exercise are output, house prices, credit, short- and long-term interest rates. The last row shows the number of series, in percent of the total number of series, for which the variance share explained by the common components of the included series exceeds 20 percent.

Credit and house price cycles tend to be slightly more protracted on average than business cycles... but peaks and troughs generally are not too far.

Credit and house price cycles have larger swings than real activity cycles).

In some countries, there are significant differences across these characteristics… Countries that allow MEW and refinancing tend to

have larger amplitude and longer duration in house price cycles relative to the business cycle if the share of variable rate mortgages is large (e.g., New Zealand versus Germany).

Characteristics of Cycles

Characteristics of Cycles

Financial accelerator model is not empirically supported in all countries—correlations vary in significance and sign.

Collateral is a driver of procyclicality more than lending is—stronger correlation between output and house prices than between output and bank credit.

Procyclicality

Procyclicality

Output-House Prices Output-CreditOutput-Residential

Investment Credit-House PricesCredit-Residential

InvestmentHouse Prices-

Residential Investment

Australia 0.55 0.69 0.65 0.36 0.26 0.76Austria 0.74 0.82 -0.04 0.75 0.02 -0.40Belgium 0.72 0.79 0.67 0.85 0.67 0.83Canada 0.70 0.63 0.50 0.77 0.30 0.70Denmark 0.46 0.62 0.38 0.60 0.48 0.58Finland 0.89 0.40 0.87 0.53 0.67 0.93France 0.59 0.73 0.70 0.33 0.28 0.51Germany 0.28 -0.31 0.50 -0.02 -0.36 0.28Ireland 0.53 0.81 0.50 0.54 0.37 0.14Italy 0.37 0.19 0.45 0.25 0.44 0.50Japan 0.45 0.46 0.43 0.73 0.35 -0.07Netherlands -0.14 0.69 0.44 -0.03 0.59 0.32New Zealand 0.51 0.17 0.79 0.44 0.08 0.65Norway 0.54 -0.09 0.20 -0.10 -0.20 0.59Spain 0.74 0.61 0.75 0.25 0.24 0.76Switzerland 0.36 0.11 0.26 0.17 -0.06 0.45United Kingdom 0.85 0.69 0.73 0.74 0.45 0.78United States 0.13 0.59 0.50 0.37 0.21 0.51

Mean 0.52 0.48 0.52 0.42 0.27 0.49


Table 5. Correlation Coefficients for Total Cyclical Components

Notes: The table reports the correlation coefficients for the total cyclical component of output (real GDP), house prices, residential investment, and credit series, over the entire sample period. For each pair listed in the column title, entries in the table show the correlation between the series over the whole sample period. Correlation coefficients larger than 0.40 are deemed to be significant and are highlighted.

A large share of common components drive the three cycles.

The share of the common component in GDP and in credit has increased over time.

The importance of common shocks and/or the speed of transmission of shocks has increased over time.

Common Components

Common Components

GDP

-0.05

-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

0.05

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

Common component Total cyclical movement

UNITED STATES

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

GERMANY

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

UNITED KINGDOM

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

JAPAN

Common Components

Credit

-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

0.05

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1


UNITED STATES

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

GERMANY

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

UNITED KINGDOM

-0.05

-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

JAPAN

Common Components

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1


UNITED STATES

-0.03

-0.03

-0.02

-0.02

-0.01

-0.01

0.00

0.01

0.01

0.02

0.02

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

GERMANY

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

UNITED KINGDOM

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

JAPAN

House prices

Evolution of Common Components

1980s 1990s 2000s 1980s 1990s 2000s 1980s 1990s 2000s 1980s 1990s 2000s

Australia 0.82 0.84 0.64 0.63 0.60 0.35 0.45 0.74 0.62 0.90 0.85 0.87Austria 0.30 0.72 0.91 0.59 0.90 0.54 0.84 0.09 0.81 0.49 0.91 0.89Belgium 0.90 0.89 0.97 0.90 0.78 0.92 0.88 0.84 0.90 0.81 0.91 0.89Canada 0.85 0.92 0.91 0.89 0.62 0.74 0.77 0.35 0.46 0.89 0.87 0.92Denmark 0.08 0.63 0.66 0.68 0.59 0.87 0.70 0.71 0.69 0.55 0.60 0.90Finland 0.79 0.81 0.95 0.65 0.82 0.82 0.65 0.85 0.94 0.68 0.79 0.91France 0.43 0.82 0.95 0.20 0.62 0.69 0.86 0.90 0.94 0.40 0.25 0.82Germany 0.67 0.66 0.81 0.33 0.65 -0.02 0.79 0.18 0.49 0.38 0.57 0.53Ireland 0.76 0.85 0.93 0.57 0.76 0.90 0.73 0.81 0.70 0.91 0.84 0.94Italy 0.89 0.79 0.91 0.26 0.79 0.92 0.68 0.56 0.66 0.67 0.35 0.88Japan 0.72 0.71 0.93 0.64 0.65 0.21 0.76 0.61 0.82 0.31 0.67 0.69Netherlands 0.72 0.84 0.96 0.21 0.85 0.11 0.79 0.31 0.86 0.63 0.87 0.96New Zealand 0.16 0.90 0.90 0.80 0.84 0.53 -0.16 0.64 0.84 0.78 0.67 0.84Norway 0.56 0.18 0.50 0.62 0.87 0.53 0.83 0.89 0.31 0.39 0.34 0.67Spain 0.60 0.88 0.95 0.70 0.85 0.83 0.75 0.75 0.86 0.21 0.80 0.83Switzerland 0.84 0.73 0.95 0.81 0.86 0.59 0.62 0.58 0.57 0.40 0.27 0.59United Kingdom 0.90 0.92 0.70 0.84 0.74 0.78 0.66 0.54 0.51 0.86 0.71 0.91United States 0.78 0.56 0.90 0.85 0.62 0.42 0.19 0.67 0.73 0.80 0.51 0.87

Mean 0.65 0.77 0.87 0.60 0.71 0.58 0.63 0.59 0.68 0.60 0.66 0.84Median 0.74 0.83 0.92 0.65 0.77 0.64 0.74 0.65 0.72 0.60 0.69 0.88


Table 7. Evolution of Cyclical Movements Driven by Common Components

Notes: The table reports the correlation coefficients for the total cyclical components (including the common and idiosyncratic component, as shown in Figure 2) and the common components (shown in Figure 4).

Output House Prices CreditResidential Investment

During the minor cycle, house prices lead output and credit only in a few cases.

In the long run, there is some support for financial accelerator mechanism, but which channel? balance sheet improvements -> credit -> house prices or house prices -> creditworthiness -> credit

Short-term interest rates never lead house prices. House prices lead output, which in turn leads

credit.U.S. cycles lead the corresponding cycles in the

long run in most cases, and U.S. credit cycles lead only in the short run.

Leads and Lags

Leads and Lags (Details - 1)

6-16 quarters 16-32 quarters 6-16 quarters 16-32 quarters 6-16 quarters 16-32 quarters 6-16 quarters 16-32 quarters 6-16 quarters 16-32 quarters

Australia lag lag contemp. lag lag contemp. contemp. lag lag contemp.Austria lead lag lead lead lag lag contemp. lead lag lagBelgium lag contemp. lag lag lag lag lag lag lag contemp.Canada lag lag lag lag lag lag lag contemp. lag lagDenmark lead lead lead lag contemp. lag lag lag lag lagFinland lag lag lag lag lag lag lag lag lead lagFrance contemp. lag lag lag lag lag lag lag lag contemp.Germany lead lead contemp. contemp. lead lead contemp. lag lead lagIreland lead lead lead lead lag lag lead lead lag lagItaly lead lag lead lag lag lead contemp. lead lead leadJapan lag lead contemp. lead lag lag lead lead lag contemp.Netherlands contemp. lead lag lag lag contemp. lag lag lag contemp.New Zealand lag lag contemp. lag lag lag lead lead contemp. contemp.Norway lead lead lead lead lead contemp. lag lead lead leadSpain lag lag lag lag lag contemp. contemp. lag lag leadSwitzerland contemp. lead lag lead lead lag contemp. lag lag lagUnited Kingdom lead lag lag lag lag lag contemp. contemp. lag lagUnited States lead contemp. lag lag lag lag lag lag lag lag

Lags 39 50 50 67 78 67 44 56 72 50Contemporaneous 17 11 22 6 6 22 39 11 6 33Leads 44 39 28 28 17 11 17 33 22 17

Table 6. Leads and Lags between Cycles within Countries

Source: Authors' estimates.Notes: The table reports the lead-lag relationship between pairs of series that are the focus of the analysis. Interest rates are nominal short-term rates. For each pair listed in the column title, entries in the table indicate whether the first variable leads or lags the second variable, or whether the relationship is contemporaneous, on average, over the frequency band. The numbers in the bottom indicate the percentage of countries with a given type of relation. Leads and lags are calculated using the approach suggested by Croux, Forni and Reichlin (2001).

Credit-Output Credit-House Prices Output-House PricesCredit-Residential Investment Output-Residential Investment

Leads and Lags (Details – 2)

8-16 quarters 16-32 quarters 8-16 quarters 16-32 quarters 8-16 quarters 16-32 quarters 8-16 quarters 16-32 quarters 8-16 quarters 16-32 quarters

Australia lag lag lead contemp. lead lead contemp. lead lead contemp.Austria lead lead lead contemp. lead lead contemp. lead lead leadBelgium lead lead lead lead lead lead contemp. lead lead leadCanada lag contemp. lead lead lead lead contemp. lead lead leadDenmark lead lead lead contemp. lead lead contemp. contemp. lead leadFinland lead lead lead lead lead lead lead lead lead leadFrance lead lead lead lead lead lead lead lead lead leadGermany lead lead lag lag lag lead contemp. lag lead leadIreland lead lead lead contemp. lead lead lead lead lead leadItaly lead lead lead lead contemp. lead contemp. lead lead leadJapan lead lead lag lead lead lead lead lead lead leadNetherlands lead lead lead lag lead lead lead lead lead leadNew Zealand lag contemp. lead lead lead lag contemp. lead lead contemp.Norway contemp. lead lag lag lead lead contemp. lag lead leadSpain lead lead lead lead lead lead lead lead lead leadSwitzerland lead lead lead lead lead lead contemp. lead lead leadUnited Kingdom contemp. contemp. lead lead lead lead contemp. lead lead lead

Lags 18 6 18 18 6 6 0 12 0 0Contemporaneous 12 18 0 24 6 0 65 6 0 12Leads 71 76 82 59 88 94 35 82 100 88

Source: Authors' estimates.Notes: For each pair of countries and the variable, entries in the table indicate whether the cyclical component in the United States leads or lags the cyclical component in the respective country listed in the first column, or whether the relationship is contemporaneous, on average, over the frequency band. The numbers in the bottom indicate the percentage of countries with a given type of relation. Leads and lags are calculated using the approach suggested by Croux, Forni and Reichlin (2001).

Table 8. Lead-Lag Relations between the United States and Other Countries

House prices CreditOutput Short-term interest ratesResidential Investment

Conclusions

House price cycles lead other cycles in the long run

Interest rates tend to lag other cycles at all horizons

Global factors are important for all cycles, especially for credit and real activity in the latter part of the sample period

U.S. leads other countries in all cycles

Similar conclusions from other research

A six-variable VAR: real GDP, private consumption, residential investment, CPI inflation, the nominal (short term) interest rate and real house prices. 20 countries, sample from 1986-2009.

House price shocks are identified with a Cholesky decomposition. In practice they look like “housing demand shocks” because they lead to a strong comovement between real house prices and residential investment. Alternative identification through sign restrictions.

VAR delivers average responses, but there could be asymmetries between house price booms and busts.

Impact on Real GDP of a 10% decline in real house prices

-4

-3

-2

-1

0

1

2

3

4

0 1 2 3 4 5 6 7 8 9 10 11 12

France

-2

-1.5

-1

-0.5

0

0.5

1

1.5

0 1 2 3 4 5 6 7 8 9 10 11 12

Spain

-3

-2

-1

0

1

2

3

4

0 1 2 3 4 5 6 7 8 9 10 11 12

United Kingdom

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 1 2 3 4 5 6 7 8 9 10 11 12

United States

-2

-1

0

1

2

3

4

0 1 2 3 4 5 6 7 8 9 10 11 12

Australia

-3

-2

-1

0

1

2

3

4

0 1 2 3 4 5 6 7 8 9 10 11 12

Korea

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

0 1 2 3 4 5 6 7 8 9 10 11 12

Netherlands

-6

-5

-4

-3

-2

-1

0

1

2

3

0 1 2 3 4 5 6 7 8 9 10 11 12

New Zealand

Mortgage market characteristics matter

Average estimated impact on GDP by VAR analysis

(in percent) Countries included in the calculation of average impact

Penalty-fee prepayment and refinancingavailable -2.77 Australia, Denmark, Japan, United Kingdom, United States

not available -1.71Austria, Belgium, Canada, Finland, France, Germany, Greece, Italy, Netherlands, Norway, Spain, Sweden

Option to withdraw mortgage equity

available -2.27Australia, Canada, Denmark, Finland, Greece, Ireland, Japan, Netherlands, Norway, Sweden, United Kingdom, United States

not available -1.61 Austria, Belgium, France, Germany, Italy, Spain

Typical loan-to-value ratio at originationless than or equal to 60 percent -0.06 Austria, Italybetween 60 and 79 percent -1.67 Canada, Finland, France, Germany, Norway, Spain, United Kingdom

more than or equal to 80 percent -2.21Australia, Belgium, Denmark, Ireland, Japan, Netherlands, Sweden, United States

Policy Implications

Can a uniform policy prescription of taking into account asset prices in monetary policy making be made? It seems not, statistical properties of house prices vary across countries. In addition, some shocks to house price inflation are persistent.

The U.S. business, house price, and interest rate cycles tend to lead the respective cycles in other countries over all horizons and more so recently. Questions domestically-focused economic policies.

Documents

House Price Cycles and the Real Economy