1

The Impact of Low Income Home Owners on the Volatility of Housing Markets

Peter WesterheideZEW

European Real Estate Society Conference 2009Stockholm

June 26, 2009

2

Outline

Motivation

Literature Review

Data

Methodology

Empirical Results

Conclusions and Outlook

3

Motivation

Default of low income home owners (subprime borrowers) has

triggered the current crisis

Usual focus is on the behavior of lenders, not of borrowers

• Lending without properly checking creditworthiness

• „Originate and distribute“

Our Focus: What is the role of low income housing demand?

• Can we observe any destabilizing impact of low income

home ownership in the long run?

4

Motivation

Arguments in favor of destabilizing impact:

Low income households

• face a higher income risk of unemployment

• have low liquid wealth and often no buffer stock to

compensate income fluctuations

• are usually highly leveraged and have high interest/debt

burdens

• cannot rely on bailout by relatives

5

Motivation

Competing arguments: Low income households are

• less mobile (relative income position and regional location) ->

„trading up the ladder“ is less likely

• Wages are less variable than other components of income

therefore total income might be less variable

(as long as employed)

Net effect depends on institutional framework, might be different

in the housing cycle

Political target high home ownership rate: Is there a potential

tradeoff to the stability of housing markets?

Level and volatility of income are correlated

(Diaz-Serrano 2004, Dynan/Elmendorff/Sichel 2005,

Jensen/Shore 2008)

Extent of low income homeownership depends on completeness of mortgage markets (Chiuri/Japelli 2003, Bicacova/Siermienska 2007)

Income volatility and probability of mortgage default

6

Literature Review

Income Mortgage market

Housing prices

Housing demand

Financial leverage and volatility

Volatility of income in different income classes

Access to mortgage markets Structure of

demand and volatility of markets

Sensitivity of prices to income shocks depends on

leverage (Lamont/Stein 1999, Benito 2006)

Income volatility is related to mortage default (Diaz-Serrano 2004)

Low income homeowners increase price volatility (Ortalo-Magné/Rady 2002)Down payment constraints affect the stability of housing markets (Ortalo-Magné/Rady 2005)Volatility of house prices depends on tax wedges (van den Noord 2005)

7

Our approach

Direct analysis of impact of low income home ownership on

house price volatility in a cross country comparison

13 OECD countries, 1970-2006

Panel Regression with Fixed Effects

Pooled VAR

8

Data

Most important problem:

Data on distribution of home ownership and income

• No micro data for long time horizons

• Assumption of fixed distribution not realistic

• But: home ownership rate might be used

as a proxy for share of low income households

• Empirical evidence supports this assumption

9

Data

Rat

e of

Hom

e O

wne

rshi

p

Income

HHO Country

LHO Country

Stylized fact: home ownership rate and income distribution

10

DataRatio of home ownership 2nd/9th income decile and average home ownership rate

R 2 = 0.7267

0

1

2

3

4

5

6

0% 10% 20% 30% 40% 50% 60% 70%

Averag e H ome Owners hip R ate

Rati

o o

f Ho

meo

wn

ersh

ip R

ate

2nd

/9th

Inco

me

Dec

ile

GE

IT

US

FINUK

Own calculations, based on Bicacova /Siermienska (2007). Household head/spouse18-40 years old.

11

DataRatio of home ownership 3rd/9th income decile and average home ownership rate

R 2 = 0.8705

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0% 10% 20% 30% 40% 50% 60% 70%

Averag e Home owners hip rate

Rati

o of

Hom

eow

ners

hip

Rat

e 3r

d/9t

h In

com

e D

ecile

UK

US

IT

FIN

GE

Own calculations, based on Bicacova /Siermienska (2007). Household head/spouse18-40 years old.

12

Data

Proxy Homeownership Rate

• No annual data (except UK and US)

• Survey based, 4 – 10 years

• Common definition: Share of owner occupied

dwellings in all dwellings

• Estimation of long term trends necessary

Assumption: high inertia

Short term fluctuations mainly reflect measurement error

13

Data

House Prices

• OECD Database, Girouard et al. 2006

• Heterogeneous data, focussing mostly on

used family homes

Other data OECD and UN, gaps filled by interpolation

based on national data:

• GDP per capita, long term interest rate, CPI, unemployment

rate, debt/GDP-ratio

14

Descriptive Evidence

R² = 0.3876

0.0

5.0

10.0

15.0

20.0

25.0

0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%

Standard Deviation: Annual Percentage Change of Real House Prices

Inc

rea

se

of

Ow

ne

rsh

ip R

ati

o 1

97

0-2

00

6

(Pe

rce

nta

ge

Po

ints

)

UK

ES

IT

NL

FIN

IRL

SE

CH

CA

JP

FR

GE

US

15

Descriptive Evidence

FIN

ES

SE

JPCA

NL

US

GE

FR

CH

IRL

UKIT

R2 = 0.1857

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%

Standard Deviation of Annual Percentage Change of Real House Prices

Av

era

ge

Ow

ne

rsh

ip R

ati

o 1

97

0-2

00

6

(%)

16

MethodologyMultivariate Regression: Volatility of house prices

• Volatility of Real Income Growth (proxied by Real GDP per Capita)

• Unemployment rate

• Interest Rate

• Debt-to-GDP ratio

• House Price growth

• Volatility of Inflation Rate (CPI)

• Home ownership rate

Income

Credit Market

Prices

17

MethodologyTime Period 1970 – 2006

• Problem: Measure volatility in sub periods that are

long enough to show substantial variation…

• …but short enough to have sufficient data points in

the time series dimension

• Tests with several period lengths

• Finally: 11 overlapping periods of 7 years

18

Econometric ResultsResults of Regression for Nominal House Price Volatility

Dependent Variable: Volatility of Nominal House Prices

Coeff. Std. Error t-value P>|t|95 % confidence

interval

Volatility of CPI -0,026 0,214 -0,120,90

4 -0,450 0,398

Volatility of GDP 1,460 0,364 4,010,00

0 0,737 2,184

Interest Rate 0,709 0,309 2,290,02

4 0,095 1,322

Unemployment Rate -0,327 0,244 -1,340,18

3 -0, 81 0,156

Debt to GDP Ratio 0,041 0,034 1,200,23

4 0,026 0,108

Growth Rate of Nominal House Prices 0,261 0,081 3,20

0,002 0,099 0,422

Ownership ratio 0,780 0,344 2,270,02

5 0,098 1,463

Const. -0,005 0,005 -1,020,31

1 -0,016 0,005

R2 = 0,366

117 Observations (9 periods/13 countries)

Fixed effects regression, adjusted for serial correlation.

19

Econometric ResultsResults of Regression for Real House Price Volatility

Fixed effects regression, adjusted for serial correlation.

Dependent Variable; Volatility of Real House Prices

Coeff. Std. Error t-value P>|t|95 % confidence

interval

Volatility of CPI-

0,079 0,181 -0,440,66

3 -0,439 0,280

Volatility of GDP 1,896 0,321 5,910,00

0 1,259 2,532

Real Interest Rate 0,122 0,193 0,630,53

0 -0,261 0,505

Unemployment Rate-

0,194 0,216 -0,90,37

2 -0,623 0,235

Debt to GDP Ratio 0,052 0,027 1,930,05

7 0,002 0,105

Growth Rate of Real House Prices 0,174 0,067 2,62

0,010 0,042 0,306

Ownership ratio 0,504 0,278 1,820,07

2 0,046 1,057

Const.-

0,013 0,004 -3,280,00

1 -0,020 -0,005

R2 = 0,332

117 Observations (9 periods/13 countries)

20

MethodologySpecification of a VAR with annual values 1970-2006

• Are house prices more sensitive to shocks in HHO

vs. LHO countries?

• Variables: real house prices, real interest rates,

real GPD per capita, ownership ratio

• 2 lags

21

Econometric Results

0

0.5

1

1.5

2

2.5

3

3.5

4

1 2 3 4 5 6 7 8 9 10

GDP OR < 60

GDP OR >= 60

0

0.5

1

1.5

2

2.5

3

1 2 3 4 5 6 7 8 9 10

RHP OR < 60

RHP OR >= 60

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

1 2 3 4 5 6 7 8 9 10

LI OR < 60

LI OR >=60

Reaction to House PricesReaction to GDP

Reaction to Interest Rates

22

ConclusionOwnership rate is proxy for share of low income home

owners

Some evidence for a positive correlation of home

ownership rate and volatility of house prices

Confirms theoretical findings

23

Outlook Inclusion of better credit market indicator (mortgage

market index)

Account for age structures/demographic structures

Test with US micro data for different regions?

Refine estimation of VAR

…

24

Thank you for your attention!!!

Contact:

Peter Westerheide

++49 621 1235 146

westerheide@zew.de