An empirical study of efficiency of the Austrian residential markets

Shanaka Herath, Gunther MaierResearch Institute for Spatial and Real Estate Economics

The 18th Annual Conference of the European Real Estate Society17th June 2011

The Eindhoven University of Technology

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Outline of the presentation

Introduction The conceptual framework Literature on efficient markets (from finance to real

estate) Our study

Data Methodology Empirical results Conclusion

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Introduction

Maier et al. (2010) – “energy efficient improvements in residential properties were not capitalized into prices” suggests that Austrian residential markets are not efficient

Purpose Formally testing the informational efficiency of the Austrian

residential markets

Why is it important? House price models assume informationally efficient markets Provide opportunities for arbitrage

This is not operational/ allocative efficiency Deviation of real estate prices from fundamental value

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The conceptual framework:What is an Efficient Market?

“Efficient market hypothesis” (EMH) (Fama et al., 1969) about financial markets: “The prices of traded assets already include/reveal all known information“

Implication: “random walk hypothesis” as long as fundamentals do not change, fluctuations are

random follow a random walk

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Market efficiency research

Originally on financial markets Early research on market efficiency (Samuelson 1965, Fama

et al. 1969, Fama 1970) A market is efficient when it “adjusts rapidly to new

information” (Fama et al. 1969) An efficient market is one where prices “fully reflect all

available information” (Fama 1991) EMH in financial markets

Early decade:“no other proposition in economics which has more solid empirical evidence supporting it” (Jensen 1978)

Today:EMH appears more controversial (Beechey et al. 2000)

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Market efficiency research (contd.)

Market efficiency depends on a specific information set (not an absolute characteristic)

Efficiency with respect to some set of information

Three forms of market efficiency Weak

information set consists of only past prices Semi-strong

information set consists of past prices and all publicly available information

Stronginformation set also includes non-public information

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Real estate market efficiency

Later, extended to the real estate market (Gatzlaff & Tirtiroglu 1995, Cho 1996, Maier & Herath 2009)

First efficiency tests of the real estate market (Gau 1984, 1985, Linnemann 1986)

Real estate market efficiency

Theoretical argument Empirical argument

Test specific versions of the efficient market hypothesis (EMH)

characteristics of the real estate market

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The empirical study

Test specific versions of the efficient market hypothesis (EMH) Weak form and semi-strong form of EMH On Viennese and Austrian residential markets

Research question: “Whether the Austrian residential markets are informationally efficient?”

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Methodology

Examine the autoregressive structure of the house price series Check for stationarity Determine appropriate order of the autoregression

(based on BIC and AIC) Check each process for white noise residuals

Form of the final regression utilized/ length of the autoregressive lag structure provide insights about efficiency Price changes from many lags explain current price

changes -> failure of the market to absorb information

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Data

Weak form of the EMH Austrian National Bank Residential real estate price indices (annual and quarterly) The price index uses hedonic approach, with the district as a

location variable and various variables describing the object (year of construction, stat of repairs, amenities,..)

Semi-strong form of the EMH Austrian National Bank Austrian Stock Exchange Prices [AUSTRIAN TRADED INDEX

(ATX)] - 1999=100 Other fundamental economic indicators

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Empirical resultsAnnual price index and appreciation- Vienna and Austria

Fig. 1 Hedonic log residential price series for Vienna (1986-2010)

0

1

2

3

4

5

6

1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

Ind

ex

Fig. 2 Annual appreciation of the residential price index for Vienna (1987-2010)

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Year

Ch

an

ge in

in

dex

Fig. 3 Hedonic log residential price series for Austria (outside Vienna) (2000-2010)

4.5

4.55

4.6

4.65

4.7

4.75

4.8

4.85

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

Ind

ex

Fig. 4 Annual appreciation of the residential price index for Austria (outside Vienna)

(2001-2010)

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

Ch

an

ge in

in

dex

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Empirical results (contd.)Quarterly price index – Vienna and Austria

Fig. 4 Quarterly hedonic log residential price series for Vienna (1986 Q3- 2010 Q3)

0

20

40

60

80

100

120

140

160

180

Sep-

86

Sep-

88

Sep-

90

Sep-

92

Sep-

94

Sep-

96

Sep-

98

Sep-

00

Sep-

02

Sep-

04

Sep-

06

Sep-

08

Sep-

10

Year

Inde

x

Fig. 5 Quarterly hedonic log residential price series for Austria outside Vienna (2000 Q1- 2011 Q1)

0

20

40

60

80

100

120

140

Mar

-00

Sep-

00

Mar

-01

Sep-

01

Mar

-02

Sep-

02

Mar

-03

Sep-

03

Mar

-04

Sep-

04

Mar

-05

Sep-

05

Mar

-06

Sep-

06

Mar

-07

Sep-

07

Mar

-08

Sep-

08

Mar

-09

Sep-

09

Mar

-10

Sep-

10

Mar

-11

Year

Inde

x

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Empirical results (contd.) Weak form test

Autocorrelation in annual log prices and log returns

Substantial autocorrelation in quarterly log prices and log returns Is it a consequence of

seasonality?

Analysis using a specific quarter of every year

Maximum significant lag length is 2

Weak-form efficient

Table 1 Lag structure estimates

  Number of lags with autocorrelation

  log of price log returns (appreciation)

Annual    

Vienna 2 1

Austria (outside Vienna) 1 0

Quarterly    

Vienna 4 5

Austria (outside Vienna) 2 1

Quarterly- Q1    

Vienna 2 1

Austria (outside Vienna) 1 0

Quarterly- Q2    

Vienna 2 1

Austria (outside Vienna) 2 1

Quarterly- Q3    

Vienna 1 0

Austria (outside Vienna) 1 0

Quarterly- Q4    

Vienna 0 0

Austria (outside Vienna) 1 0

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Empirical results (contd.)Semi-strong form test

Literature on semi-strong form version of EMH Macroeconomic fundamentals/ economic indicators

(Gatzlaff, 1994) Movements of stock prices (Gyourko and Keim, 1992) Industry/firm related public announcements (Gyamfi-

Yeboah et al (2011)

Can the Austrian Stock Index (ATX) and past house prices predict present prices?

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Empirical results (contd.)Semi-strong form test

Fig. 6 Viennese house price index and the Austrian stock index

0

50

100

150

200

250

300

350

400

450

1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

Vien

nese

resi

dent

ial p

rice

inde

x an

d st

ock

inde

x

Viennese house price index Stock index

)27.0%,52(38.074.2 2 DWRlstklprice tt

)3.2%,98(60.044.105.052.0 221 DWRlpricelpricelstklprice tttt

)61.0%,96(79.006.071.0 21 DWRlpricelstklprice ttt

Residuals are white noise!Semi-strong version of the EMH rejected!

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Conclusions

Austrian residential markets are weak-form efficient

Viennese residential market is inefficient in the semi-strong form Nature of the real estate market Transaction level prices are not publicly available

Way forward: An answer to critics on using index values (use Geltner

(1993) method to unsmooth price index values) examine semi-strong from version of the EMH using

public information on market fundamentals or economic indicators

FUßZEILESEITE 17

Thank you for your attention!

VIENNA UNIVERSITY OF ECONOMICS AND BUSINESSAugasse 2-6, 1090 Vienna, Austriawww.wu.ac.at

SPATIAL AND REAL ESTATE ECONOMICS RESEARCH INSTITUTENordbergstraße 15 (UZA4, Kern B, 4. Stock)A-1090 Vienna, Austriahttp://www.wu.ac.at/immobilienwirtschaft

SHANAKA HERATH PROF. GUNTHER MAIERT +43-1-31336-5764 T +43-1-31336-4780F +43-(0)1-31336 705 F +43-(0)1-31336 705Shanaka.herath@wu.ac.at gunther.maier@wu.ac.at