1. Gunther Maier, Shanaka Herath
The efficiency of the real estate market: a meta-analysis of the empirical literature
Gunther Maier, Shanaka Herath
Research Institute for Spatial and Real Estate Economics

2. Real estate and real estate market
Real estate and economic activities
Real estate = wealth of economies and households
Structural deficits have consequences
Loans
Security
Financial
market
Real estate
market
Location decisions
Land use patterns
Infrastructure needs
Energy consumption
Environmental hazards
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3. 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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4. 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
the relevant information set consists of only past prices
Semi-strong
information set consists of past prices and all publicly available information
Strong
information set also includes non-public information
Efficiency of the real estate market (Gau 1984, 1985, Linnemann 1986)
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5. Real estate market efficiency
Later, extended to the real estate market (Gatzlaff & Tirtiroglu 1995, Cho 1996, Maier & Herath 2009)
Real estate market efficiency
Theoretical argument Empirical argument
characteristics of the
real estate market
* Test specific versions of the
efficient market hypothesis (EMH)
* Test whether house prices were
driven by market fundamentals
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6. Theoretical arguments on market efficiency
These arguments cast some doubt on the validity of the EMH
Need to distinguish between different contexts: types of real estate, countries & regions, levels of aggregation
Heterogeneous product
Production lags
High transaction costs and infrequent transactions
Information asymmetries
Long term contracts
Regulations and strong role of policy
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7. Our emphasis: empirical studies
Analysis of different types of real estate › different countries-cities-regions › different levels of aggregation › different time period
Literature provide no decisive result
Whether some of the parameters of these analyses make the conclusion of an efficient real estate market more or less likely
A meta-analysis of empirical studies
A Binary Logit Model
Discrete dependent variable: binary indicator whether or not a certain study concluded the real estate market is efficient
Explanatory variables: factors characterizing the sample (the study)
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8. Meta-analysis
No original data about the phenomenon, unit of analysis is previous studies on that
Proper construction of the data set is a challenge
Previous studies do not describe all the characteristics of the analysis
Differences in the quality of the studies (and how to take this into account)
Advantages: * can detect some dependence of empirical results on the context of the analysis; * can identify the risk of taking the empirical results of one study at face value
Disadvantages: * publication bias (potential selectivity of the peer review and publication process) -Jensen (1978) › file- drawer problem –Rosenthal (1979).
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9. Data Literature survey Maier & Herath (2009)
New addition of relevant literature
Selection of empirical studies directly testing EMH (51 papers published from )
What we left out:
Papers indirectly deal with market efficiency
Conceptual papers and papers using simulations
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10. Categories in the analysis
Year
Potential change in attitude towards market efficiency & potential publication bias
Type of property
Differences in market efficiency in sub-markets
Scale of analysis
Geography
Role of real estate and structure of the real estate market
Type of market
Aggregation level of data
Theoretical arguments (issues) may appear in studies using individual level data, but may be aggregated out in a more aggregated dataset (Capozza & Seguin, 1996)
Aggregation eliminates most of the noise contained in individual data, so the charcateristics of the market will become more easily visible (Rayburn et al., 1987)
Type of investigation
Weak form, semi-strong form, test of market fundamentals
Higher chance for weak form tests to show efficiency
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11. Descriptive statistics
Table 1: Descriptive statistics for dataset “papers”
Table 2: Descriptive statistics for dataset “analyses“
Variable
Obs.
Mean
Std.dev
Min.
Max.
Efficient 44
0.386
0.493 1
Year 51
1993.2
5.659
1984
2007
Residential
0.765
0.428
Income generating
0.294
0.460
Land
0.020
0.140
Regional
0.157
0.367
National
0.353
0.483
International
0.039
0.196
USA
0.686
0.469
Europe
Urban
0.627
0.488
Urban/rural
0.275
0.451
Individual
0.529
0.504
Aggregate
0.412
0.497
Weak form
0.373
Semi strong form
variable
Obs.
Mean
Std.dev
Min.
Max.
Efficient 60
0.417
0.497 1
Year
1993.1
5.540
1984
2007
Residential
0.750
0.437
Income generating
0.283
0.454
Land
0.033
0.181
Regional
0.167
0.376
National
0.367
0.486
International
USA
0.717
Europe
0.133
0.343
Urban
0.617
0.490
Urban/rural
0.300
0.462
Individual
0.500
0.504
Aggregate
0.450
0.502
Weak form
Semi strong form
0.350
0.481
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12. Estimation results Full model & reduced model
Table 3: Results of the meta analysis
Full model & reduced model
Indicators of model quality (likelihood ratio probabilities): dataset “papers” √
Smaller prob›chi2
Larger Pseudo R2
Coefficients of the full model
Coefficients of the reduced model
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Significance indicators: * … < 10%, ** … < 5%, *** … < 1%

13. Further analysis: Likelihood Ratio Test
Whether the reduction of the variables from the full sixteen to the three significant ones is justified
Table 4: Likelihood-ratio tests for reduced models
Dataset “papers”
Dataset “analyses”
Log-likelihood (full model)
Log-Likelihood (reduced model)
Likelihood ratio (degrees of freedom)
7.851 (12)
6.291 (13)
Probability (LR > chi2)
0.797
0.935
Reduction in the maximum log-likelihood is very small as compared to the gain in degrees of freedom resulting from the smaller number of explanatory variables
Probabilities of the likelihood-ratio tests › typical threshold values (reduced models are superior)
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14. Further analysis: Choice probabilities
Effect of the significant explanatory variables on the probabilities of finding the result of an efficient real estate market
Choice probabilities for all combinations of the possible values of the explanatory variables
Table 5: Choice probabilities for dataset “papers”
Table 6: Choice probabilities for dataset “analyses”
Lower explanatory power of the model using the dataset “analysis”
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15. Conclusions Real estate market and market efficiency
The model with all the variables yield no significant coefficients
“Reduced model” is statistically superior to the full model
Variables “income generating” and “individual” are significant
Significantly positive influence in both cases
When income generating properties are analyzed in a study, it is more likely to conclude that the market is efficient
Studies that use individual level data are significantly more likely to find an efficient real estate market
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16. Thank you! VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS
Augasse 2-6, 1090 Vienna, Austria
SPATIAL AND REAL ESTATE ECONOMICS RESEARCH INSTITUTE
Nordbergstraße 15 (UZA4, Kern B, 4. Stock)
A-1090 Vienna, Austria
UNIV.PROF. DR. GUNTHER MAIER SHANAKA HERATH
T T
F +43-(0) F +43-(0)
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