Veblen Effect in the U.S. Housing Market: Spatial and Temporal

Variation

Kwan Ok Lee and Masaki Mori

National University of Singapore

European Real Estate Society Annual Conference 2013Vienna, AustriaJuly 3-6, 2013

• “Chanel has raised the prices of its popular handbag lines by 20 to 30 percent per year for the last several years, yet consumers buy its products under any circumstances…customers spend recklessly due to their label addiction.” (The Chosunilbo, 20 January, 2012).

• The LVMH revenue and profit in the fashion and leather goods segment have increased every year from 2008 to 2011 (LVMH annual reports).

Background

Research Framework

Data & Methods

ResultsIntroduction

Conclusions

• The “Veblen effect” shown in the consumption of non-housing luxury goods

• Potential translation of the Veblen effect into housing consumption behavior• The premium paid for high-end homes• Deviation from fundamental house prices• Pricing bubbles

Background

Research Framework

Data & Methods

ResultsIntroduction

Conclusions

Research Framework

Data & Methods

ResultsIntroduction

Conclusions

New York Seattle

Housing Market Dynamics

Veblen Effect in Non-Housing Goods

Research Framework

Data & Methods

ResultsIntroduction

Conclusions

New York Las Vegas

Housing Market Dynamics

Veblen Effect in Non-Housing Goods

Research Questions• What is the role that the Veblen effect

plays in housing market dynamics?• Does the higher Veblen effect lead to a higher

housing premium?

• Is there temporal and spatial variation in this role? • Is the Veblen effect more or less associated

with house price premium during the boom or bust periods?

• Does the Veblen effect drive higher house price premium in some MSAs than other MSAs?

Research Framework

Data & Methods

ResultsResearch Framework

ConclusionsIntroduction

Research Framework

Data & Methods

ResultsResearch Framework

ConclusionsIntroduction

• Luxury goods such as• Woman’s cosmetic products (Chao and Schor 1998) and

automobile (Shukla 2008)• Investment

• Link between stock investors’ behavior and the Veblen effect (Ait-Sahalia et al. 2004; Hiraki et al. 2009)

• Very low observed returns on art investments (Mandel 2009)

• Spatial variation in Veblen effect• Veblen (1899)

The Veblen Effect in Non-housing Consumption

• Relative house size• People want to have a house larger than their nearest

neighbor and pay premiums for that (Leguizamon 2010)• Property names

• Wealthier property buyers pay price premiums for “country club” (Zahirovic-Herbert and Chatterjee 2011).

• Other reasons for house price premiums in some MSAs• Higher variation in demographics across neighborhoods

(e.g. racial segregation) within the MSA (Cutler et al. 1999)

• Heterogeneity in neighborhood quality• Economic capacities to pay the premium

Potential Veblen Effect in Housing Consumption

Research Framework

Data & Methods

ResultsResearch Framework

ConclusionsIntroduction

• Google Insights for Search• Volume of Google searches for non-housing

luxury goods in a given Metropolitan Statistical Area

• Indicator of consumers’ appetite for luxury goods

• DataQuick• Median house prices collected quarterly in the

US Metropolitan Statistical Areas (MSAs)• Premium paid for houses in the highest decile

in each MSA• 101 MSAs from 2004 Q1 to 2011 Q4

Data

Research Framework

Data & Methods

Results ConclusionsIntroduction Data & Methods

• Dynamic panel system GMM regressions • Variables

• A dependent variable: the log of house price premium

• A main independent variable: the ratio of the luxury brand searches to the product searches (automobile, fashion, watch, and perfume)

• Control variables• Demographics (population, age, household size)• Income (median household income, income

distribution)• Housing markets (% newly built units, % high-cost

rental units)• Degree of racial segregation (dissimilarity indices)

Methods

Research Framework

Data & Methods

Results ConclusionsIntroduction Data & Methods

Descriptive Statistics (Mean)

Research Framework

Data & Methods

ResultsResults ConclusionsIntroduction

Variables Full SampleMSAs with top 30% premiums

MSAs with bottom 30% premiums

House price premium $182,003 $288,595 $112,069 Veblen effect (automobile) 0.255 0.313 0.193Population 1,790,471 3,549,962 605,926Median age 36.369 36.505 36.081Median household size 2.573 2.653 2.538Median household income 50,054 57,705 45,330Ratio of top 10% to median household income

2.594 2.665 2.53

% units built after 2000 0.133 0.141 0.127% units with contract rent>=$1500

0.047 0.104 0.017

D-index (Asian) 0.296 0.332 0.296D-index (Black) 0.542 0.565 0.504# of observations 3232 960 992

House Price Premium

Regression Results for the Full Sample

Research Framework

Data & Methods

ResultsResults ConclusionsIntroduction

<Dependent variable = log of house price premium>

(101 MSAs for 32 quarters from 2004 Q1 to 2011 Q4)

Independent Variable Beta z Beta z Beta z Beta zOwn lag Housing premium(log, t-1) 0.864 72.9 *** 0.843 65.37 *** 0.837 63.5 *** 0.723 41.34 ***Luxury Veblen effect Lux search(automobile) 0.364 6.67 *** 0.286 4.74 *** 0.292 4.82 *** 0.292 5.00 ***Demographics Population(log) 0.015 4.99 *** 0.026 6.24 *** 0.019 5.07 *** Median age 0.003 2.83 *** 0.004 3.97 *** 0.003 3.77 *** Median household size 0.061 4.39 *** 0.048 3.50 *** -0.018 -1.47Dissimilarity index D-index (Asian) 0.008 0.32 0.019 0.85 D-index (Black) -0.133 -6.10 *** -0.039 -2.11 **Income Median household income(log) 0.083 4.57 *** Ratio of top10% to median household income -0.023 -0.99Housing Markets % units built after 2000 -0.034 -1.08 -0.137 -3.5 *** 0.200 4.97 *** % units with contract rent>=$1500 1.069 12.6 ***

Wald chi2 11858.2 *** 23509.9 *** 26219.8 *** 48918.2 ***

1 2 3 4

Independent Variable Beta z Beta z Beta z Beta zOwn lag Housing premium(log, t-1) 0.678 12.850 *** 0.722 21.740 *** 0.619 10.610 *** 0.452 10.380 ***Luxury Veblen effect Lux search(automobile) 0.485 3.980 *** 0.311 2.120 ** 0.345 2.990 *** -0.074 -0.570Demographics Population(log) -0.023 -2.980 *** -0.026 -3.800 *** -0.029 -1.930 * 0.039 3.210 *** Median age -0.005 -1.620 -0.014 -4.680 *** 0.000 0.000 -0.001 -0.660 Median household size -0.079 -1.690 * -0.116 -2.810 *** -0.037 -0.840 -0.106 -3.900Dissimilarity index D-index (Asian) 0.191 2.870 *** 0.252 3.950 *** 0.067 0.970 -0.032 -0.560 D-index (Black) 0.068 1.170 0.186 3.350 *** 0.178 2.670 *** -0.111 -1.850 *Income Median household income(log) -0.170 -3.400 *** -0.192 -3.970 *** 0.032 0.460 0.246 3.820 *** Ratio of top10% to median hhy -0.043 -0.700 0.036 0.680 0.043 0.600 0.202 3.060 ***Housing Markets % units built after 2000 -0.168 -1.540 -0.181 -2.320 ** 0.907 3.840 0.169 1.080 % units with contract rent>=$1500 1.133 4.860 *** 0.924 6.770 *** -0.584 -0.800 1.720 3.550 ***

# of observations 307 623 318 643Wald chi2 5173.6 *** 12183.6 *** 533.8 *** 831.1 ***

Before peak year After peak year Before peak year After peak yearMSAs with top 30% premiums MSAs with bottom 30% premiums

Regression Results for the Sub-samples

Research Framework

Data & Methods

ResultsResults ConclusionsIntroduction

<Dependent variable = log of house price premium>

Summary of Findings

IntroductionResearch

FrameworkData &

MethodsResults ConclusionsConclusions

• Higher Veblen effect in MSAs drives the higher house price premium, even after controlling for • fundamental demographics• income distribution• housing conditions and the degree of racial segregation

• The Veblen effect in housing markets is more significant in MSAs with higher price premiums.

• During the bust period, the Veblen effect contributes to maintaining the higher level of housing premiums.

Implications

IntroductionResearch

FrameworkData &

MethodsResults ConclusionsConclusions

• The areas where consumers’ desire for luxury consumption changes dramatically may be more vulnerable to pricing bubbles.

• The Veblen effect dynamics could be a potential indicator of the housing booms and busts in certain MSAs.

• In the areas and periods where consumers’ desire for luxury consumption is high, people may have higher demand for high-end houses and be willing to pay higher premiums.

• Causality of the revealed relationship• Veblen effect vs. tastes• Observable vs. unobservable preferences• Instrumental variables or other controls?

• Variation in the relationship of the Veblen effect with housing bubbles and busts across different states, regions, or divisions

IntroductionResearch

FrameworkData &

MethodsResults Conclusions

Directions for Future Research

Conclusions