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

1. 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 2013 Vienna, Austria July 3-6, 2013

2. Research Framework Data & Methods Introduction Results Conclusions Background • “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).

3. Research Framework Data & Methods Introduction Results Conclusions Background • 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

4. Research Framework Data & Methods Introduction Results Conclusions New York Seattle Housing Market Dynamics Veblen Effect in Non-Housing Goods

5. Research Framework Data & Methods Introduction Results Conclusions New York Las Vegas Housing Market Dynamics Veblen Effect in Non-Housing Goods

6. Research Framework Data & Methods Research Framework Introduction Results Conclusions 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?

7. Research Framework Data & Methods Research Framework Introduction Results Conclusions The Veblen Effect in Non-housing Consumption • 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)

8. Research Framework Relative house size People want to have a house larger than their nearest neighbor and pay premiums for that (Leguizamon2010) Property names Wealthier property buyers pay price premiums for “country club” (Zahirovic-Herbert and Chatterjee2011). 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 Data & Methods Research Framework Introduction Results Conclusions Potential Veblen Effect in Housing Consumption

9. Research Framework Data & Methods Data & Methods Introduction Results Conclusions Data • 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

10. Research Framework 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) Data & Methods Data & Methods Introduction Results Conclusions Methods

11. Research Framework Data & Methods Introduction Results Conclusions Results Descriptive Statistics (Mean)

12. Research Framework Data & Methods Introduction Results Conclusions Results Regression Results for the Full Sample <Dependent variable = log of house price premium> (101 MSAs for 32 quarters from 2004 Q1 to 2011 Q4)

13. Research Framework Data & Methods Introduction Results Conclusions Results Regression Results for the Sub-samples <Dependent variable = log of house price premium>

14. Research Framework Data & Methods Introduction Results Conclusions Conclusions Summary of Findings • 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.

15. Research Framework Data & Methods Introduction Results Conclusions Conclusions Implications • 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.

16. Research Framework Data & Methods Introduction Results Conclusions Conclusions Directions for Future Research • 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