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. “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 Results Introduction Conclusions

3. 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 Results Introduction Conclusions

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

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

6. 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 Results Research Framework Conclusions Introduction

7. Research Framework Data & Methods Results Research Framework Conclusions Introduction 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

8. 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 Results Research Framework Conclusions Introduction

9. 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 Conclusions Introduction Data & Methods

10. 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 Conclusions Introduction Data & Methods

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

12. Regression Results for the Full Sample Research Framework Data & Methods Results Conclusions Introduction (101 MSAs for 32 quarters from 2004 Q1 to 2011 Q4)

13. Regression Results for the Sub-samples Research Framework Data & Methods Results Conclusions Introduction

14. Summary of Findings Introduction Research Framework Data & Methods Results Conclusions 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. Implications Introduction Research Framework Data & Methods Results Conclusions 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. 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 Introduction Research Framework Data & Methods Results Conclusions Directions for Future Research Conclusions