Why U.S. Homeowners Should Not Hold The Market Portfolio

Why U.S. Homeowners Should Not Hold The Market Portfolio

Guoliang FengPh.D. Candidate

Department of Economics

The George Washington University

April 10th, 2013

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Research Question

how should

consumption constrained households allocate wealth to housing and risk assets

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wealthhouse stock

Introduction

1. Problem and Motivation2. Literature Review3. Theory and Model of Homeower’s Problem4. Empirical Tests and Data5. Simulation6. Conclusions and Implications

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1. Problem: Failure to own equities

1. Previous research focuses on portfolio problems of the unconstrained households

2. Consumption constrained households behave very differently

I. They consume far more housing than they would hold in diversified portfolios

II. They hold a single house as their only risk asset along with risk free assets

III. As wealth increases, they pay down mortgage rather than owning other risk assets

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1. Problem: Low participation rate

Empirical evidence from research

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Data CountryParticipation rate in equity market

PSID 1984 US 27.6%

SHIW 1998 Italy 48.2%

SCF 2010 US 49.9%

1. Problem: Low equity holding

Source: SCF chart book 2010

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1. Problem: Low equity holding

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Source: SCF 2010. stock share and riskless asset shares are their ratios in financial assets (without housing wealth). Renters are excluded.

1. Problem: behavior vs theory

What households should do Equity should be a significant share of the risk assets in a well

diversified household portfolio What households actually do

Households appear to be taking substantial unique risk in holding housing as a large single risk asset along with government guaranteed (often riskless) assets

Motivation: stockholding puzzle Divergence between standard theoretical prediction and

empirical evidence The existing literature contributes little to explaining this

puzzle

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1. Problem: contribution of this paper

1. Estimating housing return for 38 cities

2. Explain the low participation rates of equity market

3. Explain the weak representation of S&P500 as stock market

4. Introduce the diversification gains from holding individual stocks

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2. Literature : Positive analysis

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Mankiw and Zeldes (1991)1984 PSID

75% of the households hold no stocks

13% of the poor (with liquid assets lower than $1,000) hold stocks

Haliassos and Bertaut (1995) 1983 SCF

44%-55% of U.S households hold stocks

the 70th income percentile hold stocks with average value < $800

Fratantoni (2001)1989 SCF

median share of risky assets for younger homeowners is just 3.32%

Guiso and Jappelli (2005) 1995 &1998 SHIW

conditional shares of risky assets (mutual fund, stocks and investment accounts) in household portfolios are only 10.2% and 20.1%

2. Literature : Normative analysis

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Brueckner(1997) “the investment constraint” proposed by Henderson and Ioannides (1983) leads to mean-variance inefficiency

Fratantoni (2001) committed mortgage expenditure risk of owning a house can help explain the empirical puzzle

Cocco (2005)fixed cost of equity market participation and correlation of housing price with labor income and stock returns explains the puzzle in portfolios of younger and poorer households

Pelizzon and Weber (2008)

negative correlation between housing and financial returns should be used to diversify portfolio risk

Chetty and Szeidl (2012)

as homeowners become less consumption constrained, they include equities in their portfolios but prepayment of the mortgage balance has an initial priority for recent homebuyers

2.Literature : this paper builds on previous research

1. Make use of Correlation between housing and stock returns Flavin and Yamashita (2002): use 4 cities’ housing return and S&P 500 to find

no correlation Pelizzon and Weber (2008) etc.: use market portfolios (not stocks) to

calculate correlation

2. More accurate estimation of total housing returns Method I: use HPI appreciation rate: Cocco (2005) Method II: use returns of REITs: Yang et al (2012) Method III: use returns of composite price index: Bucciol and Miniaci (2011) Method IV: use constant CAP rate across MSAs: Flavin and Ymashita (2002 My method: accurate estimate local housing return

Total housing returns=capitalization rate (CAP)+ appreciation rate

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3. Model

1. Variable definition1. Mortgage schedule

2. Home equity return: Leveraged & Unleveraged

2. Model solving1. Bench mark model (no leverage)

unconstrained households

2. Primary model (leverage)consumption constrained householdsNet Wealth=Housing Value*20%

3. proposition

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3.1 Model: variable definition

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0.1

3.2 Model: solve bench mark model

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Bench mark model: Unconstrained households’ maximization problem:

3.2 Model: solve primary model

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Primary model: constrained households’ maximization problem

3.3 Model: difference between two models

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Portfolio Risk

Portfolio Returns

Bench mark model

primary model

3.3 Model: propositions

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4. Empirical Tests and Data

1. asset and city definition 2. estimating home CAP rate3. calculate home equity return4. calculate variance-covariance matrix of asset

returns

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4.1 asset definition

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• Household allocates wealth to financial assets and housing equity. • There are two models: choosing individual stocks or market portfolios.

• Stocks: choose 10 representative stocks for 10 sectors• American Electric Power Co Inc. (AEP)• British Petroleum Plc. (BP)• DuPont Chemical (DD)• General Electric Co (GE)• International Business Machines (IBM)• Procter & Gamble Co (P&G)• Progressive Corp (PROG)• Universal Health Services Inc. (UHS)• Verizon Communications Inc. (VZ)• Wal-Mart Stores Inc. (WMT)

• Market portfolios: only choose one of the 5 quasi mutual funds• market value-weighted portfolio (Vrate)• market equal-weighted portfolio (Erate) • S&P 500 Index (SP500)• 10-stock value-weighted portfolio (Vfund) • 10-stock equal-weighted portfolio (Efund))

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• 38 cities

Atlanta Dallas L.A. Jersey City San Francisco

Baltimore D.C.-Arlington Miami Newark San Jose

Boston D.C.-Rockville Milwaukee Oakland Santa Ana

Cleveland Detroit Minneapolis Philadelphia Seattle

Cambridge Farmington Hills Suffolk Phoenix St. Louis

Camden Fort Lauderdale Nassau Pittsburgh Tampa

Chicago Fort Worth New Orleans Providence

Cincinnati Houston New York San Diego

4.1 city definition

4.2 estimating CAP rate

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4.2 Data

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Data for computing total housing return Mortgage interest rate: 30-year fixed mortgage rate,

Freddie Mae Annual property tax and cost rate: 2% Data for CAP estimation: AHS Data for Appreciation estimation: FHFA

Data for Stock and market portfolio: CRSP Data for Inflation: BLS Year: 1985-2009

4.3 Data –Housing Return & Risk by City

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4.3 Data – return & risk of housing and other assets

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4.3 Data – return & risk of housing equity and other assets

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4.3 Data: Leveraged housing equity return fluctuation at Washington DC

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0 5 10 15 20 25-15

-10

-5

0

5

10

15

20

25

period: 0-25 (or year: 1985-2009)

HousingReturn

(%)

Housing Returns for different LTVs at Washington DC: 1985-2009

4.4 Data- negative correlation between housing and stock returns

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Number Of Negative Correlation city

0 Cambridge, Detroit, Farmington Hills

1 Atlanta, Boston, Cincinnati, Cleveland, Oakland, San Diego, San Francisco, San Jose, Santa Ana

2 Chicago, Fort Worth, L.A., Minneapolis, Providence, Seattle, St. Louis, Tampa

3 Dallas, Fort Lauderdale, Milwaukee, Suffolk, Nassau, New Orleans, New York, Jersey City, Newark, Phoenix, Pittsburgh

4 Baltimore, Washington D.C.-Rockville, Miami, Philadelphia

5 Camden, Washington D.C.-Arlington

7 Houston

Note: the first column counts the number of negative correlations between 10 stocks and housing returns.

4.4 Data- negative correlation between housing and market portfolio returns

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Number Of Negative Correlation city

0

Atlanta, Boston, Cambridge, Chicago, Cincinnati, Cleveland, Dallas, Detroit, Fort Lauderdale, Fort Worth, L.A., Miami, Milwaukee, Minneapolis, Suffolk, Nassau, New Orleans, New York, Jersey City, Newark, Oakland, Phoenix, Pittsburgh, Providence, San Diego, San Francisco, San Jose, Santa Ana, St. Louis, Tampa, Farmington Hills, D.C.-Arlington

1 Baltimore, Washington D.C.-Rockville, Seattle

2 Camden, Philadelphia

4 Houston

Note: the first column counts the number of negative correlations between 5 quasi-mutual funds and housing returns

4.4 Data: Covariance between fluctuation leveraged asset return: Washington DC

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1 2 3 4 5 6 7 8 9 10-80

-60

-40

-20

0

20

40

GE PROG IBM AEPVZDDPGWMTUHSBP

4.4 Data: Covariance between fluctuation leveraged asset return: Houston

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1 2 3 4 5 6 7 8 9 10-60

-50

-40

-30

-20

-10

0

10


4.4 Data: Covariance between fluctuation leveraged asset return: Detroit

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1 2 3 4 5 6 7 8 9 10-10

0

10

20

30

40

50

60

70

80

90


5. Simulation

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Bench mark model Simulation Case I: choose individual stocks and house Case II: choose market portfolios and house

Primary Model Simulation Case III: choose individual stocks and house Case IV: choose market portfolios and house

5. Simulation

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5.1 Bench mark model: case I (individual stocks)

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city Stock share House share City Stock share House shareAtlanta 100.00% 0.00%Nassau 72.17% 27.83%Baltimore 72.50% 27.50%New Orleans 95.03% 4.97%D.C.-Rockville 72.77% 27.23%New York 71.10% 28.90%Boston 84.55% 15.45% Jersey City 71.43% 28.57%Cambridge 88.13% 11.87%Newark 76.43% 23.57%Camden 76.64% 23.36%Oakland 85.68% 14.32%Chicago 82.18% 17.82%Philadelphia 72.60% 27.40%Cincinnati 96.63% 3.37%Phoenix 97.34% 2.66%Cleveland 100.00% 0.00%Pittsburgh 89.47% 10.53%Dallas 100.00% 0.00%Providence 77.66% 22.34%Detroit 100.00% 0.00%San Diego 81.89% 18.11%Fort Lauderdale 91.56% 8.44%San Francisco 67.67% 32.33%Fort Worth 100.00% 0.00%San Jose 80.73% 19.27%Houston 98.80% 1.20%Santa Ana 80.05% 19.95%L.A. 79.54% 20.46%Seattle 66.66% 33.34%Miami 78.81% 21.19%St. Louis 93.83% 6.17%Milwaukee 82.38% 17.62%Tampa 95.87% 4.13%Minneapolis 88.55% 11.45%Farmington Hills 100.00% 0.00%Suffolk 72.17% 27.83%D.C.-Arlington 74.56% 25.44%Notes: A=4, annual cost=2%

5. Simulation

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5.2 Bench mark model: case II (market portfolio)

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city S&P500 share city S&P500 shareAtlanta 0.00%Nassau 0.00%Baltimore 0.00%New Orleans 0.00%D.C.-Rockville 0.00%New York 0.00%Boston 0.00% Jersey City 0.00%Cambridge 0.00%Newark 0.00%Camden 0.00%Oakland 0.00%Chicago 0.00%Philadelphia 0.00%Cincinnati 0.00%Phoenix 10.53%Cleveland 0.00%Pittsburgh 0.00%Dallas 3.60%Providence 0.00%Detroit 0.00%San Diego 0.00%Fort Lauderdale 7.33%San Francisco 0.00%Fort Worth 3.34%San Jose 0.00%Houston 4.48%Santa Ana 0.00%L.A. 0.00%Seattle 0.00%Miami 0.41%St. Louis 0.00%Milwaukee 0.00%Tampa 0.00%Minneapolis 0.00%Farmington Hills 0.00%Suffolk 0.00%D.C.-Arlington 0.00%Notes: A=4, annual cost=2%

Summary I

1. Households have different portfolios as they live in different cities

2. Low shares of housing in standard portfolio model can only be applied to the unconstrained households

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5. Simulation

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5.3 Primary model: case III (individual stocks)

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city LTV House share city LTV House shareAtlanta 84.60% 77.00% Nassau 85.40% 73.00%Baltimore 91.60% 42.00% New Orleans 87.00% 65.00%D.C.-Rockville 89.20% 54.00% New York 86.60% 67.00%Boston 81.80% 91.00% Jersey City 86.60% 67.00%Cambridge 84.60% 77.00% Newark 86.40% 68.00%Camden 91.60% 42.00% Oakland 87.60% 62.00%Chicago 86.80% 66.00% Philadelphia 92.40% 38.00%Cincinnati 82.80% 86.00% Phoenix 87.00% 65.00%Cleveland 83.40% 83.00% Pittsburgh 88.80% 56.00%Dallas 88.20% 59.00% Providence 92.00% 40.00%Detroit 80.20% 99.00% San Diego 85.40% 73.00%Fort Lauderdale 89.80% 51.00% San Francisco 86.20% 69.00%Fort Worth 87.60% 62.00% San Jose 87.40% 63.00%Houston 97.00% 15.00% Santa Ana 84.60% 77.00%L.A. 86.80% 66.00% Seattle 84.80% 76.00%Miami 91.80% 41.00% St. Louis 86.00% 70.00%Milwaukee 87.20% 64.00% Tampa 85.00% 75.00%Minneapolis 86.40% 68.00% Farmington Hills 81.40% 93.00%Suffolk 85.40% 73.00% D.C.-Arlington 92.20% 39.00% Notes: A=4, annual cost=2%

5.3 Primary model: case III (individual stocks)detail 1

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city PROG BP UHS WMT PG DD IBMAtlanta 0.00% 0.00% 3.43% 0.00% 0.00% 15.08% 4.49%Baltimore 0.00% 0.00% 15.27% 0.00% 0.00% 0.00% 42.73%Boston 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 46.00%Cambridge 0.00% 0.00% 9.00% 0.00% 0.00% 0.00% 0.00%Camden 0.00% 0.00% 6.73% 0.00% 15.48% 0.00% 0.79%Chicago 0.00% 0.00% 39.54% 0.00% 0.00% 0.00% 18.46%Cincinnati 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 34.00%Cleveland 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 14.00%Dallas 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 17.00%Detroit 0.00% 41.00% 0.00% 0.00% 0.00% 0.00% 0.00%Fort Lauderdale 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 1.00%Fort Worth 0.00% 0.00% 0.00% 49.00% 0.00% 0.00% 0.00%Houston 0.00% 38.00% 0.00% 0.00% 0.00% 0.00% 0.00%L.A. 20.32% 64.68% 0.00% 0.00% 0.00% 0.00% 0.00%Miami 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 34.00%Milwaukee 0.00% 0.00% 0.00% 59.00% 0.00% 0.00% 0.00%Minneapolis 0.00% 0.00% 0.00% 13.27% 0.00% 0.00% 22.73%Suffolk 0.00% 0.00% 0.00% 14.41% 0.00% 12.71% 4.87%Nassau 0.00% 0.00% 27.00% 0.00% 0.00% 0.00% 0.00% Notes: notes: GE, Verizon and AEP are not held. A=4, annual cost=2%

5.3 Primary model: case III (individual stocks)detail 2

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city PROG BP UHS WMT PG DD IBMNassau 0.00% 0.00% 27.00% 0.00% 0.00% 0.00% 0.00%New Orleans 0.00% 0.00% 0.00% 35.00% 0.00% 0.00% 0.00%New York 0.00% 0.00% 33.00% 0.00% 0.00% 0.00% 0.00%Jersey City 0.00% 0.00% 33.00% 0.00% 0.00% 0.00% 0.00%Newark 0.00% 0.00% 32.00% 0.00% 0.00% 0.00% 0.00%Oakland 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 38.00%Philadelphia 0.00% 0.00% 42.60% 0.00% 0.00% 0.00% 19.40%Phoenix 0.00% 0.00% 0.00% 35.00% 0.00% 0.00% 0.00%Pittsburgh 0.00% 2.24% 0.00% 0.00% 0.00% 16.76% 25.00%Providence 0.00% 0.00% 40.94% 0.00% 0.00% 0.00% 19.06%San Diego 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 27.00%San Francisco 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 31.00%San Jose 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 37.00%Santa Ana 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 23.00%Seattle 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 24.00%St. Louis 0.00% 0.00% 2.93% 12.81% 0.00% 0.00% 14.26%Tampa 0.00% 0.00% 0.00% 25.00% 0.00% 0.00% 0.00%Farmington Hills 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 7.00%D.C.-Arlington 0.00% 0.00% 0.00% 16.37% 0.00% 0.00% 44.63% notes: GE, Verizon and AEP are not held. A=4, annual cost=2%

5. Simulation

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5.4 Primary model: case IV (market portfolio)

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City S&p500 City Sp500Atlanta 0%Nassau 0%Baltimore 0%New Orleans 7%D.C.-Rockville 0%New York 2%Boston 0% Jersey City 2%Cambridge 0%Newark 0%Camden 0%Oakland 0%Chicago 0%Philadelphia 16%Cincinnati 0%Phoenix 0%Cleveland 0%Pittsburgh 21%Dallas 2%Providence 0%Detroit 0%San Diego 0%Fort Lauderdale 0%San Francisco 18%Fort Worth 4%San Jose 18%Houston 17%Santa Ana 0%L.A. 0%Seattle 47%Miami 0%St. Louis 15%Milwaukee 0%Tampa 0%Minneapolis 0%Farmington Hills 7%Suffolk 0%D.C.-Arlington 0%

Summary II

1. Consumption constrained households have different optimal housing shares as city changes

2. Consumption constrained households hold much higher housing compared with those in fully diversification case

3. Negative correlation between housing return and stocks returns bring diversification benefit

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6. Conclusion

1. Households have different optimal portfolios as their cities change

2. Individual stocks can bring more diversification benefits than market portfolios do to household portfolios

3. Consumption constrained households face higher portfolio risk, hold higher housing than that for investment purpose alone

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APPENDIX I: Portfolio performance in primary model: Washington DC

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APPENDIX II: Portfolio performance in primary model: Houston

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APPENDIX IIII: Portfolio performance in primary model: Detroit

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Documents

Why U.S. Homeowners Should Not Hold The Market Portfolio