Consumer Borrowing Behavior of Non-Hispanic White Homeowners

Transcription

1 CONSUMER BORROWING BEHAVIOR OF U.S. HOMEOWNERS: A STUDY BY RACE DISSERTATION Presented in Partial Fulfillment of the Requirements for thedegreedoctorofphilosophy in the Graduate School of The Ohio State University By Indrashis Chaudhuri, M.A. ***** The Ohio State University 2007 Dissertation Committee: Approved by Professor Donald Haurin Professor Lucia Dunn Professor Stephen Cosslett Advisor Graduate Program in Economics

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3 ABSTRACT The dissertation is aimed at developing an understanding of the consumer borrowing behavior of U.S. homeowners who experienced house price shocks. The research focuses on the differences in behavior comparing non-hispanic whites and African-Americans. The first essay develops a theoretical intuition as to how homeowners experiencing housing capital gains should respond. It is shown that liquidity constrained households and households that smooth consumption over time are the likely candidates to react strongly to any housing wealth windfall. In addition, collateralized borrowing should be the instrument of choice for cashing out equity from homes and spending on consumer good. An interesting finding is that though white homeowners seemed to have adjusted their consumption level upwards in response to increased home equity, but they have not made any significant attempt to bring down their level of high interest unsecured debt. In the second essay of the dissertation, the focus is on the behavior of African-American homeowners. The instrumental variable regression results on a sample of black homeowners from the three recent survey years of SCF find that the consumer credit behavior of homeowners differ by race in various consumer credit markets and blacks are not behaving in line with the theoretical predictions. The chapter also finds some evidence that financially constrained blacks refinance to only cash out equity where as constrained whites achieve the dual objectives of lowering interest rate and cashing out equity from refinancing. This chapter uses data from a 2005 survey sponsored by Fannie Mae of 501 homeowners divided equally across African-Americans and non-hispanic whites. In this chapter the new data has been utilized to test alternative hypotheses on the factors generating disparate financial behavior across non-hispanic White homeowners and their African-American counterparts. Differential credit profile across the homeowners in the two races, differential exposure to discrimination or perception of existence of discrimination in therealestatemarketanddifferential tax itemization tendency across the homeowners in the two races turned out to be the important factors driving the disparate borrowing behavior however there was no substantial evidence of racial difference in financial sophistication. ii

4 Dedicated to the memory of my elder brother, Somashis Chaudhuri and my parents iii

5 ACKNOWLEDGMENTS I will first like to express my deepest gratitude to my advisor Professor Donald Haurin. He not only advised me in every stage of this dissertation but has also taught me invaluable research techniques. His active co-operation and mentorship has played an important role in shaping my research effort and my professional career. He has been extremely supportive time and again in multiple aspects ranging from financial support to boosting morale through kind gestures of encouragement. I will also like to thank Professor Lucia Dunn who has always provided very interesting insights into my research and has been a wonderful support in my professional development. Next, I will like to extend my gratitude to Professor Stephen Cosslett and Professor Gene Mumy for their helpful comments and advice during the process of my research. I will also like to thank all my colleagues in the Economics Department of The Ohio State University and some very special friends who have made my graduate student life in Columbus an enriching experience. My friend and colleague Sougata Kerr deserves a special mention in this respect. He has time and again provided valuable feedback on my research and I have learned a great deal about consumer finance and life in general from him. I am indebted to him for a lot of things. Last but definitely not the least, I am greatly indebted to my family for their unconditional love and support. I am dedicating this dissertation to my elder brother Somashis and my parents, Phullotpal and Manisha Chaudhuri who have been my constant source of strength in the difficult periods of my life. I deeply regret the fact that my elder brother who met an untimely death from a cardiac arrest some days prior to my Final Oral Examination could not see me finish the doctoral degree that he so much aspired for me. Without the emotional and financial support of my parents and my elder brother none of my achievements would have ever been possible. iv

6 VITA December 17, 1978 Born - Kolkata, India 2000 B.Sc. (Honours) in Economics, University of Calcutta 2002 M.S. in Quantitative Economics, Indian Statistical Institute, Kolkata 2003 M.A. in Economics, The Ohio State University 2003 ~ 2006 Graduate Teaching Associate, The Ohio State University 2006 ~ present Asst. Vice President, JPMorgan Chase Bank, N.A. FIELDS OF STUDY Major Field: Economics Areas of Specialization: Studies in Consumer Finance Studies in Urban Economics Studies in Econometrics Studies in Labor Economics v

7 TABLE OF CONTENTS Page Abstract... ii Dedication... iii Acknowledgement... iv Vita...v List of Tables...x Introduction...1 Chapters: 1. Consumer Borrowing Behavior of Non-Hispanic White Homeowners in U.S Introduction Background and Previous Research on Impact of Housing Capital Gains on Consumption Theoretical Framework Model Implications from the Theoretical Model Data Descriptive Results: Homeowner Characteristics, Assets and Debts Basic Demographic and Economic Characteristics Financial Characteristics and Liquidity Constraint Housing and Mortgage Related Characteristics...19 vi

8 1.5.4 Line of Credit Borrowing Characteristics Automobile, Auto Loan and Education Loan Characteristics Regression Analyses Probit Regression Results Regression Results Discussion Conclusion Consumer Borrowing Behavior of African-American Homeowners in U.S Introduction Previous Research Racial differences in refinancing/ borrowing behavior The Crucial Role of Information in Real Estate and Mortgage Decisions Data Data One: Survey of Consumer Finances Data Two: Fannie Mae Foundation Data Descriptive Results: Homeowner Characteristics, Assets and Debts Basic Demographic and Economic Characteristics Financial Characteristics and Liquidity Constraint Housing and Mortgage Related Characteristics Line of Credit Borrowing Characteristics Automobile, Auto Loan and Education Loan Characteristics Regression Analyses Probit Regression Results Regression Results...49 vii

9 2.6 Discussion and Hypotheses Generation Discussion of SCF Results Hypotheses Generated from SCF Results Descriptive Results: Fannie Mae Foundation Data Discussion of Fannie Mae Foundation Survey Results Hypotheses Testing Hypotheses Testing Using FMF Data Hypotheses Testing Using SCF Data Conclusion Dissertation Summary...68 Appendices: Appendix A...71 Appendix B...73 Appendix C...74 Appendix D...82 Appendix E...88 Appendix F...89 Appendix G...97 Appendix H Appendix I Appendix J Appendix K Appendix L viii

10 Appendix M Appendix N Appendix O Appendix P Bibliography ix

11 LIST OF TABLES Page Table 1.1: Propensity to Shop for Loans & for Savings/ Investment...17 Table 1.2: Likeliness to Spend Following an Increase in Asset Value...18 Table 1.3: Risk Taking Ability...19 Table 1.4: Average Housing Characteristics...20 Table 1.5: Average Line of Credit Balance and Interest Rate...20 Table 1.6: Average Automobile Related Characteristics...21 Table 1.7: Education Loan Related Characteristics...21 Table 2.1: Propensity to Shop for Loans & for Savings/ Investment...41 Table 2.2: Proportion Itemizing Income Tax...42 Table 2.3: Likeliness to Spend Following an Increase in Asset Value...43 Table 2.4: Risk Taking Ability...43 Table 2.5: Average Housing Characteristics...44 Table 2.6: Average Line of Credit Balance and Interest Rate...46 Table 2.7: Average Automobile Related Characteristics...47 Table 2.8: Education Loan Related Characteristics...47 Table 2.9: Not Refinance Due to Bad Credit...53 Table 2.10: Not Refinance Due to Low Savings...53 Table 2.11: Borrowing Pattern by the Extent of Actual Housing Appreciation...54 Table 2.12: Know Actual House Price Increase...55 Table 2.13: Know Expected House Price Increase...55 Table 2.14: Financial Knowledge by Second Mortgage Indicators...56 Table 2.15: Financial Knowledge by Refinancing Indicators...57 x

12 Table 2.16: Family Member as Most Important Information Source...57 Table 2.17: Professional Financial Advisors as Most Important Information Source...58 Table 2.18: Percentage Refinanced by SCF Survey Years...60 Table B.1: Variable Types, Definitions and Means...73 Table C.1: Probit Explaining the Decision to Carry Credit Card...74 Table C.2: Probit Explaining the Decision to Carry HELOC...75 Table C.3: Probit Explaining the Decision to Refinance...76 Table C.4: Probit Explaining the Decision to Refinance to Cash-Out Equity...77 Table C.5: Probit Explaining the Decision to Refinance to Lower Mortgage Interest Rate...78 Table C.6: Probit Explaining the Decision to Carry Second Mortgage...79 Table C.7: Probit Explaining the Decision to Carry Auto Loan...80 Table C.8: Probit Explaining the Decision to Carry Education Loan...81 Table D.1: OLS & 2SLS Estimates of Credit Card Debt (in $1000) Equation...82 Table D.2: OLS & 2SLS Estimates of HELOC Debt (in $1000) Equation...83 Table D.3: OLS & 2SLS Estimates of Refinanced Amount (in $1000) Equation...84 Table D.4: OLS & 2SLS Estimates of Second Mortgage Debt (in $1000) Equation...85 Table D.5: OLS & 2SLS Estimates of Auto Debt (in $1000) Equation...86 Table D.6: OLS & 2SLS Estimates of Education Debt (in $1000) Equation...87 Table E: Variable Types, Definitions and Means...88 Table F.1: Probit Explaining the Decision to Carry Credit Card...89 Table F.2: Probit Explaining the Decision to Carry HELOC...90 Table F.3: Probit Explaining the Decision to Refinance...91 Table F.4: Probit Explaining the Decision to Refinance to Cash-Out Equity...92 Table F.5: Probit Explaining the Decision to Refinance to Lower Mortgage Interest Rate...93 Table F.6: Probit Explaining the Decision to Carry Second Mortgage...94 Table F.7: Probit Explaining the Decision to Carry Auto Loan...95 xi

13 Table F.8: Probit Explaining the Decision to Carry Education Loan...96 Table G.1: OLS & 2SLS Estimates of Credit Card Debt (in $1000) Equation...97 Table G.2: OLS & 2SLS Estimates of HELOC Debt (in $1000) Equation...98 Table G.3: OLS & 2SLS Estimates of Refinanced Amount (in $1000) Equation...99 Table G.4: OLS & 2SLS Estimates of Second Mortgage Debt (in $1000) Equation Table G.5: OLS & 2SLS Estimates of Auto Debt (in $1000) Equation Table G.6: OLS & 2SLS Estimates of Education Debt (in $1000) Equation Table H.1: Probit Explaining the Decision to Refinance Table H.2: Probit Explaining the Decision to Refinance to Lower Mortgage Interest Rate Table H.3: Probit Explaining the Decision to Refinance to Cash-Out Equity Table H.4: Probit Explaining the Decision to Carry Second Mortgage/ HELOC Table H.5: Probit Explaining the Decision to Carry Credit Card Table I.1: Probit Explaining the Decision to Carry Second Mortgage/ HELOC Table I.2: Probit Explaining the Decision to Carry Credit Card Table J.1: Probit Explaining the Decision to Carry Second Mortgage/ HELOC Table J.2: Probit Explaining the Decision to Carry Credit Card Table J.3: Probit Explaining the Decision to Carry Second Mortgage/ HELOC Table J.4: Probit Explaining the Decision to Carry Credit Card Table K.1: Probit Explaining the Decision to Carry Second Mortgage/ HELOC Table K.2: Probit Explaining the Decision to Carry Credit Card Table L.1: Probit Explaining the Decision to Carry Second Mortgage/ HELOC Table L.2: Probit Explaining the Decision to Carry Credit Card Table M.1: Probit Explaining the Decision to Carry Second Mortgage/ HELOC Table M.2: Probit Explaining the Decision to Carry Credit Card Table N.1: Probit Explaining the Decision to Carry Second Mortgage/ HELOC Table N.2: Probit Explaining the Decision to Carry Credit Card xii

14 Table O.1: Probit Explaining the Decision to Carry HELOC Table O.2: Probit Explaining the Decision to Refinance Table O.3: Probit Explaining Decision to Refinance to Lower Interest Rate Table O.4: Probit Explaining the Decision to carry Credit Card Table P.1: Probit Explaining the Decision to Carry Table P.2: Probit Explaining the Decision to Refinance Table P.3: Probit Explaining Decision to Refinance to Lower Interest Rate Table P.4: Probit Explaining the Decision to Carry Credit Card xiii

15 INTRODUCTION The dissertation is aimed at developing an understanding of the consumer borrowing behavior of U.S. homeowners who experienced house price shocks. The research focuses on the differences in behavior comparing non-hispanic whites and African-Americans. The first chapter of my dissertation develops a theoretical intuition as to how homeowners experiencing housing capital gains should respond. The theoretical model in chapter one is aimed at analyzing the circumstances under which increased equity in homes results in increased consumption by homeowners. It is shown that liquidity constrained households and households that smooth consumption over time are the likely candidates to react strongly to any housing wealth windfall. In addition, collateralized borrowing should be the instrument of choice for cashing out equity from homes and spending on consumer goods. In this chapter, the hypotheses are tested on a sample of white homeowners from the three recent survey years of the Survey of Consumer Finances (referred to as SCF hereafter). The empirical analysis highlights the fact that there has been a very high tendency to cash-out refinance and increase in the balance of HELOC and second mortgage debt among white homeowners experiencing gains in home equity. The balance in non-collateralized debt instruments like credit card and education loan did not go down as was predicted by the theoretical framework which suggests that there is not much evidence of any attempt on the part of the homeowners to consolidate in terms of secured borrowing instruments. Therefore an interesting finding is that though white homeowners seemed to have adjusted their consumption level upwards in response to increased home equity, but they have not made any significant attempt to bring down their level of high interest unsecured debt. In chapter two of the dissertation, the focus is on the behavior of African-American homeowners. Given that on average African-Americans are more likely to have a smaller asset base and more volatile income than non-hispanic whites, the theoretical model suggests that Blacks should react strongly to any house price shock, as they are more likely to be financially constrained. However, there exists some literature on the racial differences in refinancing behavior which suggests that African-American homeowners are less likely to refinance. In this chapter, the motivation is to investigate whether there exists a racial difference in borrowing response subsequent to experiencing capital gains in house prices. The instrumental variable regression results on a sample of Black homeowners from three recent survey years of SCF finds that 1

16 the consumer credit behavior of homeowners differ by race in various consumer credit markets. Another finding is that Blacks are not behaving in line with the theoretical predictions. The chapter also finds some evidence that financially constrained Blacks refinance to only cash out equity where as constrained whites achieve the dual objectives of lowering interest rate and cashing out equity from refinancing. In this chapter I used a different data set in an attempt to check the robustness of my previous analysis and to identify the factors responsible for the racially disparate consumer borrowing behavior of U.S. homeowners. The data are from a 2005 survey sponsored by Fannie Mae of 501 homeowners divided equally across African-Americans and non-hispanic whites. The survey, in addition to covering a rich set of information on households financial knowledge and tenure status, also reports information on respondents socio-demographic and economic characteristics. In short this survey is quite well suited to pinpoint the possible causes of disparate behavior across the races. In this chapter the new data has been utilized to explore the differences in household economic characteristics, financial sophistication, perceptions about occurrences and sources of discrimination, and also the proportion of financially constrained households across the two races. The study tested alternative hypotheses on the factors generating disparate financial behavior across non-hispanic White homeowners and their African-American counterparts. Differential credit profile across the homeowners in the two races, exposure to discrimination or perception of existence of discrimination in the real estate market and differential tax itemization tendency across the homeowners in the two races turned out to be the important factors driving the disparate borrowing behavior however there was no substantial evidence of racial difference in financial sophistication. The study however suggests that no single factor can entirely explain the borrowing gap across the races, it is likely the result of an interplay of multiple factors. The results from this analysis will be of importance to policy makers concerned about homeownership among minorities and also to financial institutions marketing various types of collateralized borrowing instruments such as cash out refinancing, home equity loans, and lines of credit. 2

17 CHAPTER 1 CONSUMER BORROWING BEHAVIOR OF NON-HISPANIC WHITE HOMEOWNERS IN U.S. 1.1 Introduction This chapter examines representative non-hispanic white homeowners borrowing and consumption behavior in response to house price appreciation. To illustrate the importance of this understanding, I will draw your attention to a comment made by the former Chairperson of Federal Reserve Dr. Alan Greenspan in a 2002 report to Congress where he stated that... mortgage markets have also been a powerful stabilizing force over the past two years of economic distress by facilitating the extraction of some of the equity that homeowners had built up over the years. This effect occurs through three channels: the turnover of the housing stock, home equity loans, and cash-outs associated with the refinancing of existing mortgages...that realized capital gain is financed essentially by the mortgage extension to the homebuyer, and the proceeds, in turn, are used to finance some combination of a down payment on a newly purchased home, a reduction of other household debt, or purchases of goods and services or other assets. 1 Therefore, according to Dr. Greenspan it is the consumer spending of U.S. homeowners that kept U.S. economy from sliding in any deeper recession during that period. The analytical exercise in this paper is aimed at analyzing the circumstances under which increased equity in a home can result in increased consumption by homeowners. It is shown that liquidity constrained or buffer stock consumers are the likely candidates to react strongly to any housing windfall and also collateralized borrowing should be the instrument of choice for cashing out equity from home and spending on consumer goods. The study then uses the Survey of Consumer Finances to empirically verify the theoretical intuitions. House prices have experienced large increases during the late 1990s and early 2000s. As illustrated by Labonte (2003) in a report for Congress, from 1975 through second quarter of 1997 house prices rose on an average by 5.3 percent a year in nominal terms. From third quarter of 1997 till 2003 house prices have 1 Testimony to the Joint Economic Committee, Congress, November 13,

18 increased by an average of 6.5 percent a year. Because inflation rate averaged 4.3 percent in the earlier period and only 1.6 percent in the later period, in real terms, house prices increased by about 1 percent a year before the third quarter of 1997 and about 5 percent a year since then. 2 Families can use their house price appreciation to finance consumer expenditure in one of two ways: families can borrow more or they can divert funds from other financial assets. In their empirical analysis, Haurin and Rosenthal (2004) found evidence that house price appreciation does not have a significant effect on changes in non-housing financial wealth. Thus the various other mechanisms that a household may employ in the face of house price appreciation to increase their consumption are the following: i> by drawing on home equity loan lines ii> by realizing capital gains through sale of existing homes iii> by extracting cash as part of refinancing existing mortgages (cash-outs) iv> increased non-collateralized borrowing. Dey (2004) has shown that there is sorting-by-private information in the market for Home Equity Line of Credit (HELOC). This implies that financial institutions with an informational disadvantage with respect to borrower default probabilities will offer a lower interest rate to borrowers pledging more home equity as collateral against their loans. Also, mortgage interest unlike credit card interest is income-tax deductible if the homeowner itemizes deduction. Therefore, it seems reasonable that from the late 1990s on, there should be a trend of lowering noncollateralized debt among homeowners experiencing large capital gains in their home equity. The first three options listed above therefore should have become more popular methods of borrowing compared to the last one. This is more so because around this period the mortgage interest rate experienced a steady decline and so refinancing loans was an especially lucrative option for homeowners with higher interest mortgage loans. There is another strand of literature that studies the consumption or borrowing response to the increase in price of house from a more micro economics perspective: Carroll, Dynan and Krane (2003); Skinner (1996); Hurst and Stafford (2004); Andreas Lehnert (2004); Lustig and Van Nieuwerburgh (2002); Nothaft and Chang (2004); Leth-Petersen (2004). This strand of literature tends to suggest that people who are precautionary savers or are liquidity constrained are more likely to respond by increasing consumption when home equity rises. The theoretical model in this chapter also suggests that financially constrained homeowners experiencing housing capital gains are more likely to exhibit a stronger response. There also is some empirical support to this intuition from SCF data as shown in the empirical section of this chapter. 2 The source of this report has been the house price index published by Office of Federal Housing Enterprise Oversight (OFHEO) 4

19 1.2 Background and Previous Research on Impact of Housing Capital Gains on Consumption There are a few papers that have looked into the consumption impact of housing capital gains. First is a recent paper by Haurin and Rosenthal (2004) who showed that impact of housing capital gains on nonhousing net wealth approximately equals that impact of housing capital gains on consumption. Also they found empirical evidence that house price appreciation does not have significant effects on non-housing financial wealth. Consumption in their definition includes both non-durable goods and the flow of services from durable goods. Therefore, summing up they showed that impact of housing capital gains on the household s level of debt approximately equals the effect of the former on a household s expenditure on durable and non-durable consumption goods. Their study further showed that high income households take on additional debt worth a significant fraction of house price appreciation to finance non-durable consumption as opposed to investment in financial and non-housing durable assets. Low income households in their analysis also borrowed a significant fraction of their house price appreciation and spend it on nonhousing durable goods. There is an interesting but very simple analytical framework in their paper to show the mechanism by which an increase in dollar value of a house can affect consumption. The exercise has been reproduced in the appendix A to stimulate the current discussion. A recent paper by Li and Yao (2005) presents an interesting theoretical analysis of the consumption and welfare consequences of house price changes both at the aggregate level and over the life cycle. Their analysis explicitly incorporates housing consumption in the household utility function. That helped them to derive the welfare consequences of house price changes. This study did not find any substantial aggregate effect of housing capital gains, however it did show that consumption and welfare consequences vary substantially at the individual level. In line with the existing literature it found that the non-housing consumption of young and old homeowners is more sensitive to house price shocks compared to middle aged homeowners. The study showed that house price appreciation increases the net worth and consumption of all homeowners. However in terms of welfare consequences, price appreciation only improves the welfare of middle-aged and old homeowners, while young homeowners suffer adverse consequence. In the paper by Li and Yao (2005), young homeowners suffer adverse welfare effects following housing capital gains because their investment gain from existing housing positions are not sufficient to compensate them for the increase in their lifetime housing consumption costs. This result arises because young homeowners have a longer horizon of housing consumption and are expected to up-size their house. 5

20 As already mentioned in section 1, there are some papers in the literature that provide microeconomic intuition as to why there should be a consumption response if there is a house price shock. In this literature the authors mainly rely on buffer stock savings or liquidity constrained consumer behavior to explain this phenomenon. A very well cited paper in this area is by Carroll, Dynan and Krane (2003) (referred to as CDK hereafter). This paper essentially tried to estimate the strength of the precautionary savings motive by relating uncertainty associated with the risk of being unemployed to the cross-sectional distribution of household net worth. The paper finds empirical evidence that a statistically significant precautionary effect emerges for households at a moderate level of income. However interestingly, the finding is not robust to a measure of wealth that does not include home equity. In other words, this study finds a significant precautionary motive in broad measures of wealth that include home equity, but not in a narrow definition that includes only financial assets and liabilities. It is counterintuitive to a certain extent that consumers prefer to hold precautionary wealth in housing even though it is considered as one of the least liquid asset that a household can hold. CDK explained that their theoretical as well as empirical models focused on net worth, comprised of only one type of liquid asset and one type of unsecured liability, and hence provided very little guidance. The authors referred to the finding by Laibson (1997) where he argued that consumers with a hyperbolic time discount factor will want to buffer against income risk in the long run but are so impatient that they must force themselves to save by committing assets to instruments that are costly to liquidate. It is also possible, according to the authors, that the illiquidity of an asset is a necessary characteristic to use it for precautionary savings. Also CDK correctly pointed to the findings of Carroll and Sawmick (1998), who argued that a less liquid asset may not be an inappropriate buffer if a household s concern is a high cost, low probability event such as job loss. This is the case because the transaction cost to tap into a less liquid asset will be insignificant in the expected value term. Also in case of bankruptcy, some state laws allow a person to retain her primary residence which makes this expected portfolio cost even lower. Again as correctly pointed by CDK, home equity is not a highly illiquid asset. Recent financial innovations allow one to draw through multiple channels on home equity at a relatively low transaction cost. Most of the time the interest cost of borrowing on home equity is lower than that on credit cards. Interestingly, in their analysis, CDK found that housing wealth has been a driving factor in precautionary savings even when home equity related borrowing was not very prevalent, as in early 1980s. There are some other papers which have emphasized the importance of housing wealth as a buffer stock savings. One of the early papers in this line is by Skinner (1996). The paper is an attempt to reconcile theoretical predictions of the life cycle model with empirical findings. The paper uses the 1989 wave of the Panel Study of Income Dynamics (referred to as PSID hereafter) and aggregate time series data, and finds a high marginal propensity to consume out of housing windfalls among young homeowners. However 6

21 PSID data indicate that few retirees draw down their housing wealth in any given year. According to the life cycle model, if households respond to housing windfalls while young by saving less for retirement, they should use them after retirement to finance retirement consumption. However, Skinner s analysis found that only a very small fraction of retirees buffeted by adverse economic events respond to a housing windfall through consumption. Skinner suggests that one way to reconcile these two empirical regularities is to view housing wealth as a precautionary buffer that can be cashed out in the event of an income or health downturn or widowhood, when the demand for housing services is likely to decline as well. Hurst and Stafford (2004) used the PSID to show that mortgage refinancing plays a major role in consumption insurance. This paper makes an important observation that it becomes difficult for a person to buffer income shocks when liquid asset balances are low, especially if non-collateralized borrowing rates are high. Households who have some equity in their home can choose to refinance by paying a fixed cost to refinance. However for a subset of homeowners with equity trapped in their home, the gain in lifetime utility achieved by refinancing and increasing current consumption would not be sufficient to offset the costs incurred to access the home equity. These households, according to Hurst and Stafford, also are liquidity constrained even though they have equity in a home. They have developed a model of optimal refinancing incorporating a household s desire to access home equity. The model highlights two distinct reasons that households refinance. One is a financial motivation associated with the lower stream of mortgage payments and associated rise in lifetime wealth which has already been highlighted by the existing literature on mortgage refinancing. The second is a consumption smoothing motivation where households experiencing negative income shocks and having a lower level of liquid wealth to buffer the shocks are more likely to refinance. Hurst and Stafford (2004) found strong empirical support for their theoretical prediction using micro data from Panel Study of Income Dynamics (PSID). The authors regressed whether households refinanced any time between 1991 and 1996 on permanent income, demographics, the present value financial gain achieved by refinancing, and controls for the consumption smoothing motivation to refinance. The study found that households who experienced a spell of unemployment between 1991 and 1996, and who had zero liquid assets going into 1991, were 25 percent more likely to refinance than otherwise similar households. Also, the propensity to refinance and remove equity for households who experienced a negative shock declined as the amount of liquid assets they held increased. The study also showed the extent to which declining mortgage rates can create a spending stimulus as otherwise liquidity constrained households can now access their home equity at a lower cost. 7

22 Disney, Henley and Jevons (2003) examined the impact of unanticipated housing capital gains on consumption behavior in the UK and found a strong marginal propensity to consume out of unanticipated gains in housing wealth. They also found some evidence of asymmetric consumption response to house price changes depending on whether it rises or falls. One interesting finding in this paper is that elasticity of consumption with respect to house price increases is greatest for households with zero or negative equity values in their housing stock. The authors interpreted that as a result of spontaneous response by precautionary savers who are being liberated from a negative equity situation by housing capital gains. A recent paper by Andreas Lehnert (2004) used household level data from the waves of the PSID to test whether consumption caused by house price shocks is greater among credit constrained households than among other households. Being credit constrained is identified by age in this paper. Lenhert finds a strong marginal propensity to consume out of housing wealth and a non-monotone age pattern to the marginal propensity to consume estimates. The effect is greatest among people in their late middle ages and then among young households. In a sense, this paper supports the analysis of Skinner (1996) that younger households decrease their active savings in response to housing wealth shocks. The late middle age group is on the eve of their retirement and hence is on the verge of moving to smaller houses so their relatively large sensitivity to housing wealth gains is not much of a surprise according to the author. The author advances two reasons for why young households consume a significant portion of their housing wealth gains. First, they are more likely to move and realize their housing wealth gains. Second, they are buffer stock or liquidity constrained households. As a result they are more likely to borrow against their future income gains or at least not save as much and essentially are more likely to consume out of any wealth shock including housing wealth. Lustig and Van Nieuwerburgh (2002) in their study of impact of house price appreciation on stock market returns, suggest that increases in house value can lower the aggregate savings rate. Their argument is that relaxation of the borrowing constraint associated with the increased collateral value of a house is the main cause of lower savings rates. Nothaft and Chang (2004) have made an interesting observation as to explaining why home equity growth imparts a greater stimulus to consumption compared with an equivalent growth in stock market wealth. According to them, due to the lower volatility of housing wealth, it is considered more an increase in permanent income than an equivalent seemingly transitory increase in stock market wealth. The permanent aspect of a housing wealth shock is due to less volatility of housing prices compared to stock prices. Other recent research shows that the propensity to consume out of housing capital gains is significantly higher than the propensity to consume out of stock market capital gains: Bayoumi and Edison (2002); Case, Quigley, and Shiller (2003); Benjamin, Chinloy, and Jud (2004); Haurin and Rosenthal (2004). 8

23 Leth-Petersen (2004) did an interesting analysis that also has emphasized the role of home equity as collateral for liquidity constrained households. In 1991, a financial reform in Denmark allowed homeowners to establish a mortgage and use the proceeds for non-housing related expenditures. The theoretical model in their paper showed that with the reform, expenditures will expand more for liquidity constrained households than otherwise similar households who are not liquidity constrained. The model was tested on household level panel data that contained information about total expenditures. Effects of the reform on total expenditure were estimated by statistical matching by comparing the change in total expenditure around the reform for constrained with that of otherwise identical unconstrained households. Significant effects of the reform on total expenditures were found for constrained households, but that on unconstrained households was less Theoretical Framework Model An intertemporal model of consumption with two goods, a nondurable good and a durable good (nonhousing) is presented below. The objective of this analytical exercise is primarily to generate hypotheses on the consumption and borrowing responses of U.S. homeowners experiencing changes in house price levels. For simplicity, the utility generated by consuming the flow of housing services is ignored. Also, the paper does not allow the housing stock to adjust as a choice variable. A house is treated as an asset in which households can choose to add or remove savings by paying a fixed cost. A special interest is to note the predicted responses by liquidity constrained homeowners as their constraints change following changes in their levels of home equity. In the face of a positive price shock, we determine whether the constrained households necessarily increase their non-durable and durable goods consumption. The model also determines whether the increased consumption is likely to be financed by taking loans collateralizing home equity, by non-collateralized borrowing instruments like credit cards, or else by drawing down other financial assets. In this simple model the representative household has the option to cash-out equity from home by placing a new mortgage, they have the option to draw from a noncollateralized credit source, and there is also a certain level of liquid financial asset at their disposal. In each period, the household chooses the consumption of the durable and nondurable goods. The consumers who are homeowners have two types of assets: a risk free asset that is perfectly liquid and a less liquid asset, which is home equity. As a result, in addition to the consumption decision, they also have to decide on how much of the liquid asset to (dis)save, whether to get a new mortgage, and the extent of home 9

24 equity to add or remove from their home conditional on getting a new mortgage. This modeling set up is inspired by the work of Chah, Ramey, and Starr (1995), Hurst and Stafford (2004), Alessie, Devereux, and Weber (1995) and Leth-Petersen (2004). Assume that ith consumer solves the following optimization problem (i subscripts are omitted for notational convenience but should appear on all but the price variables): P (1) max β t U(C t,s t ) C t,s t, A t,l t (subject to R t =1) t=0 subject to the following constraints (2) A t =(1+r)A t 1 +Y t -C t -p d [S t -(1-δ)S t 1 ]+(L t -F t )R t -M t (3) A t =-φ 1t -φ 2t (p H t H t -B t )-φ 3t p d S t (4) B t =B t 1 +L t (5) M t =r m B t (6) R t =1if the household takes a new mortgage; (7) B t =0 (8) S t =0 Notation: =0 otherwise β : Household s intertemporal time discount factor C t : Nondurable consumption in period t S t : Durable (non-housing) stock at the end of period t A t : Liquid assets at the end of period t R t : Takes value one, if new mortgage is established in period t L t : Equity removed from (or added to) home subject to the decision to take a new mortgage F t : Transaction cost of establishing a new mortgage in period t r : Interest rate on risk free asset Y t : Disposable income in period t p d : (Constant) relative price of durables in terms of non durables δ : Depreciation rate of durables M t : (Interest only) mortgage payment in period t p H t : House price at the end of period t H t : Stock of housing at the end of period t r m : Mortgage interest rate in period t B t : Outstanding mortgage debt at the end of period t 10

25 φ 1t : Parameter indicating time varying access to non-collateralized credit φ 2t : Parameter indicating access to home equity for consumption purposes φ 3t : Parameter indicating the fraction of durable stock that can be financed The standard model of life-cycle behavior is extended here by adding an illiquid asset in the form of housing along with a borrowing restriction. Equation (2) gives the period-to-period budget constraint; the household enters period t with liquid asset A t 1 that earns interest r, and it receives disposable income Y t.outofy t they spend an amount for non durable expenditure C t, durable expenditure (S t -(1-δ)S t 1 ), and a mortgage interest payment M t (an interest-only mortgage). Subject to the decision to take a new mortgage (i.e., R t =1), the household decides on whether to cash-out home equity L t after paying a fixed transaction cost F t. The borrowing restriction as described in equation (3) says that households can borrow in three ways: a non-collateralized instrument (like a credit card) with a fixed time varying upper bound, collateralizing a fraction of home equity (the size of the fraction is determined by the institutional restrictions), or collateralizing a fraction of durable goods. I assume that a household can collateralize only a fraction of durable stock while purchasing it. This occurs for two reasons; one being small durables like clothing can not be collateralized (φ 3t =0). The second is that the extent one can collateralize big durables like automobiles or furniture depends on the financial regulations on hire purchase in a country. By hire purchase, I mean purchasing goods under financing schemes without making a full payment at the time of purchase. There can be financial regulations stipulating a minimum down payment requirement. If there is a minimum down payment restriction then φ 3t cannot be 1. Therefore for the aggregate durable stock, φ 3t must lie between 0 and 1. Also note that the upper bound of non-collateralized credit φ 1t can be a function of observable characteristics of the representative agent, as long as it is not a function of the choice variables in our optimization process. The difference in the outstanding balance in mortgage debt across time (i.e., between B t and B t 1 ) is the size of the new mortgage as described by equation (4). Households are assumed to be endowed with an interest only mortgage each period. If the household has an outstanding mortgage debt, then they make a mortgage payment (M t ) equal to the interest payment on the mortgage balance (B t ) at mortgage interest rate (r m ) as described by equation (5). Equation (6) describes R t, the dummy variable indicating the decision to refinance. This should also be a choice variable in a more general model but that is beyond the scope of the current analysis. The outstanding mortgage balance (B t ) and the stock of durables (S t ) are constrained to be non-negative by equations (7) and (8). The difference between the mortgage interest rate and the interest rate earned on liquid assets is an important feature of this model. In the absence of transaction costs, we will not see agents simultaneously possess both financial assets and housing. However, we observe both being held because there are transaction costs associated with accessing home equity. 11

26 There are a number of restrictions built into this model. The stock of durables is treated as a continuous variable. This assumption is not very realistic but it can be partially justified by the fact that some durables such as cars have second-hand markets and have continuous quality. Also the relative price of durable goods in terms of non durables is kept constant. It is also assumed that the real interest rate on savings and the real interest rate on mortgages are time invariant. Moreover households are assumed to be endowed with an interest only mortgage each period. The corresponding Lagrange function is given by P (9) L= β t {U(C t,s t )+λ t [(1+r)A t 1 +Y t -C t -p d [S t -(1-δ)S t 1 ]+ t=0 (L t -F t )R t -M t -A t ]+μ t [A t +φ 1t +φ 2t (p H t H t -B t )+φ 3t p d S t ]+ν t B t +γ t S t } where λ t denotes marginal utility of wealth μ t reprsents the shadow price associated with the borrowing constraint ν t and γ t are multipliers associated with non-negativity restrictions of B t and S t respectively Now, substituting M t =r m B t and B t =B t 1 +L t the Lagrange function changes to P (10) L= β t {U(C t,s t )+λ t [(1+r)A t 1 +Y t -C t -p d [S t -(1-δ)S t 1 ]+ t=0 (L t -F t )R t -r m (B t 1 + L t )-A t ]+μ t [A t +φ 1t +φ 2t (p H t H t -B t 1 -L t )+φ 3t p d S t ]+ ν t (B t 1 + L t )+γ t S t } The first-order conditions of this optimization problem are given by (11) U t C t =λ t (12) U t S t =λ t p d -β (1 δ) λ t+1 p d -μ t φ 3t p d -γ t (13) λ t =μ t +β (1 + r) λ t+1 (14) λ t (1-r m )=μ t φ 2t -γ t +βr m λ t+1 +βμ t+1 φ 2(t+1) -βν t+1 (15) μ t At + φ 1t + φ 2t (p H t H t B t )+φ 3t p d S t =0 (16) ν t B t =0,ν t = 0 (17) γ t S t =0, γ t =0 The relevant conditions after suitable substitutions are (18) U t C t μ t =β(1 + r) Ut+1 C t+1 (19) Ut S t = ³ r+δ 1+r p d U t 1 δ C t +μ t 1+r φ 3t p d γ t Equation (18) states that if households are constrained in period t (μ t > 0), then the marginal utility of nondurable consumption is higher in t than in (t+1). If the utility function is monotone this implies that nondurable consumption is lower in periods when households are liquidity constrained. Therefore, if that constraint is lifted then we should see households boost their nondurable consumption. Next equation (19) shows the relation between the marginal utilities of durable and nondurable consumption. We can 12

27 use this equation to compare this relationship under two situations: no credit constraint (μ t =0) and credit constrained (μ t >0). In both cases we will assume that households have a strictly positive durable stock (γ t =0). When (μ t =0) then (19) gives the usual equality between the marginal rate of substitution of nondurable and durable goods and their relative prices. (20) U t S t = r+δ 1+r p d U t C t When (μ t >0) then (19) changes to (21) U t S t = ³ r+δ 1+r p d U t 1 δ C t +μ t 1+r φ 3t p d Therefore, nonzero values of μ t affect the intertemporal relation between the two goods in period t because they alter the shadow price of durables vis a vis the non durables. Note that the nature of this alteration depends on the extent to which durables can be financed. Consider equation (21) under two extreme situations: φ 3t =0; i.e., durables cannot be financed at all and φ 3t =1; i.e., durables can be completely financed. When φ 3t =0 then (21) becomes (22) U t S t = ³ r+δ 1+r p d U t 1 δ C t +μ t 1+r p d When φ 3t =1 then (21) becomes (23) Ut S t = ³ r+δ 1+r p d U t r+δ C t μ t 1+r p d In equation (22) coefficient of μ t is positive (because δ<1), therefore the marginal utility of durable consumption will be higher than that of nondurable consumption; and therefore households will cut back more on durable consumption than on nondurable consumption. When the constraint gets lifted, then households are expected to increase their durable consumption more than their non-durable consumption. This result supports the analysis of Leth-Petersen (2004) who did not consider the possibility of financing durable purchases. Here the intuition is straightforward, with a liquidity constraint, if you need to pay for durable goods upfront then their current user cost is very high because they use up funds for necessary nondurable purchases. One will prefer to continue using his/her existing durable stock and durable expenditures will fall temporarily in anticipation of the constraint being lifted the following period. However as correctly pointed out by Chah, Ramey and Starr (1995), this is not the only possible situation. As in (23), if φ 3t =1, then the coefficient of μ t becomes negative. Then, even if you are liquidity constrained in the current period, you will not necessarily cut down your durable consumption and you will pay for them in the following period when the constraint gets lifted. Next, consider the implications of equation (14). We assumethathouseholdshavestrictlypositive durable stock in both periods t and t+1 and that the credit constraint is not binding the following period t+1 (i.e., μ t+1 =0). Consider equation (14) under two circumstances, the credit constraint is binding in the current period (μ t > 0) and it is not binding in the current period (μ t =0). 13

28 When μ t > 0, then combining (11) and (14) we get, (24) U t C t = μ t φ 2t 1 r +β rm U t+1 m 1 r m C t+1 When μ t =0,then combining (11) and (14) we get, (25) U t C t = β rm U t+1 1 r m C t+1 Comparing (24) and (25) we get the same intuition as we already found from equation (18). These two equations point to the fact that if households are constrained in the current period, the marginal utility of non durable consumption is higher for the current period compared to the next period Implications from the Theoretical Model The first prediction of the model is that households should respond to a relaxation of the credit constraint by a consumption hike, especially non-durable consumption. Durable consumption however, may or may not increase for reasons discussed above. A close look at the constraint (3) shows possible ways of relaxing the constraint: i: an increase in house price ii: an increase in the credit limit for non-collateralized borrowing iii: an increase in the fraction of home equity that you are allowed to cash-out iv: a decrease in the down payment requirement for durable goods purchase An increase in house prices (p H t ) should increase consumption for the constrained households. One other possible situation where consumption can potentially respond strongly to appreciation in house prices occurs for precautionary savers. For them, if they experience unexpected increases in house prices then they will have more home equity to buffer future income uncertainty and will therefore increase current period consumption. Additionally, there are people who have sufficient liquid assets to buffer income uncertainty in any period. They also may increase their consumption due to an unexpected housing wealth increase, if they consider it to contribute to their permanent income. Therefore, the intuition developed so far suggests that if there is a shock in house price, then we should see either an increase in the borrowing level or a reduction in liquid financial assets. Note that for the precautionary savers or unconstrained households the increase in house prices must have an element of surprise for them to respond. However for the credit constrained homeowners even if this increase was fully anticipated they are not able to adjust their consumption until the house price has actually increased. A caveat in this context is that homeowners experiencing a fall in house prices will not necessarily behave in a symmetrically opposite manner. If the households are current period credit constrained then with fall in house prices the constraint (3) above is still strictly binding and there is no change in the relevant first 14