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1 HOUSING MARKET IMPERFECTIONS: THE LIFE CYCLE HYPOTHESIS AND HOMEOWNERSHIP By JORGE RUIZ MENJIVAR A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2013
2 2013 Jorge Ruiz Menjivar
3 ACKNOWLEDGMENTS I would like to give my sincere thanks to my committee members and my dear friends, all of who m have repeatedly proven themselves and made the course of these two years at the University of Florida truly memorable. Additionally, I would like to express my gratitude to my family for their unconditional support.
4 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 3 LIST OF TABLES ................................ ................................ ................................ ............ 6 ABSTRACT ................................ ................................ ................................ ..................... 7 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ ...... 9 2 LITERATURE REVIEW ................................ ................................ .......................... 14 Neoclassical Economic Theories: General Postulates ................................ ............ 14 ................................ .................... 15 Demogra phic Variables ................................ ................................ ........................... 19 Market Imperfection Variables ................................ ................................ ................ 23 Information Asymmetry ................................ ................................ ..................... 23 Borrowing Constraints ................................ ................................ ...................... 24 Structural Barriers ................................ ................................ ............................ 26 Tax Preferential Treatment ................................ ................................ ............... 27 Time Preference ................................ ................................ ............................... 29 Summary ................................ ................................ ................................ ................ 30 Hypotheses ................................ ................................ ................................ ............. 30 3 DATA AND METHOD OLOGY ................................ ................................ ................ 32 Data Set ................................ ................................ ................................ .................. 32 Dependent Variable ................................ ................................ ................................ 35 Independent Variables ................................ ................................ ............................ 35 Demographics: Life Cycle Variables ................................ ................................ 35 Other Demographic Variables ................................ ................................ .......... 36 Market Imperfection Variables ................................ ................................ .......... 37 Information Asymmetry ................................ ................................ .............. 37 Borrowing Constraints ................................ ................................ ................ 38 Structural Barriers ................................ ................................ ...................... 39 Tax Preferential Treatment ................................ ................................ ........ 39 Market Uncertainty ................................ ................................ ..................... 40 Sample Size ................................ ................................ ................................ ............ 40 Analysis ................................ ................................ ................................ .................. 41 4 FINDINGS AND RESULTS ................................ ................................ ..................... 4 2 Bivariate Analysis ................................ ................................ ................................ ... 42 Life Cycle Variables ................................ ................................ .......................... 42
5 Other Demographics Variables ................................ ................................ ........ 43 Market Imperfection Variables ................................ ................................ .......... 44 Information Asymmetry ................................ ................................ .............. 44 Borrowing Constraint ................................ ................................ ................. 45 Structural Barriers ................................ ................................ ...................... 46 Housing Tax Preferential Treatment ................................ .......................... 47 Time Preference ................................ ................................ ........................ 47 Binomial Logistic Regression ................................ ................................ .................. 48 Demographic Variables ................................ ................................ .................... 48 Market Imperfection Variables ................................ ................................ .......... 49 Information Asymmetry ................................ ................................ .............. 49 Borrowing Constraints ................................ ................................ ................ 49 Structural Barriers ................................ ................................ ...................... 49 Tax Preferential Treatment ................................ ................................ ........ 50 Market Uncertainty ................................ ................................ ..................... 50 5 DISCUSION, CONCLUSIONS AND IMPLICATIONS ................................ ............. 56 Discussion ................................ ................................ ................................ .............. 56 Hypotheses ................................ ................................ ................................ ...... 56 Discussion of Findings ................................ ................................ ..................... 58 Conclusions ................................ ................................ ................................ ............ 64 Implications ................................ ................................ ................................ ............. 67 LIST OF REFERENCES ................................ ................................ ............................... 69 BIOGRAP HICAL SKETCH ................................ ................................ ............................ 75
6 LIST OF TABLES Table page 4 1 Bivariate analysis: Sample profile and demographics by homeownership status ................................ ................................ ................................ .................. 51 4 2 Bivariate analysis: S ample profile and market imperfection variables by homeownership status ................................ ................................ ........................ 52 4 3 Logistic regression for likelihood of being a homeowner demographics ............. 54 4 4 Logistic regression for likelihood of being a homeowner market imperfection .... 55
7 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science HOUSING MARKET IMPERFECTIONS: THE LIFE CYCLE HYPOTHESIS AND HOMEOWNERSHIP By Jorge Ruiz Menjivar August 2013 Chair: Michael S. Gutter Major: Family, Youth and Community Science s Homeownership is one of the most common financial aspirations of households in the United States. It has historically been an integral part of the so associated positive externalities to homeownership (Glaeser & Sacerdote 2000; Glaeser & Shapiro 2003); and as a result of these multifaceted benefits, policy makers and academicians have paid careful attention to its determinants. The present study employ s the 2010 Survey of Consumer Finances (SCF) to explore the relationship of housing market hypothesis under the assumption of a non frictionless market. Binomial logistic re gression is used to survey the impact of the following: information asymmetry, borrowing constraints, structural barriers, housing tax preferential treatments and time preference on the probability of being a homeowner. Important implications derived from this study, as it relates to the notion of household life cycles, might benefit multiple interested parties such as financial planners and housing professionals who are rendering recommendations to their clients, educators and researchers assisting the
8 pub lic in making informed choices, and governmental policymakers attempti ng to secure the financial well being and interests of households.
9 CHAPTER 1 INTRODUCTION One of the most common financial aspirations of households in the United States is homeownership. Indeed, the desire to be a homeowner has historically been an integral part o f the so (Bostic, Calem & Wachter, 2004). Numerous studies outline the spillover benefits of homeownership (Glaeser & Sacerdote 2000; Glaeser & Shapiro 2003). Its impact is reflected at the macro and micro level in the economy. For instance, from a macro economic perspective, housing is v construction, remodeling and other aspects of the industry. A micro level perspective is and equity accumulation, amon g others. Nonetheless, homeownership benefits are not only of an economical nature as research studies have shown that homeownership is related to increased education for children, residential stability, lower teen pregnancy rates and a higher lifetime an nual income for children (DiPasquale & Glaeser 1999; Galster 1983; Rossi & Weber 1996; Aaronson, 2000). Also, it has been linked to pride, partially to social status, and to amenities both within the dwelling unit and within the environment (C arliner, 1974 ). As a result of these multifaceted benefits that homeownership brings for both households and the economic system as a whole, policy makers and academicians have paid careful attention to the determinants of homeownership. Over the past 150 years, U.S. h omeownership trends have experienced several significant changes resulting from various socio economic and political events. A homeownership rate of 48% remained relatively constant from 1890 up to the Great Depression that started in 1921. As a result of this economic downturn, homeownership
10 rates progressively declined to 44% by 1940. Rates then began increasing for the subsequent decades. Such an increase was significant and evident; the homeownership rate rose to 65% by 1970 (Masnik, 2001). This dramat ic and uninterrupted growth for farm and non farm housing was attributed to the prosperous period after World War II (Gale, Gruber & Stephens Davidowitz, 2007). Specific contributors to this increase were the steady rise of m ortgage programs, the rise in marginal tax rate for middle income households, and the introduction of other pivotal federal housing policies through the creation and expansion of several national institutions devoted to support homeownership. The rate cons istently grew during the 1990s, and by 2005 the homeownership rate had reached an historical high of 69% although this represent ed a real growth of only less than 4 points over the preceding 30 years (Gale, Gruber & Stephens Davidowitz, 2007). Later, the r ate experienced a significant decrease due to the 2007 global financial crisis during which there was a house price crash. In an attempt to bring relief for current homeowners and encourage homeownership, the government enacted the American Recovery and Re investment Act of 200 9. Nonetheless, the 2012 house market remains unstable and fragile at an estimated 65.5% homeownership rate ( Callis & Kresin, 2013 ). When considering renting versus buying, many households simply have, or believe they have, no other ch oice but to rent. The result of such dilemma is reflected in the consecutive decrease in homeownership rate for the last five years ( Callis & Kresin, 2013 ). Economists and researchers predict this rate will remain steady for approximately two m ore years be fore bottoming out. E ventually, it is expected that the nation will reach a long term housing demand and supply equilibrium (Guggenmos et al, 2012). Though,
11 this will depend on several factors such as interest rates, the tax code and individual attitudes towards housing as an investment. Being that homeownership rates are of extreme importance for households and the economy, it is crucial to scrutinize its determinants. Many have been identified throughout time (Maisel 1966; Kain & Quingley, 1972). For ex ample, the relative price of rental and owner occupied housing is an underlying factor that researchers commonly agree plays an important role in the likelihood of owning a house Carliner (1974) explains that the relative price of owner occupied housing i s composed of tax rates, mortgage terms, and income tax deductions which at any given time are essential functions of a complex model of the after One major theory that has been widely used in the field of economics is the life cyc le hypothesis (LCH). Researchers and economists have utilized the LCH for making important macroeconomic inferences about the private and public provision of social security, the implications of the stock market on the economy, the impact of demographic ch anges on national saving and in economic growth, and the determinants of national wealth (Deaton, 2005). Additionally, microeconomic questions can be partially answered through use of the LCH. How much should be saved for retirement or cation? How much insurance should one purchase? How should households allocate their portfolios across various assets? These are just some examples of such microeconomic decisions (Bodie, Treussard & Willen, 2007). One main postulate of this theory is tha life cycle determined by age and present wealth (Artle & Varaiya, 1978). The standard
12 LCH model 1 posits that perfectly rat ional individuals will choose consumption, not expenditure, as a path for maximizing life utility. It is important to highlight that the level of consumption will be governed by intertemporal preferences. Ultimately, the goal should be to keep their margin al utility of expenditure constant over time. However, for optimum consumption to be constant, the LCH model assume s there is no uncertainty that is the interest rate and the rate of time preference is zero (Japelli, 1999). Generally, the simplified model is divided into stages through which individuals pass. For instance, the early or youth stage refers to the years in which little income is earned. The middle aged stage, the working years, sees most of the wealth and resource accumulation. The model culminates in the final stage of retirement whereupon individuals begin to dissipate wealth and social security. In other words, the wealth of the idea l individual increases up to retirement after which there is a smooth decrease. Although LCH has provided much guidance for making decisions at the macro and microeconomic level, many researchers have challenged the standard model as it makes particular a ssumptions. Barnheim and Scholz (1993) explicate d that in practice, life cycle decision s are closely dependent on factors such as labor earnings, investment assumes that the individual has an almost perfect expectation regarding future economic prices, household composition, and life span, along with the other assumptions about rationality and self control. These numerous assumptions simply limit the model. Japelli (199 9) support ed these allegations by arguing that realistic examples of wealth accumulation depend on household preferences, interest rates, 1 Modigliani & Brumberg (1954)
13 market imperfections and uncertainty, and life cycle variations within household structure. In this study, I questio n the assumption of a frictionless market. I presume that if the market is imperfect, rather than being idealized, then, risk factor may shift and financial bar riers should arise over time (Yang, 2009 ). Such barriers and variations play an important and i nfluential role in general homeownership rates. Thus, the main research objective of this study is to explore market constraints specifically, financial barriers, and their impact on the likelihood of homeownership by utilizing the LCH under the assumption of a non frictionless market. Important implications and conclusions of this study as it relates to the notion of household life cycles might benefit multiple interested parties such as financial planners and housing professionals rendering recommendati ons to their clients, educators and researchers helping the public to make informed choices, and governmental policymakers attempting to secure the financial well being and interests of households.
14 CHAPTER 2 LITERATURE REVIEW The life cycle h ypothesis (Modigliani & Brumberg, 1954; Mondigliani & Ando, 1957; Ando & Mondigliani, 1963; Modigliani, 1986) and more specifically its application on consumption and investment in housing is used as a cornerstone for the formulation of hypotheses to be proposed in this study. Before introducing the life cycle hypothesis (LCH) itself, it is relevant to first understand neoclassical economic theories that branch of economic theories in which LCH is comprised. Afterwards, the premises of life cycle hypoth esis and assumption s relevant to this study are further explained. A survey of literature is offered for each variable utilized in this research. F inally, the chapter concludes with a statement of the proposed hypotheses pertinent to this study Neocla ssical Economic Theories: General Postulates Neoclassical economic theories of asset accumulation and consumption are characterized by several mutual assumptions. For example, under the lens of these theories, individuals are deemed as rational beings seeking to maximize benefits and minimize costs; individual utility is assumed to be a function of consumption. In addition, neoclassical economic theories assume there is little difference between income and assets as both are seen as economic resources that might be utilized for consumption. Particular ly, this set of theories pays special attention to the variable of time by proposing that individuals have to make choices between present and future consumption. An important assumption is that such choices are normally a product of independent individual
15 (Friedman, 1957). Modli Consistent with the general postulates of neoclassical economic theories, the LCH proposes that households and individuals aim to increase and maximize their lifetime utility by leveling consumption while navigat ing through periods in the life cycle stage s which are generally pr oxied by age and wealth. C onsumption and asset time, LCH is an intertemporal framework that posits th at both individuals and hous eholds pay close attention to long term consumption opportunities, as there is awareness that current decisions may affect future options. In turn, this notion is then applied to investment an d asset accumulation decisions more specifically to dura ble goods, an important aspect considered in this study. As with most durable goods, purchasing a home is seen as an investment decision ; a nd for most families equity in owner occupied homes is the dominant form of wealth (Kain & Quing ly, 1975). LCH expectations about future financial assets and resources, and family structure. These factors (commonly known as life cycle variables) also change over the ti me. As these factors fluctuate as individuals move through various life cycle stages so also does consumption, which in this case represents the demand for housing. In the housing literature, researchers agree that multiple factors influence the likelihoo d of being a homeowner, including the life cycle variables. Previous studies have examined the impact of income, race, age, marital status and family size on the likelihood of being a homeowner (Maisel, 1966; Kain & Quigley, 1972; 1975; David,
16 1962). Using data from the 1967 Survey of Economic Opportunity (SEO), Carliner (1974) extrapolate d previous findings (Maisel, 1966; Kain & Quingly, 1972) to the nation as a whole. He f ound that demographic variables such as marital status, age, and family size are hig hly correlated with the likelihood that a household owns its home. Additionally there was a positive correlation between income and homeownership rates. More figuratively, an increase of income of $1,000 increase d the likelihood of homeownership by 2% (19 71 homeownership rates). However, although the LCH has been highly and prominen tly used in the area of housing and consumer economics as it provides a foundation for understanding economics pa tterns across life cycle stages, t he original and simplified ve rsion of this theory fails to realistically illustrate asset accumulation and housing demand in the real world financial market context (Beverly & Sherraden, 1999). For example LCH has previously assumed that financial capital markets are perfect This ca n be appreciated in this prototype where earnings are stochastic and are the only source of uncertainty used in this model; market frictions are not considered. Significant and pragmatic variables such as inequality on market access to information and pric es, governmental regulation or taxes, and barriers to entry or exit in the market are completely ignored in this model (Dornbusch & Fischer, 1993 ). Because of these limitations, the theory has been subject to several appropriate criticisms. For example, B arheim and Scholz (1993) highlight that, in practice, access to market information and financial preparedness among individuals is not egalitarian. A household, in isolation, with no training, practice or access to information is not likely to act as the l ooking forward utility maximizer suggested by LCH. Bernheim (1994)
17 concluded from his empirical studies that a great number of households in America lack financial sophistication and relevant access to information. These limitations impede their making opt im al decisions regarding consumption. Further, Lusardi (2007) determined that financial literacy is linked with poor financial decision making, lack of stock market participation and poor borrowing constraints. The research highlight ed the widespread finan cial illiteracy among particular groups such as women, African American and Hispanics. Moreover, sev eral studies (Barneim & Scholz, 1993; Bunt ing, 1991; Diamond and Hausman, 1984) have shown that this notion of market inequality harshly affects low income ho useholds. Based on these previous findings, these households were less prone to save and accumulate assets at an optimum rate. Also, they exhibited more often very low levels of savings and asset accumulation; and even worse, often had negative levels. No netheless, other reasons lead individuals to vary from the optimal lifetime stages is smooth. For this to be possible, it is required that individuals have current inflows greater that their outflows in order to liquidate liabilities (if any), contribute towards retirement, and have credit vehicles available to compensate should inflows fall short in order to finance optimum consumption. In reality, imperfect credit markets and uncertainties pertinent to future income preclude households from access to borrow against futures income ( M odigliani, 1986 ). Consequently, some households are unable to achieve optimal consumption. For instance, individuals with irregular earnings o r with low lifetime earnings are more prone to face liquidity constraints or market access constraints. This problem is heightened in many low income households, as many may
18 never earn a n income substantial enough to exceed their consumption needs. Japell i (1999) explain ed the implication of frictions in the market; constraints early in the life cycle or uninsurable income risk may alter the process of wealth accumulation. By assuming a model with imperfect markets, households accumulate wealth before ret irement at the higher rate than would be necessary under perfect markets. Thus, accumulation function. As most households do not exist in the assumed scenario of a perfect f inancial market, this assumpt ion must be challenged (and is in this study) as it may introduce bias on the suggested optimum saving and consumption rate. And more specifically, this study examine s the financial barriers variables as part of the market impe rfection component. I hypothesize that these constraints influence the likelihood of being a homeowner during different life cycle stages. Several studies have already proposed models of the life cycle under the assumption of imperfect markets. Yang (2009) explore d the housing consumption pattern by offering a modified life cycle behavior quantitative model. The prototype t ook into consideration variables such as uninsurable income risk, borrowing constraints, the lack of an annuity market to insure against an uncertain lifetime, and t he t ransaction costs for trading houses. The author conclude d that the borrowing constraint variable help ed explain the housing consumption profiles of accumulation of housing stock early in life in the United States. Yang (2009) implie d that young agents would be renters until they accumulate enough wealth to make the down payments. Young households, in an ef fort to accumulate housing stock early in the cycle, are willing to hold a major fraction of their wealth as housing. Halk et &
19 Vasudev (2009) supported this notion that households rent in the early stage due to borrowing constraint in the mortgage market but add ed other factors in the formula, such as the profile of earning and desire for mobility. Several other studies have concluded financial market constraints to be significant factors in the decision of ownership (Linneman et al., 1997; Haurin Henders hott & Watcher 1996; Zorn, 1989). But financial position, liquidity and borrowing constraints are an incomplete set of barriers, and I propose in this study that the preferential tax treatment of housing capital in the economy affects the likelihood of b eing a homeown er as well. Gervais (2002) stated that tax code provisions offer an incentive for individuals to own rather than rent. However, this incentive is biased towards owning larger houses, thus distorting the lifetime profile and composition of ind including these imperfect market variables might help further explain the position of low income households under the lens of the LCH framework. Below, the variables considered and explored in this study a re further surveyed in the housing and economic literature. Demographic V ariables Age Consistent with LCH, in general, ownership rates continuously increase as age increases. Carliner (1974) suggest ed that age was highly and significantly correlated wit h the likelihood of being a homeowner. In his research, he f ound that homeownership rates rise from 23% for households under 25 to 84% for families with head of household 65 or over. Similarly, other studies (Maisel, 1966; Dav id, 1962) supported the idea t hat homeownership is positively cor related with age of head of household.
20 It is important to clarify that this study explores the likelihood of being a homeowner rather than the likelihood of becoming a homeowner. This is a crucial clarification consideri ng that the possibility of becoming a homeowner near retirement year becomes less likely according to life cycle theory (Bodie, Treussard & Willen, 2008). And, homeownership rates remain virtually unchanged after age 55. Some individuals in the retirement stage might decide to trade to another unit, upsizing and downsizing their current unit as a correction of the disequilibrium between occupants and house size. However, in the absence of a change in family structure, older households are unlikely t o move ( Munnel l Soto & Aubry, 2007). Painter and Lee (2009) also support ed this notion by suggesting that age is not linked to housing tenure preferences for older households. However, life changing events such health conditions could be determinant for house te nure transitions for households in the retirement stage. Household size and composition Family structure and its size are variables and co mposition change; and also they are expected to change i n conjunction with marital status (Kain & Quigley, 1972). For many households, the timing of household formation and the arrival of new members (children for example) coincide with plans to buy a hou se (Chiuri & Japelli, 2003 ; Rosen, 1979). Econometric studies on the determinant of homeownership and home purchase have indicate d for instance, that ownership rates are greater for married couples with children than for married couples without children a nd single individuals (David, 1963; Maisel, 1966).
21 Consistent with the findings from previous studies, Carliner (1974) explain ed that the probability of owning increases with household size primarily because larger households almost always included childr en. His findings state d there was a significant difference in ownership rates between the following particular comparison groups: households with less than 3 members and households with three or more members Adjusted ownership rates for one and two perso n households were 60%, for three and four person households 68%, and for househo lds with five or more 69% Marital s tatus This variable has been associated with household stability, a determinant on the decision on buying or renting Thus, this variable could serve as a mediator in the likelihood of being a homeowner. Carliner ( 1974) studied t he variable of stability of a household by measur ing specific areas of the household such as size, income, marital status, or taste. The research found that a n expec ted change in any of these would likely have an impact on the probability of being a homeowner. In addition, almost 71% of households headed by married couples own ed their homes and fewer than 46% with unmarried heads d id Interestingly, other studies indi cate d that married couples are slightly more likely to be homeowners than widows. However, both married couples and individual widows were notably more prone to be ing homeowners than single individuals (Maisel, 1966 ). Race Under some circumstances, race has been correlated to differences in homeownership among different groups. Carliner (1974) reported that after adjusting for other variables blacks own ed less often than whites : 38% of all black households were headed by women compared to 21% of all whit e households. Additionally, combined with the variab le of marital status, 12% of black households were headed by an
22 unmarried individual versus 9% for white. Only 50% of black households, compared to 70% of white household s were headed by married couples. The difference in average income between whites and black s explain ed a larger portion of differences in ownership than any other factors, which account ed to 5.1%. For t he adjusted differences in all these categories a dummy variable was included for non whites ; and the coefficient for such dummy was 0.17 significant at the 1 % level. This difference is probably due to discrimination in the market s of housing and credit. Likewise, Rosen (1979) f ound that the race and gender of the head of household was st atistically significant to the probability of owning a home. Households led by females and black households overall are less likely to be homeowners. Income In general, income, in combination with other life cycle variables, plays an important role in th e probability of being a homeowner. Cetis paribus as income rises, marginal tax rates rise, and the advantage of investment in owner occupied housing over other forms of investment also rises. Specific studies have demonstrate d the relationship between in come and homeownership rates. For instance, Carliner (1974) explicated that for the entire sample used in the study, the change on homeownership was 1.62 per each $1,0000 (1 966 dollars of family income). More precisely, he found that the income coefficient for young married families was more than twice as large, while the coefficient for older married families was slightly lower. Older owners whose incomes have decreased since buying their home s w ould be slow to readjust their consumption of housing by moving to smaller quarters. On the other hand, y oung renters whose income ha d recently increase d w ould be quick to move up T his is
23 consistent with the life cycle hypothesis. It is significant to mention that a ll the se estimates of income elastici ties have been based on one years measured income. Market Imperfection Variables Information Asymmetry Lack of significant access to information (Berheim, 1994) and lack of general financial literacy (Lusardi, 2008) could be translated as financial barriers that lead to wrong financial decision making Indeed, asymmetric information among consumers is likely to generate barriers across stages in the life cycle (Chiuri & Japelli, 2000). Braunstein and Welch (2002 ) explicate d that consumers with financial knowledge deficiencies lack the tools to make decisions that are advantageous to their particular decisions, as well as, thei r long term goals such as buying a home or financing retirement. Many studies suggest that poor borrowing decisions (i.e. excessive borrowing or high cost mortgage selection) and, in general, financial management behavior are connected to lack of knowledge and lack of basic financial concepts (Lusardi, 20 07; 2008; Moore, 2003; Bucks & Pe nce 2008 ). On the other hand, Kozup and Hogarth (2008) emphasize d the importance of financial education and its role in contributing to households making optimum decisions a bout goals, needs, and values. In general, households with higher financial literacy and financial education are positively correlated to improved credit use, a better consumer financial management, and compliance and adherence of suggested financial pract ices (Hilgert et al., 2003; Hogarth & Hilgert, 2002; Cude et al., 2006; Braunstein & Welch, 2002). It is relevant to mention that an improved financial behavior does not necessarily follow sole l y from an increase in financial information. Studies suggest t hat financial education, provided
24 through financial counseling or intervention, can certainly affect financial behavior (Lusardi, 2002 ; 2004; Bernheim & Garret, 2003; informed households help to create more a competitiv e and more efficient market (Braunstein & Welch, 2002). Borrowing Constraints Several studies have suggested that financing constraint constitutes one of the main barriers to homeownership (Rosethal, 2002; Yao & Zhang, 2005; Cocco, 2005; Luen go Prado, 200 6). Rosenthal (2002 ) provided an extensive and comprehensive literature review on the impact of borrowing constraints on homeownership In summary, he posit ed that, ceteris paribus if borrowing constraints (down payment, house to income ratios, and total debt payment to income ratios) are removed, homeownership rates w ould rise, as it was likely that households w ould change their housin g tenure Furthermore, in a effort to create a model consistent with observed life cycle housing consumption profiles, Yan g (2009 ) proposed a version of the life cycle model that t ook into consideration financing market frictions such as uninsurable labor income risk, transactions costs for trading houses and borrowing constraints. She conclude d that borrowing constraints were crucial in explicating the accumulation of housing early in the life cycle : y ounger households aim ed to accumulate housing stock quickly, thus delaying non housing consumption due to the existence of borrowing constraints. Interestingly, households in the retirement stage are unlikely to decrease their housing stock because of high transaction costs for trading housing units. In fact, Painter and Lee (2009) show ed that age was not directly associated to housing tenure choice for older households. Speci fically, for the purpose of this study, I emphasize and
25 pay careful attention to three main dimensions: credit worthiness, and liquidity constraints. Credit w orthiness Barakova et al. (2003) evaluate d the probability of credit quality as a potential barrier to homeownership. The results demonstrate d that financing constraints had significant effects on the likelihood of being a homeowner. For example, if households with poor credit scores would have ha d cleaner records, their homeownership would have increased by 10%. Consistently, Rosentha l (2002) determine d that bankruptcy and history of delinquency on loan payment important c ould be seen as actual barriers to homeowner ship. Moreover, it is important to consider not only actual barriers but also perceived barriers by consumers. The notion of discouragement from lenders is considered as a barrier in this study. Carliner (1974) suggest ed that even if families were able to save for a down payment and were willing to take the risk of ownership, mortgage lenders still might not be willing to lend to them, as there are minimum limits o f credits worthiness to which borrowers are subjec t Liquidity c onstraints Carliner (1974) explain ed that households (especially low income households) might have experience d difficulty in successfully saving and meeting the current expenses of owning a home. Hence, without emergency reserves, they might be less willing than richer households to assume the risks that homeownership demands. When exploring how homeownership rates change, Chiuri and Japelli (2000) stress ed the importance of both demand side factors (household formation and composition), and supply side factors such as mortgage marke t imperfection. In their study, market imperfection was defined as the size of mortgage
26 market and down payment ratios. Controlling for demographic factors, they f ound that credit availability and mortgage market imperfections did actually affect the homeo wnership profile. For example, the timing of buying a house was closely related the lower the down payment ratio, the earlier the purchase. The study indicate d that younger people tend to face a higher level of liquidity constraint to homeownership as they ha d to save before they c ould buy. Structural Barriers Sharraden (1991) theorize d that assets accumulations were principally the outcome of institutionalized mechanisms involving explicit connections, rule s, subsidies and incentives. His proposed theory of welfare based on assets paid special attention to the crucial role of financial institutions in savings and assets accumulation I nstitutionalized arrangeme nts very often offer access and incentives to accumulate assets (retirement plans, for example) More precisely in the context of homeownership, the use of institutionalized mechanisms to buy a house provides households with incentives and housing tax bene fits. For instance, buying a home with mortgage financing enables taxpayers to deduct the interest paid on the mortgage note. Sherraden (1991) suggest ed that it w ould be rational for households who ha d access to these institutions to use their mechanisms t o save and accumulate assets. Moreover, Beverly and Sherraden (1999) explicate d the role of institutional determinants such as facilitating savings, financial education, institutionalized mechanisms, and incentives and subsidies in saving practices. They acknowledge d that these institutional savings ed household access to institutionalized mechanisms was positively correlated with higher saving rates. They
27 elaborate d further in pointing out that ins titutionalized saving opportunities are secure and convenient. Gutter et al. (2012) explain ed the implications of structural barriers for households, mainly for those families with low and moderate income s The lack of access to financial institutions c o uld accumulation decisions, especially for some low and moderate income families. Being unbanked c ould represent a real or perceived market constraint that could result in missing market opportunities such as u tilizing mainstream financial institutions. Tax Preferential Treatment For those households that own a house and itemize their deductions, an evident tax advantage is extended through the current U.S. Tax System. Rosen (1979) explain ed that through itemiz were understated by the sum of net rent (also called imputed rent), mortgage interest and property taxes. Thus, the deductibility of property taxes and mortgage interest c ould be seen as an implicit subsidy from the system. Seve ral research studies have suggest ed a direct connection between these existing tax incentives available in the system and the likelihood of being a homeowner. For example, property taxes (Ihlanfeldt & Boehm, 1983; Slitor, 1976) and mortgage interest deduct ibility (Halket & Vasudev, 2009) encourage homeownership; or in other words, it makes the price of owning favorable to renting. Nonetheless, most of the existing home ownership incentives in the U.S. Tax System are selective in the sense that only qualify ing taxpayers are able to take advantage of them. Particularly, the deduction of mortgage interest and paid property taxes is only available to those tax filers who are in the position to itemize their deductions. In contrast, several non itemizing househo lds, mainly those with low or
28 moderate income, do not have the opportunity to perceive the benefits of such house policies if they were to buy a home. Thus, the inability to benefit from these advantages might be perceived as a barrier by those unqualified households. Indeed, the notion that the effect of taxation on homeownership is closely tied to income classes has been document by several studies (Carasso, St euerle & Bell, 2005; Bevery & Sherraden, 1999; Slitor, 1976; Carliner, 1974). The expectation is that at higher income levels, property taxes and mortgage interest deductibility increase homeownership. To some degree, high income and itemizing households with large mortgage notes could use homeownership as a tax shelter. For instance, when buying a h ouse, it encourages individuals to borrow more and buy larger homes than they otherwise might have done (Gale, Gruber & Stephens Davidowitz, 2007). On the other hand, Beverly and Sherraden (1999) reported that low income households (and homeowner who are i n a lower marginal tax rate) d id not generally benefit from tax incentive because they were less likely to ow n their own homes (Eller & Fras er 1995). Carliner (1974) hypothesize d that the reason wealthy households frequently own more than poor households was because of the imputed rent that results from owning house stock as it is exempt from income taxation; therefore, owners enjoy ed an additional return on this investment equal to their marginal tax rate times the imputed rent. Hence, these deductions are mostly beneficial for high income households rather than for those with low or moderate income, or households with lower marginal tax rates. Carasso, Steuerle, and Bell (2005) reveal ed that federal rental policies tend to subsidize low income household s, which result s in discouragement of homeownership
29 for these cohorts. Thus, itemizing versus non itemizing may be seen as a barrier by con sumers when making a decision about a housing unit purchase. Time Preference The notion of time is crucial when utili zing the life cycle hypothesis in economics, as consumption decisions are ruled by a set of intertemporal preferences. Time preference refers to the opportunity cost of trading current utility for future satisfaction (Gutter et al., 2012). James (2009) hig hlight ed the importance of considering the current predicting homeownership rates. He posit ed tha t besides external barriers (e.g borrowing constraints, unit prices and interest rates), families might face an internal barrier of behavioral choice by heavily discounting future costs and benefits. Th e study report ed that renters exhibit ed a considerably shorter financial planning horizon than homeowners did. Having a myopic planni ng horizon strategy or a high level of time becoming a homeowner usually involves the trade off or delay of current consumption for future utility. Additionally conside r a study that discusses the financial implications of future orientation bout future decisions (Shobe & Page Adams, 2001). In the study, future orientation is proposed to act as a mediator between assets and individual/societal well being. It report ed that for high and moderate income families, economic security facilitate d the process of planning for the future; whereas for low income households, financial decisions m ight have to be made on a daily basis. Sharreden (1991) noted this previously in a study showing that asset depravation limit ed future orientation. And, the study suggest ed this w ould have negative social and economic reper cussions for these households (e.g
30 households instability per sonal inefficacy, low community involvement). In more economic terms, Finke, Hus ton and Weaver (2003), and Finke and Huston (2004 ) support ed a notion that time preference w ould be a predictor for household net worth and asset accumulation. And, these house between the psychological concepts of impulsiveness or impatience and the LCH construct of utility maximization (Gutter et al., 2012). For the purpose of this study, time preference is treated as a psychological facto r. Summary Under the lens of neoclassical economics theories, rational individuals act based on a cost benefit analysis dynamic; they aim to obtain greater benefits on the situation in question or/and minimize costs. The notion of time is crucially relevan t in the possibility of adopting an action or making a particular decision over others. In the financial context, for instance, individuals constantly ponder whether to consume now or in the future. pothesis (LCH), which is founded in the neoclassical branch of economics theories. Consistently, it posits that individuals attain to maximize utility (satisfaction) by smoothing consumption through the different life cycle stages. It is crucial to mention that LCH assumes that markets are frictionless. Consistent with the postulate of the LCH, the financial and housing literature suggest that the life cycle variables that is age, marital status, and household size and composition are highly correlated wi th the likelihood of being a homeowner. Other demographic variables such as income and race exhibit a high level of significance in homeownership rates (Carliner, 1974). Other variables not comprised in the LCH framework, but under the umbrella of market i mperfections have been found correlated to homeownership rates. Previous research studies have reported that borrowing and liquidity constraints (Barakova et al., 2003 ; Rosentha l 2002; Carliner, 1974), tax preferential treatment ( Carasso, Steuerle & Bell, 2005 ; Carliner, 1974) and planning horizon (James, 2009) are significantly related to homeownership rates. Hypotheses In this study, the hypotheses are formulated under the extension of the
31 order to adapt the LCH to a more practical basis, market frictions exist and are present in th is model. The considered market imperfections in this study are conceived as potential barriers for some households in the likelihood of being a homeowner. H 1 : Households with an increase level of information asymmetry are less likely to be homeo wners. H 2 : Households with greater level of actual and perceived borrowing constraints are less likely to be homeowners. H 3 : Households that report higher levels of structural barriers (access to institutionalized mechanisms) are less likely to be homeowners. H 4 : Households that are ineligible for housing tax preferential treatments are less likely to be homeowners. Households with a more myopic time preference are less likely to be homeowners
3 2 CHAPTER 3 DATA AND METHODOLOGY Data Set The present study uses the 2010 Survey of Consumer Finances (SCF) sponsored and conducted by the Federal Reserve Board in conjunction with The U.S. Treasury Department, and collected by the National Opinion Research Center at the University of Chicago. In the 2010 Survey of Consumer Finances, 6,492 families/households were interviewed (Bricker, Kennickell, Moore & Sabelhaus, 2012). The SCF is a triennial cross sectional survey of American households. d retirement accounts, income and other sources, an d general demographics are gathered in this survey. T he survey are unique as no other study in the United States collect s similar information with level of specificity. Importantly, the SCF data is often utilized by government institutions and other agencies as well as scholars and researchers in the various branches of economics. For the purpose of this stu dy, this data set is ideal in that it includes a rich and comprehensive source of information either through direct or subjective measure and computation expectation. It is rel evant to mention some potentially problematic issues regarding this data set. T he consideration of these is of extreme importance for the purpose of this study. The first issue of concern is the SCF oversamp ling of high income households, whereby ; this data set does not offer a represent ative sample of the U.S. population as a whole. Nonetheless, the survey provides weights that might be used to approximate
33 close estimates of the U.S. population ( Kennickell, McManus & Woodburn, 1996; Aiz c orbe, Kenn ickell & Moore, 2003). W hen comparing the effect of using weighted versus unweighted data in multivariate analysis, Lindamood, Hanna and Bi (2007) find that these effects exhibited little difference. A second issue worth scrutinizing is the multiple imputation method used for missing data in thi s data set. When collecting data, it is likely to have missing data due to several reasons such as unintentional mistakes and skipped answers. Missing and omitte d data often result in issues of efficiency and bias for user (Montalto & Sung, 1996). Thus, d ifferent methods exist to deal with missing data Some methods use multiple regression to approximate and substitute the missing data based on identified and known attributes of the survey respondents. For instan ce, a valid response category might be rando mly assign ed to replace the missing data, or the mean of the values available from those partici pants who actually responded might be utilized for such replacement (Lindamood, Hanna & Bi, 2007). Since 1989, The SCF has opted to employ the multiple imputati on method with the ultimate goal of providing the best possible estimate for missing data. This method uses multivariate statistical technique s to replace the omitted data; consequently, the result of this process is m ultiple complete data sets. In the cas e of the SCF, the multiple implication results in five different sets of data. E ach of these data sets is denoted as implicate In other words, for each respondent five different sets of data are available. The main benefit of following this procedure is that the SCF data files contain no missing values; however, a careful treatment of these five sets would be required in order to aptly analyze this data. Indeed, Lindamood, Hanna and Bi (2 007) highlight the importance of how to analyze
34 these multiple impli cates. They explain that while it is possible to use just one implicate, this would defeat the purpose of multiple implicates and even worse, it could result in biased results. Ideally all five implicates should be used though the employment of Rubin (1987 1996) repeated imputation inference method (RII). Montalto and Sung (1996) explicate that the result of replacing missing values through imputation techniques is an intrinsic phenomenon of extra variability in the data. RII, then, allows researchers to e stimate such variability. The effect of using this technique is the estimation of variances that more closely represent the true variances, at least more approximate than if only one implicate is used. Montalto and Yuh (1998) elaborate further on the advan demonstrate that using point estimates add estimate of variance calculated through RII technique offer a fo undation for more valid inference and test of significance than if only one implicate would have been used (Montalto & Sung, 1996; Montalto, Hanna & Bi, 2007). Thus, it is ideal for researchers and scholars using the SCF to take full advantage of the benef its of repeated multiple imputations in an effort to make better inferences and minimize bias. In this study, for the descriptive statistics part, data is weighted in order to make the sample more representative of the U.S. population as a whole. Moreover, I utilize all (1987) repeated imputation technique in order to lump all the information into one set and adjust for imputation error.
35 Dependent Variable In this stud y, the likelihood of being a homeowner constitutes the dependent variable in this study. In order to determine homeownership two specific questions are Do you a) own this house, b) own it as part of condo, c) co op, d) townhouse association, e) own part of it f) pay rent g) or did it cost when you originally acquired respondents to be considered homeowners they must meet two requirements: they must indicate any time, they must report home value greater than 0. If answers are different than these conditions, then, households are considered non homeowners. With this screening, I am interested in in cluding individuals/households who have a legal right to sell and/or transfer the ir property. Also, it is important to highlight whether or not the r espondents live in housing unit that is a farm/ranch or mobile home. This is necessary as farm/ranch owners are excluded as they might use or potentially use the proper ty for business purposes Because preferential tax treatment is a barrier considere d in this study, I believe including them would potentially create bias on tax advan tages for homeowners. R espondents are coded 1 if they are homeowners, 0 otherwise. Independent Variables Demogr aphics: Life C ycle Variables As previous studi es well document life cycle variables are highly correlated with the likelihood of being a homeowner. For the purpose of this study, life cycle variables refer to age, marital status, household structure (specifically, the presence of a child/dependent in the household) and household size. In order to measure these variables three particular questions from the SCF are utiliz ed.
36 To determine the age of the respondents, we use the reported age. For a greater grade of accuracy and consistency, the age has been matched with the reported date of birth of the respondents. In this study, age is coded continuously. The household ty pe of respondents is directly determined from the following question re you currently married or living with a partner, separated, divorced, married or living with partner households coupled households s separated divorced widowed and single Non coupled respondents will be classified based on and male uncoupled female respondent In addition, the presence of offspring (children) is measured by using the indicated number of dependents under the age of 19. Four categories were used to measure this variable: coupled hou seholds with at least 1 child ; coupled households with 0 children; non coupled hou seholds with at least 1 child ; and non coupled household with 0 children. It is relevant to highlight that for tax purposes, an individual who is 24 or under, and full time student may qualify as dependent However, it is difficult to ascertain these attributes from the information provided in the data set. Finally, household size is included in this study. The reported number of people in the primary economic unit is used for this purpose. Note that the r eported number of members in the household excludes those who do not usually live there and who are financially independent. This variable is coded continuously. Other Demographic Variables Although the following are not seen as life cycle variables, the literature suggest s that there is a strong association between some of these variables such as race and
37 income and the likelihood of being a homeowner. The inclusion and measurement of these variables is crucial then. The SCF classif ies respondents in to the following race/ethnicity categories: white and non white I am aware of this limitation and the potential implication for this study. However, I believe this classification is adequate given the limitations on the data set. In addition, the variable e ducation is considered. The number of years of formal education i s used to measure this variable. The following c ategories are employed : less than high school, completed high school, some college, and completed college and /or more Finally, we turn our attention to the of income. The SCF gathers thorough information on the financial position o f household s and individual s Annual income is calculated by using components s uch as salary/wages, investment dividen ds, among othe rs. In this study this variable is coded in four categories: house holds earning less than $25,000, between $25,000 and 64,999, between $65,000 and $110,000, and more than $110,000 Market Imperfection Variables Information Asymmetry In this study, i nformation asymmetry is measured by surveying the numbers of sources of information used by respondents when making borrowing and credit decisions The variable is broke n down in three dimensions: asymmetry in professional, internal and external sources of information among respondents. More precisely, professional sources of information refer to contact with or financial counseling of a lawyer, accountant, banker, broker, fin anc ial planer, insurance agent, or real estate broker. Personal internal sources o f information include magazine/newspapers and
38 books, internet/online service, friend relative, past experience, material from work/business contact, other personal research, and other institutional sources. Finally, external sources of information consider material in the mail, television/rad io, advertisements, telemarketing and other media material. Borrowing Constraints For the purpose of this study, borrowing barriers are divided in to two groups: a) credit worthiness and b) liquid ity constraints. The SCF contains several questions allowing for the measurement of this variable in multiple ways. The measurements are analyzed in two dimension s actual and perceived barriers. To be more precise, to assess the relationship of estimated credit worthiness and the likelihood of homeownership, respondent FICO score s are estimated Five components determine FICO score: payment history (35%), amount owed (30%), length of credit history (15%), types of credit in use (10%) and new credit (10%). In this study, I am able to obtain approximates of 7 5 % of FICO score that is payment history, amount owed and types of credit in use. I am aware of this limit ed estimate, but I believe it still reflect s a n adequate Payment histo ry is measure d with the following question T hinking about Visa, Mastercard, Discover, American Express and store cards that you can pay off over time, do you almost always, sometimes, or hardly ever pay off the total balance is calculated by using the current debt to credit limit ration. Finally, types of credit are measured by looking at the presence of mortgage loans, student loans and car loans. Perceived credit discouragement is also ex plored under the umbrella of credit worthiness. More prec isely, I use the following question I n the
39 past five yea r s, has a particular lender o r creditor denied you (or your partner) any request or lowered the amount for which you applied? Liquidity constraints are directly measured by using the emergency fund ratio. This is calculated by dividing liquid assets (cash and cash equivalents) by monthly expenses. Ideally, households and individuals should have a 3 to 6 month emergenc y expense fund. Generally, a rate greater tha n 10 % is deemed as accept able. In this study and for comparison purposes, a 6 month fund emergency fund ratio is utilized. Structural B arriers For this variable several questions regarding access and use of fi nancial institutions are considered. Access, in particular, is measured in terms of distance from the financial institutions and workplace or residence It is asked with the question, oughly, how many miles is the office or ATM (cash machine) of this in stitution from your home or workplace/home or workplace of the The variable is coded continuousl y. Using the reported number of financial institution s in which respondents currently have an account, loan or regularly do pers onal financial business, I assess the use of financial institution For the purpose of this study financial institutions comprise banks, saving and loans, credit unions, brokerages and loan companies but no institution in which respondents have only credit cards or business accounts. Number of financial accounts is coded continuously. Tax Preferential Treatment This variable is evaluated D id/will riable is coded 1 if yes, 0 otherwise (this includes no and did not file does not expect to do so ). Secondly, I am interested in measuring the ownership of at least one tax advantaged account or investment which is defined in
40 th is study as a tax strategy Tax advantaged investments refers to tax exempt bonds and tax exempt mutual funds. Tax advantaged accounts include Keogh, 401(k), 403(b) and tax advantaged savings accounts. Market Uncertainty Economic outlook is used a s a proxy for uncertainty as measured by utilizing a five years, do you expect the U.S. economy as a whole to perform better, worse, or var iable is coded in the following categories: better, same and worse. Moreover the variable of planning horizon is utilized in this study. I use the planning or budg eting your family's saving and spending, which of the following time periods is most important to you (and those residing family members are grouped in the following categories: less than 5 years, and more than 5 years. These c ategories allo w comparing group comparisons with differing time preference s (less myopic respondents vs. more myopic respondents ). Sample Size In this study, the sample size totaled 6,473 households. Precisely, 4,067 households were homeowners and 2,406 non homeowners It is significant to mention that households who in dicated living in a mobile home or whose housing unit was identified as farm/ra nch were excluded. The reason for this decision lies primarily in the bias that housing units used as both dwelling units an d business units might potentially introduce in the study In the case of mobile home owners, it is important to treat this group separately as several factors such as credit/borrowing terms
41 time component, asset depreciation and mobility l ikely diverge from the more traditional acquisition of housing units included in the focus of this study. F or detailed information on the criteria that was used to classify households as either homeowners or non homeowners, refer to the dependent variable section in this chapter. Analysis The ana lysis started with a measurement of the sample variables or descriptive analy sis. The sample profile (frequencies and percentages/means and standard deviation) included all independent variables presented by homeowners and non homeowners It is important to highlight that for descriptive statistics the sample was weighted in order to be representative for the U.S. popula tion. Furthermore, bivariate analysis, which included chi square test and t test, was performed as a part of the descriptive analysis. For t test, Montalto and Sung (1996) coding for scalar variables was use in this study. After wards binomial regression w as used to explore the effects o f the demographic variables and the imperf ect market variables (deemed as the variable of study in this research ) on the likelihood of being a homeowner. The use of this statistical technique is appropriate for this study as the binomial regression is designed for model in which the dependent v ariable is the result of a serie s of two possible disjoint ed outcomes (homeowners and non homeowners, in this case). In other words, logistic regression is a variant of multiple regress ions. The relationship between a categorical dependent variable (homeowners or non homeowners) and several predictors or independent variables (demographic and market imperfection variables) is assessed. The odds of being a homeowner are estimated based on the values of the independent variables.
42 CHAPTER 4 FINDINGS AND RESULTS Bivariate Analysis This section presents the bivariate results for each dependent variable used in this study. The analysis compares the variables by the independent variable homeownership status, precisely that is homeowne r versus non homeowner. The tables at the end of this chapter display the complete results of the ana lysis. Life Cycle Variables Age, marital status and the presence of offspring are theoretically speaking the main variables comprise d in the life cycle theory. The consulted housing literature suggests strong associations between each of these variables and homeownership rates. Age where as non homeowner s displayed an average age of 44.34 years old. T test showed that homeowners were more likely to be older than non homeowners (t=22.96, p <0.001). Marital s tatus The results of chi square test displayed significant differences in marital status by homeowner ship status. Specifically, 42.05% of the respondents who indicated living in a coupled household were homeowners and 15.99% of similar marital p<. 001). For those respondents who specified being single or a n on coupled household, the sub variable gender was respondents were female non coupled households and homeowners, while 13.16% p<. 001). O n t he other hand, 6.92% of the
43 participants in the sample were male non coupled households and homeowners, and 8.51% were non p<. 001). Household size The average number of members in the household for those who indicted being homeown ers was 2.66; whereas for those who reported being non homeowners, the average number of members in the household was 2.46. Based on the t test results, homeowners were significantly more likely to have larger household size than their counterparts (t=5. 1703, p <0.001). Presence of o ffspring Chi square values revealed that statistical differences existed among the categories used: coupled household with at least one ch ild, coupled household with no child, non coupled household with at least one child, and non coupled household with no child. More precisely, for those respondents who indicated the presence of at least one child, 15.61% were part of a coupled household and homeowners, 7.69% coupled household and non homeowner, 2.55% non coupled household and homeowner, and 4.43% non coupled and non homeown er. O n the other hand, for tho se individuals who responded with absence of offspring in the household, 26.44% were coupled households and homeowners, 8.31% coupled households and non homeowners. Other Demogr aphics Variables Although not considered as being core factors in the life cycle hypothesis model, these variables have exhibited high and influential correlation in the likelihood of being a homeowner in previous studies. Race The chi square test indic ated that there were significant differences in race between homeowners and non p<. 001). The results were as follows: for whites, 49.07% were homeowners while 21.70% were non white; and in the
44 case of non whites, 13.36% and 15.96% were homeowners and non homeowners respectively. Education For the variable education, each of the categories used in this study exhibited significant differences by homeownership status. Precisely, for those who ind icated having attained an educational level of less than high school 5.77% were homeowners and 7.60% non p<. 001); high school, 18.17% homeowners and 12.7% non p<. 001); some college, 14.2% homeowners and 9.56% non p<. 05); and college and graduate school, 24.20% homeowners and 7.80% non p<. 001). Income following income ranges compared by homeown ership status: for those households that reported earned income less than $25,000, 9.06% were homeowners and 17.44% non p<. 001); for those in the more than $65,000 but less than $ 110,000 range, 15.63% were homeowners and 3.51% non ho p<. 001); and for those who indicated having earned more that $110,000, 13.22% were homeowners and 1.34% non p<. 001). There was no significant difference for the income category of $25,000 65,000. Market Imperfec tion Variables Information Asymmetry Homeowners were significantly more likely to consult greater number of professional sources when making financial decision than their counterpart (t=12.44, p <0.001). Similarly, homeowners were more prone to consult a g reater number of personal sources than those who reported being non homeowners (t=2.97, p <0.05). No
45 significant difference was exhibited between homeowners and non homeowners in the number of external sources used when making financial decision. Borrowing Constraint Cr edit w orthiness (FICO score components) When comparing homeowners against non for the following: the type of credit accounts that is revolving and installment credit and credit history. A total of 51.25% indicated having revolving credit and being a homeowner whereas 16.65% reported having revolving credit but not being a homeowner. Conversely, 11.08% of the r espondents specified having no revolving credit account a nd being a homeowner co mpared to 21.08% who indicated the absence of revolving credit type and not being a homeowner. M =39.13, SD =612.50) differed from the credit usage ratio reported by non homeowners ( M =16.77, SD =219.31). T test outcome suggested that homeowners were more prone to have a greater credit usage ration than non homeowners (t=2.11, p <0.5). Moreover, of the total respondents in this study, those who indicated having at least one installment credit account, 28.0 8% were homeowners while 17.43% were not. And for those who reported having no installment credit type whatsoever, 34.25% of the total respondents were homeowners compared to 20.24% who were not. As stated previously, credit history exhibited significant difference s among homeowners and non homeowners. Specifically, of those who were homeowners, 30.56% reported paying their revolving accounts always on time, 20.43% indicated paying not always or never on time, while 30.56% suggested not having a card. On t he
46 other hand, in the case of those who were not homeowners, 7.29% reported paying their revolving accounts always on time, 9.20% not always or never on time, and 21.17% informed not having at least one revolving account. Actual constraints Chi square test showed significant differences in being turned down or accepted by financial institutions when comparing respondents by p<. 001). The sample profile was as follows: 11.15% indicated being turned down and being homeowners while 9.52% were turned down and were not homeowners. Conversely, 51.1 8% reported having not been turned down and being homeowners whereas 27.85 % said not been turned down but being non homeowners. Perceived constraints Perceived d iscourageme nt ( expected credit denial by financial institutions ) exhibited significant di fferences between homeowners and non p<. 001). Of the total respondents, 2.38% reported having perceived credit c onstraints and being homeowners, compared t o 11.08% non homeowners who expected the same credit discouragement from financial institutions. Also, 54.96% said being homeowners and would not expect any perceived credit denial by financial institutions whereas 26.58% reported being non homeowners and expected no perceived credit constraints whatsoever. Liquidity constraints T test results displayed no significant difference in emergency fund ration between homeowners and non homeowners. Structural Barriers The number of financial accounts owned by hom eowners ( M =2.74, SD =1.66) differed significantly from the number owned by non homeowners ( M =1.59, SD =1.21).
47 In fact, homeowners were more prone to have a greater number of financial accounts than those who were non homeowners (t=31.92, p <0.001). No statis ti cal difference was found in the distance to financial institutions between homeowners and non homeowners. Housing Tax Preferential Treatment Significant differences in the two items employed for measuring this variable =1193.77, p <.001)., and the ownership of at least one p <.001) existed in this sample when comparing homeowners against non homeowners and able to itemize while 7 .56% indicted being non homeowners yet have the ability to itemize for the tax year. Moreover, significant differences in the ownership of at least one tax advantaged investments and accounts existed with 38.65% of homeowners and 10.13% of non homeowners. On the other hand, 23.69% of homeowners and 27.54% of non homeowners indicated not holding either a tax advantaged account or investment. Time Preference Chi square test results suggested significant differences in planning horizon between homeowner and non p <.001). Precisely, of total respondents, those who indicated a planning horizon of more than 5 years, 23.54% were homeowners and 7.97% were non homeowners. And those who indicated having a planning horizon of less than 5 years, 38.79% were homeowners while 29.70% reported being non homeowners. There were no significant differences in the other time preference variable that is expe
48 Binomial Logistic Regression Demographic Variables Regression analysis results indicated age, household type, household size and race were significantly related to the likelihood of being a homeowner. More precisely, bot h the variables age and age square were highly significant indicating a quadratic relationship between age and homeownership. Thus, it suggested that as age increases the probability of being a homeowner increases but it decreases after certain point later in the life span. As mentioned previously household type was significant in the regression model. Non coupled household s (single or others) led by females were less likely to be homeowners than coupled hou sehold (married couples and cohabitators ). Simila rly, non coupled households with a male head of household were significantly less likely to be homeowners than coupled households or female non coupled households. Moreover, race was significant associated with the likelihood of being homeowners. Non white respondents were significantly less likely to be homeowners than white household respondents. Finally, all income categories were significantly related to the likelihood of being a homeowner. Precisely, households that reported earning less than $25,000 p er year were significantly less likely to own a house than those earning in $25,000 $64,999 income range. Conversely, those respondents who reported total earnings of $65,000 110,000 or more than $110,000 were more likely to be homeow ners than those in the refe re nce group ($25,000 64,999).
49 Market Imperfection Variables Information Asymmetry None of the items used to measure information asymmetry were statistical significant in this regression model. Borrowing Constraints Credit denial and perceived credit denial were the only two items under the umbrella of borrowing constraints that were significant in this model. Being turned down by financial institutions in the last 5 yeas was negatively associated with homeownership. Likewise, a perceived expectation of credit denial was highly and negatively related to the likelihood of being a homeowner. The odds indicated that households that were denied credit were 82% less likely to be homeowner as otherwise similar households who se credit petitions were not denied. Similarly, those households who perceived a credit denial were 62% less likely to be homeowner as otherwise similar households who did not anticipate a credit denial from lenders. The rest of the items used to capture t he borrowing constraints variable FICO score c omponents and liquidity constrain ts were not statistically significant in this model. Structural Barriers The number of financial accounts owned by respondents was statistically signi fi cant in this model. Havi ng a greater number of financial accounts was highly correlated to being a homeowner. Based on the odds ratio for the number of financial accounts, t he likelihood of being a homeowner increased 1.32 times for each financial account used.
50 The distance to financial institution offices, the other item used to measure the dimension of structural barriers was not significant in this regression. Tax Preferential Treatment Both items utilized under this variable ability to itemize and ownership of tax advantage d account and investments were statistically significant to the probability of being a homeowner. Specifically, those households that were able to itemize were drastically more prone to be homeowners than those ineligible or unable to itemize In addition, owning at least one tax advantaged investment and/or account (TAIA) was significantly related to the likelihood of being a homeowner. The odd s ratio for itemizing households indicated that were 3.18 times more to be homeowner s than non itemizing household s. Likewise, h ouseholds with at least 1 TAIA were 1.40 times more likely to be homeowners than those who hold none TAIA. Market Uncertainty Economic outlook was significant in this model. More precisely, Respondents who expected the economy to worse n in the next 5 years were less likely to be a homeowner than those who expected the economy to perform the same. Finally, financial planning horizon was a lso significantly related to the likelihood of being a homeowner, indicating that less myopic households were more likely to own a home than those with lo wer financial planning horizon The odds ratio for planning horizon of more than 5 years indicated tha t less myopic households were 1.121 times more likely to be homeowners that those households with a more myopic approach to time preference.
51 Table 4 1 Bivariate a nalysis : Sample p rofile and demographics by homeownership s tatus Variable Frequency (percentage) / Mean (SD) Significance test Homeowner Non homeowners Age 54.26 (15.87) 44.34 (17.30) t= 22.96 *** Household t ype Coupled households 2723.17 (42.05%) 1035.92 (15.99%) *** Non coupled household Male 448.29 (6.92%) 551.03 (8.51%) *** Female 865.73 (13.37%) 852.48 (13.16%) *** Household s ize 2.66 (1.44) 2.46 (1.52) t= 5.17 *** Presence of o ffspring Coupled household with at least 1 child 1011.32 (15.61%) 497 (7.69%) *** Couple household with no child 1711.82 (26.43%) 538.10 (8.31%) *** Non coupled household with at least 1 child 165.08 (2.55%) 286.69 (4.43%) *** Non coupled household with no child 1148.94 (17.74%) 1116.83 (17.24%) *** Race *** White 3178.11 (49.07%) 1405.49 (21.70%) Nonwhite 859.04 (13.26%) 1033.94 (15.96%) Education Less than high school 373.60 (5.77%) 492.05 (7.60%) *** High school 1176.61 (18.17%) 822.62 (12.7%) *** Some college 919.48 (14.20%) 619.44 (9.56%) College and more 1567.48 (24.20%) 505.30 (7.80%) *** Income Less than $25,000 587.03 (9.06%) 1129.49 (17.44%) *** $25,000 $64,999 1581.49 (24.42%) 995.69 (15.37%) $65,000 $110,000 1012.51 (15.63%) 227.29 (3.51%) *** More than $110,000 856.11 (13.22%) 86.95 (1.34%) *** *p <0.05,** p <0.01, *** p <0.001
52 Table 4 2. Bivariate a nalysis : sample profile and market i mperfec tion v ariables by h omeownership s tatus Significance test Variable Frequency (percentage) / Mean (SD) Homeowner Non homeowners Information a symmetry Professional sources 0.75 (0.87) 0.50 (0.74) t= 12 .44 *** Personal sources 1.35 (1.12) 1.27 (1.07) t= 2.97 ** External sources 0.40 (0.72) 0.38 (0.6884) t=1.34 Borrowing c onstraints FICO c omponents Type of c redit Revolving c redit *** Having revolving credit 3319.29 (51.25%) 1078.27 (16.65%) Not having revolving credit 717.86 (11.08%) 1361.17 (21.02%) Installment c redit Having at least 1 1818.88 (28.08%) 1128.86 (17.43%) Having none 2218.28 (34.25%) 1310.57 (20.24%) History p ayment pay off almost always 1979.57 (30.56%) 472.43 (7.29%) *** pay off almost never 1322.24 (20.42%) 595.61 (9.20%) *** do not have cards 735.34 (11.35%) 1371.28 (21.17%) *** Credit usage r atio 39.13 (612.50) 16.77 (219.31) t= 2.11 *p <0.05,** p <0.01, *** p <0.001
53 Table 4 2. Continued Variable Frequency (percentage) / Mean (SD) Significance test Homeowner non homeowners Credit d enial *** Being turned down 722.39 (11.15%) 635.73 (9.82%) Not being turned down 3314.76 (51.18%) 1803.7 (27.85%) Perceived credit d enial *** Perceived being turned down 477.87 (7.38%) 717.87 (11.08%) Perceived not being turned down 3559.28 (54.96%) 1721.56 (26.58%) Emergency f und ratio 779.51 (45450.98) 16.59 (523.55) t =1.07 Structural b arriers Distance to financial institutions 8.75 (73.33) 11.88 (101.58) t= 1.32 Number of financial accounts 2.74 (1.66) 1.59 (1.2110) t= 31.92 *** Tax preferential t reatment Ability to i temize Households itemize 2597.13 (40.10%) 489.77 (7.56%) Households do not itemize 1440.03 (22.23%) 1949.66 (30.10%) Tax advantaged investment and accounts *** Having at least 1 2503.16 (38.65%) 655.96 (10.13%) Having none 1534 (23.69%) 1783.47 (27.54%) Market u ncertainty Expectation of e conomy's overall performance in the next 5 years Better 2114.88 (32.65%) 1281.21 (19.78%) Worse 725.82 (11.21%) 474.67 (7.33%) Same 1196.45 (18.47%) 683.55 (10.55%) Planning h orizon *** More than 5 years 1524.89 (23.54%) 515.90 (7.97%) Less than 5 years 2512 (38.79%) 1923.53 (29.70%) *p <0.05,** p <0.01, *** p <0.001
54 Table 4 3. Logistic regression for likelihood of being a homeowner d emographics Variable Parameter Odds ratio Age 0.09 *** 1.10 Age square 4.71E 04 *** 1.00 Household t ype (coupled households) Male non coupled household 0.43 *** 0.65 Female non coupled household 0.24 0.79 Household size 0.17 1.18 Presence of o ffspring Coupled household (no child) 0.03 0.97 Non coupled household (no child) 0.10 1.10 Race (white) 0.47 *** 0.63 Education (college or more) Less than high school 0.04 0.96 High school 0.17 1.18 Some college 0.01 1.01 Income ($25,000 $64,999) Less than $25,000 0.51 *** 0.60 $65,000 $110,000 0.28 1.32 More than $110,000 0.42 ** 1.52 *p <0.05,** p <0.01, *** p <0.001
55 Table 4 4. Logistic regression for likelihood of being a homeowner market i mperfection Variable Parameter Odds ratio Market imperfection v ariables Information a symmetry Professional s ource 0.03 0.96 Personal s ource 0.06 1.07 External s ource 0.04 1.04 Borrowing c onstraints FICO c omponents Type of c redit Revolving c redit (not having revolving credit) 0.71 2.04 Installment c redit (having none) 0.14 0.86 Credit u sage r atio 1.20E 04 1.00 History p ayment (almost always) Pay off almost never 0.16 0.85 Do not have cards 0.08 0.91 Credit d enial (being turned down) 0.19 0.82 Perceived credit d enial (perceived being turned down) 0.46 *** 0.62 Emergency fund r atio 2.68E 05 1 Structural b arriers Distance to financial institutions offices 3.34E 04 3 Number of financial accounts 0.28 *** 1.32 Tax p r eferential t reatment Ability to itemize (household does not itemize) 1.16*** 3.18 Tax advantaged investment and accounts (having none) 0 .34 *** 1.40 Market u ncertainty Expectation of economy's p erformance in the next 5 years (same) Better 0.02 1.02 Worse 0.24 0.78 Planning h orizon (less than 5 years) 0.19 1.21 *p <0.05,** p <0.01, *** p <0.001
56 CHAPTER 5 DISCUSION, CONCLUSIONS AND IMPLICATIONS Discussion Hypotheses Information a symmetry Hypothesis 1 indicated that households with an increase d level of information asymmetry were less likely to be homeowners. T test results showed that there were significant differences between homeowners and non homeowners in terms of the quantity of information from sources consulted when making finan level of significance in the relationship between fewer professional sources of information consulted in credit decision making, and the probability of not being a homeowner. Also, t test results moderately suggested a significant relationship between fewer personal sources of information consulted in financial decision making, and non homeowners. External sources of information such as material in the mail and television/radio adver tisements did not exhibit any significant association to homeownership. Borrowing c onstraints Hypothesis 2 stated that h ouseholds with greater level of actual and per ceived borrowing constraints were less likely to be homeowners. Chi square test showed t hat significant differences existed in several items when measuring the variable of borrowing constraints between homeowners and non homeowners. For instance, from the FICO score factors, having revolving credit type, history payment categories and debt to credit limit (credit usage) ratio significantly differed between homeowners and non homeowners. In addition, the outcome from the logistic regression model suggested association between being turned down by a financial
57 institution in the last 5 years, and the likelihood of being a non homeowner. Finally the regression model showed that the expectation or perception of being turne d down by institutions was strongly and significantly associated with non homeowners. Structural b arriers Hypothesis 3 propose d that h ouseholds with higher levels of structural barriers or access to institutionalized mechanisms would be less likely to be homeowners. T test revealed that non homeowners were more likely to have fewer financial accounts than their counterpart. Regre ssion analysis suggested the same notion by indicating a very strong and positive significant relationship between homeowners and greater numbers of accounts with financial institutions. Distance to financial institutions was not significan t in the statis tical test s, t test, nor was it for the binomial logistic regression. Tax preferential t reatment Hypothesis 4 states that households unable to afford housing tax preferential treatments or tax advantaged vehicles were less likely to be homeowners. Chi sq uare test outcome showed that there were significant differences in tax preferential treatment vari ables between homeowners and non homeowners. Additionally, based on the regression results, there were statistical indications to conclude that homeowners overall were more prone to it emize and also to own at least one tax ad vantaged investment/account as opposed to those who indicat ed being non homeowners; thus, supporting the proposed hypothesis 4. Time p reference Hypothesis 5 states that those h ouseholds with a more myopic time preference would be less likely to be homeowners First, chi square test indicated that tim e preference s significantly differed from non preferences Regression analysis confirmed these results. Respondents with a planning
58 horizon of more than 5 years were more likely to be homeowners than those who indicated a planning horizon of le ss than 5 years. Thus, it showed a strong relationship between a less myopic perspective in terms of planning horizon and homeownership. Furthermore, the outcome from the regression provided an indication that those households that expected the economy to worse n ( or in other words who anticipated a negative economic outlooks ) were less likely to be homeowners. Therefore, the strong evidence obtained from these statistical procedures was supportive to the hypothesis proposed in the dimension of time preferen ce. Discussion of Findings The housing literature has well documented the very influential role of life cycle variables and homeownership rates. The results obtained in this study reflect previous findings on the effect of life cycle variables such as age, marital status, and household size The results on this study are very consistent with the ones described in Carliner ( 1974 ) Given that the presence of offspring has bee n widely researched and that its significant relationship has been highlighted i n multiple studies (e.g. Kain & Quigley, 1972 ; Chiuri & Japelli, 2003 ; Rosen, 1979 ; David, 1963; Maisel, 1966 ) it was surprising that this particular variable exhibited little level of significant in the likelihood of being a homeowner. Although, homeowner s and non homeowners displayed differences in the presence of offspring the household, the results were not strong enough to suggest a strong correlation (in any possible direction) between the variables. From the other demographics variables utilized in this research, race and income exhibited strong statistical indications to homeownership. These findings are consistent with results presented previously by other researchers (e.g Carliner, 1974) Particularly,
59 the variable income confirmed an intuitive r elationship with homeownership. This relationship was anticipated as it is an important and influential component in the model form ulated by Modigliani & Brumberg (1954). It was unexpected, nevertheless, to observe the variable education to show no significant correlation with homeownership whatsoever. It is crucial to note that i n the current study, this variable not only measured educational attainment but also served as a proxy for financial education, which literature would suggest to be an i nfluential factor in financial decision making decisions such as borrowing and credit assessments. In fact, previous have shown that a higher level of financial education could be translated into more favorable housing decision making ( Lusardi, 20 07; 2008; Moore, 2003; Bucks & Pe nce 2008). Perhaps due to the limitation of educational attainment used as a proxy, the financial education dime nsion was explored with a very limited scope. Further research in which the employment of a different methodology would be considered for the variable financial education might be required for a better exploration of this variable. At the same time, we should ponder if really education by itself (not financial education) could necessarily influence in the likelihood of bei ng a homeowner. It is worth while to explore current s tatistical facts such as percentages of students that have access to college or who graduate from college yet are unemployed or underemployed. O r also to consider whether graduating college students wit h average student loan debt actually have the market opportunity to become homeowners as expected in the life cycle hypothesis. Information a symmetry The re sults obtained in this research reflected to a certain degree the findings of other studies. For i nstance, Chiuri and Jappelli (2000)
60 su ggested from a more general perspective that information asymmetry is likely to create some sort of barriers across stages in the life cycle. Berheim (1994) posited that inequality in access to information is likely to influence in the process of financial decision making The results confirmed both dimensions as was revealed by the cited studies above: with fewer and asymmetric access to sources of information (professional or personal), the likelihood of being a homeo wner is less likely. Therefore, to some degree, being less informed or having an unequal access to different professional and personal sources of information might represent a barrier or at the very least, it might represent a disadvantage for households i n achieving the likelihood of higher rates of homeownership. It is important to mention that although it is not absolute to believe that being a homeowner is the optimum homeownership status, it is very well known that informed households and homeownership do introduce positive externalities such as the creation of a more competitive and more efficient market that not only imminently impact the househo ld unit but the economy and society as a whole (Braunstein & Welch, 2002). Moreover and specifically to thi s study the results provided significant indications to determine that households that consulted a greate r n umber of professional (e.g. certified public accountant, certified financial planner broker insurance agent ) and personal sources (e.g. past experience, friends and relatives, own research ) of information were more prone to be homeowners. Such suggestive relationship leads to comment on the importance that professionals in the field of financial planning might play in advising clients whe n financial decision making, especially when rendering opinion on the best practices and options on credit and borrowing matters.
61 Borrowing c onstraints Out of all of the imperfect market variable s used in this study, borrowing constraints is perhaps the one that has been the most widely explored on an individual basis by researchers ( Rose n thal, 2002; Yao & Zhang, 2005; Cocco, 2005; Luengo Prado, 2006 ). In this research study, though, the borrowing constraints items are surveyed in a model that conjunctive ly test s for other imperfect market variables at the same time. The results from the bivariate analysis are extremely co nsistent with previous findings (Barakova et. al., 2003; Rosenthal, 2003; Carliner, 1974). Almost all items utilized t o measure this dim ension (i. e. FICO score components perceived and actual credit denial ) were statistically different between homeowner and non homeowners. These results to some extent were expected given the mediating interaction that borrowing constraints could have in c onsumption an important piece of the life cycle hypothesis. From the inferential statistics, two items actual credit denial, and perceived credit denial showed significant association with non homeowners. To some degree, the potential effect of being denie d when applying credit lines and other loans is somewhat intuitive in the probability of being home owner. Influential and mediator factors such as credit worthiness, liquidity constraints might be implicitly in such relationship. Nonetheless, it was surpri sing that out of all the other borrowing constraint items, the expectation of being denied for credit was highly significant and negatively related to being a homeowner. Given that the question used in this item filtered out for households who had a percep tion of being denied and thus have refrained themselves from applying to credi t, I think that the notion of a more psychological interaction in terms of borrowing discouragement may exist. Further
62 exploration on perceived financial and credit barriers coul d potentially bring meaningful implications in the study of psychological financial attitudes. Finally, it was certainly unanticipated that the FICO score compone nts would exhibit no significance in the inferential statistical test. Based on the grounds th at in practice financial institutions and lenders heavily rely on this measure to extend loans to consumers, the relationship between these factors and homeownership was expected to be clear and pronounce d Structural b arriers Consistent with the finding s and explanation on Gutter et al. (2012) that t he lack of access to financial institutions c ould savings and asset accumulation decisions the current study showed that the number of financial accounts held by households was pos itively related to being a homeowner or the likelihood of asset accumulation On the other hand, n on homeowners, based on t he bivariate analysis, were more prone to hold fewer financial accounts than homeowners. Therefore, the notion of the propensity of being unbanked and underbanked suggested by Gutter et al. (2002) may be seen as constraints in attaining higher rates of homeowners. At the same time, the r esults highlight the importance of access to institutionalize d mechanism s that could potentially provide individuals with housing benefits of multiple natures (e.g. tax benefits, credit payment flexibility). For future research, I think it would be notewor thy to explore closely and in a more individualist approach the relationship between different types of institutionalize d mechanisms and homeowners. A dimension tested under the structural barrier umbrella was the distance to financial institutions. This variable was not significant in either, bivariate or inferential
63 analysis. It is reasonable to attribute these results to the availability of electronic and data banking, and an increase in a computer network. The change in both has been positive and enormous in the recent decades: virtual banking has been more widely accepted and trusted by consumer; and hou seholds have had more access to education in new technologies. Undoubtedly, nowadays virtual communication and electronic transa ctions in the banking industry has revolutionize d the way households access and do business with financial institutions. Hence technology has greatly reduced physical barriers to access to and usage of banking in stitu tions that many house holds historicall y had (Bell & Hogarth, 2009). Tax preferential t reatment The results obtained under the scope of this variable were all highly consistent with previous findings in the housing literature. For example, several studies have suggested a positive and direct relationship between tax incentives, opportunities or strategies, and the likelihood of being a homeowner ( Ihlanfeldt & Boehm, 1983; Slitor, 1976 ; Halket & Vasudev, 2009) Such association was reflected in the results as b oth items used in this study abili ty to itemize, and the ownershi p of tax advantaged account were statistically significant in the regression model. As concluded from the results, homeowners were more likely to itemize and to own at least one tax advantaged account/investment. For homeowne rs, the ability to itemize lies in great part in whether the taxes and interests paid on the property exceed the standard deduction for a specific tax year. Thus, the combined results in this section might highlight the importance and link between institut ional mechanisms/vehicles and itemization. In other words, ceteris paribus having a home yet not a mortgage loan with
64 a financial institution would most likely result in the inability to itemize. A trichotomous model might be a representation of the inter action of these th r ee variables in a model. Time p reference Consistently with the results on James (2009), the factor planning horizon exhibited high level of significant differe nces between homeowners and non homeowners. The regression analysis confirmed indeed that those with less myopic planning horizon were more likely to be homeowners. Thus, the results led us to conclude that failing t o financially plan in advanced could represent a barrier in homeownership consumption. It is important to comment that planning horizon refers to the actual planning/budgeting for future financial course of actions. The role of financial planning and professionals in the field is then crucial in helping and advising clients on how to make optimum financial dec isions. Remember that especially in this case, the object in question is whether or not households are homeowners or not. The consumption of housing units comes with a series of financial step s and commitments (e.g. building credit score strategy, down pa yment requirement), that professionals might use in financial planning and are the ideal tools to help mitigate potential barriers that prevent households in achieving their goals. Conclusions Based on the results obtained in this research and the consul ted housing literature, two main and general conclusions can be drawn from this study. The first conclusion refers to the confirmation of several market imperfection barriers present in the interaction and likelihood of housing consumption, which have been ig nored by the empirical based model proposed in the life cycle hypothesis. The second conclusion deals with the intrinsic and developed limitations of the model, which to a degree
65 restrain the model to reflect an accurate picture of the present housing units consumption pattern. If we recall the main objective of this research was to explore the relationship between homeownership and the assumption of a non frictionless market employing the life cycle hypothesis as a base model. The challenge to the mo del assumed that the market was imperfect by controlling specific market variables. I n other word rather than idealizing the prototype, risk and pragmatic financial factors were taken into consideration in order to determine whether they would constitute a predictor to homeownership. Based on the findings in this research, we can conclude that the life cycle hy pothesis is limited in scope, failing to capture many financial aspects in this study perceived as barriers that greatly influence housing consumpti on and more precisely homeownership rates. Such limitation is partly tied to the fact that the model is an empirical approximation of the subject in study that is the likelihood of being a homeowner. However, other model constraints might come from the unfitted application of an old and outdated model to the current economics settings. This last statement leads to make important remarks on economical, sociological and psychological factors that should be re considered. The life cycle hypothesis was developed in the early 19 just a few years after the end of World War II. This period was characterized by the migration of households from rural to urban areas; the birth and development of suburbs was the result of such migration. The housing boom and grow th of suburbs was facilitated in a big part by incentives such as the availability of credit and financing packages and
66 stimulus provided by the government (e.g. Federal Housing Administration Insured loan). The life cy cle hypothesis, then, was an empirica l model adapted to approach housing consumption patterns in that period of time. Therefore, the assumptions made in this hypothesis were suitable for households with social and eco nomical characteristics particularly as sociated to households in the 19 idea of a traditional nuclear home was the prevalent household composition and common denominator in the society at that time; t he American dream was strongly associated with owning a housing unit and consequently being part of the emerging H owever, it has been well documented of the evolution an d pragmatic change in household s demographic characteristi cs over the last five decades The household structure categories have changed greatly including now a great portion of single parents in the society. The inclusion of women in the workforce has positioned households in a different scenario in terms of economic and purchasing power. T he longevity of individuals due to an improvement in quality of life and the component in this model. The exponent ial growth of racial minorities such as Hispanic/Lati nos and African Ameri cans have radically changed the composition of the U.S. population. The homeownership distribution is questionable of being normal ly distributed and may be rather more credibly binomial. The American dream has been less linked to the i dea o f owning a house ( the stigmatization of renting has seemingly decrea sed). The economic and consumption preferences and expectation of young profess ionals have evolved critically; identifying and demanding new housing
67 are just some examples that put in perspective new factors that should be taken into account when analyzing and exploring homeownership rate s in current time. Implications Researchers. Within the framework of life cycle hypothesis, there is a need of furt her exploration on s everal variables such as financial education, race (without lumping all minorities groups t ogether), tax advantaged investment and accounts among others Time series studies that might compare and contrast changes in demographic charac teristics, financial attitudes and preferences might be beneficial for a better understating of historic housing consumption patterns. Additionally, one general implication of this study is the important call for the academic community to work in the deve lopment and adaptation of an economic model preferably theory based that includes and reflects the changes experienced in demographics and consumer preferences over the last decades. Policy Makers. In the ef fort of securing financial well being and housin g interests in the U.S., policy makers must be aware of and consider the changes characteristics and preferences over the last 60 years. The identification of new housing needs, the change and emergence of new household structures, and the current distribution of homeownership in the U.S. are just a few examples of the key components that policy makers should include in the formulation, development and enactment of new housing and social policies Financial planners and other professionals in the field. The results in this s tudy confirm previous findings on the suggestive relationship between access to professional help or sources of information, and the likelihood of being a homeowner
68 Such correlation in a way highlights the importance t hat professionals in the field of f inancial planning play in advising clients with financial decision making. In housing, precisely, professional opinion on credit and borrowing issues, tax planning and the use of tax strategies might help mitigate actual and perceived barriers that restrain households in achieving their housing goals.
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75 BIOGRAPHICAL SKETCH Jorge Ruiz Menjivar attended the University of New Orleans Louisiana State University System where he obtained a Bachelor of Science in accounting in Spring 2011 He was aw arded a Master of Science with a concentration in personal and family financial planning at the University of Florida in Summer 2013. Starting in Fall 2013, Mr. Ruiz Menjivar is furthering his education wi th a Doctor of Philosophy in the D epartment of Housing and Consumer Economics at the University of Georgia. Mr. Ruiz research interests include housing, taxation, personal and family finance, behavioral and consumer economics and international economics (financial affairs in de veloping and emerging economies and Latin American economics).