The Home Purchase Mortgage Preferences of Low- and Moderate-Income Households

Transcription

1 2007 V35 3: pp REAL ESTATE ECONOMICS The Home Purchase Mortgage Preferences of Low- and Moderate-Income Households Michael LaCour-Little Housing policy in the United States has long supported homeownership, yet variation persists across income groups. This article employs recent mortgage origination data to focus on the revealed preferences of low- and moderateincome (LMI) households in home purchase mortgage choice. I identify the factors associated with conventional conforming, FHA, nonprime and specially targeted programs. Empirical results show that individual credit characteristics and financial factors, including pricing, generally drive product choice, with some variation evident when loans are originated through brokers. Results also indicate that targeted conventional programs effectively compete with government-insured products in the LMI segment. How well do the myriad mortgage financing options now available meet the needs of low- and moderate-income (LMI) homebuyers? What factors determine their choice of mortgage product? Do certain groups of LMI households have specific needs that are only met in particular product categories? What role does pricing and product substitution play in this segment of the market? Do results vary when loans are originated through mortgage brokers? This article addresses these and related questions. Fostering home ownership in the United States has long been an important public policy objective. The Department of Housing and Urban Development (HUD) regularly reports homeownership rates, and policy makers periodically set goals for increasing them. While the overall rate currently stands at 69.1%, up from 64% in 1989, there is considerable variation across population segments. Geographically, the home ownership rate in suburban areas is 73.8%, as compared to 50.7% in central cities, and the rate among households earning less than the median family income is 50.8%. Of all white households, 72.8% were homeowners as compared to 49.7% of African Americans and 49.1% of Hispanics, according to HUD s Web site. As the argument in favor of promoting home ownership among all segments of society is well known, I will not dwell on that topic here. Suffice it to say that homeownership is thought to be associated with a variety of positive social and economic outcomes. California State University at Fullerton, Fullerton, CA or mlacour-little@ fullerton.edu. C 2007 American Real Estate and Urban Economics Association

2 266 LaCour-Little Given the desirability of increasing home ownership among LMI households, considerable research has focused on constraints limiting homeownership. Early work on this topic includes that by Linneman and Wachter (1989), who reported a reduction in the effect of both income and wealth constraints with the mortgage design innovation that occurred in the early 1980s. In related work, Zorn (1993) examined the effect of constraints using survey data from 1986 and found them binding on 46% of all households, although effects were much greater on renters than on current homeowners. In more recent work, Rosenthal (2002) reports that eliminating credit-underwriting constraints could potentially increase overall homeownership rates by 4 percentage points. Of course, completely relaxing underwriting standards would impose substantial costs on both lenders and those borrowers who experience foreclosure. Indeed, Deng, Quigley and Van Order (1996) estimate that if expected losses in such programs were not priced into note rates, the losses from default alone could exceed 10% of the funds made available for loans. Most recently, Barakova et al. (2003) reexamined the three main constraints to home ownership, namely, income, wealth and credit. They find that wealth still presents the greatest constraint, though its importance has declined during the 1990s, and credit constraints persist, mostly due to the increase in the number of credit-impaired households. During the 1990s, the mortgage industry responded to increased demand and policymakers incentives with an expanded set of product offerings. Beyond the traditional conventional and government-insured categories, these may be generally classified into two main types: (1) nonprime 1 mortgage products and (2) LMI-targeted mortgage products. Loan purpose has traditionally been an important element in mortgage design. FHA-insured loans were originally designed to allow households to purchase moderately priced housing with relatively small down payments. In contrast, nonprime mortgages were originally designed as refinancing loans for the purpose of debt consolidation. More recently, nonprime home purchase loans allow borrowers who may have encountered financial difficulties in the past (such as a bankruptcy or foreclosure), or who are currently highly leveraged, to purchase a home. nonprime mortgages are now widely available and do not involve income or geographic limitations. Moreover, the nonprime segment includes the low or no doc category of loans. In contrast, specially targeted programs are often means tested (income cannot exceed a certain level) and/or are available only in designated areas. 1 Throughout this article I use the relatively more neutral term nonprime rather than subprime and intend it to include both loans to borrowers with weak credit histories as well as what have come to be called A-, Alt-A and other loan types that fail to be eligible for government-sponsored enterprise (GSE) purchase owing to factors other than credit.

3 The Home Purchase Mortgage Preferences 267 Specialized mortgage programs targeted at the LMI segment comprise still another product array, some lender specific and others investor specific. Many of these are conventional programs, such as those sponsored by the GSEs; for example, Fannie Mae s Community Home Buyer s Program and Freddie Mac s Affordable Gold Alt 97. Such programs generally involve greater credit risk than traditional conventional mortgage lending, yet they are seldom described as nonprime. In general, they offer reduced down payment requirements and greater underwriting flexibility, compared to traditional conventional loan products, but they are rationed in some form or other: by means testing, by maximum loan amount limits or by restricting availability to specific geographies. I include one such program here (the Special Program ) developed by a national bank in partnership with one of the two major GSEs. I compare and contrast underwriting standards for this program with those of traditional FHA lending later in the article. I turn now to the academic research on mortgage choice, especially the choice of contract type. After an introductory review, I focus on prior studies of the FHA conventional and prime nonprime choice. Literature Review The literature on mortgage choice is extensive, so the review here is necessarily limited. Follain (1990) defines the topic broadly to include an array of problems faced by households, including the choice of how much to borrow (the loan-to-value [LTV] ratio decision), if and when to refinance or default (the termination decision) and the choice of mortgage instrument itself (the contract decision). While my empirical focus here is on contract choice, it will be seen in my discussion of the econometric issues that many of these choices are interrelated. The demand for mortgage debt is derived from housing demand. From the economic perspective, the primary determinant of housing demand is user cost, which may be defined as the cost of using one unit of housing capital for one period. User cost is the sum of the opportunity cost of capital invested in housing, mortgage financing costs and depreciation in the housing unit, less tax benefits attributable to the tax deductibility of property taxes and mortgage interest and appreciation in capital value. If the user cost of owning is less than the user cost of renting, then households will have an economic incentive to become homeowners. Early theoretical work on mortgage demand, such as Jones (1993) and Brueckner (1994), focused on the opportunity cost of capital invested in housing. Absent frictions, if the return on other investments exceeds the return on

4 268 LaCour-Little housing, all households demand 100% LTV ratios. Conversely, if the return on housing exceeds that of other investments, then the optimal strategy is 100% equity, that is, no mortgage debt usage. Where investment returns are uncertain, however, the optimal mortgage size is ambiguous. In a world where mortgage debt is readily available and mortgage interest is tax deductible, an important comparison is between the after-tax cost of mortgage debt and the expected after-tax rate of return on housing. Hence, tax rates and tax policy have a significant impact on the demand for housing and, hence, the demand for mortgage debt. Empirical work on the demand for mortgage debt, as a function of household characteristics, has been much more limited. Ling and McGill (L&M) (1998) address the question using American Housing Survey data from the 1980s, modeling mortgage and housing demand as jointly determined. Consistent with the theoretical findings, L&M (1998) show that the tax savings associated with the interest deduction affect the level of mortgage debt usage, noting that lower-income households who do not itemize lose the associated tax benefit of the mortgage interest deduction. A detailed discussion of taxes is outside of the scope of my work here, but I refer the reader to Follain and Dunksy (1997) for a thorough treatment. Related work on contract choice has focused on the choice of rate versus points (Brueckner 1995, Stanton and Wallace 1998) and the choice of adjustable rate mortgage (ARM) versus fixed rate mortgage (FRM) (Dhillon, Shilling and Sirmans 1987, Brueckner and Follain 1988). The consensus in these areas seem to be that points signal reduced borrower mobility, that is, longer expected housing tenure, and that the ARM FRM choice is both a function of expected mobility and the slope of the yield curve, which produces the savings between short and long rates. Consequently, in flat yield curve environments ARM demand falls off. It is also generally accepted that a preference for ARMs is related to higher-cost housing markets because low initial rates stretch borrower budgets further under payment ratio tests. ARMs tend to be held in portfolio by financial institutions, whereas FRMs tend to be securitized (see Ambrose and LaCour-Little 2001 and Ambrose, LaCour-Little and Huszar 2005 on ARM performance patterns). I turn now to a more detailed review of prior research studies more directly related to my topic, namely, FHA versus conventional choice and prime versus nonprime choice. Gabriel and Rosenthal (G&R) (1991) used micro data from the 1983 Survey of Consumer Finances to study borrower choice between FHA and conventional loans among borrowers who obtained a newly originated loan between 1978 and To make a government-insured loan a meaningful choice, loan size was restricted to FHA limits (then $67,500). G&R (1991) did not have access

5 The Home Purchase Mortgage Preferences 269 to any credit-related variables but attempted to proxy for default risk. Among proxies they include expected housing appreciation rates, a proxy for borrower equity based on reported household wealth and average unemployment rates in the borrower s occupation. They also include demographic variables, such as borrower age, whether the property is located in a central city and whether the borrower is nonwhite. Their results suggest that minorities are more likely to choose FHA loans after some weak controls for default risk. That conclusion, however, may have simply reflected differential underwriting across conventional and FHA products and limited funds for down payments among minority borrowers, or other omitted credit risk variables. Hendershott, LaFayette and Haurin (HL&H) (1997) examine both the FHA conventional choice and the FRM-ARM in a nested logit framework. They use data from the 1984 American Housing Survey to study the choices of 819 young homeowners. At this time, ARMs were not a viable option for FHA borrowers. They find that mortgage choice is driven by down payment and monthly payment constraints and the desire to reduce mortgage insurance costs. HL&H (1997) do not include credit or demographic factors in their empirical analysis. Turning to more recent work, Pennington-Cross and Nichols (PC&N) (2000) also address the FHA conventional choice using a much larger and more complete data set, including credit information. PC&N (2000) combine information from four distinct sources to construct a data set of loans originated during 1996, including 21,246 FHA and 26,246 conventional loans. PC&N (2000) note that the two segments clearly have different credit score distributions: the mean FICO score for conventional borrowers is 717, while the mean for FHA borrowers is 665, with some convergence at higher LTV levels. PC&N (2000) then estimate four specifications of a logistic regression model of mortgage choice controlling for a variety of financial, credit and demographic variables, including MSA fixed effects. Their most complete specification includes FICO score (in continuous form), a limited set of credit bureau attributes, estimated LTV generated through an instrumental variables approach, an estimate of borrower permanent income and an estimate of the differential cost of mortgage insurance under the two programs for high-ltv categories. PC&N (2000) find that credit history plays an important role in product selection; for example, an increase of only 10 points in FICO score decreases the probability of FHA choice by 2.8%. In contrast to Gabriel and Rosenthal (1991), who reported greater use of FHA by a combined category of nonwhite minority groups without controlling for credit history, PC&N find no effect for African American borrowers but a significant preference for FHA among Hispanic borrowers. Focusing on the choice of prime versus nonprime, the literature is much more limited, partly because the nonprime market is relatively new. Courchane,

6 270 LaCour-Little Surette and Zorn (CS&Z) (2004) use survey data to examine differences in both circumstances and experience for nonprime compared to prime borrowers, using rate-based definition of nonprime together with the HUD lender name approach, producing a sample used in regressions of 4,571 loans. Like PC&N (2000), they find that credit score classifies borrowers reasonably well, for example, 87% of prime borrowers have FICO scores greater than 620, whereas only 38% of borrowers in the nonprime group are in this score range. CS&Z (2003) show that adverse life events, such as marital dissolution, a major medical expense, unemployment or change in income, all contribute to nonprime product usage. In an interesting extension, CS&Z (2003) also analyze outcomes and transitions among product types, noting that approximately 40% of nonprime borrowers transition to prime via refinancing. On the basis of this finding, it may be helpful to think of nonprime product usage as episodic, more like a spell of unemployment than a fixed borrower characteristic. Pennington-Cross, Yezer and Nichols (PCY&N) (2000) address much the same question as that considered here: the choice between FHA, conventional and nonprime. They use the same data as PC&N (2000) to study the FHA conventional choice. But they use the HUD list to identify nonprime loans, probably leading to significant underestimation, because only 612 of 48,105 (1.3%) are classified as nonprime using this criterion. In their data, the mean FICO score of prime borrowers is 717, whereas FHA and nonprime borrowers have means of 665 and 669, respectively, while FHA borrowers have much higher LTVs than do nonprime borrowers. PCY&N (2000) conclude that credit risk factors and the relative cost of mortgage insurance are the predominate factors in sorting borrowers into conventional, FHA and nonprime categories, although there are some unexplained demographic residual effects as well. PCY&N (2000) tested both an ordered and multinomial logit (MNL) and found that the multinomial has better explanatory power. As in PC&N (2000), LTV is treated as endogenous and estimated via instrumental variables. The Federal Reserve has shown great interest in the growth of the nonprime market and analyzed the results of 2004 data supplied under provisions of the Home Mortgage Disclosure Act (HMDA), under which yield spreads on mortgages exceeding certain triggers are now reported. 2 Higher yield spreads provide an imperfect signal of nonprime lending. A comprehensive analysis of 2004 data (Federal Reserve 2005) found that choice of lender has as much explanatory power as borrower and loan attributes in explaining why some loans have reportable yield spreads. 2 Specifically, HMDA now requires the yield spread be reported if the APR exceeds 300 basis points over comparable maturity treasuries for first liens and 500 basis points over treasuries for junior liens.

7 The Home Purchase Mortgage Preferences 271 In summary, research on the FHA conventional choice to date has focused on the effects of income and wealth constraints, loan size limits, differences in underwriting criteria and the relative cost difference of FHA versus nongovernment mortgage insurance. There are mixed results on the question of whether demographic characteristics are important, and the finding in some research that minority borrowers are more likely to choose FHA is consistent with a relatively weaker financial picture and limited funds for down payments given FHA s relatively more liberal underwriting standards. Results from research on nonprime lending has encountered the problem of category definition though, regardless of definition used, the research has generally found borrower sorting based on credit characteristics and mixed evidence on the effect of demographics. It is clear, however, that many nonprime borrowers, unlike FHA borrowers, have sufficient funds to make relatively large down payments or have significant equity if already homeowners. But unlike prime borrowers, they have weaker credit histories and often carry higher current debt burdens. In all of these analyses, the low doc (sometimes called Alt-A ) phenomenon has remained unexplored. Moreover, insufficient attention seems to have been directed at the pricing of various mortgage choices, a topic that has become of particular interest because limited pricing data has now become publicly available under HMDA. Assuming rationality, consumers would be expected to seek out the lowestpriced mortgage for which they could qualify. We address this issue here by estimating the price, that is, contract note rate, the borrower would have paid, given their credit and loan characteristics, for each of the various loan program alternatives. The plan for the balance of the article is as follows. In the next section I discuss research methodology. In the third section, I describe the data used for the empirical analysis. In the fourth section I present and discuss regression results and robustness tests. The fifth section summarizes findings and offers policy implications and further research needs. Research Methodology A variety of difficult questions arise in mortgage choice model specification. Fundamentally, I would like to estimate a general demand function, but typically observe only realized outcomes, conditional on the borrower s decision to apply to the particular lender and the lender s subsequent decision to approve. Moreover, the lender s decision to approve is conditioned on assessment of default risk, and the borrower s assessment of default risk may affect choice of loan terms. One could argue that because borrowers choose many of the loan terms, in particular, the amount to borrow (equivalently, the LTV ratio), this

8 272 LaCour-Little variable is not truly exogenous. A similar argument can be made about reduced documentation: in most cases borrowers could choose to provide more complete financial information, allow verification of employment, income, assets and so forth. So, reduced documentation is not a loan characteristic, but an endogenously determined borrower choice. Neglected here is the issue of time, often quite important in home purchase transactions, because home buyers under typical contract terms face a deadline by which they must remove their financing contingency and a date by which they must close escrow. Penalties for failure to meet these dates can be potentially severe: forfeiture of the right to purchase the house at the agreed-upon price and, sometimes, forfeiture of the earnest money deposit. One of my contributions here is to consider the role of time, particularly the time required for the loan to proceed from application to closing. I find fairly significant differences, with government lending taking the longest time and nonprime loans the shortest. At the suggestion of a reviewer of an earlier draft of this article, I also treat time to close as an endogenous variable and use its fitted value in the choice regressions estimated. While these regressions results are not particularly strong, I do find a positive relationship between new construction and time to close, which makes intuitive sense, given exogenous construction delays that may be outside of either borrower or lender control. As mentioned previously, LTV ratio is another choice variable. Borrowers needing higher LTVs because of liquidity constraints are likely to seek out products and programs that meet those needs. Accordingly, I estimate LTV and use its fitted values in the choice models. Results of this regression confirm several findings in the literature; for example, higher-income borrowers (who are more likely to benefit from the interest rate deduction for tax purposes) choose higher LTV ratios, and higher-risk borrowers choose to borrow more (Harrison, Noordewier and Yavas 2004). To address the reduced documentation issue, I estimate the probability of a reduced documentation level and employ that fitted probability as an explanatory variable in the mortgage choice model. While, like the other models described above, this is only an intermediate step along the path to a final choice model, it does represent an additional contribution, because it clearly shows the strong relationship that exists between self-employment and the choice of a reduced documentation loan. In addition to choice of loan terms, including level of documentation, borrowers must factor loan prices into their decision calculation. Previous research (e.g., Pennington-Cross, Yezer and Nichols 2000) has addressed relative pricing differences by estimating the present discounted value of interest and

9 The Home Purchase Mortgage Preferences 273 mortgage insurance payments for each mortgage alternative. My approach here is slightly different. I simply estimate the price (i.e., note rate) that each alternative mortgage choice would entail, conditional on borrower selection of contract type (including a wide array of specific products), the standard credit risk pricing variables of FICO score and LTV and whether the loan was originated by a mortgage broker or not. The cost of each alternative is then a factor in the MNL model that is used to estimate mortgage choice. The signs of coefficients here may be informative about product substitution effects. Finally, mortgage brokers have come to play an increasingly important role in the market, reportedly now originating more than half of all loans. Accordingly, I estimate MNL models separately for retail versus brokeroriginated loans, because outcomes may well vary across distinct origination channels. One can conceptually represent the set of equations estimated as follows: LTV = f (Rates, Down Payment, Property Type, Credit Risk, Income, Market Area) (1) No Doc = f (Self-Employment, Credit Risk, Liquid Assets, Age) (2) Price = f (Rates, Loan Amount, Product, Close Time, Credit Risk, Market Area, Channel) (3) Close Time = f (Property Type, Season, Price, Loan Amount) (4) Choice = f (Loan Amount, Fitted Price, Fitted Close Time, Fitted Probability No Doc, Fitted LTV, Credit Risk, Channel, Other Borrower Characteristics). (5) Equations (1), (3) and (4) are estimated by ordinary least squares. Equation (2) is estimated by logistic regression, and Equation (5) is estimated by MNL regression. I further discuss precise specifications and expected signs of coefficients later in the text, but I turn now to the data.

10 274 LaCour-Little Data The data come from recent loan origination records of a major national lender that prefers anonymity. The company is a leader in FHA lending and holds a substantial market share in both lending to ethnic minority groups and to LMI segments. In addition, it has a relatively small, though growing, market share in the nonprime segment, which anecdotally includes an above-industryaverage percentage of home purchase loans. For purposes of this discussion, I define nonprime as any loan originated by the institution s nonprime lending divisions, either retail or wholesale, and note that such loans are manually underwritten as opposed to processed through any sort of automated underwriting system. The lender runs the loan applications through a GSE-underwriting engine to ensure that borrowers cannot qualify for conventional conforming loan alternatives prior to processing them as nonprime. Because my focus here is on home ownership, I restrict the data to loans originated for the purpose of home purchase, including both single-family and twoto four-unit multifamily, as well as single housing units within condominium properties. I exclude manufactured housing, cooperative apartments and second homes. About a third of the loans were originated by mortgage brokers: 6,207 individual brokers, in fact, none of which contribute even 1% of the total population. I exclude some specialized programs, such as VA loans and renovation loans, because they employ unique underwriting standards. Some of these exclusions may be material for the LMI segment of the market; for example, in rural areas of the South and West, manufactured housing is a popular choice for many lower-income households. In earlier versions of this research, I followed the previous literature and limited loan sizes to the FHA loan limits as they vary by MSA. However, because the Special Program loans are available up to the conforming loan size limit (as are, of course, standard conforming and nonprime loans), I have expanded the data set by considering all loans falling below the conforming loan size limit for the year 2002 ($300,700 for a single family unit), following the suggestions of a reviewer. Clearly, FHA size limits impose additional constraints on borrowers in some of the higher-cost housing markets, such as those in California and the Northeast. Subject to the usual problem of occasional missing values, I have near-complete micro-level data for each loan in the sample. In addition to product choice, location, loan amount, note rate and transaction dates, all major credit quality indicators are observed, including loan-to-value ratio (LTV), debt-to-income ratio (DTI) and borrower credit score at time of origination (FICO). An important variable infrequently available to researchers is liquid assets (Liquid Assets), which consists of funds for down payment and closing costs plus postclosing reserves. Unfortunately, I do not have points, fees or APR, so I am limited to

11 The Home Purchase Mortgage Preferences 275 contract note rate as a measure of loan price. 3 Borrower demographic characteristics available include income, age and race, many of which are reported under provisions of the HMDA. Income is missing in a small fraction (less than 5%) of cases and I estimate it on the basis of census tract income in a few instances, such as for purposes of the descriptive statistics. I began with a larger data set consisting of 226,933 loans extended to all income groups. I follow the standard regulatory approach and define a loan as LMI if made to a household with income less than 80% of area median, regardless of location, or to a household buying in an area where median income is less than 80% of the area median, regardless of household income. In earlier versions of this research, I compared LMI to non-lmi borrowers using analogous methods, whereas here, following reviewer recommendations, I narrow the focus to just LMI borrowers, because they are most often of particular of interest to policy makers. FHA-insured loans have been widely studied and are well understood, but the Special Program targeted at the LMI population deserves further description. As with other targeted programs, it does limit eligibility, specifically by restricting borrower income to a maximum of 120% of HUD median family income for the area, but this requirement can be waived for properties in city centers. Special Program loans offer higher loan limits (the GSE-conforming loan limits) compared to the FHA loan limits, higher maximum LTV ratios (100% for borrowers with a minimum FICO score of 620 and 97% for borrowers with FICO less than 620) and higher allowable payment ratios. In contrast, FHA does not have a stated FICO standard. The maximum allowable front-end to back-end ratios under FHA are currently 29:41, while the Special Program allows 45:45 if income is documented and 40:40 if stated income is used. Up to 25% of income can be stated income under the Special Program. Neither the Special Program nor the FHA requires reserves (verifiable liquid assets over and above closing costs including funds for down payment). Special Program loans offer both fixed-rate and hybrid ARM structures; in contrast, during 2002 FHA offered only the one-year ARM design. The Special Program offers both borrowerpaid and lender-paid mortgage insurance options; under the latter, the note rate is increased to cover costs. I note in passing that nonprime typically employs lender-paid mortgage insurance, which automatically results in a slightly higher note rate relative to conventional, even for borrowers with comparable risk profiles. Likewise, FHA loans carry the additional cost of a mortgage insurance 3 Many lenders did not begin retaining APR information (because it is not used for loan servicing) until the HMDA requirements compelled them to do so starting in 2004.

12 276 LaCour-Little Table 1 All channels. Full Population of LMI Loans Variable N Mean Standard Deviation 30-Year FRM 75, ARM (several types) 75, Broker-originated 75, Note amount 75, Note rate 75, Time to close 75, Missing DTI 75, DTI 75, LTV 75, No Doc 75, FICO 75, First-time homebuyer 75, Income 75, Liquid assets 75, Borrower age 75, Black or Hispanic 75, New house 75, Condo 75, MFDU (2 4 units) 75, FHA 75, Special Program 75, Nonprime 75, Conventional conforming 75, premium that is not included in the contract note rate, so FHA loans may appear to be lower in cost than they actually are when all ancillary costs are considered. After exclusions described above, I have a data set of 75,695 LMI loans. This count includes 28,471 FHA loans, 8,031 Special Program loans and 2,744 nonprime loans, as well as 36,179 conventional conforming loans. As a starting point in the analysis, I compare characteristics across choices (aggregate descriptive statistics shown in Table 1 and segmented by choice in Table 2). In some ways, borrowers and loans are remarkably similar across choices (all channels have an average borrower income of less than $40,000 annually, loan amounts around $100,000 and first-time homeowners as the majority of borrowers 4 ), yet on other dimensions there is wide variation. Across all choices, the majority of loans are fixed-rate 30-year contracts. nonprime are an exception, however, with the majority of that channel category (89%) in the ARM category. While loans are from across the country, average 4 Because such a high fraction of cases are first-time home buyers, I was unable to use this variable in many of the regressions, due to sparse cell problems.

13 The Home Purchase Mortgage Preferences 277 Table 2 By choice. FHA Program Special Nonprime Conventional Conforming Standard Standard Standard Standard Variable N Mean Deviation N Mean Deviation N Mean Deviation N Mean Deviation 30-Year FRM 28, , , , ARM (several types) 28, , , , Broker-originated 28, , , , Note amount 28, , , , Note rate 28, , , , Time to close 28, , , , Missing DTI 28, , , , DTI 28, , , , LTV 28, , , , No Doc 28, , , , FICO 28, , , , First-time homebuyer 28, , , , Income 28, , , , Liquid assets 28, , , , Borrower age 28, , , , Black or Hispanic 28, , , , New house 28, , , , Condo 28, , , , MFDU (2 4 units) 28, , , ,

14 278 LaCour-Little loan size is generally similar, ranging from $91,000 for nonprime loans to $117,900 in the conforming conventional category. Mean note rates are similar (in the 6.60% 6.70% range), except for the nonprime loans, which have a mean note rate of 9.30%. The average time from application to closing varies, from a low of 30 days in the nonprime channel to a high of 48 days for FHA. Credit and other risk characteristics show some of the widest variation; for example, the average FICO score ranges from 612 for nonprime borrowers to 727 for borrowers choosing conventional conforming products; FHA and Special Program loans are in between. Average DTI ratios are generally similar, again, except for nonprime where average DTI exceeds 43%. Average LTVs are very high for both FHA and Special Program loans: in excess of 95%. However, nonprime and conventional conforming loans have much lower LTVs. In part, these differences reflect differential levels of liquid assets held by borrowers, ranging from $4,200 for Special Program loans to more than $65,000 for borrowers in the conventional conforming choice category. Liquid assets include funds for down payment as well as savings, including vested retirement assets such as IRAs and 401k plans. Regression Results Tables 3, 4, 5 and 6 present preliminary regressions to generate fitted values for use in the MNL models presented in Tables 7 and 8. Table 3 presents a simple model of LTV choice, in which LTV is modeled as a function of the level of interest rates (as measured by the monthly 10-year treasury), MSA dummy variables (to capture the local housing market effects), the dollar amount of borrower s down payment (expected negative sign), borrower credit score (expected negative sign), borrower income (expected positive sign), Table 3 Ordinary least squares regression with the dependent variable LTV. Estimate Standard Error T-Ratio Intercept Level of 10-year treasury rate Borrower down payment FICO Income Borrower age New house Condo MFDU (2 4 units) R Not reported: MSA dummy variables

15 The Home Purchase Mortgage Preferences 279 Table 4 Logistic regression, with the dependent variable No Doc. Parameter Estimate Standard Error Chi-Square Intercept Self-employed borrower Borrower liquid assets FICO Borrower age Concordance 0.73 Likelihood ratio test 1,510 (p value = ) borrower age (expected negative sign) and a series of indicators for property type (new construction, condo or multifamily two- to four-unit). Results are satisfactory with an R-squared of Higher levels of interest rates reduce LTV, as do greater funds for down payment, a higher credit score and greater borrower age. LTV is increasing in borrower income, reflecting perhaps greater capability to service debt and the tax benefit of the mortgage interest deduction, although whether borrowers at LMI levels of income and debt would be itemizing deductions is certainly questionable. Table 4 presents a logit model of the probability of a borrower selecting a No Doc loan. Some reduced documentation programs simply accept, without verification, borrower statements of income and assets; others accept such statements subject to a general test of reasonability (a salaried retail sales clerk is unlikely to have an annual income of $250,000, for example); some perform limited verification based on review of bank statements; still others do not even inquire, so that payment-to-income ratios are not even calculated. 5 I cannot distinguish the exact program type in the data, so I simply exclude income as a potential covariate (if it is not already missing, of course). I can observe, even for the No Doc loans, that liquid assets and credit score, and a model combining these variables, together with an indicator for a self-employed borrower, does a reasonable job (concordance = 0.73) of predicting the probability of a No Doc loan. Coefficients are positive for all covariates, although borrower age proved not to be statistically significant. Although not the focus of the analysis here, I note that the probability of a self-employed borrower choosing a reduced documentation loan is dramatically higher, with an odds ratio of about 13.5, compared to a salaried worker. In addition, use of this product type seems to be more prevalent among borrowers with substantial funds for down payment and better credit scores. Minimum FICO scores often apply for these reduced documentation products. 5 The indicator variable DTI Missing captures this small number of cases.

16 280 LaCour-Little Table 5 Ordinary least squares regressions, by choice, with the dependent variable Note Rate. Estimate Standard Error T Ratio FHA Intercept Level of 10-year treasury rate ARM (one-year adjustable) Loan amount LTV FICO Condo MFDU New house Time to closing Broker-originated R Not reported: MSA dummy variables Special Program Intercept Level of 10-year treasury rate ARM (five-year) ARM (seven-year) ARM (10-year) FRM (20-year term) Loan amount LTV FICO Condo MFDU New house Time to closing Broker-originated R Not reported: MSA dummy variables Table 5 presents results on the models used to generate predicted Price (i.e., note rates) for alternative loan types. Here I attempt to mirror general industry pricing practice, 6 in which loans are priced according to market rates, product type and general risk factors such LTV and credit score, and specific risk factor add-ons, such as for property type and loan size, which reflects the economics 6 Many rate sheets, especially in the nonprime category, explicitly price loans based on LTV, credit score and loan amount.

17 The Home Purchase Mortgage Preferences 281 Table 5 continued Estimate Standard Error T Ratio Nonprime Intercept Level of 10-year treasury rate ARM (two-year) ARM (three-year) Loan amount LTV FICO Condo MFDU New house Time to closing Broker-originated R Not reported: MSA dummy variables Conventional Conforming Intercept Level of 10-year treasury rate ARM (three-year) ARM (five-year) ARM (six-month) ARM (one-year) ARM (three one hybrid) ARM (five one hybrid) ARM (seven one hybrid) ARM (ten one hybrid) FRM (20-year term) Loan amount LTV FICO Condo MFDU New house Time to closing Broker-originated R Not reported: MSA dummy variables of the loan origination process. All models in this preliminary OLS regression use the same specification: note rate is a function of the rate lock date, product type (ARM, including several types, vs. 30-year FRM), loan amount, credit score, LTV, property type and whether the loan was originated by a mortgage

18 282 LaCour-Little broker. Results are quite consistent and model fit reasonably good, with R-squared values ranging from 0.57 to Moreover, signs of coefficients are as expected: higher LTVs are associated with higher note rates, higher credit scores are associated with lower note rates, multifamily properties typically require a price premium and larger loans carry lower note rates. The one result that appears inconsistent across the four models is the effect of broker origination. In the FHA segment, loans originated by mortgage brokers carry note rates that are about 8 basis points higher, whereas across other choices loans originated by brokers carry rates that are 4 38 basis points lower. 7 We should note that, in the interest of simplicity, we have modeled broker pricing effects here as fixed using an indicator variable, although whether this is appropriate or not is certainly an open issue. The effect of mortgage brokers on pricing and other market outcomes is fertile ground for additional research. Table 6 models time to closing (defined as time from application to loan closing) channel by channel, on the view that borrowers implicitly choose closing time in their product selection process. Recall that we estimate this model to obtain fitted values for use as control variables in the MNL model of choice reported in Tables 7 and 8. 8 These regression results have relatively lower R-squared values, suggesting that other, unobserved factors are at work, too. It is possible to improve model fit slightly by, for example, using a logarithmic transformation on the dependent variable and/or by pooling all choices together and using indicator variables for product choice; however, resulting adjusted R-squared are at best around Regardless of specification, however, several patterns emerge. First, new construction consistently has a large positive value implying that such loans take longer to close, consistent with unexpected construction delays or a need to apply (and perhaps receive conditional approval) well before the dwelling unit is actually finished. Second, multifamily properties take longer to close, consistent with the need to evaluate rental income from tenants as well as borrower income. There is also some weaker evidence that loans originated during peak season (defined as June, July or August) take longer to close, consistent with capacity constraints at lenders and title companies during the peak home-buying period. Finally, the sign on Broker-Originated is consistently negative, suggesting that broker-originated loans close faster, an obvious advantage for borrowers who must satisfy a financing contingency within a contractually defined time period. 7 These results are consistent with Anshasy, Elliehausen and Shimazaki (2005), who found that nonprime loans originated through mortgage brokers had lower APRs than those originated through traditional retail lender channels. However, LaCour-Little (2006) finds the opposite effect for prime loans. 8 In an earlier version of this article, I simply used actual closing time with generally similar results.

19 The Home Purchase Mortgage Preferences 283 Table 6 Ordinary least squares regressions, by choice, with the dependent variable Time to Closing. Estimate Standard Error T Ratio FHA Intercept Peak season (June, July, August) New house MFDU (2 4 units) First-time homebuyer Loan amount Note rate Broker-originated R Special Program Intercept Peak season (June, July, August) New house MFDU (2 4 units) First-time homebuyer Loan amount Note rate Broker-originated R Nonprime Intercept Peak season (June, July, August) New house MFDU (2 4 units) First-time homebuyer Loan amount Note rate Broker-originated R Conventional Conforming Intercept Peak season (June, July, August) New house MFDU (2 4 units) First-time homebuyer Loan amount Note rate Broker-originated R

20 284 LaCour-Little Table 7 Multinomial logit, retail loans only, with the dependent variable Choice. FHA Special Program Nonprime Standard Chi Standard Chi Standard Chi Estimate Error Square Estimate Error Square Estimate Error Square Intercept , Loan characteristics Loan amount Price and closing time of alternatives Fitted FHA rate Fitted EMNP rate Fitted nonprime rate Fitted conventional rate Fitted FHA close time Fitted EMNP close time Fitted nonprime close time Fitted conventional close time Credit and other risk characteristics DTI missing DTI Fitted LTV Fitted probability of No Doc FICO < FICO FICO FICO FICO FICO FICO Borrower liquid assets Borrower characteristics Borrower age Borrower is Black or Hispanic

21 The Home Purchase Mortgage Preferences 285 Table 8 Multinomial logit, broker-originated loans only, with the dependent variable Choice. FHA Special Program Nonprime Standard Chi Standard Chi Standard Chi Estimate Error Square Estimate Error Square Estimate Error Square Intercept Loan characteristics Loan amount Price and closing time of alternatives Fitted FHA rate Fitted EMNP rate Fitted nonprime rate Fitted conventional rate Fitted FHA close time Fitted EMNP close time Fitted nonprime close time Fitted conventional close time Credit and other risk characteristics DTI Missing DTI Fitted LTV Fitted probability of No Doc FICO < FICO FICO FICO FICO FICO FICO Borrower liquid assets Borrower characteristics Borrower age Borrower is Black or Hispanic