Electronic copy available at: http://ssrn.com/abstract=1726566

DOES SOCIAL CAPITAL AFFECT MORTGAGE LOAN TERMINATION BY LOW-

INCOME HOMEOWNERS?

Abstract

We study whether social capital affects low-income borrowers’ decisions to terminate their

mortgage loans, originated after church leaders connected these borrowers with a lender. Loan

termination is modeled in a competing-risks framework, where social capital, demographic and

religious characteristics as well as traditional economic factors affect mortgage termination. We

find evidence that heterogeneity of social trust in the wider community is associated with

decreased default and prepayment hazards, indicating that low-income borrowers value

“bridging” social capital linking them to the wider community. The evidence suggests a role for

social capital in mortgage loans and perhaps other financial contracts.

JEL Classification: R10, Z13

Keywords: low-income borrowers, social capital, mortgage loan termination, competing-risks

Electronic copy available at: http://ssrn.com/abstract=1726566

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DOES SOCIAL CAPITAL AFFECT MORTGAGE TERMINATION BY LOW-INCOME

HOMEOWNERS?

INTRODUCTION

While there is evidence that low- and moderate-income homeowners are less educated, misjudge

their credit rating and may buy houses they are not able to afford, solutions targeting information

disclosure only are not likely to improve consumer welfare (e.g. Perry, 2008; Bone, 2008).

Through securitization risky loans are re-sold and mortgage loan default risk is dispersed through

the economy, diminishing primary lenders’ incentives to develop lending practices appropriate

for their specific clientele. The real estate bubble and the following financial crisis provoke re-

examination of many assumptions about borrower behavior and justify analyses from diverse

perspectives to answer questions that seem puzzling from more conventional point of view. For

example, an unexpectedly large number of households continue to live in houses worth less than

the balance of their mortgage loans while according to the dominant option theory of mortgage

termination many should have defaulted.

We contribute to the literature by exploring what factors affected mortgage loan

termination by low-income borrowers who participated in a bank outreach program established

through collaboration with community church leaders. We test a hypothesis that in addition to

factors typically shown to affect mortgage loan termination, social factors also play a role. This

hypothesis comes from the literature studying targeted lending practices where the lender

establishes links to the community and understands how social capital, such as connectedness to

local community, trust in people and institutions within the community etc., affects individual’s

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loan repayment (Ferrary, 2003). In addition, several other studies find that social capital is

relevant in financial contracts (Baku and Smith, 1998; Karlan et al 2009; Guiso, 2004).

The link between mortgage termination and social capital is explored with data from

mortgage loans extended to low-income households in several Midwest states, for the period

1992 to 2000. The social capital data come from indexes developed by the Social Capital

Community Benchmark Survey of 2000, conducted by the Civic Engagement in America Project

at Harvard's John F. Kennedy School of Government. The empirical model is specified within

the competing-risks approach, where mortgage termination hazards are functions of the value of

the options to terminate via prepayment or foreclosure as well as other factors affecting borrower

behavior, such as shock events, transaction costs, and borrower and community characteristics.

To control for the specific nature of the program we also include variables that measure possible

role of religious institutions.

For the sample analyzed, we find that low-income borrowers living in counties with more

variation in social capital by various neighborhoods are less likely to terminate their loans which

we interpret to suggest that borrowers value the bridging social capital and ability to link to their

larger diverse community. We also find suggestive evidence that borrowers in counties with a

higher proportion of urban population (where social capital is harder to accumulate) and with a

higher number of congregations per person are more likely to experience foreclosure, all else

equal.

The paper is organized as follows: part two briefly summarizes the relevant literature on

social capital and on its role in financial contracts; part three introduces the empirical model, part

four describes the data, part five discusses the results, and part six offers conclusions.

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LITERATURE REVIEW

Social Capital and Financial Contracts

Social capital is most often defined by sociologists as “advantages and opportunities accruing to

people through membership in certain communities” (Bourdieu 1986), by political scientists as

“the resources of the individual that emerge from social ties and participation in networks”

(Coleman, 1988) and, according to economists, as “community relations that affect personal

interactions” (Durlauf and Fafchamps, 2004).

Two types of social capital are distinguished. The first is bonding social capital (social

glue), which consists of connections of individuals and groups with similar backgrounds (Lang

and Hornbourg, 1998; Flora and Flora, 2003). These connections are valuable in financial

contracts because they can serve as collateral substitutes. For example, in France, brasserie

owners from the same province secure their loans only by the value of the businessman’s social

capital in the brasserie community (Ferrary, 2003). Similarly, microfinance institutions in

developing countries offer loans secured by the joint liability of group members, who are

collectively responsible for repayment if one of them fails to pay back her loan. In these financial

contracts, lenders rely on the group’s information advantages for screening and monitoring and

on peer pressure for contract enforcement. The risk of losing social capital motivates repayment

as much as the fear of losing any physical assets pledged (Besley and Coate, 1996).

Some studies show, however, that the success of microfinance contracts varies by country

and depends on the strength of social ties in the community (Armendariz de Aghion, 1999).

Guiso et al.. (2004) establish a link between social capital and use of financial services.

Specifically, households in areas with high levels of social capital use more sophisticated

financial instruments, make less use of informal credit, and enjoy more access to institutional

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credit. Moreover, the authors find that the impact of social capital is stronger among less

educated people, who possess less of the more traditional forms of capital.

Second, bridging social capital connects diverse groups in the community with groups

outside the community. By accessing the community’s bridging capital, financial institutions

may find new markets and provide new opportunities to disadvantaged borrowers. For example,

Baku and Smith (1998) report that nonprofit low-income mortgage lenders which involve local

business leaders benefit from the community’s social capital and experience lower delinquency

rates than other lenders. In general, Karlan et al.. (2009) argue that financial products, such as

microfinance loans, use and create social capital. Availability of bridging social capital can also

help link low income borrowers to the larger community, and thus influence their willingness to

move and or terminate their mortgage loans.

Church leaders are held with respect and are trusted by their congregation. They possess

significant stock of bridging social capital, which may be used to link borrowers and lenders.

Moreover, religion is important for social capital formation, and it is especially important in the

African American community, where members have historically found refuge from injustice

(Carter, 1999; Putnam, 2000). The data used in the analysis come from a program where

bridging social capital was used to bring together church leaders, bank officials, and the

congregation in order to overcome the traditional mistrust of banks in low-income communities

and increase access to services.

Mortgage Loan Termination

Mortgage loans are long-term contracts designed to fund the purchase of a home and are among

the most complex financial instruments. While there is a growing literature on the performance

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of these loans, the impact of social capital on their repayment has not been explored. Unlike

other financial contracts, mortgages combine two major decisions—a financing decision and a

home-purchase decision. The financing decision involves determining the amount of debt a

borrower can afford and the terms of the loan contract, such as downpayment, interest rates, and

duration. The housing decision involves choosing a house and a community in which the

household would reside. Borrowers have the option to terminate via default or by early

prepayment. Lenders ability to correctly assess these risks affects their willingness to serve low-

income borrowers. When typical loan application information cannot be collected or is not

useful, lenders could use alternative information.

The hypothesis here is that in addition to initial financing and housing decisions, the

default or repayment of the loan are likely to be affected by the social capital in the community.

There is evidence suggesting that home ownership encourages investment in social capital and in

local amenities and that social capital, when combined with variables such as credit availability

and housing condition, affects neighborhood stability (DiPasquale and Gleaser, 1999; Temkin

and Rohe, 1998). More stable communities, in turn, may be more attractive to live in and

borrowers who live in these communities may have lower rates of mortgage loan termination.

The mainstream literature on mortgage termination evaluates default risk and the risk of

early refinancing, both unprofitable for the lender. Modern finance theory evaluates these risks in

a competing-risk framework, where borrowers are assumed to default or prepay when it is

profitable to do so, namely, when their options to default or to prepay are in-the-money. The low-

income borrowers’ ability to repay is related to the initial financing and housing decisions, and it

is explored in the literature by controlling for additional factors such as borrower wealth and

income constraints, events that may trigger termination such as unemployment and divorce,

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household mobility, neighborhood characteristics or borrower heterogeneity (Ambrose and

LaCour Little, 2001; Clapp et al 2001; Calhoun and Deng, 2002; and Vandell 1995). The

contribution of this paper is that it evaluates if the level and the distribution of social capital in

the community affected mortgage loan termination by low-income borrowers in late 1990s.

METHODOLOGY

The competing-risks framework used here is rooted in the option theory of mortgage termination,

which originally viewed mortgage termination as a financial decision, independent of the

housing decision. Specifically, when deciding on how to act on a loan obligation, a borrower

decides whether to (1) make the payment on the loan and continue in good standing as a debtor.

(2) pay in full the remaining balance on the loan, by refinancing (prepayment, or call option), or

(3) surrender the house to the lender in exchange for cancellation of the debt (put, or default

option). Thus, prepayment and default are two choices, driven by the value of the underlying

prepayment (call) and default (put) options that borrowers make in order to increase their wealth

(Foster and Van Order, 1984). Since by exercising one option the borrower gives up the other,

the extent to which one option is in-the-money (that is, it has value) affects the exercise of the

other option (Kau et al. 1992). For instance, the probability of prepayment is a function of the

extent to which the default option is in-the-money and vice versa.

Empirical studies recognize, however, that factors such as borrower characteristics,

income shocks (trigger events), and transaction costs also influence default, even if it is non-

value enhancing (Deng et al. 1996). The literature on social capital is also suggestive of the

potential impact of social trust on financial transactions (Ferrary, 2003; Guiso et al. 2004). The

empirical model proposed here allows for mortgage termination to be evaluated in a competing-

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risk framework and also to control for the impact of non-financial characteristics, including the

value and distribution of social capital.

Within the competing risk approach we estimate two hazard models where the

termination-specific hazard function is

)](;[ tXtj ])(exp[)(0 jj tZt for j=1,2 (1)

and )](;[ tXtj represents the instantaneous rate of termination (by default or by prepayment),

given left continuous with right-hand side limits X(t); Z(t) is a vector of possibly time-varying

covariates defined as a function of X(t); the baseline hazard )(0 tj and the regression

coefficients j can vary arbitrarily over the termination types; namely, the baseline hazard of

default and prepayment and the estimated coefficients are allowed to be different as required.

The model is estimated with MLE in STATAi.

Within this framework we include variables that measure the value and distribution of

social capital to test whether and how these variables impact mortgage loan repayment.

Community characteristics, such as median house value and median income, the percentages of

African-American and urban population, and variables that describe the congregations in the

county are also included. The expectation is that borrowers in counties with a higher level of

social capital and living in counties with more homogeneous social capital will be less likely to

terminate their mortgage loans, as default may result in scarred reputation and worsening of the

stock of social capital (both social glue and bridging social capital linking low-income

individuals to the larger community) that these borrowers possess.

An important advantage of this framework is that it accommodates time-constant and

time-variant explanatory variables. Time-varying variables included are those that approximate

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the value of the two options, as well as trigger events such as unemployment and divorce. The

values of the put and call options are approximated using formulas introduced by Deng et al.

2000. The basic idea is that movements in house prices and, thereby, the value of the equity in

the house, together with changes in interest rates, would determine the value of the two options.

The value of the option to default (put option) is approximated by Default Option which

is the probability that the equity in the house is negative; that is, the borrower owes more to the

bank than the house is worth in the market.ii The higher the value of Default Option , the higher

the probability that the equity in the house is negative and the more “profitable” it would be to

default or walk out of the house in exchange for cancellation of the debt.

The value of the repayment (call) option is Prepay Option, which is calculated as one

minus the ratio of the present value of the unpaid mortgage balance at the current market interest

rate, ii km , and the same value discounted at the contract interest rate.iii Positive values of

Prepay Option would indicate that the option is out-of-the-money; that is, it is not in the

borrower’s interest to prepay. The option will move in-the-money as Prepay Option becomes

negative because negative values would indicate that the contract interest rate is higher than the

market rate and that it will be profitable to refinance.

In addition to the value of the options, the analysis includes variables that measure the

impact of individual shocks, unemployment, and divorce. Time-invariant covariates included are

the purchase price of the house and the monthly mortgage payment, which serve as proxies for

the borrower’s original income and wealth levels, and the loan-to-value ratio at time of

origination, which serves as a proxy for the downpayment.iv

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DATA

The study uses data from the Community Mortgage Loan Program (CML) which is part of an

umbrella project called Community Centered Banking Program. The latter program was

organized by a large bank in Ohio to fulfill Community Reinvestment Act requirements and

targeted low-to-moderate income households who did not routinely use the banking system and

who typically were denied loans. The objectives of the Community Centered Banking Program

were to improve the integration of the financial services offered to a community and to enhance

opportunities available to low-to-moderate income households. The program was organized in

collaboration with Community Churches and a local consulting firm with experience in

implementing community outreach programs.

Potential low-income clients were approached through a series of seminars organized by

Community Churches. Through this program, low-income households gained access to a full

range of banking services—checking and savings accounts, student and consumer loans, and

educational services. The purpose of the CML program was to offer cost-effective mortgage

loans to low-income households, in a fashion profitable to the bank.

The features of the program were designed for the specific market segment. Borrowers

could get mortgage loans for up to $75,000 with a downpayment of the lesser of $1000 or 5

percent of the loan. The bank offered eased credit restrictions, a one-percent origination fee, and

no discount points. In addition, all applicants were required to go through credit counseling

organized and paid by the bank. To cover its costs, the bank charged an interest rate 150 points

above the Fannie Mae 60-days average rate on 80 percent LTV-conforming loans. The mortgage

loan data consist of all mortgage loans originated from 1992 to 2000 to borrowers from 25

counties mainly from Ohio but also Florida, Kentucky, Michigan, and West Virginia.

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Social capital data come from a Social Capital Community Benchmark Survey of the

Suguaro Seminar 2000, available from the Roper Center at the University of Connecticut

(http://www.ropercenter.uconn.edu). This survey was conducted by the Civic Engagement in

America Project at the Harvard's John F. Kennedy School of Government. The SCCBS was the

first nationwide survey to measure "social capital" or how “connected people are to family,

friends, neighbors, and civic institutions.” This survey provides several indexes that capture the

level of social capital in the community. The social trust index is based on general interpersonal

trust, trust of neighbors, trust of co-workers, trust of fellow congregants, trust of store employees

where people shop, and trust of local police. The faith-based social capital index covers church

membership, church service attendance, non-religious service church participation, and

affiliation with non-church religious groups.

This survey contains more than 2000 observations from the counties for which mortgage

loan data are available. These observations were used to create four variables: (1) the average

value of social capital in the county, calculated as the mean value for the observations from this

county. (2) county social capital heterogeneity approximated by the standard deviation of the

social trust index in the county; (3) the average value of faith-based social capital, calculated as

the mean value for the observations from this county, and (4) county faith-based social capital

heterogeneity approximated by the standard deviation from the mean value of the faith-based

social capital index for each county. To create county homogeneity variables, only counties with

at least 3 observations from the Social Capital Survey are used resulting in a total of 504 loan

observations.v

Repayment records span up to nine years, with most loans still outstanding. The

performance of the portfolio of all counseled loans is shown in Table 1. At time of data

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collection in 2000, this portfolio had 400 current loans, 45 loans (8.9 percent of the number) in

default, and 59 prepaid loans (11.7 percent).

[Insert Table 1 about here]

Loans in default are defined as loans for which foreclosure took place, loans tied up in

bankruptcy procedures and/or loans for which a loss was realized, as well as loans coded as DIL.

(deed-in-lieu or foreclosure). Default is recorded at a time when these loans became 90-days

overdue. Regarding prepayment, the available information is less detailed. The bank has not

collected information on the reason for prepayment—either refinancing or moving.

The variables used in the analysis are defined in Table 2. Several variables that describe

the surrounding county community are considered as potential indicators of the influence of

social capital on loan termination. Since borrowers were approached through religious

organizations, data on the number of congregations and congregation size in the county were

added. These data come from Churches and Church Membership in the United States 1990 and

Religious Congregations and Membership in the United States 2000 data, collected by the

Association of Statisticians of American Religious Bodies (ASARB), and are available online

from the American Religion Data Archive, www.thearda.com. The specific variables used in this

study are the size of the congregation, the number of congregations in the county scaled by

county population, and the change in the number of congregations from 1990 to 2000. County

population, county median household income, and county median house value for the year 2000,

available from the census database, are also included to isolate the impact of economic

characteristics of the community.

[Insert Table 2 about here]

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Monthly county level unemployment data come from the Bureau of Labor and Statistics.

Data on annual divorce rates for Ohio counties are obtained from the Center for Public Health

Data and Statistics, Ohio Department of Health. County divorce rates for the rest of the states (10

percent of the sample) were not available, so state divorce rates from various issues of U.S.

National Center for Health Statistics, “Vital Statistics of the United States, Volume III, Marriage

and Divorce,” and in “Statistical Abstract of the U.S.” were used instead.

Summary statistics for these variables are presented in Table 3.

[Insert Table 3 about here]

DISCUSSION OF RESULTS

Estimation results are presented in Table 4. We find evidence that social capital affects mortgage

loan termination. The hypothesis that low-income borrowers living in counties with higher levels

of social capital have lower termination hazards is not supported by the results. In fact, we find

that low-income borrowers living in counties with more variation in the index of social trust are

less likely to default or prepay. We interpret this to mean that while the level of trust within the

low-income community may impact financial transactions and the creation of social bonds, this

impact does not appear to be captured by the county average of the social trust index. However,

the sign of the significant standard deviation of this index suggests a role for the bridging social

capital. Low-income borrowers living in counties with more variations in the social trust index

may value bridging social capital and the links that more diverse and larger community provides

and be more likely to stay within this larger community.

[Insert Table 4 about here]

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These results show that low-income borrowers value social capital and may successfully

be serviced by lenders who are willing to develop lending technologies that use the bridging

social capital provided by local church leaders. Borrowers in counties with more diversity of the

social capital may even be less “risky,” because they rely more on bridging social capital. The

results also suggest that the impact of bonding social capital has to be explored below county

level, perhaps within the zip code or more narrowly defined community level. May change this

slightly to match my point above.

We find evidence that borrowers living in counties with a higher number of

congregations per capita default more often, suggesting that religious fragmentation may be

associated with less trust. Also, this may reflect the fact that congregation members have more

alternatives and thus may value less the social capital within their current congregation. This

result is reminiscent of the erosion of repayment discipline observed in microfinance markets

when the number of lenders increases and they do not share information about borrower

performance (Rhyne 2001). The change in the number of congregations from 1990 to 2000, as a

proxy for opportunities to switch, does not seem to have an impact on loan termination, however.

The average size of the congregation also does not affect termination outcomes. This may be

due to the fact that, while the value of faith-based social capital may be larger in larger

communities, where it could be deployed in multiple transactions, it may be easier to accumulate

this capital in smaller communities. The size of the congregation would have ambiguous

influence on the outcome.

Model 2 in Table 4 adds faith-based social capital index and its standard deviation. These

two variables do not affect default or prepayment hazards. This result is similar to that on social

capital county level index. While collaboration with church leaders helped the lender to access

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low-income clientele, if the faith-based social capital index matters in assessing risk, it probably

matters on a level less aggregate than captured by county level indexes. Thus, because religious

institutions operate on more local level and provide the bridging capital valued by low-income

borrowers, collaboration between financial institutions and local churches remains an option for

lenders considering serving these communities.

The results also indicate that the economic characteristics of the county affect mortgage

termination. The low-income borrowers in the sample living in counties with higher average

incomes were less likely to default, possibly in reflection of better employment opportunities

there. In contrast, low-income borrowers who bought a house in counties with a higher median

house price were more likely to terminate by either default or prepay.

In addition, borrowers who live in counties with a higher percentage of African

Americans are less likely to default, and there is weak evidence that borrowers living in counties

with a higher percentage of urban population are more likely to default. Although this coefficient

is significant only in Model 2, it is not surprising, as the formation of bonding social capital may

be harder and less valuable in impersonal urban areas than in less urban setting.

The results also indicate that the competing-risks framework is appropriate to study

mortgage termination by low-income borrowers. Results show that these borrowers terminate

when it makes financial sense, namely, when the put and call options are in-the-money. These

results are consistent with previous studies such as Hartarska and Gonzalez-Vega (2005 & 2007).

As expected, and in both models, default is positively and significantly influenced by the

probability of negative equity (Default Option) and by the value of the prepayment option

(Prepay Option). Also as expected, and in both Model 1 and Model 2, the value of the

prepayment option significantly affects prepayment; that is, the more negative Prepay Option is,

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the more profitable it is to prepay. The sign of Default Option in the prepayment hazard is

positive as expected, but it is not significant. This suggests that the borrowers’ prepayment was

not affected by the high probability that their equity was negative. This result might indicate that

low-income borrowers value their reputation. The ability to get a loan in the future seems

important and low-income borrowers may have even taken a financial loss (by selling the house

or refinancing and prepaying) even when defaulting for purely financial considerations would

have been wealth increasing.

The value of the property and the level of the monthly payments did not affect default,

but borrowers who bought more expensive houses were more likely to prepay their loans. Loans

with higher monthly payments were less likely to be prepaid in advance of term. It is widely

accepted that loans with higher LTV (that is, a smaller downpayment) are more risky. The results

show that this was not the case with this portfolio. Instead, borrowers with higher LTV exhibit

lower default hazards. Although such a result is not unusual in lending to low-income

households, it may also reflect the willingness of the bank to add a consumer loan to allow the

borrower to generate the down payment, thus biasing the results. In general, microfinance

organizations lending in low-income communities have discovered that, the poorer the borrower

(that is, the less collateral she has), the more important reputation becomes in gaining access to

loans. This frequently translates into fewer defaults among the poorest (Armendariz de Aghion

and Morduch, 2000).

Trigger events and personal circumstances impacted mortgage termination by the

borrowers in the sample. As expected, the proxy variable for the shock is positive and significant

in the default hazard. After controlling for borrower-specific shocks, as revealed by the

borrowers themselves, county unemployment and divorce rates do not impact default, but the

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coefficient for the unemployment rate is statistically significant in the prepayment hazard. This

indicates that borrowers living in counties with higher unemployment rates were more likely to

prepay, perhaps because they needed to move elsewhere or wanted to use some of their equity as

a risk-coping tool. This result is consistent with findings of an optimal prepayment behavior

among low-income households, reflected by a statistically significant value of the call option

coefficient.

CONCLUSIONS

A better understanding of the factors affecting low-income borrowers’ mortgage loan

termination could improve these borrowers’ access to mortgage loans and help lenders in

managing risks and continuing to serve these consumers. Initiatives to bridge the gap between

low-income borrowers’ demand and lenders willingness to supply such loans will require

genuine innovations, unlike “innovations” of the past decade. This study presents evidence that

social capital may be useful in designing such innovations.

In the credit program analyzed here, the lender used local church leaders’ bridging social

capital to gain access to low-income clients otherwise deemed not creditworthy. The key issue

explored is whether social capital served as a factor influencing low-income borrowers’ behavior

and therefore credit risk. Specifically, the paper examines whether county level social capital and

its distribution reflecting variation in neighborhood social capital (since neighborhood levels are

not available) affect the default and prepayment hazards of mortgage loans received by the

participants in a low-income mortgage loan program. The results indicate that the dispersion of

social trust in the county is significantly associated with decreased default and prepayment

hazards. That is, the value of social capital as a constraint on mortgage loan termination seems

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to be higher in communities with more variability in the level of local social trust, suggesting

that low-income borrowers valued links to a wider community with diverse levels of social trust,

and captures the importance of the “bridging” aspect of social capital. The impact of social trust

or “social glue” may be best explored with neighborhood level measures of social trust.

This paper also finds that borrowers living in counties with a higher number of

congregations per capita default more often, suggesting that religious fragmentation may be

associated with less trust. County economic characteristics also affect mortgage termination

hazards and borrowers living in counties with a higher percentage of urban population are more

likely to default. The paper confirms that low-income borrowers just like wealthier borrowers

respond to financial incentives, exercise optimally their default and prepay options and are

affected by shock events, and socio-economic factors.

These results warrant further research on whether the wider population of middle class

borrowers value social capital in their community, and whether social capital is a factor

contributing to the currently observed phenomenon of families staying in houses worth less than

the mortgage loan or how neighborhoods with high foreclosure rates may or may not be able to

recover. Since data measuring social capital have become more available in recent years (see for

example Rupasingha and Goetz, 2008), the results of this paper suggest that such data may be

pertinent in studies of consumer behavior in various financial transactions such as mortgage

loans, microfinance business loans, and community development programs.

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DiPasquale, D. and E. L.Glaeser. 1999. Incentives and Social Capital: Are Homeowners Better Citizens? Journal of Urban Economics, 45: 354-84. Durlauf S. and M. Fafchamps. 2004. Social Capital NBER Working Paper No. W10485. Ferrary, Michael, 2003. Trust and Social Capital in the Regulation of Lending Activities. Journal of Socio-Economics, 31: 673-699. Flora, C. B. and J. L. Flora. 2003. Rural Communities: Legacy and Change, Westview Press. Foster, C. and R. Order. 1984, An Option-Based Model of Mortgage Default, Housing Finance Review, 3: 351-372. Guiso, L., P. Sapienza, and Luigi Zingales. 2004. The Role of Social Capital in Financial Development, American Economic Review, 94: 526-56. Hartarska Valentina, and Claudio Gonzalez-Vega. 2006. Evidence on the Effect of Credit Counseling and Mortgage Loan Default by Low-Income Households. Journal of Housing Economics, 15(1): 63-79. Hartarska, Valentina, and Claudio Gonzalez-Vega. 2005., “Credit Counseling and Mortgage Termination by Low-Income Households,” Journal of Real Estate Finance and Economics, 30(3):227-243. Kalbfleisch, J. and R. Prentice. 2002. The Statistical Analysis of Failure Time Data, New Jersey: John Wiley & Sons, Inc. Karlan, Dean, Markus Mobius, Tanya Rosenblat and Adam Szeidl. 2009 . Trust and Social Collateral. Quarterly Journal of Economics, 124( 3): 1307-61. Kau, J. B., D. C. Keenan, W. J. Muller III, and J. F. Epperson. 1992. A Generalized Valuation Model for Fixed-Rate Residential Mortgages,” Journal of Money, Credit and Banking, 24: 279-299. Lang and Hornbourg. 1998. What is Social Capital and Why is it Important to Public Policy? Housing Policy Debate, 9: 1-17. Perry, Vanessa Gail. 2008. Is Ignorance Bliss? Consumer Accuracy in Judgments about Credit Ratings. Journal of Consumer Affairs, 42(2): 189-205. Putnam, Robert, 2000, “Bowling alone: The Collapse and Revival of American Community.New York: Simon and Schuster.

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21

Table 1. Portfolio Performance Loan Status Total

Number %

In default 45 8.9 Prepaid 59 11.7 Current 400 79.4 Total 504 100

Table 2. Definitions of Variables

Variable Name Description of the Explanatory Variables Default Option Approximated by the probability that the borrowers’ equity is

negative (as in Deng et al., 2000.. Prepay Option Approximated by 1 minus the ratio of the discounted value of the

remaining mortgage payment, at the current market interest rate, to the discounted value of the remaining mortgage payment, at the contract interest rate (as in Deng et al., 2000..

LTV Loan-to-value ratio at loan origination House Value House value at loan origination Monthly Pay Monthly payment on the loan (principal and interest.. It does not

include insurance and taxes Median HHINC Median household income in the county Median HPRICE Median house price in the county PBLACK Percentage of blacks in the community PURBAN Percentage of urban population in the community Congregation SIZE Congregation size Nu congregations pc Number of congregations per capita in the county Change Nu churches Percentage change in the number of churches in the county Average ST Average value of the index of social trust in the community St. Dev. ST Standard deviation of the index of social trust in the community Average FAITH Average value of the index of faith-based social capital St. Dev. FAITH Standard Deviation of the index of faith-based social capital

22

Table 3. Descriptive Statistics for the Explanatory Variables Variable Mean Std. Dev. Min Max

Default Option 0.39 0.23 0.00 0.97 Prepay Option -0.12 0.09 -0.43 0.06

Default Option a 0.73 0.26 0.00 1.00

Prepay Option a -0.19 0.11 -0.37 0.73

Default Option b 0.58 0.24 0.01 0.94

Prepay Option b -0.22 0.09 -0.43 0.00

LTV (%. 92 6 40 119 House Value ($. 52,671 15,352 11,000 139,905 Monthly Pay ($. 398 117 73 1,019 Median HHINC ($. 40,112 3,230 30,160 67,258 Median HPRICE ($. 98,462 14,062 62,000 188,000 BLACK (%. 15.8 6 0.4 27.3 URBAN (%. 90.2 13.9 0 99.2 Avg congregation size 369 63 151 606 Nu congregations pc 3.8 2.6 1.8 14.3 Change Nu churches 0.12 0.10 -0.24 0.44 Social trust index -0.12 0.31 -0.47 0.93 Variability in of Social trust

0.78 0.23 0.06 1.07

Faith index -0.02 0.22 -0.93 1.47 Variability in Faith index FAITH

0.70 0.08 0.00 0.88

a at default b at prepayment

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Table 4. Maximum likelihood estimates of a competing-risks model of mortgage termination Model 1 Model 2 Default Prepay Default Prepay Default Option 15.151*** 2.050 15.304*** 2.274 (3.16. (1.02. (3.16. (1.11. Repay Option 15.600*** -8.072*** 15.741*** -7.683*** (2.98. (2.62. (2.99. (2.41. LTV -0.308*** -0.004 -0.309*** -0.006 (4.29. (0.06. (4.29. (0.10. House Value -3.178 7.940*** -3.125 8.039*** (0.70. (2.99. (0.69. (3.03. Monthly Pay 2.560 -7.805*** 2.565 -7.873*** (0.58. (3.09. (0.58. (3.3. Shock 1.181*** -0.314 1.155*** -0.332 (2.91. (0.68. (2.84. (0.72. DIVORCE 0.534 0.726 0.704 0.834 (0.60. (1.35. (0.77. (1.48. UNEMPL -0.195 0.421*** -0.228 0.385*** (0.55. (2.91. (0.60. (2.51. Median HHINC -27.806* -8.729 -28.014* -12.262 (1.88. (1.27. (1.89. (1.53. Median HPRICE 22.287* 12.050** 23425* 15.126** (1.84. (2.12. (1.69. (2.28. PBLACK -0.171* -0.021 -0.168* -0.032 (1.76. (0.27. (1.76. (0.40. PURBAN 0.300 0.021 0.300* 0.014 (1.55. (0.41. (1.68. (0.25. Avg congregation size -0.017 -0.001 -0.016 -0.001 (0.97. (0.18. (0.83. (0.15. Nu congregations pc 26.589 0.923 22.237* 3.730 (nochcap. (1.45. (0.38. (1.68. (0.48. Change Nu churches -8.336 1.982 -8.057 1.785 (1.28. (0.38. (1.32. (0.35. Social trust index 0.744 0.839 -0.379 0.325 (0.61. (0.71. (0.27. (0.24. Variability in Social trust index

-2.007* -1.858* -3.422** -2.312*

(1.86. (1.61. (2.08. (1.61. Faith index 0.102 0.055 (0.10. (0.06. Variability in Faith Index

2.5701 1.919

(1.13. (0.88. Log likelihood -824 -824 No. observations 504 504

24

i For more details and description of the estimation procedure see Kalbfleisch and Prentice 2002 and Crowder 2001.

ii As in Deng et al. (2002), Default Option is defined as:

2

,,

,,

loglog)0(OptionDefault ,

w

MVEprob iikij

ii

kimi

kiki

(8)

where ikiE , is the equity in the house for the ith individual, evaluated k periods after origination; (.) is a

cumulative standard normal distribution function; ikijmiV

,, is the present value of the outstanding loan balance at the

ii km market interest rate, and w2 is the estimated variance from repeat (paired) sales, by state, provided by the

Office of Federal Housing Oversight (OFHEO). Here, ikiM , is the market value of the property, purchased at cost Ci

at time τi. Evaluated ki months thereafter, it is

i

ii

i

j

kjiki I

ICM

,

,, (9)

where the term in parenthesis follows a log-normal distribution and ijI , is an index of house prices by state j, at

time i . iii That is

*,

,

,,1_ri

mi

ki V

Voptionprepay ikij

(10)

Where ikijmiV

,, =

ii

ii

kTM

tt

k

i

m

P

1 )1(

(11)

*, iriV =

ii kTM

tt

i

i

r

P

1 )1( (12)

and where Pi is the monthly payment in principal and interest and ri is the contract interest rate and ii km is the

market interest rate.

25

iv Monthly payments and house value do not necessarily measure the same thing because, although most of the loans

were 30-year fixed rate loans, on occasion the bank granted fixed-rate loans for 10, 15, 20 or 25 years. No

information on these outliers was available, however.

v Unfortunately, within the loan data only an insignificant part of loans have information on more narrow geographic

location of the property (such as zip code) and this data shortcoming combined with smaller number of observations

per zip code from the social capital survey renders the use of smaller geographic unit non-feasible.