Interstate Non-Local Mortgage Lending: Features and Explanations

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Interstate Non-Local Mortgage Lending:

Features and Explanations

Yilan Xu Jipeng Zhang

[email protected] [email protected]

Department of Economics

University of Pittsburgh

Abstract

The mortgages from out-of-state non-local banks have a higher proportion of high-priced loans and are sold much more to the secondary market than other types of mortgages. Our interpretation is the following. When searching for a loan, borrowers first apply to banks’ local branches; if this fails, they next seek loans from non-local sources through brokers or the internet. Thus, less creditworthy applicants tend to end up with non-local loans. Then we expect that the areas that have high denial rates by local banks have more non-local lending. The empirical evidence supports this conjecture, and also shows that the non-local loans from out-of-state banks are even less favorable by borrowers. Additionally, we argue that banks have incentives to make non-local loans to such less creditworthy applicants in other states because they transfer the risk by selling most of their loans to the secondary market. If this is true, we expect that the banks that have better abilities or are more active in participating the secondary market have higher proportions of out-of-state non-local mortgages. Our findings support this hypothesis.

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1. Introduction

As the communicating, credit-screening, underwriting and monitoring techniques develop, banks can now make loans from a distance and enter a local market without physical presence. In the home mortgage market particularly, lending through brokers, on the internet and by correspondent has been an increasing trend in recent years. Out-of-state banks benefit more from the remote entries than their in-state counterparts because they face higher branching costs than the latter due to the interstate banking restrictions. To investigate the features of loans by different banks and lending channels, the home mortgage loans in this study are classified by whether the bank is in-state or out-of-state and whether it has branch offices in the local market.

Using a sample of home purchase loans from the Home Mortgage Disclosure Act (HMDA) data, we find that both in-state non-local and out-of-state non-local loans are significantly more likely to be subprime than their local counterparts, yet only out-of-state non-local loans are significantly more likely to be sold to the secondary market. The purpose of this study is to explain the reason of high price for non-local loans, to investigate the relationship between high-price lending and securitization for out-of-state non-local loans, and to provide empirical evidence for the moral hazard problem underlying the innovation of securitization.

The challenge to interpret the high subprime rate for the non-local loans lies in the absence of the credit information and the loan outcome information in HMDA data (Avery, Brevoort and Canner 2007). The high prices charged by the non-local lenders could be legitimate risk premium for high-risk borrower pools, or compensation to the long distance associated with increased transaction fees, monitoring and servicing costs, or it could be the discrimination from the non-local lenders. Without proper information such as borrowers’ credit scores, loan delinquency, default and foreclosure, we cannot distinguish those explanations or draw any conclusion about the true credit quality of the loans.

Nevertheless, we provide indirect evidences to show that the loans with non-local lenders are the ones with poorer unobservable credit qualities, due to the borrowers’ credit searching process. The hypothesis is that when trying to secure a loan, most borrowers first apply to banks’ local branches -- if this fails, they next seek loans from non-local sources through brokers or the internet. We show that the non-local credit supply, especially out-of-state non-local supply is greater in neighborhoods where the local banks have high lending standards. As a result of the screening of the local branches, we argue that only borrowers with lower credit end up with non-local loans. Since the non-local lending business is exposed to low-quality borrowers, why would some banks do this business? How do they manage the high risks to make profits? We find that nearly 80 percent of the out-of-state non-local loans are sold to the secondary market, which is around 30 percent higher than others. Moreover, the subprime loans originated by the out-of-state non-local banks are more likely to be sold to the secondary market, while subprime loans are generally less likely to be sold. We argue that as a result of poor credit pool for the out-of-state non-local banks, the high prices are not sufficient to compensate for the high risks of some loans. Therefore, the banks have to dump the bad loans in the secondary market by taking advantage of their information of the loan quality. We show that the banks with better ability to sell the

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loans to the secondary market originate a higher proportion of out-of-state non-local loans.

Given that the out-of-state non-local banks originate 20%-30% of the total home purchase mortgages, and that they have a much higher subprime rate, their lending practices may be a contributing factor to the subprime crisis. Gerardi, Shapiro and Willen(2007) find that homeownerships that begin with a subprime purchase mortgage end up in foreclosure almost 20 percent of the time, or more than 6 times as often as experiences that begin with prime purchase mortgages. As the securitization of home loans becomes a trend, the secondary market may take up too many loans with low qualities because the secondary market purchase decision are not based on the attributes of the individual borrowers, as suggested by Gabriel and Rosenthal (2007).

In previous literature, interstate banking and lending channels have been well studied respectively for small business lending. We are the first to analyze the home mortgages by combining both perspectives. This study contributes to the literature of interstate banking regulation in that it highlights the remote entries by out-of-state banks, and provides the insight on consumers’ welfare under this framework. It also extends the existing analysis of distant lending in small business lending to home mortgage lending, and links the non-local lending with the subprime lending. Finally, this study suggests that the securitization in the secondary market provides bad incentive for banks to lend to high-risk borrowers outside their branching areas.

1.1Literature Review

The deregulation of geographic expansion for banks both intra- and inter-state happened in three steps: intrastate branching, interstate banking by Bank Holding Companies, and interstate branching by independent banks. The interstate branching by independent banks permitted by the Riegle-Neal Act of 1994 is a huge advancement in the interstate banking because acquiring or establishing a branch office is much cheaper than acquiring a whole bank. Moreover, the delegation and management costs are lower within a bank than between a bank and its parent company. The cross-state variation in the availability of in-state and out-of-state banks may affect the proportion of non-local lending in a given market, all else equal. As Hannan (2003) points out, the share of the non-local lending in the local market is positively determined by the degree of local competition.

Peterson and Rajan (2002) argue that the distant borrowers are not necessarily the ones with high quality credits, due to the improvement in lenders’ productivity and the increasing competition in credit market. DeYoung, Frame, Glennon, McMillen, Nigro (2008) finds out that increase in the distance between small business borrowers and their lenders during the 1990s was disproportionately large for borrowers located in low-income and minority neighborhoods. Those findings suggest that the banks mainly develop their non-local lending business in historically underserved markets, where the borrowers are less creditworthy in the conventional sense. However, little is known about the impact of extending credit to those markets without local information and local services. This study fills this gap in the literature.

DeYoung, Glennon and Nigro (2008) showed that the production efficiencies in the credit scoring encouraged the lender to expand the credit to the marginal borrowers. For

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non-local lending in the home mortgage market, the production efficiency comes from not only from credit scoring and but also the scale of economy. The non-local lenders extend the output by offering on-line banking and hiring brokers in the local market, both of which rely on the hard information such as credit scores. Moreover, the non-local lenders usually offer loans in wide geographic areas across the country given the marketing strategy they employ. This also generates scale of economy that encourages lending to marginal lenders.

Degryse and Ongena (2005) showed empirical evidence that loan rates decrease with the distance between the firm and the lending bank and increase with the distance between the firm and competing banks. They argue that the transportation costs to travel to lender are the reason for the spatial price discrimination. However, the 2004 HMDA data suggest that the incidence of subprime loans is significantly higher for borrowers outside the lenders’ branching areas (Avery, Brevoort, Canner 2006). The reason for the difference is because the transportation costs are not a concern for home mortgage lending since most non-local loans are made through local brokers, in the internet and by correspondent. Ergungor (2007) demonstrates that the presence of local bank branches in the low-to-moderate income neighborhood increases the origination rate of home mortgage loans, and decreases the interest rate spread because the local branches have information advantage from relationship-lending.

Facing the high-risk lending in non-local market, the home mortgage lending banks can easily get rid of those high risk loans from their balance sheet by selling the loans to the secondary market. Loutskina and Strahan(2009) show that securitization in the secondary market reduces the effect of lenders’ financial condition on credit supply. Using the HMDA data between 1992 and 2004, Gabriel and Rosenthal (2007) show the secondary market purchase helps to expand the credit supply in terms of higher origination rates, and the effect is even greater for by the subprime lenders. Keys et al. (2010) and Dell'Ariccia et al. (2008) points out that securitization adversely affect the screening incentives of lenders, and therefore the banks lowers the lending standard when making home mortgage loans with securitization. Mian and Sufi (2008) argue that the sharp increase in the fraction of loans sold to secondary market expands the home mortgage credit supply to the neighborhoods of low creditworthiness in terms of low denial rates and high origination rates. Our results confirms that the securitization in the secondary market provide incentives for the banks to expand the risky out-of-state non-local lending.

2. Background of the Mortgage Market

The home mortgage market has grown rapidly in the past decade. The home mortgage debt as percentage of GDP has increased from 40-50% in 1990s to more than 70% in 2003 and 2004 (Green and Wachter 2005). The growth is largely attributable to the homeownership encouragement policy that the government adopted. Several programs were established to foster mortgage lending, construction and encourage home ownership. These programs include the Government National Mortgage Association (known as Ginnie Mae), the Federal National Mortgage Association (known as Fannie Mae) and the Federal Home Loan Mortgage Corporation (known as Freddie Mac). As a result of readily available funding for home mortgages, denial rate for conventional home purchase loans in 2002 and 2003 decreased to 14%, half of the denial rate in 1997 (Source: Federal Financial Institutions Examination Council, Press Release). The lax

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lending also helped the U.S. homeownership rate to peak with an all time high of 69.2% in 2004 (Source: U.S. Census Bureau.). As the demand for housing increased, the housing prices also soared. In 2004 and 2005, Arizona, California, Florida, Hawaii, and Nevada record price increases in excess of 25% per year.

Other than the government policies, the innovation in the home mortgage market also helped the growth of the home mortgage lending. Many mortgage products were introduce to the market, such as Adjustment Rate Mortgage (ARM), balloon loans, interest-only loans, piggy back loans. More importantly, the borrowers with poor credit in the conventional standard can now get loans in the subprime segment of the home mortgage market. The subprime market is meant to overcome the credit rationing (Stiglitz and Weiss 1981, Woosley 2004). However, there are evidence indicates that the probability of default is at least six times higher for subprime loans than prime loans, and more than 5 times more like to delinquent, and more than 10 times likely to be foreclosed (Chomsisengphet and Pennington-Cross 2006) .

Thirty years ago, the deposits of banks are the only source of the home mortgage lending. The banks kept the loan on its balance sheet until the loan was repaid. Nowadays, banks and other mortgage companies can originate loans, and they sell the loans to a third party in exchange for funding. The third party can be a government agency like Ginnie Mae, or Government-sponsored enterprises (GSEs) like Fannie Mae and Freddie Mac; or a private sector financial institution like Lehmen brothers. The third party then packages the mortgages and sells the payment rights to investors, whose gains are based on the payments of a collection of individual mortgages. The investors of the mortgage baked securities (MBS) can be individual investors or institutional investors like insurance companies, mutual fund companies, unit trusts, investment trusts, commercial banks, investment banks, pension fund managers, private banking organizations.

As broader investors get involved, derivatives like collateralized debt obligations (CDOs), and structured investment vehicles (SIVs) are invented, and more parties get involved. In the event of huge volume of mortgage defaults, the whole chain breaks down: the originators fail to collect the payment from the borrowers, hence profits of MBSs drop, investors sell out their bonds, the stoke holders of the third parties sell their stokes, the third parties who issues the MBSs face illiquidity. By 2008, the losses to bank capital were already in the range of $150 billion and a large number of specialized mortgage banking institutions had been sold or went bankruptcy (Kregel 2008).

3. Data Analysis

3.1 Data Resources

Our understanding of the home mortgage market is largely derived from the Home Mortgage Disclosure Act (HMDA) data. The home mortgage lending institutions include depository institutions such as commercial banks, saving and loan associations, and credit unions; and the non-depository institutions such as impendent mortgage companies and mortgage companies that are subsidiaries of commercial banks or Bank Holding Companies. It is estimated that the more than 8,800 lenders currently covered by the law account for approximately 80% of all home lending nationwide. For more analysis of HMDA coverage, see Bercovec and Zorn(1996).

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The data used in this paper come from three resources of HMDA: the Loan Application Register (LAR), the Institution Record, and the MSA Office Information. They contain detailed information of the loan applications, the applicants, the lender and its branch offices, for the time period of 2005-2008. Each observation in the data set is a loan application which may result in rejection or acceptance, and further origination. The interest rates spreads are reported if the difference between the annual percentage rate and the applicable treasury yield are equal or greater than 3 percentage point for the first lien loans. The loans with interest rates spreads reported are categorized as “subprime”. As a starting point, the bench mark sample includes the applications of conventional home purchase loans for 1- to 4-family housing units secured by first lien. The lenders include only state chartered commercial banks, which are identified by their regulatory authorities, i.e. FRS for the member banks and FDIC for the non-member banks. The mortgage banking subsidiaries of banks or Bank Holding Companies (BHCs) are excluded from the lenders because they are non-depositary institutions. Only complete applications initiated by a nature person with the banks are included. The applications withdrew by the applicants and the loans purchased by banks are excluded, as well as the loans initiated by a corporation, partnership or other entity that is not a nature person. The market is defined by MSA/MD. A loan is defined to be out-of-state if the state where the property locates differs from the state where the lender is chartered. The charter state of a bank is identified by the headquarter state according to the Institution Record. A loan is defined to be non-local if the bank who takes the loan application does not have a branch office in the MSA where the property that secures the loan locates. Compared to the in-state banks, out-of-state banks have little or no commitment to local prosperity, have higher cost of branching in the local market due to the regulation, yet they may be exempt from some of the state banking laws. Compared to the local banks, the non-local banks have no access to borrowers’ information through deposit and transaction accounts (Mester, Nakamura and Renault, 2007), and they cannot form up relationship lending with the local borrowers through in-person interactions.

3.2 Descriptive Analysis

Table I exploits the loan application information to understand the market share and origination of loan applications with different type of banks and lending channel. The total applications with in-state banks are around twice of the applications with out-of-state banks. Among the applications with in-out-of-state banks, more than two-thirds are local applications; while for the application with out-of-state banks, around two thirds are non-local application. The majority of the local applications are with in-state banks; however, more non-local applications are with out-of-state banks than with in-state banks.

The share of out-of-state non-local applications gets smaller over time, from around 30 percent in 2005 and 2006 to 16 percent in 2009; the share of out-of-state local is stable; the share of in-state applications increases. This implies that out-of-state non-local applications are sensitive to the time-varying shocks, such as the subprime crisis. The share of out-of-state non-local applications become loans (origination rate) is also sensitive to the shocks, decreasing from more than 70 percent in earlier years to 64

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percent in 2008, while the origination rate of other types of applications remain almost the same over years. The origination rate is the highest for in-state local applications and the lowest for out-of state non-local.

Table II describes the risk profile for the loans applications that eventually become loan originations. Both in-state and out-of-state non-local loans have higher subprime rate than local loans. Compared to other types of loans, the non-local loans from out-of-state banks have salient features such as significantly higher sales rate to the secondary market, higher subprime rate, higher interest rate spread, and smaller proportion of loans with co-applicants. The subprime rate of out-of-state non-local loans drops substantially over the years, while the rate remains relatively stable for the other types, narrowing the gaps between the two. The sales rates drop for all types but the out-of-state local loans, indicating that the secondary market activities are sensitive to the shock of the subprime crisis. However, the sales rate of the out-of-state non-local loans is still much higher than the other types in 2008.

In our research, we found a particular bank that originates a large amount of out-of-state non-local lending, of which a great proportion is subprime.1 To check whether this bank drives the stylized features mentioned above, we do the summary statistics in Table II without this bank. The results are shown in Table III. The sales rate and the subprime rate of out-of-state non-local loans become smaller, but are still significantly higher than the other types. Therefore we can conclude that the higher sales rate and higher subprime rate are not driven entirely by this particular bank.

Regardless the effect of this major bank in the out-of-state non-local lending market, the market has very different structure from the market for other type of loans. The market is concentrated on a few banks. The Herfindahl-Hirschman Index (HHI) for this market is more than 30 percent with the biggest bank, and more than 10 percent without this bank, while the HHI for in-state local market is less than one percent. Table IV provides a snapshot of the market concentration of the out-of-state non-local lending market in 2005, and the features of the top specialized banks. The top 13 banks account for 80 percent of total loans. Among those banks, the ones with smaller assets are more specialized in non-local lending, have higher proportion of the subprime loans, and sell much more loans to the secondary market, compared to those banks with larger total assets. Moreover, most of those banks are subsidiaries of Bank Holding Company. 3.3 Logit Model Estimations

The statistical results only compare the unconditional means of the subprime loan incidence, the sales incidence for the four types of loans. However, the subprime status and the sales status may be correlated with some observable characteristics of the loans and the borrowers. To control for those factors, we run pooled logit regressions to see the determinants of the incidence of subprime loans and sales to secondary market. Due to the possible self-selection into a particular type of loan based on unobervables, we do

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FREMONT INVESTMENT & LOAN, which originated 67.77% (48.83%) of the out-of-state non-local loans in 2005 (2006), and was closed in August, 2008. According to the Federal Deposit Insurance Corporation (FDIC) press release, "On March 8, 2007, the FDIC issued a cease and desist order against Fremont Investment & Loan, Brea, California (Bank), and its parent corporations, Fremont General Corporation and Fremont General Credit Corporation."

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not interpret the results here as causal.

Column 1 and column 2 in Table VI are the pooled logit estimations for the subprime status. The dependent variable is a discrete variable which takes value of one if the loan is subprime, and zero otherwise. The results report the marginal effects and the clustered standard errors. The baseline case is the in-state local loans. The marginal effects for the out-of-state non-local and in-state non-local indicators are both positive and significant, indicating that non-local loans from both in-state and out-of-state banks are more likely to be subprime loans. However, the marginal effect for out-of-state loans is bigger. It decreases from 12 percent in column 1 to 11.8 percent in column 2 after controlling for the occupancy of the property, the presence of co-applicants, the loan amount, the gender and ethnicity of the borrower, the neighborhood income, minority share and population. Both results account for state and year effects, the impact of the major out-of-state non-local bank, and the impact of the loans being in a border MSA market. Column 3 to column 5 in Table VI shows the sales regression. The dependent variable is a discrete variable which takes value of one if the loan is sold to the secondary market in the same year as the loan origination, and zero otherwise. The baseline case is again the in-state local loans. Column 3 is the simple regression, column 4 includes the controls as mentioned above, and column 5 includes interactions between subprime status and loan types as well as the major bank. The marginal effects for the out-of-state non-local loans are positive and significant, indicating that those loans are more likely to be sold to the secondary market. Moreover, the subprime loans from out-of-state non-local banks are more likely to be sold to the secondary market, compared to subprime loans originated by other groups. According to the result in column 5, the prime loans originated by out-of-state non-local banks are 20.1percent more likely to be sold to the secondary market compared to the ones originated by in-state local banks. The subprime loans originated by out-of-state non-local banks are 35.4 percent (20.1+15.3) more likely to be sold to the secondary market compared to the prime loans originated by in-state local banks, and 75.1percent (20.1+15.3+39.7) more likely than the subprime loans originated by in-state local banks. All results account for state and year effects, and the impact of the major out-of-state non-local bank, and the impact of the loans being in a border MSA market.

4. A Behavioral Interpretation and Empirical Evidence

Although there is growing understanding in the home mortgage lending practices as well as the literature that the non-local lending generally increases the loans prices, and the securitization of the home mortgage loans increases credit supply to low-income neighborhoods, there is no clear explanation of why non-local loans charge higher, and no evidences of how that relates to securitization. It is unclear whether the high prices are premium for the information asymmetry due to the distance between borrowers and lenders, or premium for high risks of lending to low-quality borrowers, or the discrimination of the non-local banks against credit-constraint borrowers. The securitization of the home mortgages in the secondary market is an extended source of funding for lending, however, it may give rise to irresponsible lending due to the limited information the buyers have about the loans quality. The study distinguishes those explanations.

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banks are more likely to be subprime, and more likely to be sold to the secondary market. Our hypothesis is the following. When applying a loan, most borrowers first consider those banks with local branches--if this fails, they next seek loans from non-local sources through brokers or in the internet. Thus, less-creditworthy and riskier applicants tend to end up with non-local loans after the screening of the local banks. Additionally, we think that banks have incentives to make non-local loans to low-quality applicants located in other states because they manage the risks by selling the majority of those loans to the secondary market.

4.1 The Local Lending Standard and the Non-local Credit Supply

To provide evidence of the searching and screening process, it is necessary to identify whether the local banks and non-local banks are serving different pools. One possible way is to investigate the relationship of current non-local credit supply and the past local supply. If it turns out the non-local banks mainly serve the neighborhoods where the past decline rates of the local banks were high, then it concludes that the non-local banks gets the residual demand after the local banks screen the potential applicants in the first applications .

Since banks’ branching decisions are largely endogenous to the neighborhood characteristics, the share of non-local lending is also endogenous. To deal with endogeneity problem, we constructed a panel data set of the neighborhoods to provide evidence of the above hypothesis. The HMDA loan applications are collapsed to the tract level for each year during 2005-2008. Each observation in the panel is a tract. The panel has several desirable features for the analysis. The tract fixed effect can control for any unobservable tract characteristics that can potentially correlated with the share of non-local lending, such as low income, high minority share, and borrower pools with poor credit. The aggregation to the tract level overcomes the main disadvantage of the HMDA data: the lack of credit information for individual applicants. At the tract level, however, unless there are immigrations or emigrations of large scale, the credit pool will remain almost the same over the short observation span. The year fixed effect can be used to control for any changes in the macroeconomics and housing policies that affect all tracts in a given year.

The pooled OLS and FE results are shown in Table VII. In the first two columns, the dependent variable is the share of loan applications with non-local banks in a given tract. In the third and forth columns, the dependent variable is the share of loan applications with out-of-state non-local banks in a given tract. The share as the dependent variable is less sensitive to the size of the tract. Given the dispersed distribution of the tract size, this measure is more desirable. The explanatory variable in interest is the lagged denial rate of the loan applications with local banks, which is perceived as a proxy for the lending standards implemented by the local banks. The lagged variable is used because the borrowers make their loan application decisions based on past information, and it takes some time for them to know the results of their initial applications and to response by reapplication if the initial ones were not approved.

The coefficients for the lagged local denial rate are positive and significant in all specifications. The FE estimators are smaller than the OLS estimators, implying that the tract fixed effect effectively controls for the unobservable factor in the tract level. The FE estimation results show that as the local denial rate in the previous year increases by

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one percent, the share of non-local loans will increase by 0.015 percent, and the share of of-state non-local loans will increase by 0.017 percent. The coefficient for the out-of-state non-local share regression is greater than the non-local share regression, implying the out-of-state non-local lending is more sensitive to the lending standards implemented by the local banks. No control variables are added to the four specifications because the FE specification can only estimate the time-varying variables, which are limited for the tracts. Moreover, the tract characteristics do not displace much variation across time in such short time span.

A nature result of the searching and screening process we proposed is that the demand faced by the local banks and the non-local banks are segmented in terms of true credit quality. Since the potential borrowers first apply to local sources, the local banks have the privilege to screen the relatively better qualified borrowers. The non-local banks get the residual demand after the screening of the local banks, and therefore the credit quality of non-local demand is lower than the quality of local demand.

4.2 The Sale Ability and the Out-of-state Non-local Lending Share

Additionally, we argue that banks have incentives to make non-local loans to such less creditworthy applicants in other states because they transfer the risk by selling most of their loans to the secondary market. If this is true, we expect that the banks that have better abilities or are more active in participating the secondary market have higher proportions of out-of-state non-local mortgages. The results in Table VIII support this hypothesis.

To measure the ability of a given bank to sell the loans to the secondary market, we use the sales rate of the bank in the previous year. This measure is a proxy for the bank’s credit rating, loan packing and selling skills, as well as the connection with the purchasers in the secondary market. We argued that the banks make loan originations based on its ability to handle the loan risks. That is, they originate high-risk out-of-state non-local loans only if they anticipate they can sell the loans to the secondary market based on the previous sales activities.

The HMDA loan origination data were aggregated to the bank level to construct a panel set. The influential bank, Fremont Investment & Loan, is no longer a concern in this analysis because it only accounts for one observation among more than 3000 thousand banks. The panel data exploits the bank fixed effect to control for any unobservable bank characteristics that correlates with the out-of-state non-local lending strategy, and the year fixed effect to control for shocks in a given year that influent all banks. Given that the out-of-state non-local lending business is highly concentrated in a few banks, we restrict the sample to banks which originate more than 10 loans in the sample and have a proportion of out-of-state non-local loans greater than 10 percent.

The outcome variable is the share of out-of-state non-local loans for a given bank. Both pooled OLS and FE specifications are used. Model 1 and model 2 do not use any control variables. Both OLS and FE estimators show the desirable signs, but only the OLS estimator is significant. Since the bank characteristics can change dramatically across years, we control for assets, loan counts, passed distribution of loan sales among different purchaser, and the subprime ratio in model 3 and model 4. Both OLS and FE estimators are positive and significant after adding the controls. We argue that the

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specifications with controls are more convincing because they control for the diverse features of different banks, and the time variation of the features. As the FE estimation in model 4 shows, the share of out-of-state non-local lending for a bank increases by 0.19 percent if the sales rate in the previous year increases by one percent.

The coefficient for banks’ total assets is negative and the coefficient for the banks’ total loan counts (including loans such as government backed loans, refinance loans, which are not included in the sample) is positive in both OLS and FE specifications. This is consistent with the statistics showed in Table IV. The banks who originate a greater proportion of out-of-state non-local loans are not necessarily the banks with big assets, thanks to the sales to the secondary market. And as the banks expand their home mortgage lending business, they involve more in the out-of-state non-local lending because it expands the market to the areas without bank branches.

5. Conclusions

This study explains the high subprime ratio for the out-of-state non-local loans by proposing a searching and screening process for the home mortgage loans. The story is the following. When applying for a loan, most borrowers first consider those banks with local branches--if this fails, they next seek loans from non-local sources through brokers or in the internet. The empirical evidences show that the non-local credit supply is greater in the neighborhoods where the borrowers get rejected by the local banks more often. This finding indicates that less-creditworthy and riskier applicants tend to end up with non-local loans because the local banks have the privileges to pick the borrowers of better qualities. Additionally, we explore the incentives to make non-local loans to low-quality applicants located in other states by investigating the market structure and the specialized banks. The empirical evidences show that banks with better abilities to sell the home loans in the past will originate a higher proportion of out-of-state non-local loans regardless the high risks.

The risky lending made by out-of-state non-local banks might be one contributing factor of the subprime crisis. Specifically, the secondary market provides bad incentive for lenders to originate low-quality loans; government might need to reinvestigate their regulation on interstate banking and brokers in the mortgage market. Explaining the salient features of subprime lending and secondary market activities that are related to out-of-state non-local lending can deepen our understanding of the subprime crisis and can contribute important insights on the ongoing regulation reforms.

For future studies, we would like to investigate the market structure in the home mortgage lending business by constructing a structural model. We would also like to look at the impact of governmental policies such as the Community Reinvestment Act (CRA), a federal law that requires banks to supply credit to low-income, minority neighborhoods, and the Affordable Housing Goals (HG) complied by the government sponsored enterprises (GSEs). By linking the HMDA data with local foreclosure data, we can also identify the true qualities of the non-local loans.

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11. Gerardi, K., A. H. Shapiro, and P. S. Willen, "Subprime outcomes: Risky mortgages, homeownership experiences, and foreclosures," Federal Reserve Bank of Boston Working Paper, (2007), 07-15.

12. Green, R. K., and S. M. Wachter, "The American mortgage in historical and international context," Journal of Economic Perspectives, 19 (2005), 93-114. 13. Hannan, T. H., "Changes in non-local lending to small business," Journal of Financial Services Research, 24 (2003), 31-46.

14. Keys, B. J., T. Mukherjee, A. Seru, and V. Vig, "Did Securitization Lead to Lax Screening? Evidence from Subprime Loans*," Quarterly Journal of Economics, 125 (2010), 307-362.

15. Kregel, J. A., "Changes in the US financial system and the subprime crisis," (2008).

16. Loutskina, E., and P. E. Strahan, "Securitization and the declining impact of bank finance on loan supply: Evidence from mortgage originations," Journal of Finance, 64 (2009), 861-889.

17. Mester, L. J., L. I. Nakamura, and M. Renault, "Transactions Accounts and Loan Monitoring," Review of Financial Studies, 20 (2007), 529.

18. Mian, A. R., and A. Sufi, "The consequences of mortgage credit expansion: Evidence from the 2007 mortgage default crisis," NBER working paper, (2008). 19. Petersen, M. A., and R. G. Rajan, "Does distance still matter? The information revolution in small business lending," The Journal of Finance, 57 (2002), 2533-2570. 20. Stiglitz, J. E., and A. Weiss, "Credit rationing in markets with imperfect information," The American economic review, 71 (1981), 393-410.

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Table I

Application and Origination by Bank Type

In-state banks Out-of-state banks All banks

Local Non-local Sub-total Local Non-local Sub-total Total Panel A: 2005-2008

Number of applications 890,282 360,444 1,250,726 223,642 486,035 709,677 1,960,403

Share of total application 0.45 0.18 0.64 0.11 0.25 0.36 1.00

Share of applications become loans 0.86 0.82 0.85 0.78 0.71 0.73 0.81 Panel B: 2005

Number of applications 266,145 105,018 371,163 63,609 169,261 232,870 604,033

Share of applications become loans 0.87 0.83 0.86 0.79 0.75 0.76 0.82 Panel C: 2006

Share of total application 0.43 0.17 0.60 0.10 0.30 0.40 1

Share of applications become loans 0.85 0.82 0.84 0.78 0.69 0.71 0.79 Panel D: 2007

Share of applications become loans 0.86 0.82 0.85 0.77 0.70 0.73 0.81 Panel E: 2008

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Table II

Characteristics of Originated Loans by Bank Type

In-state banks Out-of-state banks

Local Non-local local Non-local

Panel A: 2005

Share of sold loans 0.48 0.47 0.43 0.80

Share of subprime loans 0.12 0.19 0.12 0.67

Interest rate spread 4.71 4.53 4.48 5.69

Share with a co-applicant 0.55 0.59 0.51 0.36

Loan-to-income ratio 2.07 1.88 2.14 1.87

Loan amount(thousands) 168.98 143.24 199.36 152.78

Panel B: 2006

Panel C: 2007

Panel D: 2008

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Table III

Characteristics of Originated Loans by Bank Type (Without Fremont) In-state banks Out-of-state banks

Local Non-local local Non-local Panel A: 2005

Share of sold loans _{0.46 0.44 0.43 0.57}

Share of subprime loans _{0.08 0.14 0.12 0.18}

Interest rate spread _{4.44 4.26 4.48 4.94}

Share with a co-applicant _{0.57 0.60 0.51 0.52}

Loan-to-income ratio _{2.07 1.87 2.14 1.94}

Loan amount(thousands) _{164.68 136.32 199.36 172.52}

Panel B: 2006

Panel C: 2007

Panel D: 2008

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Table IV

Top Out-of-state Non-local Lenders of 2005

Rank Name State BHC

#OS NL Cum. share MSA * Total Assets OS-NL% Sales %** Subprime %*** 1 FREMONT INVESTMENT & LOAN CA 1 39705 56.74 10 9.91 79.51 98.01 92.14

2 REGIONS BANK AL 1 5562 64.68 96 49.21 31.77 5.99 28.66

3 BRANCH BANKING AND TRUST CO NC 1 2976 68.94 61 74.48 18.31 0.68 8.45

4 FIRST BANK MO 1 1181 70.62 20 8.71 49.75 71.5 77.26

5 AMSOUTH BANK AL 1 1123 72.23 48 49.71 12.03 3.43 53.51

6 RESOURCE BANK VA 1 919 73.54 3 1.16 65.04 19.7 97.77

7 REPUBLIC BANK MI 1 870 74.79 12 5.7 20.97 3.7 40.96

8 BANK OF THE WEST CA 1 758 75.87 63 38.77 42.7 2.52 8.64

9 FRANKLIN BANK SSB TX 0 723 76.9 5 3.48 68.02 23.02 57.72

10 BANCO POPULAR NORTH AMERICA NY 1 709 77.92 21 10.23 26.96 . 0 11 BANCORP SOUTH BANK MS 1 702 78.92 17 10.84 24.41 0.35 0.62

12 RENASANT BANK MS 1 544 79.7 4 1.44 31.26 6.02 35.5

13 FIRST INTERSTATE BANK MT 1 518 80.44 5 4.2 31.96 1.04 7.65 14 UNITED COMMUNITY BANK - GA GA 1 489 81.13 5 3.72 33.91 1.06 6.98

15 1ST MARINER BANK MD 1 482 81.82 1 1.2 36.74 9.95 88.15

16 GATEWAY BUSINESS BANK CA 1 474 82.5 2 0.22 28.11 11.16 96.32

17 CHITTENDEN TRUST COMPANY VT 0 401 83.07 2 3.17 42.89 0.78 50

18 BANKERS' BANK WI 1 353 83.58 1 0.25 65.61 1.92 100

19 SKY BANK OH 1 343 84.07 15 14.76 10.75 4.32 78.08

20 BANK OF BLUE VALLEY KS 1 280 84.47 1 0.65 95.89 2.79 100

#Abbreviations: OS-NL(out-of-state non-local loans). * Number of MSA markets.

**Share of subprime loans sold to the secondary market. ***Share of sold loans that are subprime.

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Table V

Loan Level Regression

Subprime status Sales status

Logit Logit Logit Logit Logit (1) (2) (3) (4) (5) 0.120** 0.118** 0.194** 0.193** 0.201** Out-of-state non-local (0.0430) (0.0413) (0.0687) (0.0697) (0.0689) 0.100*** 0.105*** -0.0178 -0.0119 0.0229* In-state non-local (0.00745) (0.00814) (0.0116) (0.0113) (0.0109) 0.00986 0.00878 0.00609 -0.0126 -0.0314 Out-of-state local (0.0463) (0.0435) (0.0417) (0.0431) (0.0406) 0.799*** 0.788*** 0.360*** 0.351*** 0.389*** Fremont (0.0241) (0.0271) (0.0460) (0.0496) (0.0427) 0.0194 0.00855 0.0477** 0.0540** 0.0585*** Border MSA (0.0122) (0.0110) (0.0173) (0.0166) (0.0169) -0.397 0.0246) 0.163 0.0554) 0.0632) .0699 .0250) 0.0822) *** Subprime ( ** Fremont*subprime ( Out-of-state non-local*subprime 0.153* ( -0 ** 0 In-state non-local*subpirme ( 0.156 Out-of-state local*subprime (

State dummies Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes

Controls Yes Yes Yes

N 1567367 1507404 1567367 1507404 1507404

Coefficients and Marginal effects reported for OLS and logit, respectively; Standard errors in parentheses.

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Table VI

The Local Lending Standard and the Non-local Credit Supply Non-local share Out-of-state non-local share

OLS FE OLS FE (1) (2) (3) (4) 0.0923*** 0.0146** 0.0891*** 0.0166** Lagged local denial rate _{(0.0046) (0.0055) (0.0043) (0.0053)} 2006 0.1172*** . 0.1134*** . (0.0021) . (0.0020) . 2007 0.0542*** -0.0353*** 0.0543*** -0.0379*** (0.0020) (0.0018) (0.0019) (0.0017) 2008 . -0.0755*** . -0.0797*** . (0.0021) . (0.0019) Constant 0.1942*** 0.3033*** 0.1459*** 0.2549*** (0.0018) (0.0015) (0.0016) (0.0014)

N 1.1e+05 1.1e+05 1.1e+05 1.1e+05

r2 0.0281 0.0236 0.0297 0.0294

rho 0.6016 0.6021

Each observation is a tract.

Clustered standard errors in parentheses. *

p < 0.05, **p < 0.01, ***p < 0.001

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Table VII

The Sale Ability and the Out-of-state Non-local Lending Share Share of Out-of-state Non-local

OLS FE OLS FE (1) (2) (3) (4) 0.0711** 0.0167 0.1036** 0.1881** (0.0269) (0.0490) (0.0378) (0.0653) Lagged sales rate 2006 0.0104 0.0101 0.0085 . (0.0116) (0.0111) (0.0145) . 2007 0.0214* 0.0127 . 0.0035 (0.0093) (0.0097) . (0.0110) 2008 . . -0.0009 0.0171 . . (0.0125) (0.0135) Assets -0.0013* -0.0014*** (0.0006) (0.0004) Loan count 0.0019*** 0.0007*** (0.0006) (0.0001) 0.0134 0.0579 (0.0251) (0.0533) Lagged GSE purchase 0.0337 -0.0229 (0.0627) (0.0260) Lagged private securitization 0.1868* 0.0339 (0.0918) (0.0273) Lagged affiliation purchase 0.1908** -0.0268 (0.0587) (0.0777) Lagged subprime ratio _cons 0.1884*** 0.2102*** 0.1358*** 0.1105* (0.0109) (0.0180) (0.0217) (0.0476) N 669.0000 669.0000 407.0000 407.0000 r2 0.0318 0.0079 0.1968 0.1175 rho 0.7554 0.8639

Each observation is a state-chartered commercial bank, mortgage banking subsidiaries excluded. The sample includes the banks that have out-of-state shares greater than 10% and originate more than 10 loans in a given year.

Clustered standard errors in parentheses. *