Federal Reserve

BULLETIN

A Profile of the 2011 HMDA Data

For 2011, a total of 7,632 institutions reported on their home-lending activity under HMDA: 4,497 banking institutions; 2,017 credit unions; and 1,118 mortgage companies, 812 of which were not affiliated with a banking institution (these companies are referred to in this article as "independent mortgage companies") (table 1). The number of reporting institutions changes some from year to year. Some of the fluctuation is due to changes in reporting requirements, primarily related to increases in the minimum asset level used to determine coverage.⁹ Mergers, acquisitions, and failures also account for some of the year-over-year changes. Finally, periodic changes in the number and geographic footprints of metropolitan areas influence reporting over time, as HMDA's coverage is limited to institutions that have at least one office in an MSA. For 2011, the number of reporting institutions fell nearly 4 percent from 2010, continuing a downward trend since 2006, when HMDA coverage included just over 8,900 lenders.¹⁰

Reporting Institutions by Size and Mortgage Lending Activity

Most institutions covered by HMDA are small, and most extend relatively few loans. For 2011, 57 percent of the depository institutions (banking institutions and credit unions) covered by HMDA had assets under $250 million, and 76 percent of them reported information on fewer than 100 loans (data derived from table 2). Among all depository institutions, nearly 55 percent reported on fewer than 100 loans. Across different types of lenders, mortgage companies tend to originate larger numbers of loans on a per-reporter basis than the other institutions (38 percent of the mortgage companies reported more than 1,000 loans, a share equal to about six times that for depository institutions).

In the aggregate, reporting institutions submitted information on 11.7 million applications for home loans of all types in 2011 (excluding requests for preapproval), down about 10 percent from the total reported for 2010 and far below the 27.5 million applications processed in 2006, just before the housing market decline (data derived from table 3.A). The majority of loan applications are approved by lenders, and most of these approvals result in extensions of credit. In some cases, an application is approved but the applicant decides not to take out the loan; for example, in 2011, about 5 percent of all applications were approved but not accepted by the applicant (data not shown in tables). Overall, about 60 percent of the applications submitted in 2011 resulted in an extension of credit (data derived from tables 3.A and 3.B), a share little changed from 2010. The total number of loans reported in 2011, 7.1 million (as shown in table 3.B), was about 10 percent lower than in 2010 and is the lowest number of mortgage loans reported under HMDA since about 6.2 million loans were reported in 1995 (data prior to 2000 not shown in tables).

The HMDA data also include information on loans purchased by reporting institutions during the reporting year, although the purchased loans may have been originated at any point in time. For 2011, lenders reported information on 2.9 million loans that they had purchased from other institutions, a decline of nearly 9 percent from 2010. Finally, lenders reported on roughly 186,000 requests for preapproval of home-purchase loans that did not result in a loan origination (table 3.A); preapprovals that resulted in loans are included in the count of loan extensions cited earlier.

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Home-Purchase and Refinance Lending

In June 2006, the peak month for home-purchase lending that year, nearly 712,000 home-purchase loans were extended, compared with only 254,000 such loans in June 2011, the most active month that year (figure 1).¹¹ On an annual basis, the number of home-purchase loans (including both first and junior liens) reported in HMDA in 2011 was down about 5 percent from 2010 and was 64 percent lower than in 2006 (data derived from table 3.B).

One factor that may help explain the drop in home-purchase lending between 2010 and 2011 is the ending of the first-time homebuyer tax credit program in April 2010.¹² The first-time homebuyer tax credit program likely stimulated homebuying in the first half of 2010 as individuals sought to purchase their homes before the sunset date.¹³ Data from the National Association of Realtors (NAR) support this view: The NAR annual survey of home buyers and sellers indicates that first-time buyers accounted for about 47 percent of all home purchases in 2009 and half of home sales in 2010 before falling to a 37 percent share in 2011.¹⁴

To a greater extent than for home-purchase borrowing, the volume of refinance lending over time generally follows the path of interest rates (typically with a fairly short lag), expanding as mortgage rates fall and retrenching when rates rise. The interest rate environment over the past few years has generally been quite favorable for well-qualified borrowers who have sought to refinance. In some cases, the same individuals have refinanced on more than one occasion to take advantage of the declining interest rate environment. However, many other individuals with outstanding loans have not been able to refinance, either because they could not meet income-related or credit-history-related underwriting standards or because of collateral-related issues, including situations where the outstanding balance on the loan exceeds the home value.¹⁵

Compared with 2010, the number of reported refinance loans in 2011 was down about 13 percent (table 3.B). Although the total volume of refinancing in 2011 was down quite a bit from 2010, lenders experienced much higher demand in some months than others. In 2011, the peak month for refinance issuance was November, with nearly 504,000 loans, compared with only 230,000 loans in May (figure 1). The surge in refinance activity toward the end of 2011 reflects the steady drop in mortgage rates over the course of the year, which by November and December saw annual percentage offer rates on 30-year fixed-rate loans dip to about 4 percent.

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Non-Owner-Occupant Lending

Individuals buying homes either for investment purposes or as second or vacation homes are an important segment of the housing market in general, and in some areas of the country, they are particularly important. In the current period of high foreclosures and elevated levels of short sales, investor activity helps reduce the overhang of unsold and foreclosed properties. In some cases, investors or second-home buyers are able to purchase their properties for cash; in other cases, they choose to borrow and finance their purchases. Surveys sponsored by the NAR find that in 2011, about half of investors paid cash for their purchases and 42 percent of vacation-home buyers paid cash for their properties.¹⁶

The HMDA data help document the role of non-owner-occupant lending over time. The data show a sharp increase in non-owner-occupant lending used to purchase one- to four-family homes (site-built and manufactured properties) during the first half of the previous decade (table 4). The volume of non-owner-occupant lending fell sharply beginning in 2007 and has remained at comparably low levels through 2011. Although non-owner-occupant lending in 2011 remained subdued compared with levels reached in the middle of the previous decade, such lending did pick up from 2010, increasing nearly 10 percent.

As shown in table 4, the post-2007 decline in non-owner-occupant lending has been more severe than that in owner-occupant lending. Between 2000 and 2005, the share of non-owner-occupant lending used to purchase one- to four-family homes rose, increasing over this period from about 9 percent to 16 percent (data derived from table 4).¹⁷ The share fell to about 11 percent in both 2009 and 2010 but rebounded to 13 percent in 2011.

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Conventional versus Government-Backed Loans

Although the total number of home-purchase loans has fallen substantially since 2005, virtually all of the decline has involved conventional lending; the volume of nonconventional home-purchase loans (sometimes referred to as "government backed" loans)--including loans backed by insurance from the FHA or by guarantees from the VA, the Farm Service Agency (FSA), or the Rural Housing Service (RHS)--has increased markedly since the mid-2000s. From 2006 to 2009, the total number of reported conventional home-purchase loans fell 77 percent, while the number of nonconventional home-purchase loans more than tripled (table 4). Although the number of nonconventional home-purchase loans has fallen since reaching its high mark in 2009, such loans still accounted for about 43 percent of home-purchase lending in 2011. The increase in nonconventional lending in recent years reflects several factors, such as increased loan-size limits allowed under the FHA and VA lending programs and reduced access (including more-stringent underwriting and higher prices) to conventional loans, particularly those that allow the borrower to finance more than 80 percent of the property value.¹⁸

Nonconventional lending has also garnered a larger share of the refinance market. In 2006, only 2 percent of refinance loans were nonconventional, compared with 12 percent in 2011. This share dropped some from 2010, as the number of nonconventional refinance loans fell about 21 percent (table 4).¹⁹

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The Private Mortgage Insurance Market

In the conventional loan market, lenders typically require that a borrower seeking to purchase an owner-occupied property make a down payment of at least 20 percent of a home's value unless the borrower obtains some type of third-party backing, such as mortgage insurance. For a borrower seeking a conventional loan with a low down payment, a lender can require that the borrower purchase mortgage insurance from a private mortgage insurance (PMI) company to protect the lender against default-related losses up to a contractually established percentage of the principal amount. As a form of protection for lenders against losses from defaulting borrowers, PMI competes with FHA insurance and VA loan guarantees.

The seven companies that reported data for 2011 dominate the PMI industry.²⁰ Thus, the reported data cover the vast majority of PMI written in the United States. For 2011, the seven PMI companies reported on nearly 409,000 applications for insurance leading to the issuance of 312,000 insurance policies, up from about 370,000 applications and 260,000 policies in 2010 (data derived from table 6). Reported volumes of PMI issuance in 2011, as in recent years, have been substantially smaller than levels prior to 2009. The large reduction in PMI issuance reflects several factors, including tighter underwriting adopted by the PMI companies in response to elevated claims and losses experienced during the recent recession and the ongoing recovery.²¹

Overall, 64 percent of the PMI policies issued in 2011 covered home-purchase loans, and the remainder covered refinance mortgages (home-improvement loans are classified as refinance loans by the PMI reporters). Virtually all of the applications for PMI policies issued involved loans to purchase site-built properties, and almost all of the applications for PMI related to owner-occupied units.

The data reported by the PMI industry over the years have consistently shown that most applications for insurance are approved, as lenders are very familiar with the underwriting policies of the insurers and generally are not going to submit an application that is unlikely to be approved. Overall, about 5 percent of PMI insurance applications were denied in 2011, down from about 10 percent in 2010 and 12 percent in 2009 but still notably higher than in 2006 and 2007, when only about 2 percent of the requests for insurance were turned down (data not shown in tables).²² As with the HMDA data, PMI companies report the reason for denial. The most commonly reported reason cited by lenders related to an issue with the collateral, most likely property value.

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Junior-Lien Lending

Junior-lien loans can be taken out either in conjunction with the primary mortgage (a piggyback loan) or independently of the first-lien loan. As noted, piggyback loans can be used by borrowers to avoid having to pay for private or government mortgage insurance. Similarly, piggyback loans can also be used to reduce the size of the first-lien loan to be within the size limits required by Freddie Mac or Fannie Mae without requiring a larger down payment by the borrower. Junior-lien loans that are taken out independently of a first lien can be used for any number of purposes, including to finance home-improvement projects or, in the case of open-ended home equity lines of credit, to provide a readily available source of credit that can be drawn on at the time the borrower needs the funds. Under the regulations that govern HMDA reporting, most of these standalone junior-lien loans are not reported.²³

In 2006, close to 1.3 million junior liens used for the purchase of owner-occupied properties were reported under HMDA (table 7). This number fell by more than one-half in 2007, dropped sharply again in each of the ensuing years, and decreased to less than 42,000 such loans in 2010 and 2011. More than 1 million junior-lien loans were taken out to refinance loans backed by owner-occupied properties in 2006, and this number also fell substantially starting in 2007 and continued to fall, reaching a low point of less than 74,000 in 2011.

The HMDA data also include information on junior-lien loans used for home-improvement purposes. In 2011, nearly 66,000 junior-lien loans were used for such a purpose, down some from about 80,000 reported in 2010. Both the 2010 and 2011 totals are sharply below the historic high mark of nearly 570,000 reached in 2006. Overall, junior-lien loans used for home improvement accounted for 35 percent of junior-lien loans reported under HMDA.

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Loan Sales

For each loan origination reported under HMDA in a given year, lenders report whether that loan was sold during the same year, and the type of institution to which the loan was sold.²⁴ Broadly, these purchaser types can be broken into those that are government related--Ginnie Mae, Fannie Mae, Freddie Mac, and Farmer Mac--and those that are not. Ginnie Mae and Farmer Mac focus on loans backed directly by government guarantees or insurance, while Fannie Mae and Freddie Mac purchase conventional loans that meet certain loan-size and underwriting standards.

Overall, about 78 percent of the first-lien home-purchase and refinance loans for one- to four-family properties originated in 2011 were reported as sold during the year (data not shown in tables). The share of originations that are sold varies some from year to year and by type and purpose of loan (table 8).²⁵ For example, 69 percent of the conventional loans extended in 2011 for the purchase of owner-occupied one- to four-family dwellings were sold that year. In contrast, nearly 94 percent of the nonconventional loans used to purchase owner-occupied homes were reported as sold in 2011. The share of conventional loans made to non-owner occupants that are reported as sold is notably smaller than that of such loans made to owner occupants. Also, the vast majority of conventional loans extended for the purchase of manufactured homes are held in portfolio; only about 10 percent of such loans were sold in 2011.

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Borrower Incomes and Loan Amounts

Under HMDA, lenders report the loan amount applied for and the applicant income that the lender relied on in making the credit decision, if income was considered in the underwriting decision. Lenders do not necessarily collect and report loan applicants' entire income, because in some cases borrowers have more income than is needed to qualify for the loan.

Borrower Income

The vast majority of loan applications and loans reported under HMDA include income information. For example, in 2011, income information was not reported for less than 1 percent of the borrowers purchasing a home with a nonconventional loan and for 3 percent of those using a conventional loan (data not shown in tables). Income information is reported less often for refinance loans, particularly those that are nonconventional (about one-third of the FHA loans and 63 percent of the VA loans), most likely because of streamlined refinance programs that do not require current income to be considered in underwriting.

While the available information on amounts borrowed and applicant income can be evaluated in many ways, we focus here on patterns by loan product and purpose. For home-purchase or refinance lending, borrowers using FHA and VA loans have lower mean or median incomes than borrowers using other loans, despite the fact that the FHA (and VA) loan limits were increased substantially in 2008, potentially allowing the program to be used much more widely than by the LMI households that have been the traditional focus of the program (table 9). Although the share of FHA home-purchase borrowers with incomes above $100,000 has roughly doubled since 2007 (the year before the increase in loan limits) to about 15 percent, the median income of borrowers getting FHA home-purchase loans was still about 30 percent lower than that of those getting conventional loans (data derived from table 9). The relatively low down-payment requirements on FHA-insured loans--the average loan-to-value ratio for FHA home-purchase loans was over 95 percent in 2011--may be continuing to attract lower-income borrowers.²⁶

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Loan Amounts

Unlike the data on borrower incomes, loan amounts are provided for all applications and loans reported in the HMDA data. Loan amounts differ across loan types, with FHA or VA loans, on average, being smaller than conventional loans (which make up most of the "other" category in table 10). However, an upward shift in the distribution of loan amounts for both FHA and VA home-purchase loans has occurred in the past couple of years, continuing into 2011 (data for only 2011 shown in tables). The shift reflects several factors, including the higher loan limits allowed under these programs.

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Application Disposition, Loan Pricing, and Status under the Home Ownership and Equity Protection Act

In tables 11 and 12, we categorize every loan application and request for preapproval reported in 2011 into 25 distinct product categories characterized by type of loan and property, purpose of loan, and lien and owner-occupancy status. Each product category contains information on the number of total and preapproval applications, application denials, originated loans, loans with prices above the reporting thresholds established by HMDA reporting rules for identifying higher-priced loans, loans covered by the Home Ownership and Equity Protection Act of 1994 (HOEPA), and the mean and median annual percentage rate (APR) spreads for loans reported as higher priced.

Disposition of Applications

As noted, the 2011 HMDA data include information on 11.7 million loan applications, nearly 86 percent of which were acted on by the lender (data derived from table 11). With respect to the disposition of applications, patterns of denial rates are largely consistent with what had been observed in earlier years.²⁷ Denial rates on applications for home-purchase loans are notably lower than those observed on applications for refinance or home-improvement loans. Denial rates on applications backed by manufactured housing are much higher than those on applications backed by site-built homes. For example, the denial rate for first-lien conventional home-purchase loan applications for owner-occupied site-built properties was 14.8 percent in 2011, compared with a denial rate of 52.7 percent for such applications for owner-occupied manufactured homes.

Under the provisions of HMDA, reporting institutions may choose to report the reasons they provide consumers whose applications are turned down. Reporting institutions may cite up to three reasons for each denied application, although most of those that provide this information cite only one reason. An analysis of the reasons for denial provided to prospective borrowers whose applications for conventional credit for the purchase of owner-occupied homes were turned down finds that collateral-related issues and debt-to-income considerations were the two categories of reasons that have seen the largest increase since 2006 (data not shown in tables). Debt-to-income issues were also cited somewhat more often for applications for FHA or VA home-purchase loans, but collateral was the category that had the largest percentage increase. These relationships are not surprising, given the changes in underwriting practices and the widespread decline in home values since 2006.

In addition to the application data provided under HMDA, nearly 430,000 requests for preapproval were reported as acted on by the lender in 2011, down about 3 percent from 2010 (table 12). The majority of requests for preapprovals involved conventional loans. About 30 percent of these requests for preapproval were denied by the lender in 2011, a proportion that is higher than in 2010. Not unexpectedly, the number of requests for preapproval is down substantially from the levels recorded at the height of the housing boom, when market conditions favored home sellers and preapproval letters were a factor that enhanced the position of prospective homebuyers. In 2006, covered institutions reported that they received nearly 1.2 million requests for preapproval on which they took action (data not shown in tables).

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The Incidence of Higher-Priced Lending

Price-reporting rules under HMDA since late 2009 define higher-priced first-lien loans as those with an APR of at least 1.5 percentage points above the average prime offer rate (APOR) for loans of a similar type (for example, a 30-year fixed-rate mortgage).²⁸ The spread for junior-lien loans must be at least 3.5 percentage points to be considered higher priced. The APOR, which is published weekly by the FFIEC, is an estimate of the APR on loans being offered to high-quality prime borrowers based on the contract interest rates and discount points reported by Freddie Mac in its Primary Mortgage Market Survey (PMMS).²⁹

The data show that the incidence of higher-priced lending across all products in 2011 was about 3.7 percent, up about 50 basis points, or 0.5 percentage point, from 2010 (table 11).³⁰ The incidence varies across loan types, products, and purposes. First, in almost all cases, nonconventional loans have a lower incidence of higher-priced lending than do comparable conventional loan products, although the differences in incidence are much smaller than in the period when many conventional loans were subprime or near prime. In 2011, among first-lien home-purchase loans for site-built homes, 3.9 percent of conventional loans had APRs above the price-reporting threshold, versus 2.8 percent of nonconventional loans. (Among nonconventional loans, those backed by VA guarantees have a particularly low incidence of being higher priced: In 2011, less than 0.04 percent of the VA-guaranteed first-lien home-purchase loans were higher priced.)

Second, with few exceptions, first-lien loans have a lower incidence of higher-priced lending than do junior-lien loans for the same purposes. For example, in 2011, the incidence of higher-priced lending for conventional first-lien refinance loans was 1.6 percent, whereas for comparable junior-lien loans it was 13.4 percent. This relationship is found despite the fact that the threshold for reporting a junior-lien loan as higher priced is 2 percentage points higher than it is for so reporting a first-lien loan. Third, manufactured-home loans exhibit the greatest incidence of higher-priced lending across all loan categories. For 2011, nearly 82 percent of the conventional first-lien loans used to purchase manufactured homes were higher priced.

The HMDA data also show that the incidence of higher-priced lending is related to borrower incomes and the amounts borrowed, with borrowers with lower incomes and those receiving smaller loans more likely to obtain a higher-priced loan. For example, 56 percent of home-purchase loans were extended to borrowers with incomes under $75,000, while such borrowers account for 72 percent of all higher-priced home-purchase loans (table 9). Across loan amounts, 19 percent of home-purchase loans were under $100,000, whereas 45 percent of higher-priced home-purchase loans were under $100,000 (table 10).

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Rate Spreads for Higher-Priced Loans

In 2011, the mean APOR spread reported for higher-priced first-lien conventional loans for the purchase of an owner-occupied site-built home was about 2.5 percentage points, compared with about 2.0 percentage points for higher-priced first-lien nonconventional loans used for the same purpose (table 11). Average spreads for first-lien conventional and government-backed refinance loans were 2.5 percentage points and 2.6 percentage points, respectively.

It is worth noting that the vast majority of nonconventional loans reported as higher priced in 2011 exceeded the HMDA price-reporting thresholds by only a small amount: Specifically, 71 percent of the higher-priced nonconventional first-lien home-purchase loans had reported spreads within 50 basis points of the threshold. By comparison, only about 42 percent of the comparable conventional loans reported as higher priced had prices this close to the margin of reporting. In contrast, the share of higher-priced nonconventional refinancing loans with APORs close to the margin of reporting (32 percent) is a little less than the share of higher-priced conventional refinancing loans with such APORs (about 47 percent).

As expected, consistent with the higher reporting threshold of junior-lien lending, higher-priced junior-lien loan products have higher mean and median APOR spreads than do higher-priced first-lien loans. Higher-priced loans for manufactured homes differ from other loan products in that they generally have the highest mean spreads. In 2011, the typical higher-priced conventional first-lien loan to purchase a manufactured home had a reported spread of about 5.7 percentage points, compared with an average spread of roughly 2.5 percentage points for comparable higher-priced loans for site-built properties.

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HOEPA Loans

The HMDA data indicate which loans are covered by the protections afforded by HOEPA. Under HOEPA, certain types of mortgage loans that have interest rates or fees above specified levels require additional disclosures to consumers and are subject to various restrictions on loan terms.³¹ For 2011, 574 lenders reported extending 2,387 loans covered by HOEPA (table 11; data regarding lenders not shown in tables). In comparison, 655 lenders reported on about 3,400 loans covered by HOEPA in 2010. In the aggregate, HOEPA-related lending made up less than 0.05 percent of all the originations of home-secured refinancings and home-improvement loans reported for 2011 (data derived from tables).³²

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Lender Concentration in the Mortgage Market

Recent press accounts have highlighted the outsized role of a few larger lending organizations in the mortgage market.³³ Table 13 lists the top 10 mortgage originating organizations (inclusive of their reporting mortgage lending affiliates and subsidiaries) according to the HMDA data. Wells Fargo tops the list, having originated over 900,000 loans in 2011, which translates into a market share of about 13 percent.³⁴ JPMorgan Chase and Bank of America each had a market share of over 5 percent, followed by U.S. Bank and Quicken Loans with over 2 percent. Wells Fargo, JPMorgan Chase, and Bank of America had considerably larger market shares in 2011 than in 2006, in part because of their acquisitions of Wachovia, Washington Mutual, and Countrywide, respectively. The remainder of the top 10 organizations had market shares under 2 percent, and the top 10 collectively issued about 37 percent of all mortgage originations reported in the HMDA data in 2011, roughly the same as in 2006.

Notably, market shares derived from the HMDA data differ markedly from market shares recently reported in the press based on information compiled by Inside Mortgage Finance. It is important to note that for HMDA reporting purposes, institutions report only mortgage applications in which they make the credit decision. Under HMDA, if an application is approved by a third party (such as a correspondent) rather than the lending institution, then that party reports the loan as its own origination and the lending institution reports the loan as a purchased loan. Alternatively, if a third party forwards an application to the lending institution for approval, then the lending institution reports the application under HMDA (and the third party does not report anything). In contrast, Inside Mortgage Finance considers loans to have been originated by the acquiring institution even if a third party makes the credit decision. Thus, many of the larger lending organizations that work with sizable networks of correspondents report considerable volumes of purchased loans in the HMDA data, while Inside Mortgage Finance considers many of these purchased loans to be originations.

To be sure, both market share numbers are important for understanding the supply side of the mortgage market. The HMDA data, by focusing on the entity that makes the approval decision, highlight that the mortgage market continues to be highly decentralized along certain dimensions, with a large number of relatively small entities operating at the retail level, working with mortgage applicants, evaluating their applications, and making lending decisions. That said, overall credit availability and pricing depend on a multitude of additional factors, such as government-sponsored enterprise and FHA practices, lenders' willingness and ability to take risk, competition between wholesale lenders, and general credit conditions and investor appetite for risk.

Table 13 shows that among the top 10 organizations, many of them reported a large number of purchased loans in 2011, particularly Wells Fargo, Bank of America, and Ally Financial. As discussed earlier, many of these purchases are likely to be from correspondents, though it is not possible from the HMDA data to determine how many. It is also worth noting that organizations often turn around and resell loans that they purchased (see last two columns of table 13).

Finally, the HMDA data indicate that the business strategies among the top 10 organizations appear to vary considerably. For example, around 30 percent of Wells Fargo's and Bank of America's originations were for home-purchase loans, compared with less than 10 percent for JPMorgan Chase and Quicken Loans. Citigroup and Ally Financial concentrated relatively more heavily on refinance loans than on home-purchase loans. These institutions also differ considerably in terms of the fraction of loans held in portfolio beyond the year of origination.³⁵ For example, U.S. Bancorp and Citigroup each held in portfolio 40 percent or more of the conventional loans they originated, compared with less than 10 percent for Wells Fargo and JPMorgan Chase. The HMDA data also reveal considerable variation across these larger lenders in the types of loans (conventional compared with FHA, VA, or FSA) they tend to extend. For example, about half of the home-purchase loans reported by Wells Fargo were conventional, whereas about 90 percent of those originated by Citigroup were of this type.

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The Credit Scores of Home-Purchase Mortgage Borrowers

Additional information about individuals obtaining mortgages to purchase homes can be gained by a review of credit record data collected by credit-reporting agencies. These data can be used to identify individuals taking out mortgages to finance a home purchase and, among these, individuals who are first-time homebuyers. Because the credit record data used here include the credit scores of individuals, we can use this metric to gauge the credit risk profile of home-purchase borrowers.

The data are from the FRBNY/Equifax Consumer Credit Panel. The panel is a nationally representative longitudinal database of individuals with detailed information, at a quarterly frequency beginning in 1999, on consumer and mortgage debt and loan performance drawn from the credit records collected and maintained by Equifax, one of the three national credit bureaus.³⁶ The data include three key pieces of information with respect to this analysis: (1) details on each mortgage outstanding for a given consumer, including the year of origination; (2) each consumer's credit score as of the end of each quarter; and (3) each consumer's residential location at the level of the census block (a subunit of a census tract).³⁷ The data used here are through the end of 2011.

Home-purchase loans are not explicitly identified in credit record data, but the panel nature of the data used here allows us to follow a given individual over time and infer whether that borrower purchased a home during any particular period. Specifically, we classify an individual as a homebuyer if the credit record indicates that he or she took out a new mortgage and moved to a different location (the credit record shows that the individual moved from one census block to another). First-time home-purchase borrowers are identified in a similar manner, but their credit records must show no evidence of a previous mortgage. The credit record data show that for home-purchase borrowers in general, as well as for first-time homebuyers financing their purchase, the median credit score has increased about 40 points since 2006. Furthermore, median scores now exceed by a considerable margin the median scores for home-purchase borrowers at any time in the past 12 years (figure 2).

From the perspective of changes in access to credit, a particular group to focus on is that consisting of individuals with scores in the bottom decile of all home-purchase borrowers. Here the data show that the score that delineates the bottom decile has increased nearly 50 points since the end of 2006. Individuals with scores below this increased threshold are likely to have a very difficult time qualifying for credit and, if they manage to qualify for a loan, are likely to pay higher prices. Consistent with this observation, overall, the share of home-purchase borrowers with scores below 620, a traditional demarcation line for individuals who are typically characterized as having a credit history that would be considered subprime, fell from about 19 percent of borrowers at the end of 2006 to about 7 percent at the end of the third quarter of 2011 (data not shown in tables).

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Lending across Population Groups and Neighborhoods

One of the strengths of the HMDA data is that the annual data can be merged to track changes in lending activity across population groups and areas. In this section, we show changes in lending, from 2010 to 2011, to borrowers sorted by income, race, or ethnicity and by the income or minority population characteristics of the areas where they reside. We also present an analysis of lending in areas characterized by their degree of economic distress.

Changes in Lending, 2010 to 2011

As noted earlier, both home-purchase and refinance lending fell from 2010 to 2011. Virtually all population segments experienced these declines, although the falloff in activity was more severe for some groups than for others (table 14, memo items). ³⁸ Across racial or ethnic groups, all minority populations except Hispanic whites experienced relatively large declines in activity; Hispanic whites and non-Hispanic whites both experienced relatively smaller declines in activity. Lower-income borrowers, those purchasing homes in lower-income census tracts, and those residing in areas with larger minority populations also experienced relatively large reductions in home-purchase lending.

Patterns for refinancing differed from those for home-purchase lending, as the largest declines were among non-Hispanic whites, middle- and higher-income borrowers, and those residing in areas with smaller shares of minorities and populations with relatively higher incomes. The only group to experience an increase in refinance lending was low-income borrowers; refinance lending to this population segment increased about 3 percent from 2010 to 2011.

Populations differ considerably in their use of various loan products. Most notably, black, Hispanic white, and lower-income borrowers, and those residing in areas with larger shares of minority populations, use nonconventional loans to purchase homes to a greater extent than other groups. Greater reliance on nonconventional loans may reflect the relatively low down-payment requirements of the FHA and VA lending programs. The HMDA data indicate that all groups were a little less dependent on nonconventional loans in 2011 than in 2010. Reduced reliance on nonconventional loans occurred for both home-purchase and refinance lending.

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Credit Circumstances in Distressed Neighborhoods

Since the start of the housing downturn, access to mortgage credit has been an acute public policy concern, particularly for households with lower incomes or in neighborhoods that have been hardest hit by foreclosures. Mortgage originations have declined broadly since 2005, and, as we discussed in the review of last year's HMDA data, these declines have been greater in highly distressed neighborhoods. To determine if credit has yet begun to flow more freely in such neighborhoods, we use the HMDA data to compare mortgage credit flows from 2010 to 2011.

As in last year's review, we identify distressed neighborhoods using the scores produced by the Department of Housing and Urban Development (HUD) for the NSP.³⁹ The NSP was created by the Housing and Economic Recovery Act of 2008 to provide funds for state and local governments seeking to support neighborhoods with high levels of property abandonment and foreclosure. In deciding which neighborhoods to target, HUD uses a statistical model that estimates the likelihood that the neighborhood is experiencing high rates of foreclosure and mortgage delinquency. The outputs of this model are used to assign to each tract an NSP score ranging from 1 to 20, with a higher score indicating a greater likelihood of distress and with the scores scaled so that each score point is given to 5 percent of census tracts. While an evaluation of the success of the NSP itself is well beyond the scope of this article, we can use these scores to classify census tracts according to the degree of distress they face.

The change from 2010 to 2011 in home-purchase lending for owner-occupied properties, broken down by quintiles of the NSP score, is shown in table 15. Lending declined 7.2 percent overall, though the declines were substantially greater in high-distress neighborhoods. In tracts with NSP scores of 17 to 20, home-purchase lending decreased 13.8 percent, compared with 3.3 percent in tracts with NSP scores below 5. The steeper decline in mortgage credit flows to highly distressed areas continues a trend that has been observed since the onset of the housing market downturn.

Differences in the extent of decline are also observed across borrower income levels. Lending fell more substantially for lower- and middle-income borrowers (12.3 percent and 11.3 percent, respectively) than it did for high-income borrowers (3.8 percent). Indeed, for high-income borrowers, the decline in lending appears unrelated to the degree of neighborhood distress, as indicated by the nonmonotonic relationship between lending declines and NSP score quintile. However, for lower- and middle-income borrowers, the decreases were notably larger when neighborhood distress increased. Somewhat surprisingly, lending to middle-income borrowers fell more than it did for lower-income borrowers in the bottom three quintiles of the NSP score (scores of 1 to 12). In tracts with NSP scores above 12, lending to lower-income borrowers fell off by a larger percentage than it did for high-income borrowers.

Attributing these declines to supply- or demand-side factors is not straightforward. As shown in table 15, the number of applications for home-purchase loans fell by slightly more than the number of loan originations, a pattern that holds for almost all NSP quintiles. The sharper decline in applications suggests that reduced mortgage flows may primarily reflect a drop in demand; however, since potential applicants may have foregone applying because they suspected their application would be denied, the sharper fall in applications is insufficient to prove that these declines represent demand-side factors alone. Most likely, these changes reflect a combination of changes in supply and demand.

One supply factor that may be influencing how mortgage credit is flowing is the mix of lenders extending credit. In percentage terms, the largest changes involved thrift institutions, whose lending fell by almost one-fourth in 2011, and credit unions, whose lending increased by over 8 percent. While these institution types accounted for only a small share of lending in 2011 (13 percent; data not shown in table), in neither case was there a clear relationship between the change in lending and the degree of neighborhood distress. Instead, the more rapid decline in lending to distressed neighborhoods appears to involve lending by commercial banks and independent mortgage companies. Both institution types experienced larger declines in lending to tracts with higher NSP scores. While lending by commercial banks was down in 2011 for all NSP quintiles, lending by independent mortgage companies increased in tracts in the least amount of distress (the bottom quintile of NSP scores) in 2011 and fell 11 percent in tracts in the most distress. Nevertheless, both institution types had a spread of about 15 percentage points between the changes in lending in the highest and lowest NSP quintiles.

In addition to types of lenders, we can also examine lending activity by largest lenders. Home-purchase lending by the 10 largest lenders in 2011 fell more sharply in 2011 (17 percent) than lending by other financial institutions (2.6 percent). However, lending by both declined more in highly distressed neighborhoods than in neighborhoods experiencing less distress.

The results of this analysis suggest that highly distressed neighborhoods continue to experience reduced mortgage flows, which mirrors the pattern observed for the 2005-10 period discussed in last year's review. These declines were particularly pronounced for lower-income borrowers. And while it is difficult to apportion these declines to demand and supply considerations, the sharper declines in distressed areas appear, for the most part, to have been widespread across lenders.

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Differences in Lending Outcomes by Race, Ethnicity, and Sex of the Borrower

One reason the Congress amended HMDA in 1989 was to enhance its value for fair lending enforcement by adding to the items reported the disposition of applications for loans and the race, ethnicity, and sex of applicants. A similar motivation underlay the decision to add pricing data for higher-priced loans in 2004, although such data serve other purposes, including to help identify lenders active in the higher-cost or higher-risk segments of the mortgage market and provide information on the volume and locations of borrowers receiving higher-priced loans.

Over the years, analyses of HMDA data have consistently found substantial differences in the incidence of higher-priced lending and in application denial rates across racial and ethnic lines, differences that cannot be fully explained by factors included in the HMDA data.⁴⁰ Analyses also have found that differences across groups in mean APR spreads paid by those with higher-priced loans were generally small.⁴¹ Here we examine the 2011 HMDA data to determine the extent to which these differences persist.

The analysis here presents aggregated lending outcomes across all reporting institutions. Patterns for any given financial institution may differ from those shown, and for any given financial institution, relationships may vary by loan product, geographic market, and loan purpose. Further, although the HMDA data include some detailed information about each mortgage transaction, many key factors that are considered by lenders in credit underwriting and pricing are not included. Accordingly, it is not possible to determine from HMDA data alone whether racial and ethnic pricing disparities reflect illegal discrimination. However, analysis using the HMDA data can account for some factors that are likely related to the lending process. Given that lenders offer a wide variety of loan products for which basic terms and underwriting criteria can differ substantially, the analysis here can only be viewed as suggestive.

Comparisons of average outcomes (both loan pricing and denials) for each racial, ethnic, or gender group are made both before and after accounting for differences in the borrower-related factors contained in the HMDA data (income; loan amount; location of the property, or MSA; and presence of a co-applicant) and for differences in borrower-related factors plus the specific lending institution used by the borrower.⁴² Comparisons for lending outcomes across groups are of three types: gross (or "unmodified"), modified to account for borrower-related factors (or "borrower modified"), and modified to account for borrower-related factors plus lender (or "lender modified").⁴³ The analysis here distinguishes between conventional and nonconventional lending, reflecting the different underwriting standards and fees associated with these two broad loan product categories.⁴⁴

Incidence of Higher-Priced Lending by Race and Ethnicity and Sex

As noted earlier, 2010 was the first HMDA reporting year for which all of the loans subject to higher-priced loan reporting used the new Freddie Mac PMMS threshold (the PMMS threshold was also used for the last three months of 2009). Before October 1, 2009, a Treasury-based threshold was used. The change in threshold makes it problematic to compare the reported incidence of higher-priced lending in 2010 or 2011 with the incidence reported for previous years. Nevertheless, in previous articles, we have employed a methodology that adjusted the Treasury-based spread to a spread over the 30-year fixed-rate mortgage APOR reported in the PMMS. For almost all of the period from 2006 to 2009, this methodology gave a good approximation of the incidence of loans with APOR spreads more than 1.75 percentage points above the PMMS (25 basis points higher than the cutoff for higher-priced reporting in 2010). Calculations using the "adjusted spread" showed that the estimated incidence of loans more than 1.75 percentage points above the PMMS is significantly reduced from 2006 to 2008 for all racial and ethnic groups and that the differences across groups are considerably smaller since 2008 than in the years prior.⁴⁵ Data reported for the last three months of 2009 using the new threshold showed only modest differences across groups.

As noted earlier, the overall reported incidence of higher-priced lending was about 50 basis points higher in 2011 than in 2010 (data for 2010 not shown in tables). Pricing relationships observed in the 2011 HMDA data are very similar to those found in the 2010 data. The 2011 HMDA data indicate that black and Hispanic-white borrowers are more likely, and Asian borrowers less likely, to obtain conventional loans with prices above the HMDA price-reporting thresholds than are non-Hispanic white borrowers. These relationships hold both for home-purchase and refinance lending and for nonconventional loans (tables 16.A and 16.B). For example, for conventional home-purchase lending in 2011, the incidence of higher-priced lending was 7.8 percent for black borrowers, 7.3 percent for Hispanic white borrowers, and 1.3 percent for Asian borrowers, compared with 3.9 percent for non-Hispanic white borrowers.

The gross differences in the incidence of higher-priced lending between non-Hispanic whites and blacks or Hispanic whites in 2011 are significantly reduced, but not completely eliminated, after controlling for lender and borrower characteristics. For example, the gross 2011 difference in the incidence of higher-priced conventional lending for home-purchase loans between Hispanic whites and non-Hispanic whites of 3.4 percentage points falls to only about 0.55 percentage point when the other factors available within the HMDA data are accounted for. The large gap in pricing between blacks and non-Hispanic whites is similarly reduced when other factors are considered. The pricing disparities across groups are significantly lower than the higher-priced incidence disparities observed from 2004 to 2007 using both the old Treasury-based threshold and our PMMS-based adjusted spread.

With regard to the gender of applicants, we find relatively small differences in the incidence of higher-priced lending between single applicants of different genders and dual applicants of different genders once all available factors are taken into account.

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Rate Spreads by Race, Ethnicity, and Sex

The 2011 data indicate that among borrowers with higher-priced loans, the gross APOR spreads are similar across groups for both home-purchase and refinance lending. This result holds for both conventional (table 17.A) and nonconventional lending (table 17.B). For example, for conventional home-purchase loans, the gross mean APOR spread was 2.49 percentage points for black borrowers and 2.76 percentage points for Hispanic white borrowers, while it was 2.49 percentage points for non-Hispanic white borrowers and 2.41 percentage points for Asian borrowers. Accounting for borrower-related factors or the specific lender used by the borrowers has little effect on the differences across groups.

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Denial Rates by Race, Ethnicity, and Sex

Analyses of the HMDA data in previous years have consistently found that denial rates vary across applicants grouped by race or ethnicity. This continues to be the case in 2011. As in past years, blacks and Hispanic whites had notably higher gross denial rates in 2011 than non-Hispanic whites, while the differences between Asians and non-Hispanic whites generally were fairly small by comparison (tables 18.A and 18.B). For example, the denial rates for conventional home-purchase loans were 30.9 percent for blacks, 21.7 percent for Hispanic whites, 14.8 percent for Asians, and 11.9 percent for non-Hispanic whites. The pattern was about the same for nonconventional home-purchase lending, although the gap in gross denial rates between blacks or Hispanic whites and non-Hispanic whites was notably smaller than for conventional home-purchase loans.

For both conventional and nonconventional home-purchase lending, controlling for borrower-related factors in the HMDA data generally reduces the differences among racial and ethnic groups. Accounting for the specific lender used by the applicant reduces differences further, although unexplained differences remain between non-Hispanic whites and other racial and ethnic groups. An analysis of refinance loans shows similar patterns, although the differences in gross denial rates between blacks and non-Hispanic whites and between Hispanic whites and non-Hispanic whites tend to be larger than for home-purchase lending. For example, the gross difference between black and non-Hispanic-white borrowers refinancing using a conventional loan was 20.5 percentage points.

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Some Limitations of the Data in Assessing Fair Lending Compliance

Previous research and experience gained in the fair lending enforcement process show that unexplained differences in the incidence of higher-priced lending and in denial rates among racial or ethnic groups stem, at least in part, from credit-related factors not available in the HMDA data, such as credit history (including credit scores), loan-to-value ratios, and differences in loan characteristics. Differential costs of loan origination and the competitive environment also may bear on the differences in pricing, as may differences across populations in credit-shopping activities.

Despite these limitations, the HMDA data play an important role in fair lending enforcement. The data are regularly used by bank examiners to facilitate the fair lending examination and enforcement processes. When examiners for the federal banking agencies evaluate an institution's fair lending risk, they analyze HMDA price data and loan application outcomes in conjunction with other information and risk factors that can be drawn directly from loan files or electronic records maintained by lenders, as directed by the Interagency Fair Lending Examination Procedures.⁴⁶ The availability of broader information allows the examiners to draw firm conclusions about institution compliance with the fair lending laws.

It is important to keep in mind that the HMDA data, as currently constituted, can be used only to detect differences in pricing across groups for loans with APRs above the reporting threshold; pricing differences may exist among loans below the threshold. This gap in the loan pricing information will be addressed in coming years as the Consumer Financial Protection Bureau implements the expanded data reporting requirements set forth in the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010, including the provision requiring the reporting of rate spread information for all loans.

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Assessing the Accuracy of Borrower Income Reported in the HMDA Data

During the housing boom of the 2000s, one underwriting practice that proliferated was the granting of mortgages with little or no documentation of income and assets. To investigate the extent to which borrower incomes may have been overstated on mortgage applications as a result of such practices, we compare the incomes reported for home-purchase borrowers in the HMDA data with the incomes of homebuyers taking out mortgages reported in Census 2000 and the ACS for 2005 through 2010.⁴⁷ While incentives to overstate income on mortgage applications sometimes exist, no such incentive exists when reporting income for the census or ACS. Thus, the Census 2000 and ACS data may provide "true" measures of income of homebuyers with which to gauge the accuracy of income reported on mortgage applications.⁴⁸

The Census Bureau annually conducts the ACS, a household survey gathering a wide variety of information, including overall family income, homeownership status, and mortgage status. Because the survey was conducted on a somewhat smaller scale prior to 2005, we use only ACS data for 2005 and after, and we use Census 2000 data to measure borrower income at the beginning of the decade.⁴⁹ For each year of the analysis, we compute average family income at the state level for home-purchase borrowers in the HMDA data and for families in the ACS and Census 2000 data that appear to have recently purchased their home with a mortgage (those that reported they own their home, have a mortgage, and moved in the past year).⁵⁰ We then compute the ratio of HMDA income to ACS income (or, from Census 2000, census income), state by state and for three different periods: 2000, 2005 to 2006, and 2009 to 2010. Ratios substantially greater than 1 imply widespread overstatement of income on mortgage applications.

Figure 3 suggests that income on mortgage applications was widely overstated in a number of states in 2005 and 2006, particularly California, Hawaii, Massachusetts, Nevada, and New York. In these states, average borrower income as reflected in the HMDA data was 30 percent or more above the average ACS borrower income. In contrast, HMDA borrower income was no more than 10 percent above borrower income as reported in Census 2000 in almost all states. Finally, in 2009 and 2010, we observe a return to consistent incomes across data sources, with borrower incomes reported in HMDA and the ACS within 10 percent of each other in almost every state.

Users of the HMDA data should be aware that borrower income was likely significantly overstated during the peak of the housing boom, particularly in some areas of the country. One potential implication of this finding is that lending to lower-income borrowers, as measured in the HMDA data, may be attenuated around the peak of the housing market.

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Transition to the 2010 Census Data and Revised Census-Tract Boundaries

Census data are used to evaluate the performance of lending institutions in complying with the CRA and the nation's fair lending laws. For example, family income data derived from the census are used to categorize census tracts by their relative median family income, and race and ethnicity data are used to characterize the minority population status of census tracts and other geographies.⁵¹ In the CRA context, the relative income of census tracts is used to identify which census tracts are considered lower income (low or moderate income) and, as a consequence, a focus of CRA attention. In the fair lending enforcement context, census-tract minority population characteristics are used, for example, to help detect potential redlining behavior, where, for example, a lender has a policy or practice that results in little or no lending in a geographic area because of its racial or ethnic composition.

Using census sources to identify income, population, and housing characteristics of census tracts and broader areas has become more complicated recently. Unlike Census 2000, which used a survey questionnaire that asked a great many detailed questions (often referred to as the "long form"), the 2010 census used a brief questionnaire (referred to as the "short form"). In particular, the 2010 census focused on gathering household population counts and race, ethnicity, sex, and age characteristic information, but it provides relatively little other information--and no data on household or family income.

In lieu of collecting extensive detailed information from every household once a decade in conjunction with the decennial census, the Census Bureau now annually conducts the ACS. The ACS collects detailed population, income, and housing information from a representative sample of about 3 million households using a long-form questionnaire. Because of a relatively small sample size, the annual ACS data do not provide sufficient information to establish reliable estimates of census-tract characteristics. However, the Census Bureau aggregates ACS data across years and publishes data for each census tract based on the most recent five-year combined ACS data. The first five-year ACS aggregate data made available were derived from the 2005-09 annual surveys and used the census-tract boundaries established for Census 2000. The more recent 2006-10 combined ACS data were released to the public in December 2011 and are available from the FFIEC at its HMDA website. The 2006-10 ACS data use the census-tract boundaries created for the 2010 census. Using five-year aggregated data derived from the ACS, it is possible to categorize each census tract by its relative median family income.

FFIEC Treatment of Updated Census and ACS Data

The FFIEC has announced that, for purposes of preparing HMDA disclosure reports and for CRA performance evaluations, the 2006-10 ACS data will be used to classify census tracts by relative median family income and that these classifications will not be changed for a period of five years.⁵² Five years hence, updated relative-income information will be derived from the combined 2011-15 ACS data, and census tracts will be reclassified according to their updated income profiles. Although, in principle, annual updates from the ACS could be used to reclassify census tracts by their relative incomes each year, the potential movement of census tracts from one relative-income category to another would greatly complicate CRA enforcement and make it difficult for lending institutions to plan and monitor their own activities.

A key aspect of the HMDA reporting rules is the requirement that lenders identify the census-tract locations of the properties involved in the applications and loans they report on each year. The 2011 HMDA data used census tracts as enumerated for Census 2000 and do not reflect any of the updated 2010 census or ACS data. Census-tract identifiers for the forthcoming 2012 HMDA data will be those enumerated for the 2010 census: Analysis of these data will use the 2010 census data and the 2006-10 ACS data.

There were substantial changes in the number and boundaries of census tracts between the 2000 and 2010 censuses. As a consequence of population growth and migration, as well as other factors, such as new road construction, the 2010 census includes many more census tracts than Census 2000, and the geographic areas of many census tracts used for Census 2000 have been altered. Overall, Census 2000 included about 66,300 census tracts; the 2010 census includes about 74,000 census tracts. About 46 percent of the 2010 census tracts have the same geographic boundaries as in 2000, and about 72 percent have a land area that is 95 percent or more identical to the area in 2000. For purposes of this article, the census tracts that have 2010 areas that are 95 percent or more the same as in 2000 are referred to as "substantially similar" census tracts.

The shift from the 2000 to the 2010 census has important implications for those using the HMDA data. Perhaps most important is the possibility that a loan related to a given property may have been identified as being in a census tract in a particular relative-income group one year, but a loan on that same property may be reclassified into a different relative-income category the next year simply because of the shift from the income data based on Census 2000 to the income data based on the 2006-10 ACS. Reclassification could occur because the income profile of the population in the census tract has changed (altering the numerator in the relative-income calculation), because the income profile of the broader area has changed (altering the denominator in the relative-income classification), or both.

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Evaluating the Effects of Census Data Changes

In order to gauge the potential effects of census data changes on the classification of lending activity, we undertook some simulations using the 2011 HMDA data. The analysis here focuses on the reclassification of census tracts due to changes in their relative family incomes and the reclassification of home lending (of all types) due to the reclassification of the census tracts where the properties associated with the loans are located. Because the location of branch offices may influence an institution's home-lending activity and because branch locations are an important component of CRA performance evaluations, we also assess the effects of the census data changes on branch officeclassification by census-tract income. Unlike lending, where an institution can potentially alter the geographic pattern of the home loan applications it receives by changes in marketing, outreach to real estate agents and homebuilders, and other techniques, branch office locations cannot be readily changed.

We evaluate the "pure" effects of updated population income estimates by comparing census-tract income classifications using Census 2000 data with classifications derived from the 2005-09 ACS surveys. Both Census 2000 and the 2005-09 ACS use the same census-tract boundaries. Also, to ensure that changes in MSA boundaries over the course of the past decade do not affect the analysis, we use the census-tract relative-income classifications as carried on the 2011 FFIEC HMDA data files. These files reflect the 2000 decennial estimates of median family income for each census tract but use current MSA boundary definitions. Thus, the only factors that can affect our estimates of income reclassifications are the updates to census-tract or broader area median family incomes that come about because of changes in family income estimates from shifting from Census 2000 to the more recent data based on the 2005-09 ACS.⁵³

Census-Tract Reclassification

Our analysis indicates that the transition from the Census 2000 to the 2005-09 ACS data for classifying census tracts by relative income would result in significant changes in census-tract income category classification. For example, 17 percent of the census tracts that were classified as moderate income using the 2000 income data would be reclassified as middle income, and 1 percent would be reclassified as higher income (table 19). Because these census tracts would no longer be classified as falling in the lower-income category, lending and other activities, including branch office locations, in these census tracts would no longer be a focus of CRA attention. However, about 15 percent of middle-income census tracts would be reclassified as moderate income, and activities in these census tracts would gain emphasis in CRA performance evaluations.

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Loan Reclassification

Results are similar when the analysis considers reclassification of home loans instead of census tracts, but some of the transitions are more pronounced. An analysis using the Census 2000 and the 2005-09 ACS data indicates that about 24 percent of the home loans extended in 2011 and classified as falling in moderate-income census tracts would transition and be reclassified as falling in a middle-income census tract and that 2 percent of the loans would transition to a higher-income census tract. At the same time, about 9 percent of the loans falling in middle-income areas would be reclassified as falling in moderate-income areas. However, in terms of the absolute number of loans, had the new census-tract relative-income classifications been used in 2011, there would have been a net increase in mortgage lending to low- and moderate-income neighborhoods of about 150,000 loans, about 22 percent higher than the number of LMI loans in 2011 under current census-tract relative-income classifications (data derived from table 19).

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Branch Office Reclassification

For our analysis of the effects of the transition from the Census 2000 to the ACS-based data on the classification of branch offices by census-tract relative income, we use the location of branch offices as reported in the Summary of Deposits (SOD) as of June 30, 2011. The SOD is an annual survey, compiled by the Federal Deposit Insurance Corporation (FDIC), of branch office deposits for all FDIC-insured banking institutions.⁵⁴ The data include the location (state, county, and census tract) of each branch (and headquarters) office and the dollar amount of deposits that are allocated to that branch by the banking institution. For this exercise, we excluded the locations of automated teller machines (ATMs). Although ATMs are considered in CRA performance evaluations under the "services test," it seems unlikely that ATM locations have much influence on home-lending activity, the main focus of this article.⁵⁵ In total, the branch office analysis included about 98,000 branch offices.

As in the analysis of census tracts and home lending described earlier, our analysis of branch office reclassification indicates that the switch from Census 2000 data to the more recent ACS-based income data would have a notable effect on the classification of branch offices by census-tract relative income. For example, 20 percent of the branch offices that were classified as located in a moderate-income census tract using the 2000 income data would be reclassified as middle income, and 2 percent would be reclassified as higher income, using the 2005-09 ACS data. Because these branch offices would no longer be classified as located in lower-income census tracts, they would no longer be a focus of CRA attention. However, about 14 percent of branches classified as being located in middle-income census tracts based on Census 2000 data would be reclassified as being located in moderate-income census tracts, and consequently, these offices would gain emphasis in CRA performance evaluations. Because there are more branch offices in middle-income census tracts than in low- or moderate-income census tracts, the transition to the updated census information will result in a net increase of about 3,400 branch offices in areas that are the focus of CRA attention.

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Appendix A: Requirements of Regulation C

The Federal Reserve Board's Regulation C requires lenders to report the following information on home-purchase and home-improvement loans and on financings:

For each application or loan

• application date and the date an action was taken on the application
• action taken on the application
  • approved and originated
  • approved but not accepted by the applicant
  • denied (with the reasons for denial--voluntary for some lenders)
  • withdrawn by the applicant
  • file closed for incompleteness
• preapproval program status (for home-purchase loans only)
  • preapproval request denied by financial institution
  • preapproval request approved but not accepted by individual
• loan amount
• loan type
  • conventional
  • insured by the Federal Housing Administration
  • guaranteed by the Department of Veterans Affairs
  • backed by the Farm Service Agency or Rural Housing Service
• lien status
  • first lien
  • junior lien
  • unsecured
• loan purpose
  • home purchase
  • finance
  • home improvement
• type of purchaser (if the lender subsequently sold the loan during the year)
  • Fannie Mae
  • Ginnie Mae
  • Freddie Mac
  • Farmer Mac
  • private securitization
  • commercial bank, savings bank, or savings association
  • life insurance company, credit union, mortgage bank, or finance company
  • affiliate institution
  • other type of purchaser

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For each applicant or co-applicant

• race
• ethnicity
• sex
• income relied on in credit decision

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For each property

• location, by state, county, metropolitan statistical area, and census tract
• type of structure
  • one- to four-family dwelling
  • manufactured home
  • multifamily property (dwelling with five or more units)
• occupancy status (owner occupied, non-owner occupied, or not applicable)

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For loans subject to price reporting

• spread above comparable Treasury security for applications taken prior to October 1, 2010
• spread above average prime offer rate for applications taken on or after October 1, 2010

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For loans subject to the Home Ownership and Equity Protection Act

• indicator of whether loan is subject to the Home Ownership and Equity Protection Act

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