INTEREST RATE DIFFERENTIALS BETWEEN 

JUMBO AND CONFORMING MORTGAGES, 1995-2000

May 2001

PREFACE

This Congressional Budget Office (CBO) paper estimates the difference between

interest rates on two types of mortgage loans: conforming loans, which are for

amounts of $275,000 or less, most of which are ultimately purchased by one of the

three government-sponsored enterprises (GSEs) that deal with housing finance

(Fannie Mae, Freddie Mac, and the Federal Home Loan Banks); and jumbo loans,

which are larger than $275,000 and may not be purchased by the GSEs. 

The degree to which interest rates on conforming loans are lower than rates

on jumbo loans serves as a rough measure of the benefits that the housing GSEs pass

on to borrowers in the mortgage market. This paper explains in more detail some of

the estimates contained in CBO’s new study Federal Subsidies and the Housing

GSEs, prepared at the request of the Subcommittee on Capital Markets, Insurance,

and Government-Sponsored Enterprises of the House Committee on Financial

Services.

David Torregrosa of CBO’s Microeconomic and Financial Studies Division

wrote this paper under the supervision of Marvin Phaup and Roger Hitchner.

Coleman Bazelon, Chuck Capone, Debbie Lucas, and Angelo Mascaro of CBO

reviewed the analysis at many stages, as did Wayne Passmore of the Federal Reserve

Board, Ron Feldman of the Federal Reserve Bank of Minneapolis, and Robert Seiler

Jr. of the Office of Federal Housing Enterprise Oversight (OFHEO). Their assistance

is greatly appreciated. Barry Anderson, Perry Beider, Arlene Holen, Susanne

Mehlman, Preston Miller, David Moore, Nathan Musick, and Tom Woodward of

CBO provided helpful comments, as did Patrick Lawler and Tom Lutton of OFHEO

and Mario Ugoletti of the Treasury Department. Tim Forsberg and Joe McKenzie

of the Federal Housing Finance Board provided the data used in this analysis and

patiently responded to numerous questions. Eric Warasta and John McMurray of

CBO and DaRon Ross and Sean Corcoran, formerly of CBO, provided research

assistance at various stages of the analysis. 

Chris Spoor edited the paper, and John Skeen proofread it. Rae Roy prepared

the paper for publication, and Lenny Skutnik provided the printed copies. Annette

Kalicki prepared the electronic versions for CBO’s Web site (www.cbo.gov). 

Dan L. Crippen

Director

May 2001

CONTENTS

INTRODUCTION AND SUMMARY 1

Introduction: The Role of Government-Sponsored Enterprises 

in Housing Markets 1

Summary: Determining the Benefits That the GSEs Pass

Through to Borrowers 3

THE SOURCES AND SIGNIFICANCE OF DIFFERENCES 

BETWEEN JUMBO AND CONFORMING MORTGAGE RATES 5

GSE Status 5

Other Determinants of Interest Rates and Spreads 7

Using the Jumbo/Conforming Differential as a Proxy for the

Benefits Passed Through to Borrowers 8

PREVIOUS RESEARCH ON JUMBO/CONFORMING

DIFFERENTIALS 12

ESTIMATING NATIONAL JUMBO/CONFORMING DIFFERENTIALS

FOR 30-YEAR FIXED-RATE MORTGAGES SINCE 1995 14

The Data Set 16

Results 18

The Role of Other Explanatory Variables 20

THE ROBUSTNESS OF CBO’S ESTIMATES 21

Alternative Ways to Specify Loan Size 22

Differentials for 15-Year Fixed-Rate Mortgages

Relative to 30-Year Mortgages 23

Using Rates on No-Point Loans to Determine Differentials 23

Nonregression Methods to Estimate Differentials 26

Comparisons with Other Estimates 26

CONCLUSIONS 32
vi JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

APPENDIXES

A Regression Estimates with Loan Size Specified

in Logarithmic Form 33

B Regression Estimates with Loan Size Specified

in Quadratic Form 41

C Regression Estimates for No-Point Loans 49

TABLES

1. Single-Family Conventional Loans and the Conforming Loan

Limit, 1995-2000 10

2. Unadjusted Differences in Interest Rates Between Jumbo and

Conforming Loans, 1995-2000 14

3. Differences in Effective Interest Rates Between Jumbo and

Conforming 30-Year Fixed-Rate Loans, by Quarter, with Loan

Size Specified as a Natural Log, 1995-2000 19

4. Differences in Effective Interest Rates Between Jumbo and

Conforming 30-Year Fixed-Rate Loans, by Quarter, with Loan

Size Specified in Quadratic Form, 1995-2000 24

5. Annual Differences in Effective Interest Rates Between

Jumbo and Conforming 15-Year and 30-Year Fixed-Rate Loans,

with Loan Size Specified as a Natural Log, 1995-2000 25

6. Differences in Effective Interest Rates Between Jumbo and

Conforming 30-Year Fixed-Rate Loans with No Points, by

Quarter, 1995-2000 27

7. Unadjusted Differences in Interest Rates Between Jumbo and

Conforming 30-Year Fixed-Rate Loans, by Quarter, 1995-2000 28

A-1. Regression Estimates When Loan Size Is Specified in Log

Form, 1995 34

A-2. Regression Estimates When Loan Size Is Specified in Log

Form, 1996 35
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 vii

A-3. Regression Estimates When Loan Size Is Specified in Log

Form, 1997 36

A-4. Regression Estimates When Loan Size Is Specified in Log

Form, 1998 37

A-5. Regression Estimates When Loan Size Is Specified in Log

Form, 1999 38

A-6. Regression Estimates When Loan Size Is Specified in Log

Form, First and Second Quarters 2000 39

B-1. Regression Estimates When Loan Size Is Specified in Quadratic

Form, 1995 42

B-2. Regression Estimates When Loan Size Is Specified in Quadratic

Form, 1996 43

B-3. Regression Estimates When Loan Size Is Specified in Quadratic

Form, 1997 44

B-4. Regression Estimates When Loan Size Is Specified in Quadratic

Form, 1998 45

B-5. Regression Estimates When Loan Size Is Specified in Quadratic

Form, 1999 46

B-6. Regression Estimates When Loan Size Is Specified in Quadratic

Form, First and Second Quarters 2000 47

C-1. Regression Estimates for No-Point Loans, 1995 50

C-2. Regression Estimates for No-Point Loans, 1996 51

C-3. Regression Estimates for No-Point Loans, 1997 52

C-4. Regression Estimates for No-Point Loans, 1998 53

C-5. Regression Estimates for No-Point Loans, 1999 54

C-6. Regression Estimates for No-Point Loans, First and Second 

Quarters 2000 55

1. Congressional Budget Office, Federal Subsidies and the Housing GSEs (May 2001).

2. See Congressional Budget Office, The Federal Home Loan Banks in the Housing Finance System

(July 1993). 

3. For a description of how Fannie Mae views its role in housing markets and the risks it assumes, as well

as its regulatory requirements, see Franklin D. Raines, “New Frontiers in Financial Institution Risk

Management” (address given at the Brookings Institution, Washington, D.C., December 15, 2000),

available at www.fanniemae.com/news/speeches/speech_158.html. 

INTRODUCTION AND SUMMARY

The federal government has a range of policies that promote home ownership. Most

are aimed at lowering borrowing costs, either directly or indirectly. The tax

deduction for mortgage interest payments, for example, is intended to make housing

more affordable for most buyers. Likewise, mortgage guarantees provided by the

Federal Housing Administration and the Department of Veterans Affairs are designed

to improve access and make homes more affordable for moderate- and low-income

borrowers. Indirectly, the federal government also subsidizes home mort-gages

through three government-sponsored enterprises (GSEs)—Fannie Mae, Freddie Mac,

and the Federal Home Loan Bank System—which serve as conduits between the

capital markets and local housing markets. 

This paper examines how those GSEs lower mortgage costs for borrowers;

it also estimates the benefits that those enterprises pass through to borrowers. Specifically, the paper explains in detail some of the estimates used in the Congressional

Budget Office’s (CBO’s) new report on the public benefits and costs of the housing

GSEs.1

 

Introduction: The Role of Government-Sponsored Enterprises in Housing Markets

Government-sponsored enterprises are hybrid organizations, created by the federal

government for a public purpose but with nongovernment ownership. Fannie Mae

and Freddie Mac, whose shares are traded on the New York Stock Exchange, are

owned by investors; the 12 Federal Home Loan Banks (FHLBs) are cooperatives

owned by their members (mainly private financial institutions).2 Those Congressionally chartered GSEs receive substantial benefits, or unpriced subsidies, from the

government in return for accepting certain responsibilities in the housing finance

markets and various restrictions on the scope of their business operations.

The GSEs do not originate mortgages; instead, they support a secondary

(resale) market for mortgages by purchasing “conforming” mortgages that banks,

thrifts, mortgage companies, and others originate. (Conforming mortgages are singlefamily loans that meet Fannie Mae’s and Freddie Mac’s underwriting standards and

are eligible to be purchased by the GSEs.3

 Most of those mortgages are “conven-
2 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

4. Outstanding loans in the banks’ Mortgage Partnership Finance Program increased from $1.8 billion

in 1999 to $15.4 billion in 2000; see Federal Home Loan Bank of Chicago, “Federal Home Loan Bank

of Chicago Reports Excellent 2000 Results” (press release, Chicago, Ill., February 20, 2001), available

at www.fhlbc.com/2000_results.htm. For current details of the FHLBs’ secondary-market activities,

see Joy C. Shaw, “Fannie Mae, Freddie Mac Outweigh Rival: The FHLB Program Still Is Vying for

Secondary-Mortgage Market,” Wall Street Journal, February 6, 2001, p. B-15. 

5. Congressional Budget Office, Assessing the Public Costs and Benefits of Fannie Mae and Freddie Mac

(May 1996), pp. 9-12.

6. Fannie Mae and Freddie Mac purchased $1.1 trillion in fixed-rate mortgages in 1998 and 1999—more

than two-thirds of the volume of conforming fixed-rate loans originated in those years. They also

purchase adjustable-rate mortgages and multifamily loans. See Department of Housing and Urban

Development, Office of Federal Housing Enterprise Oversight, 2000 Report to Congress (June 15,

2000), p. 10.

7. For an analysis of the importance of industry structure in the secondary mortgage market, see John

L. Goodman and S. Wayne Passmore, Market Power and the Pricing of Mortgage Securitization,

Finance and Economics Discussion Series Working Paper No. 187 (Federal Reserve Board, March

1993); and Benjamin E. Hermalin and Dwight M. Jaffee, “The Privatization of Fannie Mae and

Freddie Mac: Implications for Mortgage Industry Structure,” in Department of Housing and Urban

tional” mortgages, ones that have not been guaranteed or insured by the Federal

Housing Administration or the Department of Veterans Affairs.) In addition, the

GSEs guarantee securities backed by a pool of mortgages they purchase—a process

known as securitization. Those securities entitle their buyers to a share of the cash

flow of principal and interest from the underlying mortgages. In case of default on

those mortgages, the GSEs guarantee payment to the holders of the securities. Unlike

Fannie Mae and Freddie Mac, the FHLBs have only recently entered the secondary

market on a limited, but growing, basis.4 Their primary activity is making loans, or

“advances,” to their member institutions, including banks and thrifts (savings and loan

associations and mutual savings banks). They also pay dividends to their members.

Although the federal government does not explicitly guarantee or insure the

GSEs’ securities, investors generally assume that an implicit government guarantee

exists on the basis of numerous instances in which federal law treats GSE securities

as no riskier than risk-free Treasury securities.5

 The GSEs’ “agency” status effectively lowers their funding costs and allows Fannie Mae and Freddie Mac to offer

loan originators attractive prices for mortgages. It also allows the FHLBs to pass

their lower borrowing costs through to their member banks, which in turn pass part

of that subsidy through to mortgage borrowers and other loan customers. How

attractive the offering prices are and how much of the federal subsidy is passed

through to borrowers depend in part on the extent of competition between Fannie

Mae and Freddie Mac. Those two enterprises control almost all of the secondary

market for conforming conventional loans, and the federal benefits they receive

virtually preclude entry by completely private firms.6 As a result of that limited

competition, few analysts expect Fannie Mae and Freddie Mac to pass through all of

the subsidy they receive.7 Some of their federal benefits are retained as profits. 
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 3

Development, Office of Policy Development and Research, Studies on Privatizing Fannie Mae and

Freddie Mac (May 1996), pp. 225-302. For conditions under which secondary-market activities fail

to lower mortgage rates, see Andrea Heuson, Wayne Passmore, and Roger Sparks, Credit Scoring and

Mortgage Securitization: Implications for Mortgage Rates and Credit Availability, Finance and

Economics Discussion Series Working Paper No. 2000-4 (Federal Reserve Board, December 21,

2000).

8. The 1996 estimate was published in Congressional Budget Office, Assessing the Public Costs and

Benefits of Fannie Mae and Freddie Mac, pp. xi-xii and 18-20.

9. The conforming-loan ceiling is the same in all of the 48 contiguous states but is 50 percent higher in

Hawaii and Alaska, which have substantially higher housing costs. That ceiling is adjusted each year

for the change in housing prices (based on the average percentage increase in the value of homes with

conventional mortgages over a 12-month period beginning in October, using the Federal Housing

Finance Board’s Monthly Interest Rate Survey).

10. Judging by the current distribution of credit risk, depository institutions, the Federal Housing

Administration, and the Department of Veterans Affairs appear more willing to bear the mortgage

credit risk of low-income families than the GSEs are. For a description of the affordable-housing goals

that the Department of Housing and Urban Development sets for the GSEs, as well as a discussion of

the types and characteristics of loans they purchase, see Department of Housing and Urban

Development, “HUD’s Regulation of the Federal National Mortgage Association (Fannie Mae) and

the Federal Home Loan Mortgage Corporation (Freddie Mac); Final Rule,” Federal Register, vol. 65,

no. 211 (October 31, 2000), pp. 65043-65229.

At the request of the House Committee on Financial Services, CBO recently

updated its 1996 estimate of the federal subsidy to the GSEs and the distribution of

that subsidy among borrowers, the GSEs, and other beneficiaries.8

 An important

component of that estimate—and the main focus of this paper—is determining the

portion of the subsidy that benefits borrowers of conforming mortgages. 

Summary: Determining the Benefits That the GSEs Pass Through to Borrowers

This analysis estimates the amount of subsidy that the housing GSEs pass through to

borrowers by estimating a proxy measure—the average difference in interest rates (or

adjusted spread) between conforming and jumbo mortgages that is attributable to the

GSEs. (Conforming mortgages are currently subject to a ceiling of $275,000.9

Jumbo loans are single-family loans larger than that amount.) The GSEs may

provide other benefits to housing markets, such as increasing home ownership by

moderate- and low-income families, but those benefits are not measured in this

analysis.10 

Following the analytic framework of previous researchers, CBO estimated the

interest rate differential between 30-year fixed-rate jumbo and conforming mortgages

using the Monthly Interest Rate Survey (MIRS) data set maintained by the Federal
4 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

11. Using an established analytic framework makes it easier for other researchers to evaluate CBO’s

results. Moreover, Fannie Mae has validated estimates based on this approach in the past. See Fannie

Mae, “Fannie Mae Review of the Cotterman-Pearce and Ambrose-Warga Papers,” in Department of

Housing and Urban Development, Studies on Privatizing Fannie Mae and Freddie Mac, pp. 218-219.

12. CBO did not estimate spreads for adjustable-rate mortgages, which have a wide variety of pricing

provisions that complicate any comparison. Those mortgages make up less than 5 percent of the

GSEs’ mortgage holdings. 

13. For evidence about prepayment rates and default losses, see Kyle G. Lundstedt, “The Influence of NonOption-Related Variables Upon Corporate Default and Residential Mortgage Termination” (Ph.D.

dissertation, University of California, Berkeley, 1999), p. 37. 

Housing Finance Board.11 Unlike most previous studies, CBO’s analysis used pooled

data from the entire nation, had less restrictive data screens, and compared effective

mortgage rates (mortgage interest rates adjusted for any points and fees paid by the

borrower). Thus, CBO’s estimate was based on a larger sample of mortgages and a

better measure of borrowing costs than previous estimates. To isolate the difference

in mortgage rates that is attributable to the GSEs, CBO controlled for some of the

factors that affect rates, such as the size of the mortgage (the average cost of

originating and servicing a mortgage falls with loan size) and the loan-to-value ratio

(a proxy for the risk of default). As a test of the robustness of the estimates, CBO

fitted the MIRS data set to a variety of alternatively specified models and also

estimated the differential between jumbo and conforming mortgages using only zeropoint loans. 

Controlling for differences in loan characteristics, CBO estimates that rates

on fixed-rate jumbo mortgages exceeded those on similar conforming mortgages by

an average of 18 to 25 basis points (0.18 to 0.25 percentage points) between 1995 and

June 2000, depending on the estimation technique and the data sample.12 The interest

rate differentials varied significantly throughout that period, in part because of

changes in liquidity and risk premiums in the financial markets. In particular,

differentials widened during “flights to quality,” when investors sought safe, liquid

securities, including the GSEs’ debt issues and mortgage-backed securities. Spreads

tightened when liquidity and risk premiums dropped and thereby reduced the

advantages of the GSEs’ “agency” status. 

CBO’s analysis is subject to some of the same limitations as previous studies;

thus, those estimates of differentials may not be as precise as they appear. Because

the MIRS data set has no information on borrowers’ credit history, income, or

wealth—which affect the rates that borrowers pay—CBO could not control for all of

the economic factors that influence the jumbo/conforming interest rate differential.

Consequently, CBO’s estimates assume that borrowers in the conforming and jumbo

markets present the same risks. But research indicates that both the likelihood of

prepayment and the risk of default may be greater for jumbo mortgages.13 Thus, if
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 5

14. See Brent W. Ambrose, Richard Buttimer, and Thomas Thibodeau, “A New Spin on the Jumbo/

Conforming Loan Rate Differential,” Journal of Real Estate Finance and Economics, vol. 23, no. 3

(forthcoming).

15. For an analysis of which loans a bank chooses to sell, see Wayne Passmore, Roger Sparks, and Jamie

Ingpen, GSEs, Mortgage Rates, and the Long-Run Effects of Mortgage Securitization, Finance and

Economics Discussion Series (Federal Reserve Board, forthcoming); and Wayne Passmore and Roger

Sparks, “Putting the Squeeze on a Market for Lemons: Government-Sponsored Mortgage Securitization,” Journal of Real Estate Finance and Economics, vol. 13, no. 1 (1996), pp. 27-43. 

16. Automated underwriting has also facilitated the GSEs’ entry into the subprime market (which targets

borrowers with poorer credit histories). But most of their purchases have been at the upper end of that

market (so-called A- loans) rather than at the lower and riskier end (B and C loans). However,

automated underwriting may also lead originators to reduce the quality of the loans they choose to sell

to the GSEs. See Wayne Passmore and Roger W. Sparks, “Automated Underwriting and the

Profitability of Mortgage Securitization,” Real Estate Economics, vol. 28, no. 2 (2000), pp. 285-305.

all other factors are held equal, rates on jumbo mortgages are likely to be higher than

rates on conforming mortgages. In addition, CBO’s estimates do not control for

conditions in local housing markets. If, as one study has shown, housing prices are

more volatile for expensive properties, which are more likely to have jumbo

mortgages, lenders should be charging jumbo borrowers more for that additional

risk.14 In summary, the available evidence suggests that CBO’s approach probably

overstates the jumbo/conforming interest rate differential and thus the amount of

subsidy that the GSEs pass through to borrowers.

THE SOURCES AND SIGNIFICANCE OF DIFFERENCES

BETWEEN JUMBO AND CONFORMING MORTGAGE RATES

The mortgage rates that most borrowers pay are determined by prices and yields in

the secondary market. Two secondary markets for conventional mortgages exist:

one for conforming mortgages, the housing loans that the GSEs may purchase, and

one for jumbo mortgages, which they may not purchase. Selling conforming loans

to the secondary market is particularly attractive for loan originators because they

receive better prices for those mortgages than for jumbo loans.15 Most loan

originators match their underwriting criteria to the GSEs’ guidelines for purchases.

Moreover, they frequently use the GSEs’ own automated underwriting software to

identify credit risk more efficiently, speed up the loan application process, and

facilitate sales in the secondary market.16 A variety of factors influence prices and

yields in that market—and thus interest rates on mortgages. 

GSE Status

The “agency” status of the housing GSEs can be expected to lower interest rates on

conforming loans relative to those on jumbo loans because investors in mortgage-
6 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

17. For a general analysis of differences between the conforming and jumbo markets, see General

Accounting Office, Housing Enterprises: Potential Impacts of Severing Government Sponsorship,

AO/GGD-96-120 (May 1996), pp. 54-70; and Robert S. Seiler Jr., “Estimating the Value and

Allocation of Federal Subsidies to Fannie Mae and Freddie Mac” (paper presented at the American

Enterprise Institute conference “Fannie Mae and Freddie Mac: Public Purposes and Private Interests,”

Washington, D.C., March 24, 1999), revised April 1, 1999.

18. Regulators require a 50 percent risk weighting for individual (or “whole”) mortgages versus a 20

percent risk weighting for GSE mortgage-backed securities. In contrast, other types of loans to

individuals and firms, as well as corporate debt, receive a full risk weighting, which requires 8 percent

capital backing.

19. Although the jumbo market has been growing, it is still several times smaller than the market for

mortgage-backed securities guaranteed by the GSEs. For example, $133 billion in jumbo mortgagebacked securities were issued in 1998, whereas purchases of conforming loans by Fannie Mae and

Freddie Mac totaled $637 billion in 1998, which was a big year for refinancing. See The Mortgage

Market Statistical Annual, vol. 1, The Primary Mortgage Market (Washington, D.C.: Inside Mortgage

Finance Publications, 1999), pp. 1-2.

20. The level of credit enhancements varies among issuers of jumbo mortgage-backed securities (MBSs).

See Jeffrey Wolf and others, Theme and Variation—Understanding Why Credit Enhancement Levels

Vary Among Jumbo MBS Issuers, Structured Finance Special Report (New York: Moody’s Investors

Service, September 17, 1998).

backed securities are willing to accept lower interest rates on securities guaranteed

by GSEs than on jumbo loans guaranteed by private institutions.17 There are several

reasons for that greater willingness. First, most investors perceive an implied federal

guarantee of GSE securities, so they virtually ignore any risk of default on those

securities. Second, those securities enjoy the full advantages of GSE debt, so they

are highly liquid. (Liquidity, which is the ability to trade a security quickly with little

impact on its price, is particularly important during periods of stress in the financial

markets.) Third, federal regulators require banks and thrifts to hold two and a half

times less capital against GSE securities than against privately guaranteed mortgagebacked securities and whole loans.18 They also impose no limits on the amount of

GSE debt and securities that banks and thrifts can hold.

The secondary market for jumbo mortgage-backed securities is becoming

more liquid as the volume of those securities increases. As a result, interest rates in

that market are falling.19 To increase the attractiveness of those privately guaranteed

securities to investors, investment bankers generally take bundles of jumbo loans and

create different classes of securities with different levels of risk. Risk for those

securities can also be reduced through private credit enhancements, such as

additional guarantees or collateralization.20 Nevertheless, even the least risky

securities backed by jumbo mortgages will be issued at significantly higher interest

rates than GSE securities will. In large part, that difference results from the superior

liquidity of the GSE issues and the fragmented nature of the jumbo market rather

than from differences in credit risk. 
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 7

21. For an analysis of delinquency and default, see Robert B. Avery and others, “Credit Risk, Credit

Scoring, and the Performance of Home Mortgages,” Federal Reserve Bulletin, vol. 82, no. 7 (July

1996), pp. 621-648. 

22. Shifts in the yield curve (which reflects the maturity structure of interest rates) also present risks. Longterm rates are generally above short-term rates when the economy is expanding, but they have often

been below short-term rates during the onset of an economic contraction. Interest rate risk measures

the difference in sensitivity of the market value of assets and liabilities to changes in rates.

Other Determinants of Interest Rates and Spreads

Prices paid in the secondary market are generally negotiated between lenders and the

GSEs (or between lenders and investment banks in the case of jumbo loans) and

reflect the variation in expected returns on the mortgages. Interest rates on mortgages, plus fees and charges, must cover those parties’ costs, including the cost of

originating and servicing loans; the risks of default, changing interest rates, and

prepayment; capital requirements; the cost of funds; and other cost factors. Because

the cost of originating and servicing loans is basically fixed, it falls relative to the

size of the loan, which makes larger loans cheaper to originate and administer.

The risk of default varies with the relative size of the down payment, the

creditworthiness of the borrower, the presence or absence of mortgage insurance, and

the price volatility of the property carrying the mortgage. The larger the down

payment, the safer the loan.21 In fact, if the down payment is large enough, the lender

may devote less effort to evaluating the creditworthiness of the borrower. If the

down payment is small, however, private mortgage insurance may be required.

Stable housing markets present less risk to lenders than volatile markets because the

value of the mortgages’ collateral is more predictable. If home prices have been

rising rapidly in an area, for example, lenders may be more cautious because the

housing market could be more susceptible to price declines. Lenders can compensate for volatility in housing prices by raising interest rates.

Interest rate risk can arise when loan originators borrow in short-term markets

and lend in long-term markets. That mismatch of maturities between liabilities and

assets makes lenders vulnerable to a rise in interest rates.22 When rates rise, the value

of assets with a fixed stream of payments falls. For example, if rates increase, banks

receive the same stream of income from 30-year fixed-rate mortgages but pay more

interest on short-term deposits, which can reduce their profitability. 

Mortgage lenders can also be vulnerable to unexpected drops in interest rates,

because in those circumstances, borrowers may choose to prepay their mortgage and

refinance it at a lower rate. Prepayments are costly to a lender because the institution

must generally reinvest the funds at a lower rate. Most lenders do not impose

penalties for prepayment, but they try to account for that risk in their pricing, which

is one reason that fixed-rate loans are generally made at higher interest rates than
8 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

23. Prepayment penalties are common on subprime loans but not on conventional conforming loans.

24. Secondary-market transactions can also leave interest rate risk unchanged. In swap programs, banks

exchange their mortgages for GSE securities that represent ownership in the same mortgages. 

25. The subsidy passed through to borrowers reduces mortgage interest rates, which should benefit

borrowers, but other parties may also benefit. Some of the value of the subsidy could be capitalized

in higher home prices and flow to builders of new homes or owners of existing homes. 

adjustable-rate loans.23 The savings to borrowers from refinancing a loan rise with

the size of the loan, so prepayment risk increases with loan size. 

Lenders have several strategies for mitigating interest rate and prepayment

risks. First, they can shift most of the interest rate risk to borrowers by making

adjustable-rate loans. Even though adjustable-rate mortgages generally carry lower

rates than fixed-rate mortgages, they are much less popular with most borrowers in

most interest rate environments. 

Second, lenders can attempt to hedge their interest rate and prepayment risks

by purchasing derivative products or entering into interest rate swap agreements.

Hedging can reduce the variability of their income streams; however, the fees,

transaction costs, and personnel costs of hedging can be significant. In addition,

prepayment models used to decide on optimal hedging strategies are not always

reliable. 

Third, originators can use the secondary markets to reduce or eliminate

interest rate risk. Selling loans outright eliminates that risk, but most depository

institutions swap loans in the secondary market for mortgage-backed securities from

Fannie Mae and Freddie Mac. Holding those securities allows lenders to still use

mortgages as investments while guaranteeing them against default, enhancing

liquidity, and lowering the amount of required capital. In addition, institutions can

purchase mortgage-backed securities with expected payment flows that match their

risk preferences better than whole loans do.24

Using the Jumbo/Conforming Differential as a Proxy

for the Benefits Passed Through to Borrowers

No direct measure exists of the benefits that the housing GSEs pass through to

borrowers.25 Although some analysts suggest that those benefits can be measured by

comparing the advertized rates that lenders offer for conforming and jumbo loans,

that measure fails to control for other important factors that affect rates and may not

represent the mortgage rates used in transactions. To isolate the difference in rates

that is attributable to the presence of the GSEs in the conforming market, analysts use

statistical methods that first adjust gross spreads for the impact of loan size, down
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 9

26. For an analysis of spreads in other financial markets, see John V. Duca, “What Credit Market

Indicators Tell Us,” Economic and Financial Review, Federal Reserve Bank of Dallas (Third Quarter

1999), pp. 2-13. 

27. Smaller lenders may also be getting better deals; see Robert Julavits, “Fannie, Freddie Moving to Woo

Small Lenders,” American Banker (March 14, 2001), p. 9. Some analysts also argue that the decline

in the average guarantee fee the GSEs charge is evidence that they are competing strongly. However,

others contend that increased use of credit enhancements may explain much of the decrease in fees.

See Office of Federal Housing Enterprise Oversight, 2000 Report to Congress, pp. 25-26.

28. Some analysts argue that the GSEs have less effect on the rates of adjustable-rate mortgages than on

the rates of fixed-rate mortgages. See James E. Pearce and James C. Miller III, Freddie Mac and

Fannie Mae: Their Funding Advantage and Benefits to Consumers (prepared for Freddie Mac, January

9, 2001), available at www.freddiemac.com/news/analysis/pdf/cbo-final-pearcemiller.pdf. 

payments, and other factors on interest rates. This paper refers to that adjusted

interest rate spread as the jumbo/conforming differential. 

Spreads can change with investors’ preferences for risk and the premium they

place on liquidity, prepayment risk, and other factors.26 During periods of financial

crisis, investors often increase their demand for safe and liquid assets. That “flight

to quality” benefits the market for GSE securities and conforming mortgages but

penalizes privately guaranteed mortgage-backed securities and jumbo mortgages.

Thus, the jumbo/conforming differential can be expected to widen during periods of

financial stress. Increased competition between Fannie Mae and Freddie Mac would

also be likely to widen that differential because the agencies would be bidding up the

price of conforming mortgages, which would allow lenders to lower interest rates on

those mortgages. For example, the two GSEs have been competing since 1999 to

enter into special business arrangements with large mortgage originators.27 Conversely, as the market for jumbo-mortgage-backed securities matures or broadens as

more issues are funded, liquidity in that market should improve and more economies

of scale be realized in creating and administering the securities. Those structural

improvements should contribute to tightening the differential over time. 

Differences in borrowers’ behavior in the jumbo and conforming markets may

also influence spreads, but in ways that are difficult to assess. For example, borrowers in the jumbo market are less likely to have fixed-rate mortgages than borrowers

in the conforming market (as evidenced by the fact that fixed-rate loans make up a

lower percentage of jumbo mortgages than of all conventional loans; see Table 1).

Whether that difference means that jumbo borrowers are more willing to accept the

risk of changing interest rates or that lenders are less willing to originate fixed-rate

mortgages in the jumbo market is uncertain.28

The jumbo/conforming differential is merely a proxy for the benefits passed

through to borrowers because it is impossible to know what the housing market

would be like without the GSEs. In addition, some analysts argue that the GSEs push
10 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

29. Passmore, Sparks, and Ingpen, GSEs, Mortgage Rates, and the Long-Run Effects of Mortgage

Securitization, argues that combining the jumbo and conforming markets might significantly improve

the liquidity of jumbo mortgage-backed securities.

TABLE 1. SINGLE-FAMILY CONVENTIONAL LOANS AND THE CONFORMING LOAN

LIMIT, 1995-2000

As a Percentage of All Conventional Mortgagesa

Fixed-Rate

Jumbo Loans

as a Percentage of All

Jumbo Loans

Conforming

Loan Limitb

(Dollars)

Conforming

Loans Jumbo Loans 

Fixed-Rate

Loans

Fixed-Rate

Jumbo Loans 

1995 92.2 7.8 68 2.6 32.7 203,150

1996 90.7 9.3 73 3.2 34.8 207,000

1997 90.4 9.6 78 5.1 52.7 214,600

1998 91.3 8.7 88 5.2 59.2 227,150

1999 90.8 9.2 79 4.8 51.7 240,000

2000 90.1 9.9 76 3.7 37.0 252,700

SOURCE: Congressional Budget Office based on data from the Federal Housing Finance Board and the board’s Monthly

Interest Rate Survey, Tables 24 and 25.

a. Excludes loans that are insured or guaranteed by the federal government. Jumbo loans would be a larger percentage as

a share of the total dollar value of conventional mortgages. 

b. The limit on conforming loans for single-family homes is 50 percent higher in Alaska and Hawaii.

up rates in the jumbo market, in which case the differential overstates the extent to

which the GSEs lower rates in the conforming market. Those analysts’ argument is

that by segmenting the relatively small jumbo market from the rest of the mortgage

market, GSEs reduce the liquidity, size, and diversification of the jumbo market.

That point is particularly important because the greater geographic concentration of

jumbo mortgages in high-cost housing markets probably increases credit risk for

privately guaranteed mortgage-backed securities.29 Rates in the jumbo market would

fall, they argue, if those loans could be packaged with conforming loans and sold to

investors.

Research also indicates that differences in volatility between high- and lowpriced homes can produce rate differentials. If housing prices are generally more

volatile in the jumbo market—particularly at the upper end of that market—lenders

should be protecting themselves against that risk by charging higher rates on jumbo
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 11

30. Ambrose, Buttimer, and Thibodeau, “A New Spin on the Jumbo/Conforming Loan Rate Differential.”

That study looked only at the Dallas housing market, which may be atypical. Moreover, it found that

conforming loan markets were more volatile than jumbo markets in the 1990s but less volatile in the

1980s.

31. Lundstedt, “The Influence of Non-Option-Related Variables Upon Corporate Default and Residential

Mortgage Termination,” p. 37. Analyzing a sample of more than 400,000 30-year fixed-rate

conventional mortgages, Lundstedt found that 24 percent of conforming loans were prepaid and only

0.46 percent were defaulted on during the 1980-1997 period. In contrast, 37 percent of jumbo loans

were prepaid and 1.06 percent were defaulted on.

32. Some analysts argue that deposit insurance could affect interest rate differentials. But to the extent that

subsidized federal deposit insurance lowers banks’ cost of funds, interest rates on all loans are likely

to be equally affected, so the differential is unlikely to change. See Pearce and Miller, Freddie Mac

and Fannie Mae: Their Funding Advantage and Benefits to Consumers. 

33. For a discussion of the range of strategies that borrowers have at their disposal to avoid taking out a

jumbo loan, see Patrick Barta, “Jumbo Mortgages? Not A Huge Problem,” Wall Street Journal,

December 7, 2000, p. C-1.

loans.30 In addition, some researchers have found that jumbo mortgages have higher

rates of prepayment and default.31 If lenders are accounting for those factors in their

pricing, the effect of the GSEs on interest rate differentials may be overstated. 

Conversely, Freddie Mac and Fannie Mae argue that the jumbo/conforming

differential understates the benefits of the housing GSEs.32 They contend that the

GSEs lower rates in the jumbo market because the additional capital from GSEs that

flows into the conforming market allows depository institutions to increase their

supply of funds to the jumbo market, reducing rates there. However, it is unclear

why depository institutions and other investors would funnel the extra funds largely

to the jumbo mortgage market rather than to the entire range of investment opportunities, including the conforming mortgage market. Consequently, that substitution

effect is unlikely to have a significant impact on rates in the jumbo market.

The GSEs also offer a related version of the substitution effect to bolster their

claim that the differential understates the benefits they confer. To the extent that

home buyers can substitute between conforming and jumbo mortgages, competition

from the conforming market may force down rates for some jumbo loans. Some

borrowers take out a first mortgage at the conforming limit and a second and riskier

(home equity) loan for the remainder of their mortgage rather than a single jumbo

mortgage. That strategy works best for borrowers when the total amount of their

loans is not too far above the conforming limit.33 Whether jumbo rates fall to remain

competitive with conforming rates for situations in which borrowers can substitute

between jumbo and conforming mortgages depends on the ability of originators to

absorb the lower returns that come from lower rates. 
12 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

34. See Pearce and Miller, Freddie Mac and Fannie Mae: Their Funding Advantage and Benefits to

Consumers. 

35. See Department of the Treasury, Government Sponsorship of the Federal National Mortgage

Association and the Federal Home Loan Mortgage Corporation (July 11, 1996), pp. 70-75.

36. In addition, banks may raise their posted rates to discourage too many applicants and then offer rates

below those posted rates to their best credit risks. 

The observed spread may also understate the benefits that Fannie Mae and

Freddie Mac pass through to borrowers if low-cost advances (loans from the Federal

Home Loan Banks to their members) disproportionately reduce rates in the jumbo

market. Some analysts argue that member banks and thrifts direct a significant

portion of their subsidized advances to the jumbo market.34

PREVIOUS RESEARCH ON JUMBO/CONFORMING DIFFERENTIALS 

The benefits that the GSEs pass through to borrowers in the form of lower interest

rates are usually measured as the difference in rates for conforming and jumbo

mortgages that exists after accounting for factors other than the GSEs that affect

those rates. Estimates of that differential are sensitive to both the time period being

examined and the methodological approach.

The simplest method for determining the rate spread is to compare the

advertized, or “posted,” rates that lenders offer for conforming and jumbo loans.

(Those rates are readily available in newspapers and on various Web sites.) However, for various reasons, those rates are only a rough measure.35 First, not all borrowers qualify for posted rates, which may be available only to the best credit risks.

Second, buyers who are particularly sensitive to interest rates may shop for the best

rate. In particular, borrowers in the jumbo market may have a greater incentive to

spend time searching for the lowest rate. Third, some analysts contend that lenders

use their posted rates to manage their flow of mortgage applications. For example,

if lenders are receiving too few applications, they lower rates.36 For those and other

reasons, most analysts use “contract” rates instead—the interest rates actually agreed

to by lenders and paid by borrowers.

To isolate the impact of GSEs on the jumbo/conforming spread, researchers

use statistical methods (regression analysis) to control for some of the other factors

that are thought to affect rates. In particular, they generally adjust for the overall size

of the mortgage and for its size relative to the price of the house—the loan-to-value

(LTV) ratio, which is a proxy for default risk (because the larger the loan as a percentage of the home’s price, the smaller the down payment and the greater the risk

of default). 
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 13

37. The range of 25 to 35 basis points reflects the sensitivity of the results to how the model is specified;

see Patric H. Hendershott and James D. Shilling, “The Impact of the Agencies on Conventional FixedRate Mortgage Yields,” Journal of Real Estate Finance and Economics, vol. 2, no. 2 (June 1989), pp.

101-115. 

38. ICF Inc., Effects of the Conforming Loan Limit on Mortgage Markets (prepared for the Department

of Housing and Urban Development, March 1990), pp. 23-26 and 53-58. 

39. Robert F. Cotterman and James E. Pearce, “The Effects of the Federal National Mortgage Association

and the Federal Home Loan Mortgage Corporation on Conventional Fixed-Rate Mortgage Yields,” in

Department of Housing and Urban Development, Studies on Privatizing Fannie Mae and Freddie Mac,

pp. 97-168.

40. Congressional Budget Office, Assessing the Public Costs and Benefits of Fannie Mae and Freddie

Mac, pp. 18-20.

Controlling for loan size, LTV ratios, and other variables (such as the month

in which the loan was originated), Patric Hendershott and James Shilling found that

contract rates on conforming loans were approximately 25 to 35 basis points lower

than rates on jumbo loans in 1986.37 Their sample was restricted to California, the

largest housing market, for the months of May, June, and July, which were chosen

as the peak of annual housing sales. A study by ICF, a consulting firm, estimated the

jumbo/conforming differential at 10 to 23 basis points in 1987 for a seven-state

sample.38 Robert Cotterman and James Pearce, using data from 1989 through 1993,

found that interest rates on conforming loans were 15 to 60 basis points lower than

rates on jumbo loans.39 Their differential varied greatly over that period and was

lowest in the second half of 1993. That study separately analyzed California and 11

other states with the largest jumbo markets. CBO’s 1996 study of the housing GSEs

used Cotterman and Pearce’s central estimate of 35 basis points as the amount by

which the benefits of GSE status lowered mortgage interest rates.40

The importance of controlling for factors other than the GSEs that affect

interest rates is highlighted by looking at unadjusted rates. Raw data on effective

rates (contract rates that factor in any initial fees paid to lenders) indicate that in four

of the past six years, unadjusted rates on jumbo loans were lower, not higher, than

rates on conforming loans (see Table 2). Thus, unadjusted data might suggest that

borrowers in the conforming market received little, if any, benefit from the GSEs’

presence in that market.
14 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

41. As a matter of convenience, CBO’s analysis excludes the housing markets in Hawaii and Alaska, where

the ceiling on conforming loans is 50 percent higher than in the other 48 states. The estimate of

differentials is a fixed effect calculated in a regression equation that controls for various other factors

that might influence interest rates.

TABLE 2. UNADJUSTED DIFFERENCES IN INTEREST RATES BETWEEN JUMBO

AND CONFORMING LOANS, 1995-2000

Effective Rate on

 Fixed-Rate Jumbo

 Loans (Percent)

Effective Rate on

 Fixed-Rate Conforming

Loans (Percent)

Differential

 (Basis points)a

1995 8.16 8.18 -2

1996 8.08 7.98 10

1997 7.88 7.89 -1

1998 7.32 7.18 14

1999 7.38 7.44 -6

2000 8.24 8.25 -1

SOURCE: Congressional Budget Office based on data from the Federal Housing Finance Board and the board’s Monthly

Interest Rate Survey, Table 21.

NOTE: Effective rates are contract rates adjusted for any initial fees and other charges paid to the lender (amortized over

10 years). These raw data are not adjusted for various other factors that affect mortgage interest rates.

a. A basis point is one-hundredth of a percentage point.

ESTIMATING NATIONAL JUMBO/CONFORMING DIFFERENTIALS 

FOR 30-YEAR FIXED-RATE MORTGAGES SINCE 1995

CBO recently produced new estimates of jumbo/conforming differentials in effective

rates for the 48 contiguous states.41 Following the approach of Cotterman and

Pearce, CBO did not estimate differentials directly. Instead, it estimated the effective

rate on individual mortgage loans as the dependent variable in a regression equation,

with the natural logarithm of loan size, the LTV ratio, the month of origination, the

type of lender, a conforming-loan indicator (to signify whether the mortgage is a

conforming loan), and a new-house indicator serving as independent, or explanatory,

variables. The coefficient on the conforming-loan indicator picks up the size of the

differential. Because the differential is expected to vary considerably over time, CBO
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 15

42. The Federal Housing Finance Board computes effective interest rates by amortizing points over 10

years, which it assumes is the effective maturity of home mortgages. Because homeowners who use

points to buy down their mortgage rate are likely to stay in their home longer than average, that method

may overstate their effective rate. However, unless jumbo borrowers respond differently to points than

conventional borrowers do, the method used to translate points into effective rates should not bias the

estimate of the differential.

ran separate regressions for each quarter from 1995 through the second quarter of

2000. 

The main regression equation that CBO used (Equation 1) takes the following

form:

Effective rate = constant + b1(log loan size) + b2 (LTV) + b3(Month) + 

b4(Lender Type) + b5(New House) + b6(Conforming Loan) + error term 

with bi

 as the coefficient on the explanatory variable that follows in parentheses. 

Effective loan rates are contract rates adjusted for any points (initial fees and

charges expressed as a percentage of the loan) paid to the lender. Lenders sometimes

charge points to cover their origination costs. They also offer lower rates and higher

points to appeal to buyers who are less likely to prepay a mortgage. In general,

buyers who expect to stay in a home for a significant period (say, more than five

years) often find it financially advantageous to “buy down” their interest rate by

paying more points up front.42 

Effective rates can be expected to decline as loan size increases because

origination and servicing costs are essentially fixed and fall as a percentage of the

loan amount as loan size rises. LTV ratios measure default risk, so effective rates

should increase with higher LTV ratios. The constant term in the equation and the

indicators for month of loan origination pick up the effect of the level of other

interest rates, such as Treasury rates, on mortgage rates. (Given that CBO estimated

equations using cross-section data, little would be gained by adding Treasury rates

directly as an explanatory variable.) 

CBO expects the coefficient on the indicator for lender type—whether

mortgage companies (Lender 1), commercial banks (Lender 2), or thrifts (which

serve as the benchmark for that variable)—to be statistically insignificant. Although

some institutions, such as savings and loans, may have had comparative advantages

in originating mortgages in the past, the playing field is widely believed to be more

level now. The new-house indicator is intended to pick up the possibility that rates

are lower for new homes than for older homes, either because of lower default rates

or because builders sometimes offer access to preferential financing through an

authorized lender as an incentive to buy rather than lowering the price of a home. For
16 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

43. Complete details about the survey and its data set are available at the Federal Housing Finance Board’s

Web site (www.fhfb.gov/MIRS/MIRS.htm).

44. Fannie Mae and Freddie Mac can and do buy mortgages guaranteed by the FHA; however, those

purchases are an insignificant part of their business.

45. The market makes little or no distinction between mortgages used to purchase a home and those used

for refinancing. Purchases and guarantees of refinanced loans account for more than half of the GSEs’

business in some years. See Office of Federal Housing Enterprise Oversight, 2000 Report to Congress,

p. 40. 

46. The board screens out observations in which the contract rate is more than 100 basis points below the

previous month’s average rate.

47. “Teaser” rates are more prevalent on adjustable-rate mortgages; however, fixed-rate mortgages may

have “buydowns,” which act like teasers but are usually paid by the seller of the house.

example, some builders induce buyers to use their lender in exchange for paying

closing costs on the loan. 

The Data Set

CBO’s analysis used data from the Federal Housing Finance Board’s Monthly

Interest Rate Survey, which is the only large, publicly available sample of contract

rates for first mortgages.43 MIRS excludes several types of loans: those insured or

guaranteed by either the Federal Housing Administration (FHA) or the Department

of Veterans Affairs44; loans that refinance existing mortgages45; and loans above a

certain size ($636,750 in 2000), which are likely to be in the thinnest segment of the

mortgage market. Lenders who specialize in serving the subprime market (which

targets borrowers with blemished credit histories) are excluded as well. Despite

those exclusions, the sample is still large. In 1999, for example, it contained over

250,000 fixed- and adjustable-rate loans. All of those loans closed in the last five

days of the month, which is the survey’s sampling period. Fannie Mae and Freddie

Mac use information from MIRS to determine the increase in the conforming-loan

ceiling each year. (However, the survey does not indicate which conforming loans

are purchased by Fannie Mae or Freddie Mac.)

The MIRS data set suffered from measurement errors in the 1980s and early

1990s. For example, lenders frequently misreported adjustable-rate mortgages as

fixed-rate mortgages. Consequently, Cotterman and Pearce’s 1996 study filtered out

interest rates that appeared to be too low for fixed-rate mortgages. Because the

Federal Housing Finance Board now screens the data more effectively for errors in

reporting rates, the data quality is much higher.46 As a result, CBO minimized the

use of filters, although it did screen for “buydown” rates—rates that are lower in the

first year than in subsequent years.47 
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 17

48. Some analysts qualify that assertion—for example, by saying that the lack of uniformity in state laws

covering bankruptcy and foreclosure may affect mortgage rates and loan size. See Karen Pence,

“Foreclosing on Opportunity? State Laws and Mortgage Credit” (draft, University of Wisconsin at

Madison, December 2000). 

49. The log-linear specification of loan size in the regression makes both the loan-size variable and the

conforming-loan variable sensitive to very small or large loans. Cotterman and Pearce also limited

their sample for that reason, but with a higher upper bound. 

50. No evidence exists that would allow CBO to judge whether omitting private mortgage insurance from

the analysis affects the estimates of the interest rate differential. A typical fee for private mortgage

insurance is 35 to 50 basis points per year for a 90 percent LTV mortgage. For an analysis of private

mortgage insurance, see Stanley D. Longhofer, “PMI Reform: Good Intentions Gone Awry,” Economic

Commentary, Federal Reserve Bank of Cleveland (March 15, 1997); and Glenn B. Canner, Wayne

Passmore, and Monisha Mittal, “Private Mortgage Insurance,” Federal Reserve Bulletin, vol. 80, no.

10 (October 1994), pp. 883-899. 

MIRS also reports a weight for each lender that the board uses to adjust the

sample’s distribution to that of the general population. The board calculates those

weights on the basis of the type of mortgages the institution holds relative to those

of other lenders, the type of institution, and its geographic location. For instance, if

the sample contains a smaller proportion of savings and loans in Texas than exists

in the population, loans reported by Texas thrifts will receive a weight greater than

1. Cotterman and Pearce’s 1996 study used the weighted observations, but CBO

relied on the unweighted observations because the market for mortgages is now

essentially a national one in which neither region nor type of lender is likely to be

systematically related to interest rates.48 (CBO did, however, run regressions using

the sample weights as a test for robustness and comparability.)

Although CBO’s analysis filtered out some observations believed to be

misreported or unlikely to meet the GSEs’ underwriting standards, in general, CBO

screened out fewer observations than did some other studies to avoid erroneously

discarding valid data. Cotterman and Pearce, for example, restricted their sample to

loans with an LTV ratio of at least 70 percent. CBO’s sample includes loans with

LTV ratios between 20 percent and 97 percent. (Loans smaller than 20 percent of the

home’s value might be second mortgages, and until recently, the GSEs did not buy

loans with LTV ratios over 97 percent.) CBO also restricted the data to loans

between 25 percent and 200 percent of the conforming limit for technical reasons.49

The MIRS data set has no information about any of the characteristics of

individual borrowers—such as credit history, income, or wealth—that affect the rates

borrowers pay. It also lacks information about local real estate conditions. Since

jumbo loans have higher default rates than conforming loans, those omissions could

cause CBO’s estimate of the differential to be too high. In addition, MIRS does not

identify which loans have private mortgage insurance or its cost.50 That omission
18 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

51. The 22 quarterly estimates have an unweighted mean of 22.8 basis points and a standard deviation (a

statistical measure of the distribution of observations around the mean) of 7.9 basis points. As a test

for robustness and comparability, CBO ran the same regression with the weighted observations and

found relatively little difference. Using the weighted observations raised the differential by less than

0.5 basis points, on average. One reason that CBO relied on unweighted observations is that it was

uncertain how the weights were determined and how they should be interpreted. 

52. Rates on Treasury securities, for example, fell in the first half of 1998, at least in part because of the

effects of the Asian crisis. But spreads in other financial markets were relatively stable during the first

half of 1998 compared with the second half. For a description of developments in international

financial markets and domestic housing markets during that period, see Federal Reserve Board,

“Monetary Policy Report to the Congress,” Federal Reserve Bulletin, vol. 84, no. 8 (August 1998), pp.

586, 589-591, and 600-603.

could affect the precision of the estimated coefficients on the LTV ratios. For

example, a loan with a 10 percent down payment and private mortgage insurance

may be as safe, or even safer, to the lender as one with a 20 percent down payment

but no insurance.

Results

CBO’s analysis found that interest rates on jumbo mortgages remained higher than

rates on conforming mortgages after controlling for some of the other loan

characteristics that affect those rates. Based on Equation 1, the average differential

(or adjusted spread) between the first quarter of 1995 and the second quarter of 2000

was 23 basis points. The quarterly differentials ranged from 4 basis points to 35

basis points (see Table 3).51 All but one of the quarterly estimates (the 4.1 basis point

differential for the first quarter of 1995) were statistically significant—that is,

statistically different from zero. 

Spreads in other financial markets vary substantially over time as premiums

for risk and liquidity change; consequently, the estimated differentials in the mortgage market can also be expected to vary. The high differentials in 1998, which

averaged 32 basis points, stand out. The financial markets’ early flight-to-quality

response to the Asian currency crisis, which began in 1987, is the most likely

explanation for the high differentials in the first half of 1998. Other analysts point

to unusually heavy activity in mortgage markets.52 The flight to quality more clearly

explains the large differentials in the second half of the year, which persisted into the

first quarter of 1999. The spreads between Treasury rates and interest rates for most
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 19

TABLE 3. DIFFERENCES IN EFFECTIVE INTEREST RATES BETWEEN JUMBO AND

CONFORMING 30-YEAR FIXED-RATE LOANS, BY QUARTER, WITH LOAN

SIZE SPECIFIED AS A NATURAL LOG, 1995-2000 (In basis points)

First

Quarter

 Second

Quarter

Third

Quarter

Fourth

Quarter Average

1995

Differential 4.1a 20.7 32.3 32.0 22.3

Standard Error of Differential 4.3 1.9 1.6 1.6

Number of Observations 5,987 14,118 17,904 13,927

1996

Differential 28.3 19.0 17.4 15.6 20.1

Standard Error of Differential 1.8 2.0 1.7 1.6

Number of Observations 16,307 15,620 13,285 12,525

1997

Differential 17.3 10.3 17.5 22.8 17.0

Standard Error of Differential 1.6 1.1 1.0 1.0

Number of Observations 14,637 24,865 28,775 28,821

1998

Differential 33.1 30.2 30.2 35.2 32.2

Standard Error of Differential 0.9 0.8 0.8 0.8

Number of Observations 33,193 49,061 48,064 44,592

1999

Differential 28.4 19.0 20.3 23.7 22.9

Standard Error of Differential 1.0 0.8 1.3 1.3

Number of Observations 38,633 43,810 31,705 24,949

2000b

Differential 19.5 25.5 22.5

Standard Error of Differential 1.5 1.3

Number of Observations 20,860 33,539

1995-2000

Average Quarterly Differential 22.8

SOURCE: Congressional Budget Office based on the Federal Housing Finance Board’s Monthly Interest Rate Survey.

NOTES: Loans that were more than 25 percent below the conforming limit or 200 percent above the limit were dropped from

the analysis, as were mortgages with loan-to-value ratios below 20 percent or above 97 percent.

A basis point is one-hundredth of a percentage point.

a. Coefficient is statistically insignificant at the 10 percent level (that is, it may be zero).

b. CBO’s estimates cover only the first two quarters of 2000.
20 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

53. For analysis of the market’s changing risk perceptions during that period, see Counsel of Economic

Advisers, Economic Report of the President (February 1999), pp. 55-62; and Federal Reserve Board,

“Monetary Policy Report to the Congress,” Federal Reserve Bulletin, vol. 85, no. 3 (March 1999), pp.

147-177. First Manhattan, a consulting firm hired by Fannie Mae, argues that increases in Fannie

Mae’s monthly purchases significantly lowered rates for new conforming mortgages in September and

October 1998. See Alden L. Toevs, “A Critique of the CBO’s Sponsorship Benefit Analysis” (report

submitted by the First Manhattan Consulting Group to Fannie Mae, September 6, 2000). Also see

Capital Economics, “An Economic Analysis of Freddie Mac’s (and Fannie Mae’s) Contribution to

Liquidity in the Residential Mortgage-Backed Securities Market During the Credit Crunch of 1998”

(unpublished paper, May 2000). The Congressional Budget Office’s estimates of both the size and

the timing of the effect of those purchases differ from Toevs’s estimates.

54. Tables for all of the regressions not shown are available from the author.

securities expanded greatly in the second half of 1998 after Russia defaulted on its

bonds in mid-August and demand for U.S. government securities increased.53

In CBO’s analysis, estimated differentials were as large at the end of the

period as at the beginning. In contrast, Cotterman and Pearce found differentials

generally narrowing over the period of their study as the secondary market for jumbo

mortgage-backed securities became increasingly liquid.

The Role of Other Explanatory Variables

By specifying loan size in natural logarithmic form, Equation 1 embodies the premise

that as the loan amount rises, the interest rate falls because of the declining average

cost of originating and servicing loans. As expected, the results show effective

mortgage rates declining with loan size. (A complete set of regression results for

Equation 1 appears in Appendix A.) 

By contrast, rates generally rise with loan-to-value ratios. The regression

used several ranges of LTVs: 50 percent to 70 percent (LTV1), 70 percent to 80

percent (LTV2), 80 percent to 90 percent (LTV3), and 90 percent to 97 percent

(LTV4). The excluded category of loans, those with LTV ratios of 20 percent to 50

percent, has the lowest default risk in the sample, so it serves as the benchmark. The

coefficients on the variable LTV4 for 1999 indicate that the effective rates on loans

with LTV ratios between 90 percent and 97 percent were 9 to 13 basis points higher

than rates on loans with LTV ratios below 50 percent (see Table A-5 in Appendix A).

However, over the 1995-2000 period, the coefficients on the LTVs were not always

statistically significant.54 That may not be surprising given that CBO could not

identify the presence of private mortgage insurance, which can substitute for a higher

mortgage rate. The GSEs generally require that private mortgage insurance be taken

out on loans with LTV ratios above 80 percent. 
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 21

55. When CBO ran the regression equation using annual rather than quarterly data, it was able to explain

37 percent of the variation in effective rates over the period. The adjusted r-squares ranged from 0.13

to 0.52. 

56. See Cotterman and Pearce, “The Effects of the Federal National Mortgage Association and the Federal

Home Loan Mortgage Corporation on Conventional Fixed-Rate Mortgage Yields,“ pp. 161-62, Tables

16 and 17. 

Interest rates also differ by type of lender in some periods, but those differences were not always statistically significant or persistent. For example, rates on

loans originated by commercial banks were 2 to 6 basis points higher than rates on

loans originated by thrifts in the third and fourth quarters of 1998 but 5 to 12 basis

points lower than rates on loans originated by thrifts in 1999. Rates on loans

originated by mortgage companies were slightly lower than those on loans originated

by thrifts in the first two quarters of 1999 but significantly higher in the last two

quarters. 

Effective rates were not consistently lower on loans for new homes than on

loans for existing homes; sometimes they were higher, as in the first quarter of 1999.

Moreover, the new-home variable was often statistically insignificant. 

Given that CBO could not control for differences in borrowers’ credit quality

or the expected price volatility of the house, CBO’s method cannot be expected to

explain much of the variation in effective rates. Still, it is noteworthy that it explains

less than 10 percent of the variation in effective loan rates (as indicated by the low

adjusted r-squares shown in Appendix A).55 In contrast, Cotterman and Pearce were

able to explain more than 25 percent of the variation in rates in their earlier sample

using observations only from California and 20 percent of the variation in rates using

observations from 11 states.56 The lower explanatory power of CBO’s approach may

result from its focus on the national market, use of a slightly different dependent

variable, and later time period.

THE ROBUSTNESS OF CBO’S ESTIMATES

As a check on the reliability of its estimates, CBO fitted the MIRS data to a variety

of alternatively specified equations. The results varied slightly with changes in the

form of the equation. For example, specifying loan size using a quadratic transformation, using 15-year mortgages rather than 30-year mortgages, and sampling

loans with no fees or other charges produced small variations in the jumbo/conform-
22 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

57. Although CBO cannot be sure that the adjusted spreads are not sensitive to the presence of private

mortgage insurance, one admittedly imperfect test suggests that any effect is likely to be fairly small.

When CBO excluded loans with down payments of less than 20 percent, which are the loans most

likely to carry private mortgage insurance, the estimated jumbo/conforming differential was 23.9 basis

points, just 1 basis point higher than the estimates for Equation 1.

58. Department of the Treasury, Government Sponsorship of the Federal National Mortgage Association

and the Federal Home Loan Mortgage Corporation (July 11, 1996), p. 73. CBO’s approach assumes

that loan size is not affected by interest rates. In reality, however, borrowers are likely to adjust the

size of their mortgage to changes in interest rates. For example, they may take out bigger loans when

rates are lower. That potential endogenicity problem may bias CBO’s coefficients on loan size and

perhaps also spill over to the dummy variable for conforming loans. 

59. That analysis, which is not a regression result, is available from the author. The data also show a

relatively large number of loans either right at or just below the conforming limit and few loans

immediately above or even 10 percent above the limit. That clustering is additional evidence that rates

are lower for conforming loans.

ing differential.57 CBO also compared its results with raw nonregression measures

of the differential and with recent estimates by other researchers.

Alternative Ways to Specify Loan Size

Analysts have suggested that estimates of the jumbo/conforming differential may be

sensitive to the way in which the explanatory variable for loan size is specified.58

The reason is that some factors—such as origination and servicing costs, which are

relatively fixed with respect to loan size—should cause mortgage rates to decline as

loan size increases, but other factors may push up rates as loan size increases. As an

example of the latter, because the benefits of refinancing rise with loan size, so does

the risk of prepayment. In addition, the jumbo market, particularly at the high end,

is smaller than the conforming market in almost all areas of the country, which means

greater underlying volatility in home prices and thus higher risk of default. Greater

prepayment and default risks would put upward pressure on rates as loan size

increases. 

If rates do start to rise with loan size beyond some point, that effect cannot

be measured by the logarithmic functional form. CBO’s analysis of raw MIRS data

suggests that may be a problem. For example, the data for the second quarter of

1999—unadjusted for either month of origination or LTV ratio—indicate that rates

(adjusted for points) fall steadily with loan size up to the conforming limit, make a

discrete jump of 19 basis points as soon as the conforming limit is exceeded, then

trend down slightly, before rising just at or immediately above 200 percent of the

conforming limit.59

Specifying loan size in quadratic form is an alternative to the logarithmic form.

CBO’s equation with quadratic form uses loan size (Size) and the square of loan size
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 23

60. The quadratic transformation imposes a symmetry on rate changes; however, rates may fall more

quickly than they rise with loan size. If that is the case, in the above example rates may start rising

before a loan amount of $341,600. 

61. When loan size was specified as a quadratic transformation, the mean for the 22 quarterly estimates

of the spread was 21.8 basis points, with a standard deviation of 7.7 basis points.

62. The average of annual estimates is slightly different from the unweighted average of quarterly estimates

because mortgage originations show some seasonal variation. For example, they tend to be lower than

average in the first quarter. 

(Size Squared) as explanatory variables. That functional specification allows rates

to first fall and then rise with loan size. (The coefficient on the Size variable is

negative and the coefficient on the Size Squared variable is positive.) According to

regression analysis of that equation, the coefficients on the variables for loan size in

the second quarter of 1999 indicate that rates fell with loan size until

$341,600—about $100,000 above the conforming-loan limit in that year—and then

increased (with all else held constant).60 For most quarters, the quadratic

specification of loan size does a better job of handling the more expensive loans in

the sample than the log specification does, as indicated by coefficients on Size and

Size Squared that generally are statistically significant. The estimated

jumbo/conforming spread averages about 22 basis points over the period with a

quadratic specification versus 23 basis points when loan size is specified in log form

(see Table 4).61 The similarity of those estimates is evidence of their robustness.

(Complete regression results for the alternative equation appear in Appendix B.)

Differentials for 15-Year Fixed-Rate Mortgages Relative to 30-Year Mortgages

Annual estimates for 15-year fixed-rate mortgages show an average jumbo/conforming differential of 25 basis points over the 1995-2000 period (see Table 5). The fact

that the differential is only about 1 basis point higher than the comparable spread for

30-year mortgages using annual estimates is another sign of robustness.62 Some

difference in spreads can be expected because the conforming and jumbo markets for

15-year fixed-rate loans are much smaller and therefore less liquid than the markets

for 30-year loans. 

Using Rates on No-Point Loans to Determine Differentials

Many ways exist to adjust mortgage interest rates for the fees, points, and other

charges that most borrowers pay, and the method used might affect estimates of

spreads. In the MIRS data, those additional costs are amortized over 10 years and
24 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

TABLE 4. DIFFERENCES IN EFFECTIVE INTEREST RATES BETWEEN JUMBO AND

CONFORMING 30-YEAR FIXED-RATE LOANS, BY QUARTER, WITH LOAN

SIZE SPECIFIED IN QUADRATIC FORM, 1995-2000 (In basis points)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter Average

1995 

Differential 5.4a 18.4 34.4 29.4 21.9

Standard Error of Differential 6.1 2.8 2.3 2.4

Number of Observations 5,987 14,118 17,904 13,927

1996

Differential 28.7 23.7 18.0 12.7 20.8

Standard Error of Differential 2.5 3.0 2.5 2.3

Number of Observations 16,307 15,620 13,285 12,525

1997

Differential 18.7 8.3 17.2 21.5 16.4

Standard Error of Differential 2.3 1.6 1.4 1.3

Number of Observations 14,637 24,865 28,775 28,821

1998

Differential 32.0 28.6 29.5 29.3 29.9

Standard Error of Differential 1.3 1.1 1.1 1.1

Number of Observations 33,193 49,061 48,064 44,592

1999

Differential 27.2 17.2 17.0 16.8 19.6

Standard Error of Differential 1.4 1.1 1.9 1.8

Number of Observations 38,633 43,810 31,705 24,949

2000b

Differential 20.2 25.0 22.6

Standard Error of Differential 2.2 1.9

Number of Observations 20,860 33,539

1995-2000

Average Quarterly Differential 21.8

SOURCE: Congressional Budget Office based on the Federal Housing Finance Board’s Monthly Interest Rate Survey.

NOTES: Loans that were more than 25 percent below the conforming limit or 200 percent above the limit were dropped from

the analysis, as were mortgages with loan-to-value ratios below 20 percent or above 97 percent.

A basis point is one-hundredth of a percentage point.

a. Coefficient is statistically insignificant at the 10 percent level (that is, it may be zero). 

b. CBO’s estimates cover only the first two quarters of 2000.
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 25

63. MIRS still assumes that 10 years is the average life of a mortgage, but that assumption may be dated.

Many market participants argue that the average life of a 30-year fixed-rate mortgage is now closer to

five to seven years. The cost of refinancing a loan has fallen, and borrowers are now more aware of

the advantages of refinancing when interest rates decline. 

64. Borrowers in the jumbo market may well behave differently than those in the conforming market. For

example, borrowers in different tax brackets may respond differently to the trade-off between points

and the interest rate paid. Points paid on a mortgage are generally fully tax-deductible in the year the

mortgage is originated. Because jumbo borrowers are more likely to be in higher tax brackets than

borrowers with conforming loans, they might find paying more points a more attractive trade-off than

conforming borrowers would. However, the MIRS data set reveals no consistent pattern with respect

to the points paid by jumbo and conforming borrowers. In some years, the average points paid on

jumbo loans exceed the points paid on conforming loans, but between 1997 and 2000, fewer points

were paid on jumbos. See MIRS Table 21, “Terms on Conventional Single-Family Mortgages, All

Homes, Jumbo and Nonjumbo Mortgages,” available at www.fhfb.gov/MIRS/

MIRS_loans_downloads.htm.

TABLE 5. ANNUAL DIFFERENCES IN EFFECTIVE INTEREST RATES BETWEEN JUMBO

AND CONFORMING 15-YEAR AND 30-YEAR FIXED-RATE LOANS, WITH

LOAN SIZE SPECIFIED AS A NATURAL LOG, 1995-2000 (In basis points)

1995 1996 1997 1998 1999 2000a

Average,

1995-

2000

15-Year Mortgages

Differential 24.6 19.2 22.1 29.2 26.8 29.7 24.9

Standard Error of Differential 2.5 2.4 1.6 1.1 1.5 3.2

Number of Observations 7,903 9,151 12,531 23,835 15,774 4,516

30-Year Mortgages

Differential 27.0 20.3 17.4 32.0 23.4 22.9 23.9

Standard Error of Differential 1.0 0.9 0.6 0.4 0.5 1.0

Number of Observations 51,936 57,737 97,098 174,910 139,097 54,399

SOURCE: Congressional Budget Office based on the Federal Housing Finance Board’s Monthly Interest Rate Survey.

NOTES: Regressions were run for each year using dummy variables for the months, with loan size specified in log form.

The low standard errors indicate that all of the estimates of the coefficients for the differentials are statistically

significant.

A basis point is one-hundredth of a percentage point.

a. CBO’s estimates cover only the first six months of 2000.

added to the contract rate to determine the effective rate.63 Whether that method

overstates or understates effective rates is uncertain. CBO has no reason to believe

that the adjustment distorts the jumbo/conforming differential (in the absence of

different behavior by borrowers in the two markets).64 Nevertheless, for the sake of
26 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

65. The mean of the 22 quarterly estimates for no-point loans was 17.6 basis points, with a standard

deviation of 9.7 basis points. 

66. The range of 110 percent to 120 percent of the conforming limit was chosen to ensure a large number

of observations. There is widespread evidence of borrowers clustering at or just below the conforming

limit, with few loans made 10 percent above the limit. Most borrowers can easily avoid being just over

the conforming limit by taking out a second mortgage. Consequently, the relatively few mortgages in

the range of 100 percent to 110 percent of the limit may not be representative of all jumbo borrowers.

67. A full reporting of mortgage rates, including the adjustment for points, as a function of loan size by

quarter is available from the author.

comparison, CBO also estimated differentials using contract rates for loans with no

points, fees, or other charges.

In general, the spreads for no-point loans were smaller than those for the

entire sample (with loan size specified in natural logarithmic form and regressions

run on quarterly data). During the 1995-2000 period, quarterly spreads for no-point

loans averaged 18 basis points whereas those for the entire sample of 30-year fixedrate loans averaged 23 basis points (see Table 6).65 However, the number of

observations used in that regression was significantly smaller, since no-point loans

make up less than one-quarter of the loans in the entire sample. (Complete

regression results for no-point loans appear in Appendix C.)

Nonregression Methods to Estimate Differentials

Comparing effective rates on loans just above the conforming limit with those at the

limit provides a raw measure of spreads unadjusted for other factors (such as LTV

ratios) that affect rates. CBO’s analysis indicates that the difference between rates

at 110 percent to 120 percent of the conforming limit and those at the limit averaged

22.5 basis points, or about 0.3 basis points less than the estimate of 22.8 basis points

using regression analysis (see Table 7).66 That result is based on the difference

between the mean observations in those categories of loan size; however, given the

size of the estimate’s standard deviations, none of the differences are statistically

significant.67

Comparisons with Other Estimates

CBO’s finding of average jumbo/conforming differentials in the range of 18 to 25

basis points is consistent with several recent studies prepared for Fannie Mae and

Freddie Mac, a study by academic economists, and another by the Federal Reserve
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 27

TABLE 6. DIFFERENCES IN EFFECTIVE INTEREST RATES BETWEEN JUMBO AND

CONFORMING 30-YEAR FIXED-RATE LOANS WITH NO POINTS, BY

QUARTER, 1995-2000 (In basis points)

First

Quarter

 Second

Quarter

Third

Quarter

Fourth

Quarter Average

1995

Differential 9.9a 20.8 30.6 32.1 23.4

Standard Error of Differential 9.3 3.4 2.7 2.6

Number of Observations 1,696 4,702 6,262 5,166

1996

Differential 35.2 18.8 5.1a 5.3 16.1

Standard Error of Differential 2.8 3.4 3.2 2.7

Number of Observations 5,530 4,790 4,102 4,199

1997

Differential 12.1 3.1a 10.5 18.1 11.0

Standard Error of Differential 2.5 2.2 1.9 1.8 

Number of Observations 4,524 5,215 6,447 5,865

1998

Differential 23.5 21.3 24.7 30.5 25.0

Standard Error of Differential 1.7 1.4 1.5 1.6

Number of Observations 8,173 11,399 9,865 10,047

1999

Differential 23.4 12.5 3.8a 22.2 15.5

Standard Error of Differential 2.2 1.9 3.3 3.1

Number of Observations 8,859 8,487 5,635 5,084

2000b

Differential 9.4 13.3 11.4

Standard Error of Differential 3.5 2.6

Number of Observations 4,717 8,702

1995-2000

Average Quarterly Differential 17.6

SOURCE: Congressional Budget Office based on the Federal Housing Finance Board’s Monthly Interest Rate Survey.

NOTES: Loans that were more than 25 percent below the conforming limit or 200 percent above the limit were dropped from

the analysis, as were mortgages with loan-to-value ratios below 20 percent or above 97 percent.

A basis point is one-hundredth of a percentage point.

a. Coefficient is statistically insignificant at the 10 percent level (that is, it may be zero). 

b. CBO’s estimates cover only the first two quarters of 2000.
28 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

TABLE 7. UNADJUSTED DIFFERENCES IN INTEREST RATES BETWEEN JUMBO

AND CONFORMING 30-YEAR FIXED-RATE LOANS, BY QUARTER, 1995-

2000 

(In percentage points)

First

Quarter

 Second

Quarter

Third

Quarter

Fourth

Quarter Average

1995

Effective Rate at 100 Percent 9.07 8.27 7.88 7.63

of the Conforming Limit (0.46) (0.49) (0.38) (0.35)

Effective Rate at 110 to 120 Percent 9.07 8.36 8.23 7.92

of the Conforming Limit (0.65) (0.58) (0.37) (0.40)

Unadjusted Differential (Basis points) 0 9 35 29 18.3

Regression Differential (Basis points) 4.1 20.7 32.3 32.0 22.3

(4.3) (1.9) (1.6) (1.6)

1996

Effective Rate at 100 Percent 7.52 8.17 8.34 8.09

of the Conforming Limit (0.41) (0.38) (0.31) (0.33)

Effective Rate at 110 to 120 Percent 7.78 8.50 8.52 8.19

of the Conforming Limit (0.47) (0.58) (0.43) (0.45)

Unadjusted Differential (Basis points) 26 33 18 10 21.8

Regression Differential (Basis points) 28.3 19.0 17.4 15.6 20.1

(1.8) (2.0) (1.7) (1.6)

1997

Effective Rate at 100 Percent 7.90 8.14 7.72 7.49

of the Conforming Limit (0.30) (0.32) (0.33) (0.31)

Effective Rate at 110 to 120 Percent 8.13 8.24 7.92 7.76

of the Conforming Limit (0.39) (0.36) (0.34) (0.37)

Unadjusted Differential (Basis points) 23 12 20 27 20.5

Regression Differential (Basis points) 17.3 10.3 17.5 22.8 17.0

(1.6) (1.1) (1.0) (1.0)

1998

Effective Rate at 100 Percent 7.20 7.21 7.05 6.84

of the Conforming Limit (0.26) (0.26) (0.29) (0.27)

Effective Rate at 110 to 120 Percent 7.55 7.51 7.33 7.24

of the Conforming Limit (0.36) (0.32) (0.33) (0.43)

Unadjusted Differential (Basis points) 35 30 28 40 33.3

Regression Differential (Basis points) 33.1 30.2 30.2 35.2 32.2

(0.9) (0.8) (0.8) (0.8)

1999

Effective Rate at 100 Percent 6.94 7.15 7.73 7.88

of the Conforming Limit (0.30) (0.31) (0.36) (0.31)

Effective Rate at 110 to 120 Percent 7.22 7.30 7.87 8.14

of the Conforming Limit (0.35) (0.36) (0.50) (0.51)

Unadjusted Differential (Basis points) 28 15 14 26 20.8

Regression Differential (Basis points) 28.4 19.0 20.3 23.7 22.9

(1.0) (0.8) (1.3) (1.3)

(Continued)
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 29

68. That range of 18 to 25 basis points includes the average differentials with the two alternative specifications for loan size, with annual estimates, and with no-point loans (shown in Tables 3 through 6).

69. Toevs, “A Critique of the CBO’s Sponsorship Benefit Analysis,” p. 10. 

TABLE 7. CONTINUED

First

Quarter

 Second

Quarter

Third

Quarter

Fourth

Quarter Average

2000a

Effective Rate at 100 Percent 8.26 8.33

of the Conforming Limit (0.32) (0.29)

Effective Rate at 110 to 120 Percent 8.40 8.57

of the Conforming Limit (0.52) (0.45)

Unadjusted Differential (Basis points) 14 24 19.0

Regression Differential (Basis points) 19.5 25.5 22.5

(1.5) (1.3)

1995-2000

Average Quarterly Unadjusted 

Differential (Basis points) 22.5

Average Quarterly Regression

Differential (Basis points) 22.8

SOURCE: Congressional Budget Office based on the Federal Housing Finance Board’s Monthly Interest Rate Survey.

NOTE: The standard deviations for the effective rates are shown in parentheses, and the standard errors for the conforming

dummy variable are shown in parentheses for the estimate of the differential using regression techniques and

specifying loan size in log form.

a. CBO’s estimates cover only the first two quarters of 2000.

Board.68 However, researchers disagree about whether those adjusted spreads underestimate or overestimate the impact of the housing GSEs on mortgage interest rates.

Alden Toevs of First Manhattan Consulting Group, under contract with Fannie

Mae, concludes that the jumbo/conforming differential for the 1994-1999 period was

19 basis points, but he argues that the figure understates the benefits passed through

to borrowers by 10 basis points.69 He and James Pearce and James Miller also contend that to the extent that investments in the conforming and jumbo markets are

substitutes, some investors and depository institutions may react to the lower rates

and yields on GSE securities and conventional mortgages by increasing their demand
30 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

70. Pearce and Miller, Freddie Mac and Fannie Mae: Their Funding Advantage and Benefits to

Consumers, pp. 12-13. 

71. The market for GSE securities is very deep and highly liquid, so it is unclear whether much capital

would be reallocated to other markets. Moreover, the market for jumbo mortgage-backed securities

is very different from the market for conforming securities. That segmentation may limit the extent to

which any of the reallocation is directed to the jumbo market. 

72. That study filtered the MIRS data to create a more homogeneous set of observations for 30-year fixedrate loans. For example, Pearce used only loans with LTV ratios between 70 percent and 90 percent

and excluded loans with balances below 20 percent or above 200 percent of the conforming limit. In

contrast to Cotterman and Pearce’s previous study, the update did not use the MIRS weights because

they could have distorted the results in some cases. Pearce’s regression models accounted for about

25 percent of the variation in effective mortgage rates. See James Pearce, “Conforming Loan

Differentials: 1992-1999" (Welch Consulting, November 22, 2000). 

73. Pearce and Miller assume a stable relationship between the adjustable-rate and fixed-rate differential

and the share of adjustable-rate mortgages (ARMs). Specifically, their indirect estimate assumes that

a decline of 30 basis points in the spread between ARMs and fixed-rate mortgages will produce a

for privately guaranteed mortgage-backed securities and supplying more funds to the

jumbo market.70 

That argument makes little sense, however. The credit enhancement of conforming mortgages does not make jumbo mortgages a more attractive investment.

Consequently, no reason exists that Fannie Mae and Freddie Mac would cause more

investors to fund jumbo mortgages. The mortgage-backed securities guaranteed by

the GSEs do provide investors with a safer investment than would otherwise be the

case, but the risk-adjusted return should not change. Thus, without a portfolio

reallocation, rates on jumbo mortgages should be unchanged. Depository institutions

might reallocate some capital to the jumbo market, but they would have the entire

range of investment opportunities available to them and would probably direct their

funds where the risk-adjusted returns were highest. CBO does not know how much

funding would shift to the jumbo mortgage market or how significant any impact on

mortgage rates might be.71

Pearce updated his and Cotterman’s 1996 study and found that the jumbo/

conforming differential over the 1992-1999 period averaged 27 basis points in

California and 24 basis points for an 11-state sample. Differentials were higher in

1998, averaging nearly 32 basis points in California and 30 basis points in the 11-state

sample.72 However, Pearce and Miller argue that their regression estimates most

likely understate the full effect of Freddie Mac and Fannie Mae on conforming loans.

They contend that indirect estimates of the adjusted spread, based on inferences from

borrowers’ decisions on adjustable-rate versus fixed-rate jumbo mortgages, suggest

that the full effect could be considerably greater—perhaps as much as 65 basis points.

Such indirect estimates are speculative and are based on restrictive assumptions

about why borrowers choose adjustable-rate mortgages.73 
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 31

decline of 10 percentage points in the share of ARMs. Because the share of ARMs in the conforming

market is more than 20 percentage points less than the share of ARMs in the jumbo market, Pearce and

Miller contend that the difference is consistent with the GSEs’ reducing interest rates on fixed-rate

conforming mortgages by 60 basis points or more. However, that bivariate relationship is too

simplistic a model of borrowers’ behavior. For example, expectations about future interest rate

changes and mobility also affect the decision to take out an adjustable- or fixed-rate mortgage.

Moreover, some analysts believe that borrowers in the jumbo market are more income constrained and

thus are pushed into taking out adjustable-rate mortgages in order to qualify for a larger mortgage.

Because the interest rates charged on ARMs are generally below those on fixed-rate mortgages at

origination, the initial mortgage payments are generally lower on ARMs than on fixed-rate mortgages.

Finally, some analysts argue that the relatively illiquid secondary market for jumbo mortgages may lead

some lenders to offer fixed-rate jumbo mortgages at less attractive rates. 

74. Ambrose, Buttimer, and Thibodeau, “A New Spin on the Jumbo/Conforming Loan Rate Differential.”

75. The authors focused on California because they concluded that it had the most fully developed market

for jumbo loans. Moreover, jumbo loans in states with very small jumbo markets might be qualitatively different from jumbo loans in California. The authors note that the substantial variation in the

spreads lowers their confidence in the point estimates. (Spreads were negative for part of 1994.)

Using a confidence interval of two standard deviations, they suggest that spreads of zero to 50 basis

points are possible. Passmore, Sparks, and Ingpen, GSEs, Mortgage Rates, and the Long-Run Effects

of Mortgage Securitization.

Academic economists Brent Ambrose, Richard Buttimer, and Thomas

Thibodeau estimate that conforming loan rates were an average of 25 basis points

lower than jumbo rates in Dallas during the 1990-1999 period.74 However, they

argue that a considerable portion of that difference probably resulted from differences

in the risk of the underlying collateral and was not necessarily associated with

liquidity factors. After adjusting for the underlying price volatility of the homes

backing the loans, they found that, at most, 16 basis points of the differential could

be attributed to Fannie Mae and Freddie Mac. Any rate adjustments for volatility in

other local housing markets are likely to be significantly smaller because the Texas

market, particularly during that period, was probably more volatile than most.

However, the finding does establish the importance of the link between housing price

volatility and mortgage interest rates.

Looking just at selected slices of the California market, Wayne Passmore and

Jamie Ingpen of the Federal Reserve Board and Roger Sparks, an academic

economist, found that the average differential ranged from 18 to 23 basis points, with

a sizable standard error.75 They suggest that their spread probably overstates the

benefits that Fannie Mae and Freddie Mac pass through to borrowers primarily

because segmenting the market makes the pools of jumbo mortgages that back

privately guaranteed mortgage-backed securities necessarily smaller, less diversified,

and more unpredictable than pools of conforming mortgages. Those qualities raise

the risk and reduce the liquidity of jumbo mortgage-backed securities, which

increases the rates that investors require as well as the rates that jumbo borrowers

must pay for a mortgage.
32 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

CONCLUSIONS

CBO’s analysis confirms that the housing GSEs pass some of the subsidy they

receive from the federal government through to borrowers. Controlling for several

other factors that affect mortgage interest rates, CBO found that rates on jumbo

mortgages exceeded those on conforming loans by an average of 18 to 25 basis points

between January 1995 and June 2000, depending on the estimation technique and the

data sample used. Those differentials were fairly volatile throughout the period.

When conditions in other financial markets caused investors to place a high premium

on liquidity and show less tolerance for risk, the jumbo/conforming differential

increased.

CBO’s analysis is subject to some of the same limitations as previous studies,

which means that its estimated jumbo/conforming differential is an imperfect proxy

for the benefits that the GSEs deliver. The major deficiency of the differential is that

factors other than subsidies from the GSEs can create differences between jumbo and

conforming rates. CBO could not control for some important factors—including the

creditworthiness of borrowers and the price volatility in the local housing market

—that affect the rates that borrowers pay. Other researches have found higher default

rates and greater underlying volatility in home prices for jumbo mortgages.

Moreover, CBO’s estimates do not account for the adverse effects that the GSEs may

have on liquidity in the jumbo market. In summary, the available evidence suggests

that CBO’s approach most likely overstates the jumbo/conforming differential and

thus the size of the subsidy that the housing GSEs pass through to borrowers.
APPENDIX A: REGRESSION ESTIMATES

WITH LOAN SIZE SPECIFIED IN LOGARITHMIC FORM

The regression results from Equation 1 support the notion that interest rates vary

consistently with the size of the mortgage and with whether it is below the

conforming limit. The coefficients on the loan-to-value (LTV) indicators were

generally positive but frequently statistically insignificant, particularly in the early

years of the sample (see Tables A-1 through A-6). In particular, the rates for

mortgages with LTV ratios between 50 percent and 70 percent (LTV1) were often

statistically indistinguishable from those for loans with LTV ratios below 50 percent,

the benchmark range. That result may simply reflect the likelihood that default

probabilities are extremely low for borrowers who make down payments of 30

percent to 50 percent, and thus, those probabilities have little room to decline when

down payments exceed 50 percent. 

Rates on new homes were lower than those on existing homes only in 1995;

otherwise, they were generally higher. That result is inconsistent with the hypothesis

that rates are usually lower on new homes. 
34 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

TABLE A-1. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN LOG

FORM, 1995 (Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 9.09 8.31 7.97 7.78

(0.52) (0.42) (0.41) (0.38)

Independent Variables

Intercept 10.13 10.49 9.65 9.17

(0.24) (0.13) (0.11) (0.11)

Log of Loan Size -0.684 -0.143 -0.128 -0.085

(0.019) (0.010) (0.009) (0.009)

LTV1 -0.034a 0.045 0.051 0.032

(0.043) (0.022) (0.019) (0.019)

LTV2 -0.071 0.015a 0.032 -0.001a

(0.040) (0.020) (0.017) (0.018)

LTV3 -0.045a 0.056 0.070 0.038

(0.041) (0.021) (0.018) (0.019)

LTV4 -0.040a 0.076 0.087 0.024a

(0.040) (0.020) (0.018) (0.018)

Lender1 0.102 0.070 -0.025 -0.028

(0.014) (0.007) (0.006) (0.007)

Lender2 0.039a -0.045 -0.116 -0.106

(0.033) (0.018) (0.016) (0.017)

New Home -0.108 -0.022 0.009a -0.018

(0.018) (0.010) (0.009) (0.009)

Conforming Loan -0.041a -0.207 -0.323 -0.320

(0.043) (0.019) (0.016) (0.016)

Adjusted R-Square 0.094 0.362 0.048 0.092

Number of Observations 5,987 14,118 17,904 13,927

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 35

TABLE A-2. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN LOG

FORM, 1996 (Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 7.53 8.21 8.41 8.11

(0.43) (0.46) (0.38) (0.38)

Independent Variables

Intercept 7.74 7.83 9.21 9.68

(0.12) (0.13) (0.12) (0.12)

Log of Loan Size -0.003a 0.038 -0.050 -0.114

(0.010) (0.011) (0.009) (0.010)

LTV1 0.053 -0.004a 0.013a 0.053

(0.021) (0.023) (0.019) (0.019)

LTV2 0.024a -0.024a -0.004a 0.046

(0.020) (0.021) (0.018) (0.018)

LTV3 0.051 -0.007a 0.020a 0.046

(0.020) (0.022) (0.019) (0.019)

LTV4 0.037 -0.044 0.013a 0.027a

(0.020) (0.021) (0.018) (0.018)

Lender1 -0.065 -0.099 -0.007a 0.033

(0.007) (0.008) (0.007) (0.007)

Lender2 -0.109 0.004a -0.011a -0.075

(0.019) (0.022) (0.021) (0.018)

New Home 0.039 0.007a 0.042 0.112

(0.010) (0.011) (0.009) (0.008)

Conforming Loan -0.283 -0.190 -0.174 -0.156

(0.018) (0.020) (0.017) (0.016)

Adjusted R-Square 0.108 0.120 0.024 0.136

Number of Observations 16,307 15,620 13,285 12,525

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
36 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

TABLE A-3. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN LOG

FORM, 1997 (Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 8.04 8.25 7.88 7.65

(0.38) (0.36) (0.38) (0.38)

Independent Variables

Intercept 9.42 9.70 9.64 9.75

(0.11) (0.08) (0.08) (0.08)

Log of Loan Size -0.112 -0.125 -0.145 -0.166

(0.009) (0.007) (0.007) (0.006)

LTV1 0.055 0.015a 0.047 0.039

(0.019) (0.014) (0.014) (0.013)

LTV2 0.043 0.029 0.070 0.062

(0.018) (0.013) (0.013) (0.013)

LTV3 0.054 0.066 0.102 0.113

(0.019) (0.013) (0.013) (0.013)

LTV4 0.031 0.048 0.086 0.095

(0.018) (0.013) (0.013) (0.013)

Lender1 0.034 0.108 0.100 0.074

(0.007) (0.005) (0.005) (0.005)

Lender2 0.020a 0.023 0.016a -0.016a

(0.017) (0.012) (0.010) (0.011)

New Home 0.058 -0.005a 0.039 0.022

(0.007) (0.006) (0.006) (0.005)

Conforming Loan -0.173 -0.103 -0.175 -0.228

(0.016) (0.011) (0.010) (0.010)

Adjusted R-Square 0.021 0.051 0.042 0.066

Number of Observations 14,637 24,865 28,775 28,821

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 37

TABLE A-4. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN LOG

FORM, 1998 (Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 7.34 7.33 7.20 6.96

(0.36) (0.35) (0.36) (0.38)

Independent Variables

Intercept 8.95 8.89 9.02 8.60

(0.07) (0.06) (0.06) (0.06)

Log of Loan Size -0.116 -0.117 -0.137 -0.124

(0.006) (0.005) (0.005) (0.005)

LTV1 0.016a 0.035 0.043 0.042

(0.012) (0.010) (0.010) (0.010)

LTV2 0.025 0.042 0.052 0.050

(0.011) (0.009) (0.009) (0.009)

LTV3 0.063 0.086 0.104 0.087

(0.011) (0.009) (0.010) (0.010)

LTV4 0.057 0.074 0.089 0.092

(0.011) (0.009) (0.009) (0.009)

Lender1 0.057 0.054 0.082 0.051

(0.005) (0.004) (0.004) (0.004)

Lender2 -0.003a -0.014 0.017 0.063

(0.011) (0.008) (0.008) (0.010)

New Home 0.056 0.018 0.051 0.069

(0.005) (0.004) (0.004) (0.005)

Conforming Loan -0.331 -0.302 -0.302 -0.352

(0.009) (0.008) (0.008) (0.008)

Adjusted R-Square 0.049 0.039 0.083 0.061

Number of Observations 33,193 49,061 48,064 44,592

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
38 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

TABLE A-5. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN LOG

FORM, 1999 (Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 7.06 7.26 7.86 8.01

(0.39) (0.35) (0.37) (0.36)

Independent Variables

Intercept 8.82 8.95 9.93 9.90

(0.07) (0.06) (0.07) (0.08)

Log of Loan Size -0.137 -0.142 -0.188 -0.157

(0.006) (0.005) (0.006) (0.006)

LTV1 0.061 0.027 0.026 0.025

(0.012) (0.010) (0.012) (0.013)

LTV2 0.060 0.036 0.059 0.050

(0.011) (0.009) (0.011) (0.012)

LTV3 0.099 0.077 0.115 0.126

(0.011) (0.010) (0.012) (0.013)

LTV4 0.095 0.093 0.129 0.128

(0.011) (0.010) (0.012) (0.012)

Lender1 -0.021 -0.014 0.115 0.130

(0.005) (0.004) (0.006) (0.007)

Lender2 -0.049 -0.062 -0.097 -0.119

(0.010) (0.015) (0.012) (0.011)

New Home 0.112 -0.006a -0.043 -0.005a

(0.005) (0.005) (0.006) (0.006)

Conforming Loan -0.284 -0.190 -0.203 -0.237

(0.010) (0.008) (0.013) (0.013)

Adjusted R-Square 0.048 0.140 0.144 0.073

Number of Observations 38,633 43,810 31,705 24,949

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 39

TABLE A-6. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN LOG

FORM, FIRST AND SECOND QUARTERS 2000 

(Standard errors in parentheses)

First

Quarter

Second

Quarter

Dependent Variable

8.34 8.45

Effective Rate (Percent) (0.37) (0.38)

Independent Variables

Intercept 10.03 10.27

(0.09) (0.07)

Log of Loan Size -0.158 -0.155

(0.007) (0.006)

LTV1 0.017a -0.011a

(0.015) (0.012)

LTV2 0.055 0.033

(0.014) (0.011)

LTV3 0.104 0.101

(0.015) (0.012)

LTV4 0.125 0.117

(0.014) (0.011)

Lender1 0.169 0.145

(0.008) (0.006)

Lender2 -0.088 -0.107

(0.011) (0.009)

New Home 0.015 0.031

(0.007) (0.006)

Conforming Loan -0.195 -0.255

(0.015) (0.013)

Adjusted R-Square 0.132 0.098

Number of Observations 20,860 33,539

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.

APPENDIX B: REGRESSION ESTIMATES

WITH LOAN SIZE SPECIFIED IN QUADRATIC FORM

Equation 2 specifies loan size in quadratic form to permit effective rates to fall

initially with loan size (because of economies of scale in origination and servicing)

and then rise for larger loans (because of greater prepayment risk and more volatility

in home prices). In general, the regression results for Equation 2 are consistent with

that pattern of interest rates, as indicated by the negative coefficients on the loan-size

variable and the positive coefficients on the loan-size-squared variable in most

quarters (see Tables B-1 through B-6). On the basis of the estimated coefficients, it

appears that, controlling for other factors, rates generally do not start to rise until loan

size is significantly above the conforming limit, where the market is thin and

potentially more volatile. 

Comparing the results for Equation 2 with those for Equation 1 (in Appendix

A) shows that the form in which loan size is specified also affects the coefficients on

the loan-to-value (LTV) variables and the conforming-loan indicator. Overall, the

two equations are similar in how much of the variation in effective mortgage rates

they explain, as shown by comparing the adjusted r-squares.
42 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

TABLE B-1. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN

QUADRATIC FORM, 1995 (Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 9.09 8.31 7.97 7.78

(0.52) (0.42) (0.41) (0.38)

Independent Variables

Intercept 9.43 9.02 8.34 8.28

(0.07) (0.03) (0.30) (0.03)

Loan Size ($1,000s) -0.00081a -0.00231 -0.00160 -0.00148

(0.00055) (0.00027) (0.00022) (0.00023)

Loan Size Squared ($1,000s) 8.28E-10a 4.19E-9 1.83E-9 2.99E-9

(0.00000) (0.00000) (0.00000) (0.00000)

LTV1 -0.035a 0.044 0.051 0.032a

(0.043) (0.022) (0.019) (0.019)

LTV2 -0.073 0.015a 0.030 -0.001a

(0.040) (0.020) (0.017) (0.018)

LTV3 -0.047a 0.055 0.068 0.038

(0.041) (0.021) (0.018) (0.019)

LTV4 -0.041a 0.075 0.085 0.024a

(0.040) (0.020) (0.018) (0.018)

Lender1 0.102 0.070 -0.025 -0.028

(0.014) (0.007) (0.006) (0.007)

Lender2 0.041a -0.044 -0.116 -0.107

(0.033) (0.018) (0.016) (0.017)

New Home -0.109 -0.022 0.009a -0.018

(0.018) (0.010) (0.009) (0.009)

Conforming Loan -0.054a -0.184 -0.344 -0.294

(0.061) (0.028) (0.023) (0.024)

Adjusted R-Square 0.093 0.362 0.048 0.092

Number of Observations 5,987 14,118 17,904 13,927

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included indicators for the last two months of the quarter.

The coefficients on the Loan Size Squared variable are very small and thus are denoted in exponential notation.

For example, 1.0E-5 = 0.00001.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 43

TABLE B-2. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN

QUADRATIC FORM, 1996 (Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 7.53 8.21 8.41 8.11

(0.43) (0.46) (0.38) (0.38)

Independent Variables

Intercept 7.71 8.25 8.70 8.50

(0.03) (0.04) (0.03) (0.03)

Loan Size ($1,000s) -3.23E-6a 0.00101 -0.00063 -0.00198

(0.00024) (0.00029) (0.00024) (0.00023)

Loan Size Squared ($1,000s) -9.37E-11a -2.80E-9 7.68E-10a 3.87E-9

(0.00000) (0.00000) (0.00000) (0.00000)

LTV1 0.053 -0.004a 0.013a 0.053

(0.021) (0.023) (0.019) (0.019)

LTV2 0.024a -0.024a -0.005a 0.045

(0.020) (0.021) (0.018) (0.018)

LTV3 0.051 -0.008a 0.020a 0.046

(0.020) (0.022) (0.019) (0.019)

LTV4 0.037 -0.045 0.013a 0.028a

(0.020) (0.021) (0.018) (0.018)

Lender1 -0.065 -0.099 -0.007a 0.033

(0.007) (0.008) (0.007) (0.007)

Lender2 -0.109 0.004a -0.011a -0.076

(0.019) (0.022) (0.021) (0.018)

New Home 0.039 0.007a 0.042 0.111

(0.010) (0.011) (0.009) (0.008)

Conforming Loan -0.287 -0.237 -0.180 -0.127

(0.025) (0.030) (0.025) (0.023)

Adjusted R-Square 0.108 0.120 0.024 0.137

Number of Observations 16,307 15,620 13,285 12,525

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included indicators for the last two months of the quarter.

The coefficients on the Loan Size and Loan Size Squared variables are very small and thus are denoted in

exponential notation. For example, 1.0E-5 = 0.00001.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
44 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

TABLE B-3. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN

QUADRATIC FORM, 1997 (Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 8.04 8.25 7.88 7.65

(0.38) (0.36) (0.38) (0.38)

Independent Variables

Intercept 8.27 8.41 8.14 8.03

(0.03) (0.02) (0.02) (0.02)

Loan Size ($1,000s) -0.00138 -0.00190 -0.00195 -0.00237

(0.00022) (0.00015) (0.00014) (0.00013)

Loan Size Squared ($1,000s) 1.65E-9 3.27E-9 2.87E-9 3.73E-9

(0.00000) (0.00000) (0.00000) (0.00000)

LTV1 0.054 0.014a 0.045 0.038

(0.019) (0.014) (0.014) (0.013)

LTV2 0.042 0.028 0.068 0.060

(0.018) (0.013) (0.013) (0.013)

LTV3 0.053 0.065 0.100 0.112

(0.019) (0.013) (0.013) (0.013)

LTV4 0.029a 0.048 0.083 0.094

(0.018) (0.013) (0.013) (0.013)

Lender1 0.034 0.108 0.100 0.074

(0.007) (0.005) (0.005) (0.005)

Lender2 0.020a 0.023 0.016a -0.016a

(0.017) (0.012) (0.010) (0.011)

New Home 0.058 -0.005a 0.039 0.022

(0.007) (0.006) (0.006) (0.005)

Conforming Loan -0.187 -0.083 -0.172 -0.215

(0.023) (0.016) (0.014) (0.013)

Adjusted R-Square 0.021 0.051 0.041 0.066

Number of Observations 14,637 24,865 28,775 28,821

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included indicators for the last two months of the quarter.

The coefficients on the Loan Size Squared variable are very small and thus are denoted in exponential notation.

For example, 1.0E-5 = 0.00001.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 45

TABLE B-4. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN

QUADRATIC FORM, 1998 (Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 7.34 7.33 7.20 6.96

(0.36) (0.35) (0.36) (0.37)

Independent Variables

Intercept 7.74 7.67 7.59 7.28

(0.02) (0.15) (0.02) (0.16)

Loan Size ($1,000s) -0.00154 -0.00159 -0.00175 -0.00217

(0.00012) (0.00009) (0.00010) (0.00011)

Loan Size Squared ($1,000s) 2.29E-9 2.44E-9 2.46E-9 4.27E-9

(0.00000) (0.00000) (0.00000) (0.00000)

LTV1 0.015a 0.034 0.042 0.041

(0.012) (0.010) (0.010) (0.010)

LTV2 0.024 0.040 0.050 0.049

(0.011) (0.009) (0.009) (0.009)

LTV3 0.061 0.085 0.102 0.088

(0.011) (0.009) (0.010) (0.010)

LTV4 0.056 0.073 0.087 0.092

(0.011) (0.009) (0.009) (0.009)

Lender1 0.058 0.054 0.082 0.051

(0.005) (0.004) (0.004) (0.004)

Lender2 -0.002a -0.014 0.016 0.063

(0.011) (0.008) (0.008) (0.010)

New Home 0.056 0.018 0.050 0.070

(0.005) (0.004) (0.004) (0.005)

Conforming Loan -0.320 -0.286 -0.295 -0.293

(0.013) (0.011) (0.011) (0.011)

Adjusted R-Square 0.049 0.039 0.082 0.062

Number of Observations 33,193 49,061 48,064 44,592

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included indicators for the last two months of the quarter.

The coefficients on the Loan Size Squared variable are very small and thus are denoted in exponential notation.

For example, 1.0E-5 = 0.00001.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
46 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

TABLE B-5. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN

QUADRATIC FORM, 1999 (Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 7.06 7.26 7.86 8.01

(0.39) (0.35) (0.37) (0.36)

Independent Variables

Intercept 7.39 7.46 7.95 8.23

(0.02) (0.02) (0.02) (0.02)

Loan Size ($1,000s) -0.00172 -0.00185 -0.00253 -0.00255

(0.00012) (0.0000001) (0.00014) (0.00015)

Loan Size Squared ($1,000s) 2.40E-9 2.70E-9 3.85E-9 4.75E-9

(0.00000) (0.00000) (0.00000) (0.00000)

LTV1 0.060 0.025 0.024 0.024

(0.012) (0.010) (0.012) (0.013)

LTV2 0.058 0.034 0.056 0.049

(0.011) (0.009) (0.011) (0.012)

LTV3 0.097 0.075 0.112 0.126

(0.011) (0.010) (0.012) (0.013)

LTV4 0.093 0.091 0.127 0.128

(0.011) (0.009) (0.012) (0.012)

Lender1 -0.021 -0.014 0.115 0.130

(0.005) (0.004) (0.006) (0.007)

Lender2 -0.049 -0.061 -0.097 -0.119

(0.010) (0.015) (0.012) (0.011)

New Home 0.112 -0.006a -0.043 -0.005a

(0.005) (0.005) (0.006) (0.006)

Conforming Loan -0.272 -0.172 -0.170 0.168

(0.014) (0.011) (0.019) (0.018)

Adjusted R-Square 0.048 0.140 0.143 0.074

Number of Observations 38,633 43,810 31,705 24,949

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included indicators for the last two months of the quarter.

The coefficients on the Loan Size Squared variable are very small and thus are denoted in exponential notation.

For example, 1.0E-5 = 0.00001.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 47

TABLE B-6. REGRESSION ESTIMATES WHEN LOAN SIZE IS SPECIFIED IN

QUADRATIC FORM, FIRST AND SECOND QUARTERS 2000

(Standard errors in parentheses)

First

Quarter

Second

Quarter

Dependent Variable

Effective Rate (Percent) 8.34 8.45

(0.37) (0.38)

Independent Variables

Intercept 8.38 8.64

(0.03) (0.02)

Loan Size ($1,000s) -0.00171 -0.00179

(0.00015) (0.00014)

Loan Size Squared ($1,000s) 1.92E-9 2.25E-9

(0.00000) (0.00000)

LTV1 0.016a -0.013a

(0.015) (0.012)

LTV2 0.052 0.031

(0.014) (0.011)

LTV3 0.102 0.098

(0.015) (0.012)

LTV4 0.122 0.115

(0.014) (0.011)

Lender1 0.169 0.144

(0.008) (0.006)

Lender2 -0.088 -0.107

(0.011) (0.009)

New Home 0.015 0.031

(0.007) (0.006)

Conforming Loan -0.202 -0.250

(0.022) (0.019)

Adjusted R-Square 0.131 0.097

Number of Observations 20,860 33,539

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation.

Each regression also included indicators for the last two months of the quarter.

The coefficients on the Loan Size Squared variable are very small and thus are denoted in exponential notation.

For example, 1.0E-5 = 0.00001.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.

1. For example, in 2000, borrowers in the conforming market for fixed-rate mortgages paid an average

of 75 basis points for fees, points, and charges, whereas borrowers in the jumbo market paid an average

of 59 basis points. In 1995, the corresponding figures were 100 basis points for conforming loans and

116 basis points for jumbo loans. See Federal Housing Finance Board, MIRS Table 21, “Terms on

Conventional Single-Family Mortgages, All Homes, Jumbo and Nonjumbo Mortgages,” available at

www.fhfb.gov/MIRS/MIRS_loans_downloads.htm

APPENDIX C: REGRESSION ESTIMATES FOR NO-POINT LOANS

Because borrowers face a trade-off between the amount of points they pay on a loan

and the contract interest rate, estimates of the conforming/jumbo differential can be

affected by the method used to adjust mortgage rates for the fees and charges that

borrowers pay up front. In particular, the longer the amortization period, the smaller

will be the contribution of points to the effective interest rate. Moreover, the amount

of points paid by borrowers in the conforming market versus those in the jumbo

market has differed over the 1995-2000 period. Since 1997, borrowers in the conforming market have paid higher points, on average, than borrowers in the jumbo

market. In addition, the average amount of points paid on all loans has fallen during

the 1995-2000 period.1 Those trends are consistent with the notions that origination

and servicing costs fall proportionately with the size of the mortgage and that

technology lowers those costs over time. However, no public data exist comparing

points paid and the duration of mortgages. For all of those reasons, the

Congressional Budget Office also estimated the differential using contract rates for

loans with no points or other charges (see Tables C-1 through C-6).

The jumbo/conforming differential for no-point loans averaged 18 basis

points over the 1995-2000 period, whereas for the entire sample of 30-year fixed-rate

loans, it averaged 23 basis points (using Equation 1). These estimates were more

volatile than the ones using the full sample (shown in Appendix A). In four of the

quarters, the differential was statistically insignificant. The quarterly differentials for

no-point loans were highest in 1998, when they averaged more than 25 basis points.

The implications of these results are unclear. On the one hand, borrowers

who choose no-point loans may be systematically different from other borrowers.

They might expect, and be expected, to prepay their loan sooner than other

borrowers. On the other hand, the findings could imply that amortizing points over

10 years rather than over a shorter period biases the estimate of the

jumbo/conforming differential upward.
50 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

TABLE C-1. REGRESSION ESTIMATES FOR NO-POINT LOANS, 1995 

(Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 9.06 8.30 7.96 7.79

(0.56) (0.41) (0.38) (0.34)

Independent Variables

Intercept 10.39 10.24 9.28 9.02

(0.51) (0.21) (0.18) (0.18)

Log of Loan Size -0.087 -0.122 -0.099 -0.074

(0.040) (0.017) (0.014) (0.014)

LTV1 0.019a 0.062a 0.036a 0.017a

(0.091) (0.039) (0.030) (0.028)

LTV2 -0.111a 0.032a 0.037a -0.006a

(0.083) (0.036) (0.028) (0.026)

LTV3 -0.025a 0.088 0.082 0.059

(0.085) (0.036) (0.029) (0.027)

LTV4 -0.117a 0.119 0.113 0.072

(0.083) (0.035) (0.028) (0.026)

Lender1 0.017a -0.000a -0.037 -0.007a

(0.029) (0.012) (0.010) (0.010)

Lender2 -0.064a -0.057 -0.032a -0.031a

(0.082) (0.034) (0.025) (0.026)

New Home -0.197 -0.051 0.017a -0.019a

(0.043) (0.020) (0.016) (0.016)

Conforming Loan -0.099a -0.208 -0.306 -0.321

(0.093) (0.034) (0.027) (0.026)

Adjusted R-Square 0.042 0.374 0.049 0.097

Number of Observations 1,696 4,702 6,262 5,166

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 51

TABLE C-2. REGRESSION ESTIMATES FOR NO-POINT LOANS, 1996 

(Standard errors in parentheses) 

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 7.54 8.24 8.40 8.08

(0.38) (0.40) (0.35) (0.34)

Independent Variables

Intercept 8.26 8.55 8.94 9.30

(0.19) (0.21) (0.19) (0.190)

Log of Loan Size -0.043 -0.033 -0.038 -0.090

(0.015) (0.016) (0.015) (0.015)

LTV1 0.027a 0.063 0.043a 0.038a

(0.031) (0.036) (0.034) (0.031)

LTV2 0.016a 0.027a 0.017a 0.012a

(0.029) (0.033) (0.032) (0.029)

LTV3 0.080 0.064 0.078 0.061

(0.030) (0.034) (0.033) (0.030)

LTV4 0.106 0.123 0.121 0.092

(0.029) (0.033) (0.032) (0.029)

Lender1 -0.020 -0.027 -0.042 0.013a

(0.011) (0.012) (0.011) (0.011)

Lender2 -0.085 -0.141 -0.048a 0.016a

(0.027) (0.040) (0.042) (0.032)

New Home 0.036 -0.048 -0.042 0.048

(0.019) (0.020) (0.018) (0.015)

Conforming Loan -0.352 -0.188 -0.051a -0.053

(0.028) (0.034) (0.032) (0.027)

Adjusted R-Square 0.116 0.151 0.044 0.161

Number of Observations 5,530 4,790 4,102 4,199

SOURCE: Congressional Budget Office.

NOTES: The mean of the dependent variable is shown along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

 

a. Coefficient is not statistically significant at the 10 percent level.
52 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

TABLE C-3. REGRESSION ESTIMATES FOR NO-POINT LOANS, 1997 

(Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 8.00 8.20 7.85 7.61

(0.33) (0.33) (0.33) (0.33)

Independent Variables

Intercept 9.32 9.17 8.61 8.70

(0.18) (0.17) (0.15) (0.16)

Log of Loan Size -0.107a -0.080 -0.054 -0.071

(0.014) (0.013) (0.012) (0.013)

LTV1 -0.002a -0.026a 0.042 -0.026a

(0.030) (0.028) (0.024) (0.026)

LTV2 0.010a -0.020a 0.022a -0.033a

(0.028) (0.026) (0.023) (0.025)

LTV3 0.056 0.034a 0.069 0.034a

(0.029) (0.027) (0.024) (0.026)

LTV4 0.091 0.038 0.052 -0.022a

(0.028) (0.026) (0.023) (0.025)

Lender1 0.021 0.001a -0.039 -0.007a

(0.010) (0.009) (0.009) (0.009)

Lender2 0.009a 0.022a -0.008a -0.047

(0.030) (0.027) (0.029) (0.027)

New Home -0.009a -0.009a 0.065 0.032

(0.012) (0.011) (0.011) (0.011)

Conforming Loan -0.121 -0.031a -0.105 -0.181

(0.025) (0.022) (0.019) (0.018)

Adjusted R-Square 0.031 0.046 0.036 0.066

Number of Observations 4,525 5,215 6,447 5,865

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 53

TABLE C-4. REGRESSION ESTIMATES FOR NO-POINT LOANS, 1998 

(Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 7.31 7.30 7.15 6.89

(0.33) (0.32) (0.34) (0.33)

Independent Variables

Intercept 8.29 8.14 8.24 7.91

(0.13) (0.11) (0.12) (0.12)

Log of Loan Size -0.064 -0.056 -0.071 -0.068

(0.011) (0.009) (0.010) (0.010)

LTV1 -0.015a 0.012a 0.059 0.007a

(0.021) (0.017) (0.019) (0.016)

LTV2 -0.018a -0.012a 0.021a -0.004a

(0.020) (0.016) (0.018) (0.015)

LTV3 0.023a 0.032 0.099 0.051

(0.021) (0.017) (0.019) (0.016)

LTV4 0.015a 0.014a 0.075 0.094

(0.021) (0.016) (0.018) (0.016)

Lender1 -0.002a 0.032 0.041 -0.010a

(0.008) (0.006) (0.007) (0.007)

Lender2 -0.155 -0.117 -0.062 -0.023a

(0.021) (0.017) (0.019) (0.019)

New Home 0.023 -0.051 -0.036 -0.021

(0.010) (0.008) (0.009) (0.009)

Conforming Loan -0.235 -0.213 -0.247 -0.305

(0.017) (0.014) (0.015) (0.016)

Adjusted R-Square 0.032 0.033 0.100 0.064

Number of Observations 8,173 11,399 9,865 10,047

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
54 JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 May 2001

TABLE C-5. REGRESSION ESTIMATES FOR NO-POINT LOANS, 1999 

(Standard errors in parentheses)

First

Quarter

Second

Quarter

Third

Quarter

Fourth

Quarter

Dependent Variable

Effective Rate (Percent) 7.02 7.19 7.80 7.93

(0.42) (0.37) (0.45) (0.39)

Independent Variables

Intercept 8.43 8.24 9.29 9.26

(0.16) (0.15) (0.21) (0.19)

Log of Loan Size -0.108 -0.095 -0.153 -0.102

(0.012) (0.012) (0.017) (0.015)

LTV1 0.027a 0.022a -0.017a 0.034a

(0.025) (0.024) (0.036) (0.033)

LTV2 0.010a 0.014a 0.047a 0.064

(0.023) (0.022) (0.033) (0.030)

LTV3 0.119 0.068 0.121 0.107

(0.024) (0.023) (0.034) (0.031)

LTV4 0.095 0.103 0.103 0.072

(0.024) (0.023) (0.033) (0.030)

Lender1 -0.042 0.042 0.165 0.077

(0.010) (0.009) (0.014) (0.014)

Lender2 -0.084 0.010a -0.058 -0.107

(0.024) (0.023) (0.020) (0.016)

New Home -0.010a 0.029 -0.001a 0.013a

(0.013) (0.011) (0.016) (0.014)

Conforming Loan -0.234 -0.125 -0.038a -0.222

(0.022) (0.019) (0.033) (0.031)

Adjusted R-Square 0.039 0.091 0.117 0.051

Number of Observations 8,859 8,487 5,635 5,084

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.
JUMBO/CONFORMING INTEREST RATE DIFFERENTIALS, 1995-2000 55

TABLE C-6. REGRESSION ESTIMATES FOR NO-POINT LOANS, FIRST AND SECOND

QUARTERS, 2000 (Standard errors in parentheses)

First

Quarter

Second

Quarter

Dependent Variable

8.26 8.39

Effective Rate (Percent) (0.41) (0.40)

Independent Variables

Intercept 9.14 9.20

(0.22) (0.16)

Log of Loan Size -0.084 -0.071

(0.017) (0.012)

LTV1 0.006a -0.029a

(0.037) (0.027)

LTV2 0.037a -0.007a

(0.033) (0.024)

LTV3 0.079 0.061

(0.034) (0.025)

LTV4 0.040a 0.039a

(0.033) (0.024)

Lender1 0.133 0.137

(0.016) (0.012)

Lender2 -0.066 -0.089

(0.018) (0.014)

New Home 0.045 0.054

(0.016) (0.013)

Conforming Loan -0.094 -0.133

(0.035) (0.026)

Adjusted R-Square 0.074 0.077

Number of Observations 4,717 8,702

SOURCE: Congressional Budget Office.

NOTES: The table shows the mean of the dependent variable along with the root mean square error for the equation. Each

regression also included month indicators for the last two months of the quarter.

LTV = loan-to-value ratio.

a. Coefficient is not statistically significant at the 10 percent level.


This paper and other CBO publications are 

available at CBO's Web site:

www.cbo.gov