Intermediary Segmentation in the Commercial Real Estate Market*

• Published date: 01 October 2022
• Author: DAVID GLANCY,JOHN R. KRAINER,ROBERT J. KURTZMAN,JOSEPH B. NICHOLS
• Date: 01 October 2022
• DOI: http://doi.org/10.1111/jmcb.12889

DOI: 10.1111/jmcb.12889

DAVID GLANCY

JOHN R. KRAINER

ROBERT J. KURTZMAN

JOSEPH B. NICHOLS

Intermediary Segmentation in the Commercial Real

Estate Market*

Banks, life insurers, and commercial mortgage-backed security (CMBS)

lenders originate the vast majority of U.S. commercial real estate (CRE)

loans. While these lenders compete in the same market, they differ in how

they are funded and regulated, and therefore, specialize in loans with differ-

ent characteristics. We harmonize loan-leveldata across the lenders and re-

view how their CRE portfolios differ.We then exploit cross-sectional differ-

ences in loan portfolios to estimate a simple model of frictional substitution

across lender types. The substitution patterns in the model match well the

observed shift away from CMBS when spreads rose in late 2015 and early

2016. Counterfactuals suggest that the ability to substitute to other lenders

offsets about 20% of the effect of a 25 basis point CMBS supply shock.

JEL codes G21, G22, G23, R33

Keywords: commercial real estate, life insurers, segmentation

C   (CRE)  in the United

States is an important component of overall business lending, accounting for about

*We thank Lara Loewenstein and EvaSteiner for their thoughtful discussions, Mike Batty and Eileen

van Straelen for their helpful insights, and participants at the 2019 System Committee on Financial Insti-

tutions, Regulation, and Markets, 2020 American Real Estate and Urban Economics Association National

Conference, and 2020 Northern Finance Association Annual Meeting for their helpful comments. The

views expressed in this paper are solely the responsibility of the authors and should not be interpreted as

reecting the views of the Board of Governors of the Federal ReserveSystem or of anyone else associated

with the Federal Reserve System.

D G is a Senior Economist at the Division of Monetary Affairs, Federal Reserve Board

(E-mail: david.p.glancy@frb.gov). J K is a Principal Economist at the Division of Su-

pervision & Regulation, Federal Reserve Board (E-mail: john.r.krainer@frb.gov). R K-

 is a Senior Economist at the Division of Research & Statistics, Federal Reserve Board (E-mail:

robert.j.kurtzman@frb.gov). J N is a Principal Economist at Division of Research& Statis-

tics, Federal Reserve Board(E-mail: joseph.b.nichols@frb.gov).

Received February 19, 2020; and accepted in revised form April 15, 2021.

Journal of Money, Credit and Banking, Vol. 54, No. 7 (October 2022)

Published 2021. This article is a U.S. Government work and is in the public domain in the

USA.

2030 :MONEY,CREDIT AND BANKING

15% of total nonnancial business credit as of 2019:Q4.1Bank and nonbank lenders

compete in the CRE market, with U.S. commercial banks holding almost 60% of

the volume of commercial mortgages, and life insurance companies and issuers of

asset-backed securities (commercial mortgage-backed security [CMBS]) each hold-

ing about 15% of the market.2Though CRE is a large asset class and key input into

rm production (Ghent, Torous, and Valkanov, 2019), there remain a number of open

questions about the CRE market: Along what dimensions do CRE loan originations

differ by lender type? What are the underlying sources of segmentation in the market?

What are the implications of segmentation for how the market responds to shocks?

Toaddress these questions, the rst contribution of this paper is to harmonize loan-

level sources to compare CRE originations across the three lender types. Our data

include granular details on loan terms and property characteristics for the nonfarm

nonresidential CRE loan portfolios of around 30 of the largest U.S. banks, all U.S. life

insurers, and all loans in publicly issued, non-agency CMBS deals. An examination

of the loan-level data reveals a striking amount of segmentation in the CRE market:

bank, life insurer, and CMBS originations differ substantially by interest rate, loan-

to-value (LTV), size, property type, and term.

A review of the institutional setting in which the lenders operate indicates a supply-

side explanation for our ndings. Due to differences in regulation, funding structure,

and other institutional characteristics, lenders differ in their incentives to originate

particular types of loans. For example, short-duration liabilities incentivize banks to

make short-term, oating-rate loans; risk-sensitive capital requirements incentivize

life insurers to make safer loans; and greater diversication enables CMBS to make

larger loans.

Guided by our review,we build a simple model with representative lender types that

compete on interest rates but differ in how loan characteristics affectrequired returns.

In equilibrium, lenders have higher market shares for the loans for which they have

a pricing advantage. We estimate the model using the cross-sectional variationin the

characteristics of newly originated CRE loans.3The model allows us to estimate the

impact of various supply shocks on loan spreads and lender market shares.

1. See table 2 of the May 2019 Financial Stability Report of the Board of Governors of the Fed-

eral Reserve System: https://www.federalreserve.gov/publications/2020-may-nancial-stability- report-

borrowing.htm.

2. Data come from the Financial Accounts of the United States. See Figure D1 (in Appendix D) for

more details. The government accounts for much of the rest of CRE debt. Therefore, banks, life insurers,

and CMBS lenders account for the vast majority of private sector CRE nancing.

3. Our estimation method followsin the spirit of the discrete-choice industrial organizationliterature

(McFadden 1973,1984; Berry, Levinsohn, and Pakes, 1995). Borrowers face a discrete-choice problem

over lenders offering different loan contracts, and we estimate model parameters so as to match the ob-

served selection of borrowers into particular lender types. Our approach differs in that loan rates in our

model are bilateral agreements between borrowers and lenders (as opposed to posted prices), and bor-

rowers minimize the cost of borrowing over a set of offered interest rates (as opposed to maximizing

utility given known prices). Because of these differences, we are estimating supply functions rather than

demand functions.

DAVIDGLANCY ET AL. :2031

We test the validity of the substitution patterns in our model by exploiting a sup-

ply shock that occurred as a large number of pre-nancial crisis CMBS loans were

maturing and needed to renance.4We show that CMBS borrowers switch to other

sources of nance at a rate consistent with the predictions of the estimated model.

We then use the model to address the question we asked up front: What are the

implications of segmentation for how the market responds to a shock? We estimate

that the ability to switch to another lender offsets about 20% of the effect of a 25 basis

point (bp) shock to the pricing of CMBS loans. Shocks to banks are more costly due to

their larger market share and, on average, the lack of close substitutes. The estimated

effects of supply shocks are also heterogeneous: spreads rise most in response to

supply shocks in the segments with the least competition. For example, CMBS supply

shocks disproportionately affect borrowing costs for larger loans, given competing

offers from banks and insurers are less competitive with CMBS in that segment of

the market.

The extent of segmentation is also important in determining the effects of targeted

regulation. We analyze a hypothetical policy that raises the required rate of return on

high LTV bank loans. The estimated model suggests that nonbank lenders require a

much higher premium than banks to originate loans with LTVsabove 75%, given their

low market share for such loans. Since nonbanks are less competitive in this segment

of the market, a regulation that raises the cost of high LTV bank loans mostly passes

through into loan spreads rather than causing borrowers to switch to other lenders.

After we review the related literature below, the roadmap for the paper follows as

such: In Section 1, we describe the data, summarize how loan characteristics differ

across lender types, and outline institutional differences across the lenders. Section 2

describes the model, discusses how it is estimated and validated, and presents the

results of counterfactual supply shocks. Section 3 concludes.

Related Literature

Our paper ties into a large literature on nancial contracting and how borrowerssort

into different nancing arrangements. Much of the work studies this question in the

context of competition between banks and bonds for the provisioning of rm nanc-

ing.5Chernenko, Erel, and Prilmeier (2019) provide evidence that bank and nonbank

lenders utilize different lending techniques and cater to different types of rms.

This paper is also closely related to a literature studying lender behavior in the

context of the CRE market. Downs and Xu (2015) nd that banks are quicker to

resolve distressed loans than CMBS servicers. Black, Krainer, and Nichols (2017,

4. CMBS lending was elevatedbetween 2005 and 2007. Given that most CMBS loans have 10-year

terms, signicant prepayment restrictions, and minimal amortization, this resulted in high demand for

renance loans between 2015 and 2017.

5. There is a large theoretical and empirical literature on this topic. Important examples include:

Townsend (1979), Sharpe (1990), Diamond (1991), Rajan (1992), Hart and Moore (1998), Denis and

Mihov (2003), Gande and Saunders (2012), Hale and Santos (2009), and Becker and Ivashina (2014).