Homeowner Borrowing and Housing Collateral: New Evidence from Expiring Price Controls

• Date: 01 April 2018
• Published date: 01 April 2018
• Author: ANTHONY A. DEFUSCO
• DOI: http://doi.org/10.1111/jofi.12602

THE JOURNAL OF FINANCE •VOL. LXXIII, NO. 2 •APRIL 2018

Homeowner Borrowing and Housing Collateral:

New Evidence from Expiring Price Controls

ANTHONY A. DEFUSCO∗

ABSTRACT

I empirically analyze how changes in access to housing collateral affect homeowner

borrowing behavior. To isolate the role of collateral constraints from that of wealth

effects, I exploit the fully anticipated expiration of resale price controls on owner-

occupied housing in Montgomery County,Maryland. I estimate a marginal propensity

to borrow out of housing collateral that ranges between $0.04 and $0.13 and is corre-

lated with homeowners’ initial leverage. Additional analysis of residential investment

and ex-post loan performance indicates that some of the extracted funds generated

new expenditures. These results suggest a potentially important role for collateral

constraints in driving household expenditures.

LARGE FLUCTUATIONS IN HOUSEHOLD DEBT have become a common feature of

business cycles worldwide (Jord`

a, Schularick, and Taylor (2014), Mian, Sufi,

and Verner (2015)). Recent experience in the United States, which saw the

ratio of household debt to GDP increase by over 40% from 2000 to 2008 before

plummeting in the aftermath of the Great Recession, is no exception.1Under-

standing the microeconomic mechanisms driving these aggregate changes in

∗Anthony A. DeFusco is at Kellogg School of Management, Northwestern University.I am deeply

indebted to my advisors Gilles Duranton, Fernando Ferreira, Joe Gyourko, Nick Roussanov, and

Todd Sinai for their guidance and support. Orazio Attanasio, Effi Benmelech, Zahi Ben-David,

Alex Chinco, Tom Davidoff, Mike Ericsen, David Matsa, Alvin Murphy, Gary Painter, and Yiwei

Zhang as well as seminar participants at Berkeley (Haas), Harvard (HBS), Indiana University

(Kelley), LBS, LSE, Northwestern (Kellogg), Notre Dame (Mendoza), University of Chicago (Booth),

UCLA (Anderson), UPenn (Wharton), WUSTL (Olin), the CFPB, the Federal Reserve Board, the

Federal Reserve Bank of New York, the NBER Summer Institute workshops on Real Estate and

on Aggregate Implications of Micro Consumption Behavior, the Stanford Institute for Theoretical

Economics (SITE) session on Housing and the Macroeconomy, the WFA Summer Real Estate

Symposium, and the Bank of England/Imperial College conference on Housing: Microdata Macro

Problems provided helpful comments and discussion. I am also thankful to Maureen Harzinsky

and Stephanie Killian in the Montgomery County Department of Housing and Community Affairs

(DHCA) and to Diana Canales at SunTrustBank for providing useful details on inclusionary zoning

and the financing of price-controlled housing units in Montgomery County. Finally, I also thank

the Guthrie Center for Real Estate Research at Northwestern University for financial support. I

have no relevant or material financial interests that relate to the research described in this paper.

1Between 2000 (Q1) and 2008 (Q1), the ratio of household debt to GDP increased from 67.7%

to 97.6%. This ratio later bottomed out at 77.7% in 2015 (Q3). These figures were calculated using

aggregate data on household debt from the Federal Reserve Flow of Funds (Z.1 Release, Series

LA154104005.Q) and GDP data from National Income and Product Accounts of the United States

(BEA Account Code: A191RC1).

DOI: 10.1111/jofi.12602

523

524 The Journal of Finance R

household debt is of first-order importance for guiding monetary, fiscal, and

macroprudential policy.

A growing body of theoretical work has emphasized the importance of house-

hold borrowing constraints, linking changes in household debt, and economic

activity over the business cycle to changes in households’ access to credit.2

Given that the vast majority of household debt is collateralized by real estate,

much of this work has paid particular attention to the role of collateral con-

straints. However, direct causal evidence on the role that collateral constraints

play for household borrowing is difficult to establish, and answers to key empir-

ical questions have remained elusive. When provided with an additional dollar

of collateralized borrowing capacity, how much do households choose to borrow,

which households respond the most, and what do they spend the money on?

This paper sheds new light on these questions by studying how the borrow-

ing behavior of individual homeowners responds to changes in their ability to

access housing collateral. In a departure from prior work, which has focused

primarily on how borrowing responds to changes in house prices, I study home-

owner behavior in an empirical context in which the market value of housing

is held constant.3This approach allows me to overcome two of the key chal-

lenges confronted by prior empirical research. First, disentangling the effect of

collateral constraints from wealth effects in an environment of changing house

prices can be difficult. While an increase in house prices may lead homeowners

to take on additional debt, it is unclear whether this occurs because rising prices

make homeowners feel richer or because rising prices relax previously binding

collateral constraints. Second, even in the absence of wealth effects, drawing

causal inferences from changes in house prices is challenging. Concerns over

aggregate shocks to joint determinants of both house prices and homeowner

borrowing, such as interest rates and expected future income, always loom

large.4By holding the market value of housing constant and looking instead

at a policy-induced change in homeowners’ access to collateral, my approach is

able to overcome both of these challenges.

2See, for example, Eggertsson and Krugman (2012), Guerrieri and Lorenzoni (2015), Justiniano,

Primiceri, and Tambalotti (2015), Guerrieri and Iacoviello (2016), Korinek and Simsek (2016), and

Philippon and Midrigan (2016).

3Recent papers studying the role of collateral constraints through the lens of changing house

prices include Yamashita (2007), Disney and Gathergood (2011), Mian and Sufi (2011,2014),

Cooper (2013), and Bhutta and Keys (2014). Three important exceptions are Leth-Petersen (2010),

Abdallah and Lastrapes (2012), and Agarwal and Qian (2014), who study explicit policy-induced

changes in collateral constraints similar to the one studied in this paper. However, these studies

rely on national- and state-level policy variation, which makes it difficult to separately identify

aggregate trends from household-specific changes to collateral constraints.

4A frequently proposed solution to this problem is to instrument for local house prices using

Saiz’s (2010) estimates of cross-city variation in physical constraints to building (Mian and Sufi

(2011,2014), Aladangady (2013), Mian, Rao, and Sufi (2013)). However,as cautioned by Saiz (2010)

and further emphasized by Davidoff (2013,2014), physical supply constraints are highly correlated

with a host of other demand factors that might be expected to directly affect both house prices and

homeowner borrowing.

Homeowner Borrowing and Housing Collateral 525

To isolate the effect of collateral constraints on household borrowing, I ex-

ploit a unique feature of local land use policy in Montgomery County,Maryland.

Since 1974, housing developers in this county have been subject to an inclusion-

ary zoning regulation known as the Moderately Priced Dwelling Unit (MPDU)

program. This regulation requires developers to set aside at least 12.5% of all

housing units in new developments to be made available at controlled prices to

moderate-income households.5These housing units are subject to deed restric-

tions that cap their resale prices for a period of time ranging between 5 and

30 years. During this period, owners are not permitted to refinance or take on

home equity debt for an amount that exceeds the controlled resale price. Once

the price controls expire, however, owners are able to pledge the full market

value of the home as collateral. Since the duration and stringency of the price

controls are set by formula and known in advance at the time of purchase,

their expiration has no effect on the owner’s total expected lifetime wealth nor

on the market value of their home at the time the price control is lifted. How-

ever, expiring price controls directly affect the owner’s collateralized borrowing

capacity through the relaxation of the borrowing restrictions. Therefore, differ-

ential changes in the propensity for MPDU homeowners to extract equity from

their homes at the time the restriction is lifted contain explicit information

regarding the effect of collateral constraints on homeowner borrowing. I use

this information to provide new estimates of both the extensive margin effect

of relaxing collateral constraints on home equity extraction and the marginal

propensity to borrow against a $1 increase in collateralized borrowing capacity.

To conduct my analysis, I assemble a unique data set containing the precise

geographic location and detailed structural characteristics of every housing

unit in Montgomery County as well as the full history of transactions and

loans secured against each property during the period 1997 to 2012. I combine

this information with administrative records from the Montgomery County

Department of Housing and Community Affairs (DHCA), which identify the

restricted housing units and the dates for which the applicable price controls

were in effect. This data set allows me to identify the effect of expiring price

controls by comparing how the borrowing behavior of owners of controlled

housing units changes following the expiration of the price control relative to

that of owners of nearby and observationally identical never-controlled units. It

also allows me to track the borrowing behavior of a given homeowner over time,

permitting a within-ownership spell comparison of equity extraction before and

after the expiration of the price control. The added degrees of freedom afforded

by the fact that controlled units are dispersed relatively evenly throughout the

county and expire at different points during the sample period further allow

me to control flexibly for aggregate trends affecting borrowing behavior and for

unobservable but fixed differences across localities within the county.

5For a four-person household, the maximum income limit is set at 70% of the median family

income for the Washington, DC, metropolitan area. In 2014, that limit was $75,000, which is

roughly 17% higher than the national median family income that year. In Section V.E. I document

that the income distribution among MPDU buyers is quite similar to the distribution among all

homebuyers in the United States during my sample period.