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  • Working Paper/Document de travail 2013-18

    Booms and Busts in House Prices Explained by Constraints in Housing Supply

    by Narayan Bulusu, Jefferson Duarte and Carles Vergara-Alert

  • 2

    Bank of Canada Working Paper 2013-18

    June 2013

    Booms and Busts in House Prices Explained by Constraints in Housing Supply


    Narayan Bulusu,1 Jefferson Duarte2 and Carles Vergara-Alert3

    1Funds Management and Banking Department Bank of Canada

    Ottawa, Ontario, Canada K1A 0G9

    2Jesse H. Jones Graduate School of Business

    Rice University

    3IESE Business School University of Navarra

    Bank of Canada working papers are theoretical or empirical works-in-progress on subjects in economics and finance. The views expressed in this paper are those of the authors.

    No responsibility for them should be attributed to the Bank of Canada.

    ISSN 1701-9397 © 2013 Bank of Canada

  • ii


    We thank Alan Crane, Morris Davis, Eleonora Granziera, Brian Peterson, Randall Morck, Nancy Wallace and participants of sessions at the 48th Annual AREUEA Meetings, the 19th AEFIN Finance Forum and Universitat Pompeu Fabra for their helpful comments. Carles Vergara-Alert acknowledges financial support of the Public-Private Sector Research Center at IESE and the Ministry of Economy of Spain (ref: ECO2010-10102-E, ECO2009-13169). All remaining errors are our own.

  • iii


    We study the importance of supply constraints in explaining the heterogeneity in house price cycles across geographies in the United States. Comparing the equilibrium house price generated with and without supply constraints in a representative-agent model under irreversibility of housing investment, we derive a relationship between housing returns and changes in supply constraints and determinants of housing demand. Our empirical analysis shows that supply constraints play an important role in Metropolitan Statistical Areas (MSAs) with boom-and-bust behavior. We estimate that, in 19 of the largest MSAs in the United States, supply constraints contributed 25% to the dramatic rise in house prices from 2000 to 2006, and 17% to their sharp fall from 2006 to 2010.

    JEL classification: R310 Bank classification: Asset pricing; Economic models


    Dans cette étude, les auteurs examinent la mesure dans laquelle les contraintes s’exerçant sur l’offre expliquent l’hétérogénéité des cycles des prix des maisons entre différentes régions aux États-Unis. Ils comparent le prix d’équilibre des maisons généré en intégrant ou non des contraintes d’offre dans un modèle comportant un agent représentatif dont l’investissement dans le logement est irréversible, et font ressortir une relation entre le rendement de l’investissement résidentiel, d’une part, et l’évolution des contraintes de l’offre de logements et des déterminants de la demande, d’autre part. Une analyse empirique leur permet de constater que les contraintes pesant sur l’offre jouent un rôle important dans les zones statistiques métropolitaines (Metropolitan Statistical Areas ou MSA) des États-Unis où l’on a observé des cycles de flambée et d’effondrement des prix. Les auteurs estiment que, dans 19 des plus grandes MSA, les contraintes de l’offre ont contribué dans une proportion de 25 % à la hausse spectaculaire des prix des maisons enregistrée de 2000 à 2006, et dans une proportion de 17 % à leur chute brutale de 2006 à 2010.

    Classification JEL : R310 Classification de la Banque : Évaluation des actifs; Modèles économiques

  • 1 Introduction

    Considerable effort has been expended on explaining the drivers of aggregate U.S. house

    prices, and whether or not there was a bubble1 in this market. On the one hand, there is lit-

    erature that attributes the boom-and-bust pattern to investors suffering from money illusion

    (Brunnermeier and Julliard 2008), irrational optimism (Glaeser et al. 2008), possessing het-

    erogeneous beliefs about house price appreciation (Burnside et al. 2011), or placing incorrect

    belief in the efficient markets hypothesis (Peterson 2012). On the other hand, proponents of

    the rational-investors framework argue that high uncertainty in firms’ profitability (Pástor

    and Veronesi 2006), perceived changes in relative trend productivity (Kahn 2008) or chang-

    ing credit conditions (Chu 2012; Garriga et al. 2012; Favilukis et al. 2012) could generate the

    aggregate prices observed in the United States.

    Even though these mechanisms are all plausible for the aggregate, they face considerable

    challenges in explaining observed differences across Metropolitan Statistical Areas (MSAs).2

    For example, why do investors in San Francisco suffer from money illusion while those in

    Atlanta do not? Alternatively, is it reasonable to assume that credit conditions change in the

    appropriate manner over the cycle in Miami, but not in Cleveland?

    We show that constraints on housing supply – which are local in nature – are a natural

    way of generating heterogeneity in house price cycles across MSAs, while staying within

    the classical rational-investors framework.3 More concretely, we attempt to quantify the

    contribution of the constraints on housing supply in a given MSA and year to the observed

    price in the same MSA and year. In doing so, we suggest a systematic method to control for

    variations in non-housing consumption, housing stock and housing supply capacity that can

    be used in empirical studies that aim to differentiate among competing hypotheses for the

    causes of booms and busts in house prices. Our focus on explaining house price cycles differs

    from that of the literature using supply constraints, which shows either that the cross-section

    of house prices varies positively with measures of geographical (Saiz 2010) and regulatory

    (Glaeser et al. 2006) restrictions on land development, or that supply constraints are positively

    related to the first and second moments of the returns to housing (Davis and Heathcote 2005;

    Paciorek 2012; Murphy 2012).

    We formally incorporate the effects of supply constraints on house prices by comparing

    the house prices generated when exogenously imposed supply constraints do, and do not,

    1Since we do not contribute to the debate on bubbles, we use the Brunnermeier (2008) characterization that bubbles are typically associated with dramatic asset price increases, followed by a collapse. We therefore use the terms ‘boom and bust’ and ‘bubble’ interchangeably in the text.

    2Sinai (2012) documents that not only is the amplitude and timing of house price cycles different across MSAs, but also that there is greater cross-sectional variation in house price rises (falls) during booms (busts).

    3Housing supply limitations could arise due to restrictions placed by geographical features (steeply sloping land, presence of water bodies, etc.), regulations on land use or availability of labor for construction, all of which differ across MSAs.


  • bind. This gives us the specification relating housing returns in a given geographical area to

    the effect of changes to supply constraints and their interaction with changes to determinants

    of housing demand. We model an infinitely lived rational representative agent with utility

    for housing services provided by the stock of housing Ht and non-housing consumption ct, as

    in Piazzesi et al. (2007). At each period, from the stock of non-housing wealth Kt, the agent

    consumes ct and transfers ψt to augment the stock of housing. This investment is irreversible

    (ψt ≥ 0), and is subject to an exogenously determined constraint (ψt ≤ Ψmax).4 The growth of the stocks of non-housing wealth Kt and housing Ht have exogenously given drift, and suffer

    independent shocks. We assume that there are substantial barriers to moving between cities

    and abstract from the issue of migration between MSAs. Cross-location variation in house

    price patterns is driven, therefore, by differences in the evolution of supply characteristics

    across MSAs.

    The mechanism by which the model generates booms and busts in house prices is simple:

    an increase in demand for housing that can be fully supplied by new construction would see

    modest changes in prices; the presence of a binding constraint results in a larger movement

    in prices. A fall in demand that takes it back below the threshold that can be fully met by

    additional supply would result in a return to the ‘normal regime,’ but would show up as a

    large fall in prices.

    Using data from 19 MSAs in the United States to test these relationships, we find that

    a significant part of the boom and bust in prices observed between 2000 and 2011 can be

    explained by supply constraints.5 For instance, aggregate returns to housing in the 19 MSAs

    were 85% (84%) during the period 2000–06 (2000–07). This growth is not fully captured

    by empirical specifications that do not incorporate constraints on housing supply, which

    generate at most 36% (42%) returns to housing during this period. Including the effect

    of supply constraints, on the other hand, generates 61% (69%) returns to housing. More