Consumption, House Prices and Collateral Constraints: a Structural Econometric Analysis

If borrowing capacity of indebted households is tied to the value of their home, house prices should enter a correctly specified aggregate Euler equation for consumption. I develop a simple two-agent, dynamic general equilibrium model in which home (collateral) values affect debt capacity and consumption possibilities for a fraction of the households. I then derive and estimate an aggregate consumption Euler equation, and estimate its structural parameters. The results provide robust support for housing prices as a driving force of consumption fluctuations.Housing, consumption, collateral constraints

Household Debt and Income Inequality, 1963-2003

I construct a heterogeneous agents economy that mimics the time-series behavior of the US earnings distribution from 1963 to 2003. Agents face aggregate and idiosyncratic shocks and accumulate real and financial assets. I estimate the shocks driving the model using data on income inequality, on aggregate income and on measures of financial liberalization. I show how the model economy can replicate two empirical facts: the trend and cyclical behavior of household debt, and the diverging patterns in consumption and wealth inequality over time. In particular, I show that, while short-run changes in household debt can be accounted for by aggregate fluctuations, the rise in household debt of the 1980s and the 1990s can be quantitatively explained only by the concurrent increase in income inequalityHousehold Debt, Income Inequality, Incomplete Markets, Borrowing Constraints

House prices and the macroeconomy in Europe: Results from a structural var analysis

A structural vector autoregressive (SVAR) approach is used to identify the forces driving house prices fluctuations in France, Germany, Italy, Spain, Sweden and the UK over the period 1970-1998. Quarterly time series for real house prices, GDP, money, inflation and interest rates are characterised by a multivariate process driven by supply, nominal, monetary, inflationary and demand shocks. It is found that: (1) tight money leads to a concomitant fall in house prices and GDP; (2) the house price responses to a monetary shock can be partly justified by the different housing and financial market institutions across countries; (3) monetary and demand shocks drive most of the short-run house price volatility. The paper also interprets the main house price cycles and their links with the real economy in light of the estimates shocks. JEL Classification: C32, E32, E52, R21

Private Debt and Idiosyncratic Volatility: A Business Cycle Analysis

Debt, durables, volatility, borrowing constraints, housing

The Credit Channel of Monetary Policy and Housing Markets: International Empirical Evidence

This paper tests for the presence of a credit channel (particularly a bank-lending sub-channel) for monetary policy in the housing market. We argue that the importance of this channel for investment in residential housing is highly dependent on the structural features, and particularly the efficiency and institutional organization, of housing finance. We employ a VAR methodology to analyse this issue with respect to the housing markets of four European countries (Finland, Germany, Norway and the United Kingdom), which differ greatly in terms of structural features. Our results are generally consistent with the existence of a broad credit channel, whereas the bank-lending channel seems to be operational only under certain conditions. More importantly, our results are consistent with previous analyses of housing market efficiency, which strongly suggests the existence of a clear relationship between the presence of a credit (bank lending) channel, the efficiency level of housing finance, and the type of institutions that are active in mortgage provision.monetary transmission; bank lending channel; house prices; vector autoregressions

An Equilibrium Model of Lumpy Housing Investment

We formulate and solve a dynamic general equilibrium model with heterogeneous agents and lumpy housing adjustment at the household level. We use the model to ask a simple question: how does the microeconomic lumpiness of housing adjustment affect the equilibrium dynamic properties of aggregate consumption and investment? Our main conclusion is that lumpiness matters: in particular, lumpiness in housing adjustment (1) reduces the volatility of both housing and business investment; (2) increases the volatility of aggregate consumption; (3) increases the correlation of housing investment with business investment and with GDP. We also show that lumpiness of investment activity at the household level has small but significant aggregate implications, in contrast with the literature that shows that the aggregate effects of lumpy investment at the firm level are negligible.

Liquidity Cycles

We study an economy where firms face credit constraints tied to the value of their assets and financiers differ in their information on the market for firms' assets. Financiers with poor information on the asset market make mistakes in asset liquidation, hoarding assets during booms and trading them during recessions. We find that asset liquidity and the composition -informed versus uninformed- of firms' financiers breed each other in a cumulative fashion and that their interaction generates cycles in asset values and outputAsset Liquidity, Business Fluctuations, Firm Financing

Housing and Debt over the Life Cycle and over the Business Cycle

We study housing and debt in a quantitative general equilibrium model. In the cross-section, the model matches the wealth distribution, the age pro?les of homeownership and mortgage debt, and the frequency of housing adjustment. In the time-series, the model matches the procyclicality and volatility of housing investment, and the procyclicality of mortgage debt. We use the model to conduct two experiments. First, we investigate the consequences of higher individual income risk and lower downpayments, and ?nd that these two changes can explain, in the model and in the data, the reduced volatility of housing investment, the reduced procyclicality of mortgage debt, and a small fraction of the reduced volatility of GDP. Second, we use the model to look at the behavior of housing investment and mortgage debt in an experiment that mimics the Great Recession: we ?nd that countercyclical financial conditions can account for large drops in housing activity and mortgage debt when the economy is hit by large negative shocks.Housing, Housing Investment, Mortgage Debt, Life-cycle Models, Income Risk, Homeownership, Precautionary Savings, Borrowing Constraints

Housing market spillovers: Evidence from an estimated DSGE model

The ability of a two-sector model to quantify the contribution of the housing market to business fluctuations is investigated using U.S. data and Bayesian methods. The estimated model, which contains nominal and real rigidities and collateral constraints, displays the following features: first, a large fraction of the upward trend in real housing prices over the last 40 years can be accounted for by slow technological progress in the housing sector; second, residential investment and housing prices are very sensitive to monetary policy and housing demand shocks; third, the wealth effects from housing on consumption are positive and significant, and have become more important over time. The structural nature of the model allows identifying and quantifying the sources of fluctuations in house prices and residential investment and measuring the contribution of housing booms and busts to business cycles.House prices, Collateral Constraints, Bayesian methods, Two-sector Models

Housing market spillovers : evidence from an estimated DSGE model

We study sources and consequences of fluctuations in the housing market. The upward trend in real housing prices of the last 40 years can be explained by slow technological progress in the housing sector. Over the business cycle, housing demand and housing technology shocks explain one-quarter each of the volatility of housing investment and housing prices. Monetary factors explain 20 percent, but they played a bigger role in the housing cycle at the turn of the century. We show that the housing market spillovers are non-negligible, concentrated on consumption rather than business investment, and have become more important over time.Housing, Wealth E¤ects, Bayesian Estimation, Two-sector Models