Financing Housing Capital
April 25, 2014
Matteo Iacoviello and Luca Guerrieri, Federal Reserve Board
Guerrieri and Iacoviello show that occasionally binding collateral constraints on housing wealth drive an asymmetry in the relationship between house prices and economic activity. The sensitivity of macroeconomic aggregates to movements in housing prices can be large when housing wealth is low, and small when housing wealth is high. The authors develop this argument in a nonlinear general equilibrium model estimated with full information Bayesian methods. As collateral constraints became slack during the housing boom of 20016, expanding housing wealth contributed little to consumption growth. By contrast, the housing collapse that followed tightened the constraints and sharply exacerbated the recession of 20089. The empirical relevance of this asymmetry is corroborated by the results of panel regressions on state- and MSA-level data.
Stefano Giglio, University of Chicago and NBER; Matteo Maggiori, New York University and NBER; and Johannes Stroebel, New York University
Giglio, Maggiori, and Stroebel provide the first direct estimates of how agents trade off immediate costs and uncertain future benefits that occur in the very long run, 100 or more years away. They exploit a unique feature of housing markets in England, Wales, and Singapore where residential property ownership takes the form of either leaseholds or freeholds. Leaseholds are temporary, pre-paid, and tradable ownership contracts with maturities between 50 and 999 years, while freeholds are perpetual ownership contracts. The difference between leasehold and freehold prices represents the present value of perpetual rental income starting at leasehold expiry. The authors estimate the price discounts for varying leasehold maturities compared to freeholds and extremely long run leaseholds via hedonic regressions using proprietary datasets of the universe of transactions in each country. Agents discount very long run cash flows at low rates, assigning high present values to cash flows hundreds of years in the future. For example, 100-year leaseholds are valued up to 15 percent less than otherwise identical freeholds. Given the riskiness of rents, this suggests that both long-term risk-free discount rates and long-term risk premiums are low. Together with the relatively high average return to housing, this also implies a downward-sloping term structure of discount rates. The authors' results provide a new testing ground for asset pricing theories, including the analysis of bubbles, and have direct implications for climate change policy, long-run fiscal policy, and the conduct of cost benefit analyses. The authors find that households are relatively more willing to pay today to ensure reduced climate costs in the distant future, but relatively less willing to pay to only reduce the risk of bad climate outcomes compared to the leading environmental models.
Michael Sockin, Princeton University, and Wei Xiong, Princeton University and NBER
Sockin and Xiong develop a tractable model to analyze information aggregation and learning in housing markets. In the presence of informational frictions, households face a realistic problem in learning about the quality of a neighborhood, and housing prices serve as important signals. The model highlights how learning by households interacts with local housing supply and demand characteristics and affects housing price dynamics. These learning effects are particularly strong when supply elasticity is in an intermediate range, and can cause short-run price momentum even when shocks to both housing supply and demand mean revert over time.
David Scharfstein, Harvard University and NBER, and Adi Sunderam, Harvard University
Scharfstein and Sunderam present evidence that high concentration in local mortgage lending reduces the sensitivity of mortgage rates and refinancing activity to mortgage-backed security (MBS) yields. A decrease in MBS yields is typically associated with greater refinancing activity and lower rates on new mortgages. However, this effect is dampened in counties with concentrated mortgage markets. The authors isolate the direct effect of mortgage market concentration and rule out alternative explanations based on borrower, loan, and collateral characteristics in two ways. First, they use a matching procedure to compare high- and low-concentration counties that are very similar on observable characteristics and find similar results. Second, they examine counties where concentration in mortgage lending is increased by bank mergers. The authors show that within a given county, sensitivities to MBS yields decrease after a concentration-increasing merger. Their results suggest that the strength of the housing channel of monetary policy transmission varies in both the time series and the cross section. In the cross section, increasing concentration by one standard deviation reduces the overall impact of a decline in MBS yields by approximately 50 percent. In the time series, a decrease in MBS yields today has a 40 percent smaller effect on the average county than it would have had in the 1990s because of higher concentration today.
Erik Hurst, Amit Seru, and Joseph Vavra, University of Chicago and NBE, and Benjamin Keys, University of Chicago
Government-sponsored mortgage enterprises (GSEs) securitize the bulk of all mortgages originated within the United States. Hurst, Keys, Seru, and Vavra formally explore one of the aspects of the GSE pricing decision by studying the extent to which GSE policies result in the redistribution of resources across U.S. regions in a state contingent manner. They empirically establish that, despite large spatial variation in predictable default risk, there is essentially no spatial variation in GSE mortgage rates, conditional on borrower observables. While there is no regional risk-based pricing in the government-backed GSE market, the private market does set interest rates based in part on regional risk factors. The authors explore a number of explanations for the lack of spatial variation of GSE mortgage rates over time and conclude that political pressure is the most reasonable explanation for the patterns they observe. They attempt to quantify the economic impact of the GSEs' constant interest rate policy on regional risk sharing by building a structural spatial model of collateralized borrowing where households face both idiosyncratic and region-specific shocks. The model, parameterized and calibrated based on the authors' analysis of GSE and private markets, suggests that the GSE national interest rate policy has significant ex post redistribution consequences across regions: on an ex post basis, it is comparable in size to fiscal stimulus packages such as tax rebates and payroll tax holidays. Although there are a range of consequences to housing and mortgage markets that are often attributed to the presence of GSEs, this paper suggests that their common national interest rate policy may be one important and understudied dimension of their impact on household choices.
Giovanni Favara, Federal Reserve Board, and Mariassunta Giannetti, Stockholm School of Economics
Previous research has shown that mortgage foreclosures generate a negative externality on nearby house prices. In this paper, Favara and Giannetti conjecture that lenders with a larger share of a neighborhood' s outstanding mortgages on their balance sheets internalize this externality and are thus more inclined to renegotiate defaulting loans. The authors provide evidence supporting this conjecture using zip-code-level data on house prices and foreclosures during the 20079 U.S. housing market crisis. They find that zip codes with larger outstanding mortgage concentrations experienced fewer foreclosures and smaller house price declines. These findings are not driven by prior local economic conditions, mortgage securitization, or ex ante lender characteristics, and hold within geographical areas exposed to common economic shocks, such as metropolitan statistical areas or counties. The authors also find that the concentration of outstanding mortgages matters more in zip codes of non-judicial states where foreclosure procedures are less costly