Financial Fragility with SAM?

April 12, 2018

By Tim Landvoigt, Stijn Van Nieuwerburgh and Daniel Greenwald

Shared Appreciation Mortgages (SAMs) feature mortgage payments that adjust with house prices. Such mortgage contracts can stave off home owner default by providing payment relief in the wake of a large house price shock. SAMs have been hailed as an innovative solution that could prevent the next foreclosure crisis, act as a work-out tool during a crisis, and alleviate fiscal pressure during a downturn. They have inspired Fintech companies to offer home equity contracts. However, the home owner’s gains are the mortgage lender’s losses. We consider a model with financial intermediaries who channel savings from saver households to borrower households. The financial sector has limited risk bearing capacity. SAMs pass through more aggregate house price risk and lead to financial fragility when the shock happens in periods of low intermediary capital. We compare house prices,mortgage rates, the size of the mortgage sector, default and refinancing rates, as well as borrower and saver consumption between an economy with standard mortgage contracts and an economy with SAMs.

I had not heard of the concept of shared appreciation mortgages. Interesting idea with some counter-intuitive results. For example, I would have expected to see a higher steady-state mortgage interest rate, as the risk is shifted more to the lender. Well, no, because there are hardly any foreclosures, the risk is actually going down.


Out of Sync Subnational Housing Markets and Macroprudential Policies

April 10, 2018

By Michael Funke; Petar Mihaylovski; Adrian Wende

n view of regional house prices drifting apart, we examine whether regionally differentiated macroprudential policies can address financial stability concerns and moderate house price differences. To this end, we disaggregate both the household sector and the housing stock in a two-region DSGE model with out of sync subnational housing markets and compare four macroprudentail policy types: standard monetary policy by means of a standard Taylor rule, leaning against the wind monetary policy, national macroprudential policy or one that targets region-specific LTV ratios. In terms of reducing variances of house prices, regionally differentiated macroprudential policy performs best, provided the policy authorities are concerned with stabilising output and house prices rather than simply minimising the variance of inflation. Thus the findings point to a critical role for policy in regionalising macroprudential tools.

The problem with monetary unions, or very large countries, is that monetary policy cannot account for regional differences. This papers offers a policy solution by differentiating regionally the macroprudential solution. Good idea, although I worry about the political ramifications: what region can expect help is going to be a political game.

Term structure and real-time learning

April 7, 2018

By Pablo Aguilar and Jesús Vázquez

This paper introduces the term structure of interest rates into a medium-scale DSGE model. This extension results in a multi-period forecasting model that is estimated under both adaptive learning and rational expectations. Term structure information enables us to characterize agents’ expectations in real time, which addresses an imperfect information issue mostly neglected in the adaptive learning literature. Relative to the rational expectations version, our estimated DSGE model under adaptive learning largely improves the model fit to the data, which include not just macroeconomic data but also the yield curve and the consumption growth and inflation forecasts reported in the Survey of Professional Forecasters. Moreover, the estimation results show that most endogenous sources of aggregate persistence are dramatically undercut when adaptive learning based on multi-period forecasting is incorporated through the term structure of interest rates.

Adaptive learning is not new in DSGE models, but here is it done in a way that reflects current information much better than using historic information by using the yield curve. Interesting. This makes me think whether anyone used archived vintage data like from ALFRED for expectation formation.