A decade of strong expansion in the Spanish economy led by a credit-fueled housing boom was hit by three major shocks. The policy response to Spain’s economic challenges has been strong and wide-ranging, helping to strengthen market confidence. A decisive implementation of the envisaged financial sector reform strategy would help allay lingering market concerns. A bold strengthening of labor market reforms is needed to substantially reduce unacceptably high unemployment. Further progress needs to be made on enhancing competition in nontradable sectors.
After a strong performance in 2017, economic activity has moderated. The second half of 2018 was marked by a deceleration, coinciding with weaker economic activity in Europe. The headline fiscal balance improved, with a small increase in the structural primary balance reflecting a strict budget execution. The current account turned negative in 2018 in conjunction with a deterioration of the balance of trade in goods and services. Total credit to the nonfinancial private sector continued to decline in 2018. Nevertheless, over the last 4 years the Portuguese banking system has been strengthening its balance sheet and its performance.
Mr. Paul Louis Ceriel Hilbers, Angana Banerji, Haiyan Shi, and Mr. Willy A Hoffmaister
House prices in Europe have shown diverging trends, and this paper seeks to explain these differences by analyzing three groups of countries: the "fast lane", the average performers, and the slow movers. Price movements in the first two groups are found to be driven mostly by income and trends in user costs, and housing markets in these countries seem relatively more susceptible to adverse developments in fundamentals. Real house price declines among the slow movers are harder to explain, although ample supply, low home ownership, and less complete mortgage markets are likely factors. The impact of macroeconomic, prudential and structural policies on housing markets can be large and should be a factor in policy decisions.
Using zip code-level data and nonparametric estimation, I present eight stylized facts on the US housing market in the COVID-19 era. Some aggregate results are: (1) growth rate of median housing price during the four months (April-August 2020) since the Federal Reserve’s unprecedented monetary easing has accelerated faster than any four-month period in the lead-up to the 2007-09 global financial crisis; (2) the increase in housing demand in response to lower mortgage interest rates displays a structural break since March 2020 (housing demand has increased by much more than before). These results indicate either the existence of “fear of missing out” or COVID-induced fundamental changes in household behavior. In terms of distributional evidence, I find that the increase of housing demand seems more pronounced among the two ends of the income distribution, possibly reflecting relaxed liquidity constraints at the lower end and speculative demand at the higher end. I also find that the developments in housing price, demand, and supply since April 2020 are similar across urban, suburban, and rural areas. The paper highlights some potential unintended consequences of COVID-fighting policies and calls for further studies of the driving forces of the empirical findings.
Mr. Helge Berger, Mr. Thomas Dowling, Mr. Sergi Lanau, Mr. Mico Mrkaic, Mr. Pau Rabanal, and Marzie Taheri Sanjani
Potential output—in the sense of the GDP level or path an economy can sustain over the
medium term—is a crucial benchmark for policymakers. However, it is difficult to estimate
when financial “booms and busts” are driving the real economy. This paper uses a simple
multivariate filtering approach to illustrate the role financial variables play in driving
potential or sustainable output. The results suggest that it moves more steadily during
financial “boom and bust” periods than implied by conventional HP filter estimates, which
tend to more closely follow actual GDP. A two-region, multisector New Keynesian DSGE
model with financial frictions sheds light on the economic forces that could be behind the
results obtained from the filter. This has important implications for policymakers.
Mr. Ananthakrishnan Prasad, Selim Elekdag, Mr. Phakawa Jeasakul, Romain Lafarguette, Mr. Adrian Alter, Alan Xiaochen Feng, and Changchun Wang
The growth-at-risk (GaR) framework links current macrofinancial conditions to the distribution of future growth. Its main strength is its ability to assess the entire distribution of future GDP growth (in contrast to point forecasts), quantify macrofinancial risks in terms of growth, and monitor the evolution of risks to economic activity over time. By using GaR analysis, policymakers can quantify the likelihood of risk scenarios, which would serve as a basis for preemptive action. This paper offers practical guidance on how to conduct GaR analysis and draws lessons from country case studies. It also discusses an Excel-based GaR tool developed to support the IMF’s bilateral surveillance efforts.