This paper investigates the relationship between heterogeneity in sectoral price stickiness and the response of the economy to aggregate real shocks. We show that sectoral heterogeneity reduces inflation persistence for a constant average duration of price spells, and that inflation persistence can fall despite duration increases associated with increases in heterogeneity. We also find that sectoral heterogeneity reduces the persistence and volatility of interest rate and output gap for a constant price spells duration, while the qualitative impact on inflation volatility tends to be positive. A relevant policy implication is that neglecting price stickiness heterogeneity can impair the economic dynamics assessment.

In turbulenten Zeiten, die von anhaltenden geopolitischen Krisen, dem holprigen Regierungswechsel in Deutschland und volatilen Aktienmärkten geprägt sind, mögen die eine oder der andere Investor auf Betongold setzen. Ob dies eine gute Idee ist, zeigt ein Blick auf die Dynamik im europäischen Häusermarkt.

Is there a multicultural neighborhood price premium? We exploit plausibly exogenous variation in British colonization patterns in Northern Ireland during the early 1600s which created neighborhoods of varying religious composition that persists until today. These religious groups are culturally distinct, but are observationally equivalent ethnically and socioeconomically. A standard deviation increase neighborhood-level multiculturalism raises house prices by 9.6%. Multiculturalism raises property prices by increasing asset liquidity and housing demand as a wider spectrum of society demand houses in these areas. The findings and mechanism contrast sharply with prior evidence showing negative relationships due to homophily, social networks, and identification challenges.

We study whether and how EU banks comply with tighter macroprudential policy (MPP). Observing contractual details for more than one million securitized loans, we document an elusive risk-shifting response by EU banks in reaction to tighter loan-to-value (LTV) restrictions between 2009 and 2022. Our staggered difference-in-differences reveals that banks respond to these MPP measures at the portfolio level by issuing new loans after LTV shocks that are smaller, have shorter maturities, and show a higher collateral valuation while holding constant interest rates. Instead of contracting aggregate lending as intended by tighter MPP, banks increase the number and total volume of newly issued loans. Importantly, new loans finance especially properties in less liquid markets identified by a new European Real Estate Index (EREI), which we interpret as a novel, elusive form of risk-shifting.

Banks have been operating in a low interest rate environment paired with booming housing markets. For the largest banks in the euro area and the period 2015-2022, we assess whether banks reallocate their foreign loan portfolio backed by real estate as a response to differences in local lending spreads across the home and destination country and conditional on reduced information frictions due to borrowing-country exposures. The main result is that the relative share of foreign real estate backed lending increases in case of return opportunities, and this sensitivity depends on local exposures towards the borrowing country. The result is driven by subsamples for which neither the home nor the borrowing country have implemented macroprudential regulation targeting real estate lending, or for which there is a misalignment in macroprudential policies. Nevertheless, we find limited evidence that the riskiness of real estate backed loans goes up during our sample period, and we discuss potential reasons for this result including the possibility of hidden losses.

This Technical Report documents the construction and coverage of the IWH European Real Estate Index (EREI). Since 2018, we have used machine-learning methods to collect monthly listings of residential real estate available for sale or rent in up to 20 European countries. The Technical Report documents the cleaning and selection process and describes the data regarding coverage, moments, and frequencies to construct the EREI.

Our results reveal that higher lending spreads between foreign and home markets redirect real estate backed lending towards foreign markets offering a higher interest rate, which provides evidence for "search for yield" behavior. This re-allocation is found especially for banks with more expertise on the foreign market due to a higher local activity and holds for commercial and residential real estate backed loans. Furthermore, "search for yield" behavior and a resulting increase in foreign real estate backed lending is found when macroprudential regulation is missing or misaligned between a bank’s country of residence and the destination country. When turning to the question of whether the detected search for yield behavior results in more risk, we find that especially better capitalized banks report higher forbearance ratios as they might face less stigma effects compared to low capitalized banks.

This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in mean-squared prediction error relative to a random walk benchmark can be achieved in real time for forecast horizons of up to two years. A particularly promising model is a six-variable Bayesian vector autoregressive model that includes the fundamental determinants of the supply and demand for natural gas. To capture real-time data constraints of these and other predictor variables, we assemble a rich database of historical vintages from multiple sources. We also compare our model-based forecasts to readily available model-free forecasts provided by experts and futures markets. Given that no single forecasting method dominates all others, we explore the usefulness of pooling forecasts and find that combining forecasts from individual models selected in real time based on their most recent performance delivers the most accurate forecasts.