Real estate is a capstone connection between various economic agents and markets. It is the main store of household wealth, serves as collateral for mortgage loans in the banking system, aids the transmission of monetary policy, and can propagate financial crises when overvalued. Yet comparable house-price data across the European Union (EU) and the euro area is unavailable, which hinders the design and evaluation of common monetary and economic policy that operates across heterogeneous housing markets. We derive monthly subnational European Real Estate Indicators (EREI) from online residential property advertisements in 16 European countries. The release covers April 2024 to June 2026 and contains 48,168 region-month-segment observations for 1,154 NUTS 3 regions, aggregating more than 43 million listing observations across the monthly sale and rental cross-sections. Each region-month segment reports the number of advertisements and summary statistics for asking prices per square meter and listing durations. The release also includes sale-segment indices for Europe, the euro area, and individual countries. Thirteen covered countries are EU members, which represented 85% of EU-27 gross domestic product in 2024. EREI data support research on a wide range of socio-economic phenomena associated with real estate dynamics, such as the evaluation of monetary policy or macroprudential policy effects on financial stability.

We show that online listing prices closely approximate transaction prices across Europe. Our validation uses the IWH European Real Estate Index (EREI), a near-real-time database of monthly purchase prices, rents, listing counts, and time on market for 1,154 NUTS-3 regions in 16 countries since April 2024. With publication lags as short as one month, validated listing prices offer a timely, comparable complement to official house price statistics. Purchase price levels correlate between 0.94 and 0.97 with transaction prices in the five most populous markets, while calibration slopes range from 0.95 to 1.00. Aggregate listing prices exceed benchmark transaction prices by 13% to 22%.

Vietnam, a low middle-income economy, grapples with considerable climate impacts, primarily driven by heat waves, sea level rise, and tropical cyclones. Under ongoing global warming, these extreme weather events are projected to further intensify. We use a dynamic general equilibrium model to study economic transition dynamics from 2015 to 2100, accounting for heat-induced labor productivity losses, agricultural land loss from sea level rise, and residential property damage from tropical cyclones. We compare a Paris-compatible strong mitigation scenario where global warming is limited to well below 2 °C above preindustrial levels to a strong emission scenario where warming reaches 4–5 °C. We find that the impacts of climate change on output and investment are highly uncertain, with differences between the two emission scenarios remaining statistically insignificant until the end of the century, despite substantially higher climate forcing in the latter. By contrast, consumption losses are significantly larger under the high emission scenario. These negative impacts are primarily driven by heat-induced labor productivity losses, while TCs are the main source of uncertainty. Our findings highlight the need for analytical frameworks to capture the different channels through which climate and climate change affect economic development, rather than focusing mainly on output-related damage, as done in many existing studies.

Syndicated loan data provided by DealScan is an essential input in banking research to answer urging questions on bank lending, e.g., in the presence of financial or geopolitical shocks or climate change. However, many data options raise the question of how to choose the estimation sample. We employ a standard regression framework analyzing bank lending during the financial crisis of 2007/08 to study how conventional but varying usages of DealScan affect the estimates. The key finding is that the direction of coefficients remains relatively robust. However, statistical significance depends on the data and sampling choice, and we provide guidelines for applied research.

Based on a sufficient statistics approach, we show how the state of technology of European industries relative to the rest of the world can be empirically assessed in a way that is simple in terms of computation, parsimonious in terms of data requirements, but still comprehensive in terms of information. The lack of systematic cross-industry correlation between export specialization and technological advantage suggests that standard measures of revealed comparative advantage only imperfectly capture a country’s technological prowess due to the concurrent influences of factor prices, market size, markups, firm selection and market share reallocation. These findings offer policy insights relevant to the EU’s external competitiveness debate, echoing several recommendations from the Draghi report. Achieving export specialization in key sectors requires more than just technological superiority.

Vietnam, a lower-middle-income economy, faces severe climate risks from heat waves, sea-level rise, and tropical cyclones, which are expected to intensify under ongoing global warming. Using a dynamic general equilibrium model, we analyze economic transition dynamics from 2015 to 2100, incorporating heat-induced labor productivity losses, agricultural land loss, and cyclone-related property damage. We compare a Paris-compatible scenario limiting warming to below 2 °C with a high-emission scenario reaching 4–5 °C. While output and investment impacts remain highly uncertain and statistically indistinguishable across scenarios until 2100, consumption losses are significantly larger under high emissions, mainly driven by heat-related productivity declines, with cyclones contributing most to uncertainty. These findings underscore the importance of considering multiple impact channels beyond output damages in climate-development research.

Predicting IPO first-day returns is inherently challenging due to the wide range of contributing factors, each with distinct statistical properties. We assess the performance of several machine learning (ML) techniques and identify XGBoost as the most statistically effective model for forecasting first-day returns. Using a comprehensive set of 863 pre-IPO variables, our high-performing predictive model accurately estimates both the direction and magnitude of IPO first-day returns. The most influential predictors include underwriter agency measures, price revision, and the free-float fraction. Using a rolling-window predictive approach, the model demonstrates substantial practical value, generating approximately $300 billion in gains from IPOs with positive first-day returns and avoiding more than $22 billion in losses from those with negative returns over the 2000–2016 period.