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%.

Firms increasingly rely on markets for technology to acquire innovations developed outside their boundaries, yet acquiring intellectual property rights alone often does not guarantee successful implementation. Many technologies depend on tacit know-how that must be supplied by the provider after the transaction is completed. This paper examines whether common ownership between a technology provider and a potential adopter mitigates this implementation problem. I develop a model in which overlapping institutional investors cause the provider to partially internalize the adopter’s gains from successful implementation, strengthening incentives to transfer tacit know-how. This mechanism operates only when know-how is unverifiable – absent this friction, common ownership leaves matching and outcomes unchanged. Under moral hazard, the model predicts that common ownership increases the likelihood of technology transfer to a given adopter, that this effect is stronger when tacit know-how is more important, and that common ownership improves post-transfer outcomes conditional on adoption. I test these predictions using U.S. patent reassignments between publicly traded firms. Using within-deal variation across competing potential adopters and plausibly exogenous variation from passive index-fund holdings, I show that common ownership increases the likelihood that a firm acquires a technology, particularly when the transferred bundle is more tacit. Common ownership predicts stronger subsequent innovation and higher future firm value, especially when ownership overlap is concentrated among investors with stronger incentives to monitor the provider. These findings show how ownership structure shapes interfirm technology transfer by affecting not only who acquires a technology, but also how much value is created.

Using brand-level sales data from Chinese e-commerce platforms, this study examines how geopolitical tensions affect multinational brands operating in China. Merging these sales data with a U.S.–China tension index, we use panel regressions and local projections to show that rising tensions significantly reduce the market share of U.S. brands in China relative to brands from other countries, with the effects persisting for up to 12 months. An event study employing a difference-in-differences framework, centered on brand-specific incidents of political tension with China, reveals similar market share declines among affected brands, highlighting consumer sentiment as a key transmission channel.