Finance Division

Recent decades have seen house prices grow strongly relative to incomes, making housing ever less affordable. We develop and quantify a model of segmented housing markets to study the drivers of rising housing costs and to evaluate policies aimed at curbing these costs. We show that rising wealth dispersion, together with stagnating housing supply, can explain the observed increase in housing costs. Demand side policies such as down payment assistance and mortgage interest deductions inadvertently cause upward pressure on house prices and exacerbate unaffordability.

We explore indicators of market power in a data market. Markups cannot measure competition, because most data products’ marginal cost is zero, making the markup infinite. Yet, data monopolists may not exert monopoly power because they cannot commit to restricting data sales to future customers. This limited commitment and strategic substitutability of data undermine sellers’ monopoly power. But data subscriptions restore this monopoly power. Evidence from online data markets supports the model’s insight that subscriptions indicate market power.

How should an investor value financial data? The answer is complicated because it depends on the characteristics of all investors. We develop a sufficient statistics approach that uses equilibrium asset return moments to summarize all relevant information
about others’ characteristics. It can value data that is public or private, about one or many assets, relevant for dividends or for sentiment. While different data types, of course, have different valuations, heterogeneous investors also value the same data

Professional house price forecast data are consistent with a rational model where agents must learn about the parameters of the house price growth process and the underlying state of the housing market. Slow learning about the long-run mean generates overreaction to forecast revisions and a modest response of forecasts to lagged realizations. Heterogeneity in signals and priors about the long-run mean helps the model account for cross-sectional dispersion in forecasts. Introducing behavioral biases helps improve the model's predictions for short-horizon overreaction and dispersion.

How do venture capital investors react to social movements, especially those that relate to historical underrepresentation in their funding decisions? We use image and name algorithms combined with clerical review to classify race for 150,000 founders and 30,000 investors. Our new data allow us to assess the impact of George Floyd's murder on VC funding of Black entrepreneurs and identify which VCs were most responsive. Although VCs responded swiftly, investment in Black-owned startups reverted to prior levels within two years.