Abstract
We develop a new approach to understand the joint dynamics of transaction prices and trading volume in the market for commercial real estate. We start from a micro-founded model in which buyers and sellers differ in their private valuation of building characteristics, such as size, location, and quality. Consistent with the decentralized nature of the commercial real estate market, we model the probability that a seller meets a particular buyer, where the meeting probability depends on the characteristics of the buyer, the seller, and the building. In equilibrium, the mapping from building characteristics to observed transaction prices depends on the identity of the buyer and the seller, an important property missed by traditional hedonic valuation models. We estimate the model using granular data on commercial real estate transactions, which contain detailed information on the identity of buyers and sellers. Our central finding is that the identity of buyers and sellers has a first-order effect on both property valuation and the likelihood of trade. The importance of investor characteristics for valuations remains true, in fact is amplified, in a rich machine learning model that allows for non-linearities and interactions. We show how the model can be used for out-of-sample predictability and for counterfactual analyses on investment flows and prices. As a concrete example, we find that the Manhattan office market would have seen 5% lower valuations if it had not been for a large inflow of foreign buyers in 2013-2021. Our methodology extends to other private markets, including private equity, private credit, and infrastructure.