We develop a new framework to study the joint dynamics of prices and trading volume in commercial real estate markets. We start from a micro-founded model where buyers and sellers differ in private valuations of building characteristics. We model listing and meeting probabilities based on investor and property traits. The model implies that transaction prices depend on both building features and participant identities—unlike traditional hedonic models. Using detailed transaction data and machine-learning methods, we find buyer and seller identities have a first-order effect on prices and transaction likelihood. We apply the framework to out-of-sample price prediction and counterfactual analyses on investment flows and prices. The approach extends to private equity, credit, and infrastructure markets.