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.

Property tax revenues – the largest discretionary source of revenue for local governments - adjust at a pace that is inconsistent with property values in the US. We show that this form of revenue smoothing may be rooted in the political economy of municipalities. Measures of local budget stressors are positively related to upward assessments of a property’s value. Moreover, municipalities are significantly more likely to reassess in up markets as opposed to down – consistent with maximizing tax base and revenue collected. Using micro-level evidence from just-passing school referenda in Illinois, these shocks to municipal liabilities lead to significant increases in property assessments without any associated increases in market values or transactions. Passing a referendum over the prior 3 years increases the probability that a house is reassessed upward by 23%. This flexible form of revenue smoothing creates avenues for personal rent extraction. We find that local tax assessors: 1) have tax assessments on their own properties significantly lower than neighboring properties; and 2) these tax assessments grow significantly slower than neighbors – lowering their tax bills. We further document a significant connection between the underassessment of tax assessors’ own properties and the tax-maximizing assessment gaps documented in the districts they operate.

We introduce a novel framework for computing the transaction costs of trading strategies in the infrequently traded corporate bond market. Infrequency leads to delays and missed trading opportunities that reduce the performance of the strategies. Applying this method to a comprehensive library of 341 corporate bond factors from openbondassetpricing.com, we demonstrate that there are large number of factors that outperform the bond market before costs, but not after accounting for delays. Machine learning-based trading strategies that optimally combine the 341 characteristics yield positive bond CAPM alphas even after accounting for bid-ask spreads, but the alphas disappear after accounting for transaction delays. Our results underscore the critical impact of delay costs in illiquid securities and provide valuable insights for factor investing in corporate bond markets.