These seminars are sponsored by the Mitsui Life Financial Research Center.

If you would like to be added to the email distribution list, please contact Gabriella Ring at gabring@umich.edu.

march 28

Savitar Sundaresan, Imperial College London

Title: Extensive Attention, Intensive Attention and the Origins of Random Choice

Abstract: Using repeated choices and eye-tracking data across 180 menu instances from 50 subjects we link the randomness of choice to two notions of attention: extensive attention (what options are looked at) and intensive attention (how long options are looked at). We show that models that seek to explain randomness through attention should capture four key facts.  First, extensive attention and intensive attention are both related to randomness in choice, although intensive attention, on average, is a better predictor of choice.  Second, the mechanism of choice, and the degree of randomness, is very different for large compared to small choice sets.  Third, the relative importance of attention in generating randomness in choice is smaller between-person than within-person. Fourth, greater attentional capacity is not associated with reduced randomness at the individual level.  Fifth, increased attention is not strongly correlated with better choices, indicating additional attention may be deployed in situations where higher randomness is already likely.

Time: 10:30-11:50 a.m.        
Location: R2230

april 4

Stijn van Nieuwerburgh, Columbia

Title:  The Commercial Real Estate Ecosystem

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 7% 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.

Time: 10:30-11:50 a.m.        
Location: R2230

april 11

Ralph Koijen, Chicago Booth

Title:  TBD

Abstract: TBD

Time: 10:30-11:50 a.m.        
Location: R2230

april 25

Michael Ewens, Columbia

Title: TBD

Abstract: TBD

Time: 10:30-11:50 a.m.        
Location: TBD