Identification and Estimation of Seller Risk Aversion in Ascending Auctions

This paper shows how to identify and estimate the seller's risk parameter in an ascending auction. We consider a semiparametric model where the seller has a parametric utility function (such as CARA or CRRA) and the distribution of bidder valuations is modeled flexibly. We provide primitive conditions under which the risk parameter is identified and show that it can be consistently estimated with an asymptotically normal limiting distribution under standard regularity conditions. A Monte Carlo study demonstrates good finite-sample performance of the proposed estimator. We apply our approach to foreclosure real estate auction data from São Paulo. We find evidence that sellers are risk-averse, which leads to a much better fit to the data than a model with risk-neutral sellers, which would substantially underpredict the reserve price relative to what is observed.

Publication Details

Journal
arXiv (Cornell University)
Published
2025-09-24
Primary Topic
Geochemistry and Geologic Mapping
Type
article
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article

Identification and Estimation of Seller Risk Aversion in Ascending Auctions

arXiv (Cornell University)
Geochemistry and Geologic Mapping
article

Identification and Estimation of Seller Risk Aversion in Ascending Auctions

article en

Abstract

This paper shows how to identify and estimate the seller's risk parameter in an ascending auction. We consider a semiparametric model where the seller has a parametric utility function (such as CARA or CRRA) and the distribution of bidder valuations is modeled flexibly. We provide primitive conditions under which the risk parameter is identified and show that it can be consistently estimated with an asymptotically normal limiting distribution under standard regularity conditions. A Monte Carlo study demonstrates good finite-sample performance of the proposed estimator. We apply our approach to foreclosure real estate auction data from São Paulo. We find evidence that sellers are risk-averse, which leads to a much better fit to the data than a model with risk-neutral sellers, which would substantially underpredict the reserve price relative to what is observed.

arXiv (Cornell University)
Openalex Percentile: Top 26%
Geochemistry and Geologic Mapping
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Identification and Estimation of Seller Risk Aversion in Ascending Auctions · arXiv (Cornell University) (2025) | TGRS Research Map | TGRS