Alina Arefeva

Assistant Professor, Wisconsin School of Business, University of Wisconsin-Madison, 2018-

Assistant Professor, Johns Hopkins Carey Business School, 2016-2018

Stanford University, PhD, 2016

New Economics School, MA, 2011

Higher School of Economics, MA, 2010

Higher School of Economics, BA, 2008


Research interests:

Real Estate, Urban Economics, Finance, Macroeconomics

Curriculum vitae

My research studies the microstructure of housing markets, specifically search frictions and pricing mechanisms. Non-technical summary on the Wisconsin School of Business blog.

PUBLICATIONS

  1. Revealing Information in Auctions: the Optimal Auction versus the Second-Price Auction (with Delong Meng). Economic Letters, forthcoming.

Working Paper Version: Information Disclosure in Housing Auctions

We study the optimal information disclosure policy in the optimal auction and the second-price auction when the seller has information that additively adjusts the independent private values of the bidders. In this setting, information revelation could change the allocation of the good in both types of auctions. However, in the optimal auction, the change in allocation makes the revenue function convex in the additive adjustments, so the seller should always reveal information. In contrast, in the second-price auction, the change in allocation makes the revenue function non-convex, in which case the seller might benefit from withholding information.

WORKING PAPERS

  1. Job Growth from Opportunity Zones (joint with Morris Davis, Andra Ghent, and Minseon Park). 2020.

The Tax Cuts and Jobs Act of 2017 established a new program called Opportunity Zones (OZs) that created tax advantages for investing in businesses or real estate in a limited number of low-income Census tracts. We use a census of establishment-level data on employment to identify the effect of the program on job creation. We show that in metropolitan areas, the OZ designation increased employment growth relative to comparable tracts by between 3.0 and 4.5 percentage points and new jobs were created across many different industries and education levels. The OZ designation did not create jobs in rural areas.

  1. How Auctions Amplify House-Price Fluctuations. 2019. Under revision.

I develop a dynamic search model of the housing market in which prices, determined by auction, exhibit greater volatility than prices in the search and matching model with Nash bargaining from the literature. This helps solve the puzzle of excess volatility of house prices. The outcomes of the two models differ in hot markets when buyers' house values are heterogeneous. With Nash bargaining, a buyer who gets a house is chosen randomly among interested buyers, so prices are determined by the average house values. In auctions, competition among buyers drives up prices to the willingness to pay of the buyer with the highest value. In hot markets, the highest values fluctuate more than the average values, making the auction prices more volatile than the negotiated prices. This high volatility is constrained efficient in the sense that the equilibrium allocation decentralizes the solution of the social planner problem constrained by the search frictions.

WORK IN PROGRESS

  1. Impact of COVID-19 on Residential Real Estate (with Lu Han).

  2. Impact of COVID-19 on Commercial Real Estate.

  3. Bidding Wars in the Norwegian Housing Market: Evidence from Millions of Bids (joint with André K. Anundsen and Erling R. Larsen).

  4. Asymmetric Information and Search Frictions in Housing Markets (joint with Shiyan Wei).

  5. The Skyline Model of an Innovative City (joint with Nikolay Arefiev).