Alina Arefeva

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

Johns Hopkins Carey Business School, Assistant Professor, 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, Public Economics, Finance

Curriculum vitae

My research studies the interaction of institutions and economic policies with real estate and urban markets.

If you are interested in research/project assistant opportunities, please, email me.


Media Mentions: Reason Magazine

We provide evidence of intensified discriminatory behavior by landlords in the rental housing market during the eviction moratoria instituted during the COVID-19 pandemic. Using data collected from an experiment that involved more than 25,000 inquiries of landlords in the 50 largest cities in the United States in the spring and summer of 2020, our analysis shows that the implementation of an eviction moratorium significantly disadvantaged African Americans in the housing search process. A housing search model explains this result, showing that discrimination is worsened when landlords cannot evict tenants for the duration of the eviction moratorium.

2. "Monetary Policy Reestimated," with Nikolay Arefyev. 2023. Revised & Resubmitted to Economic Modelling.

We propose a new method of identification of the monetary policy rule. Using this method, we argue that, before the Great Moderation, the Federal Reserve implemented the Friedman policy of steady money growth as could be interpreted and adopted by the policymakers in the 1960s and 1970s. During the Great Moderation, the monetary policy follows the Taylor rule with interest rate smoothing instead, where the type of smoothing is more general than discussed in the literature. The estimated impulse response functions for the monetary policy shock are large and significant, even when they are estimated on the Great Moderation data.

3. "Housing Markets: Auctions, Microstructure Noise, and Weekly Patterns." 2022.  Slides. Revise and Resubmit to the Journal of Banking and Finance.

Media Mentions: Wisconsin School of Business Blog

This paper studies volatility in the housing markets by developing dynamic search and matching models with bidding wars, or auctions. The models’ moments are aggregated to the statistics on the offer acceptance dates and the closing dates to emphasize the differences between these statistics in the data. I find that up to 70% of the volatility of monthly home sales and listings in the Los Angeles metro area stems from the intra-week patterns in the data and the microstructure noise in the models. The remaining volatility can be generated by adding exogenous shocks to the models.


1. "The Effect of Capital Gains Taxes on Business Creation and Employment: The Case of Opportunity Zones," with Morris Davis, Andra Ghent, and Minseon Park. 2023. Slides. Accepted by Management Science.

Media Mentions: Brookings, Economic Innovation Group, Pew Trusts.

Briefing for the U.S. House of Representatives Committee on Financial Services. 

The Tax Cuts and Jobs Act of 2017 established a new program called Opportunity Zones (OZs) that reduces or eliminates capital gains taxes on investment in a limited number of low-income Census tracts. We provide a model illustrating how a change in capital taxation affects employment in existing and new establishments. We then use establishment-level data to show that, in its first two years, the OZ designation increased employment growth relative to comparable tracts by between 3.0 and 4.5 percentage points in metropolitan areas. The job growth occurred in multiple industries and persisted into 2021 rather than quickly disappearing. However, most of the jobs created by the program were likely taken by residents that live outside of the designated tracts, consistent with only 5% of US residents working in the same Census tract as the one in which they live.

Working Paper Versions: Information Disclosure in Housing Auctions (extended), Revealing Information in Auctions: the Optimal Auction versus the Second-Price Auction (short). 

Media Mentions: Wisconsin School of Business Blog

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.