Alina Arefeva

WORKING PAPERS

1. "Discrimination During the Eviction Moratoria," with Qihui Hu, Kay Jowers, and Chris Timmins. NBER WP #32289, 2024. 

Media Mentions: Reason Magazine, UCLA Podcast

We show that pandemic-era eviction moratoria policies exacerbated discriminatory behavior by landlords in rental housing markets. We hand-collected data on state eviction moratoria start and end dates from government mandates and merged it with data from the largest correspondence study of the rental market, including over 25,000 landlord inquiries in the 50 largest U.S. cities during the spring and summer of 2020. Leveraging the staggered adoption of the eviction moratoria, we provide evidence that African Americans were disadvantaged in the search process while the moratorium was in place. We explain these findings through the lens of a housing search model.

2. "Can Stay-at-Home Orders Create a Housing Boom?" with Shengwei Guo, Lu Han, and Victor Ortego-Marti. 2025. Under Review.

Media Mentions: UCLA Podcast

We examine the impact of stay-at-home (SAH) orders in 2020 on housing markets through the lens of a search-theoretic framework. By restricting in-person searches, SAH orders created a natural experiment to assess how temporary disruptions to search efficiency affect housing outcomes. We find that prices rose while sale hazard, sales, and listings declined, with stronger effects in markets with greater search frictions-older neighborhoods and areas with lower internet penetration. A search-matching model with heterogeneous buyers explains these patterns, showing that SAH-induced delays shifted the buyer pool toward more motivated buyers, sustaining higher prices and lower liquidity even after SAH orders were lifted.

3. "Housing Markets: Auctions, Microstructure Noise, and Weekly Patterns." 2022.  Slides. R&R2, the Journal of Banking and Finance.

Media Mentions: Wisconsin School of Business Blog

This paper examines the drivers of housing market volatility through dynamic search-and-matching models that incorporate auctions, a prevalent yet understudied mechanism in housing transactions. Two versions of the model are developed: one where buyers visit homes randomly and another where search is directed by seller reserve prices. The analysis demonstrates that microstructure noise—arising from individual decisions and the interaction of search frictions and auctions—generates persistent volatility, even in large markets. The paper also identifies systematic weekly patterns in housing activity, which account for up to 60% of monthly variation in sales and listings. Together, microstructure noise and weekly patterns explain 70-80% of market volatility, with the remainder driven by exogenous shocks. These findings underscore the importance of auctions, microstructure noise, and weekly patterns in understanding housing market dynamics.

PUBLISHED AND ACCEPTED PAPERS

1. "Playing by the Taylor Rules or Sticking to Friedman's Policy: A New Approach to Monetary Policy Identification," with Nikolay Arefyev. 2025. Economic Modelling. Working Paper.

This study develops a novel identification method to analyze key shifts in US monetary policy, contributing to ongoing debates on policy effectiveness and macroeconomic stability. We find that before the mid-1980s, the Federal Reserve adhered to Friedman's policy of a steady money growth, operating with no interest rate smoothing. In contrast, after the mid-1980s, the Fed transitioned to a Taylor rule that incorporated generalized interest rate smoothing and began relying on forward-looking indicators from financial and housing markets. The interest rate smoothing and the use of these indicators helped to reduce inflation and unemployment volatility, with the smoothing potentially reflecting the application of a Kalman-style information filter to improve data processing. Our findings underscore the importance of integrating broader economic indicators and advanced data analysis, which can enhance the Federal Reserve's ability to fight high inflation and support employment without increasing macroeconomic volatility.

2. "The Effect of Capital Gains Taxes on Business Creation and Employment: The Case of Opportunity Zones," with Morris Davis, Andra Ghent, and Minseon Park. 2024. Slides. Management Science, forthcoming. Working Paper. 

Media Mentions: Brookings, Economic Innovation Group, New York Times, 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.

3. "Revealing Information in Auctions: the Optimal Auction versus the Second-Price Auction," with Delong Meng. 2021.  Economics Letters, 204, 109895. Slides.

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. 

WORK IN PROGRESS

1. "Transition to A.I. and Robotic Self-Sustainability" with Nikolay Arefyev.

2. "Putting on the Ritz: Tax Increment Financing, Eminent Domain, and Local Economic Development," with Cameron LaPoint and Evan Mast.

3. "Expanding the Boundaries of Real Estate: From Operational to Financial Hedging'', with Erasmo Giambona

4. "House Price Momentum," with Dena Lomonosov.

5. "The Effect of Zoning Relaxation in California on Housing Affordability," with Dayin Zhang and Franklin Qian.

6. "Realtors' Networks'' with Miroslav Gabrovki, Ioannis Kospentaris, and Victor Ortego-Marti.

7. "Firm's Location and Social Connectedness," with Sean Flynn and Andra Ghent.

8. "Commercial Real Estate: Search and Matching Model."

9. "Impact of COVID-19 on Commercial Real Estate."

10. "Bidding Wars in the Norwegian Housing Market:  Evidence from Millions of Bids," with André K. Anundsen and Erling R. Larsen.

11. "A Skyline Model of an Innovative City," with Nikolay Arefyev.