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.

WORK IN PROGRESS

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

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

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


WORKING PAPERS

1. Who Benefits from Place-Based Policies? 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'' that created tax advantages for investment locating in Census tracts with relatively low income or high poverty. Importantly, only 25% of eligible tracts in each state could be designated as an Opportunity Zone. We use detailed establishment-level data and a difference-in-difference (DiD) approach to identify the designation of a tract as an Opportunity Zone on job creation. We find the Opportunity Zone designation increased employment growth relative to comparable tracts by a statistically significant 2-4 percentage points over the 2017-2019 period. This result holds for many different industries and for a variety of skill levels.

2. 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.

3. How to Set a Deadline for Auctioning a House. (with Delong Meng) 2020. Submitted.

We investigate the optimal choice of an auction deadline by a house seller who commits to this deadline before the arrival of any buyers. In our model buyers have evolving outside options, and their bidding behaviors change over time. We find that if the seller runs an optimal auction, then she should choose a longer deadline. However, if the seller runs a second-price auction, then a shorter deadline could potentially help her. Moreover, the seller can extract information about buyers' outside options by selling them contracts similar to European call options. Finally, the optimal dynamic mechanism is equivalent to setting a longer deadline and running an auction on the last day.

4. The Housing Search Model and Empirical Evidence. 2014.

This paper argues that the housing search and match model augmented with shocks of the discount factor and matching efficiency can fit the dynamics of the housing price-rent ratio in the US from 1960 to 2013. Specifically, the paper considers the transitory dynamics of the house price-rent ratio in the Piazzesi Schneider (2009) model augmented with the discount factor and matching efficiency shocks that follow a continuous-time Markov chain. The price-rent ratio in the model is the discounted present value of the rent minus a liquidity discount, where the liquidity discount is the expected present value of search and transaction costs. The liquidity discount depends on both the discount factor and the matching efficiency, which are negatively correlated based on the data on the time on the market for sellers. Then a drop in the discount factor raises the present value of the rent, increasing prices directly, but is also associated with the increase in the matching efficiency that decreases the liquidity discount, which also leads to higher prices. The joint dynamics of the discount factor and matching efficiency shocks help explain the big swings of the price-rent ratio that are observed in the data.

5. The buyer's barriers of entry to the U.S. owner-occupied housing market. 2014.

This paper quantifies the buyer's entry costs to the U.S. owner-occupied housing market from the housing search model. The entry costs are estimated to drop by $50 thousand dollars from the middle of 1980s to 2006 which may be associated with lowering of lending standards during that time, and those entry costs revert back to high levels right after the housing boom of 2005-2006.