Abdollah Farhoodi

Abdollah Farhoodi

Assistant Professor of Economics at University of Toronto

Research

Working Papers

  • Households Mobility and Housing Affordability (with Nathaniel Baum-Snow and Lu Han)

    The draft will be ready soon

    • Using the big data on the last 10 movements of a universe of American households (~2 Billion observations)
    • Developing a dynamic discrete choice model of households’ location choices
    • Developing a new technique to estimate the neighborhood-level moving costs of renters and owners
    • Running the counter-factual analysis of changes in moving costs and housing supply policies

  • Democratizing the Opportunities: Who Benefits from the Airbnb Market?

    Abstract: Peer-to-peer markets allow small suppliers to enter markets traditionally occupied by large firms and provide a potential decentralized distribution of opportunities. This paper investigates how these opportunities are distributed across agents and affected by government regulations. Using daily panel data of Airbnb rentals in Chicago, I develop an individual-level multinomial logit model to estimate consumer and producer surpluses across differentiated agents. The results find higher surpluses for low-income property owners but indicate a disproportionate concentration of welfare in high-income neighborhoods. The counterfactual analysis shows that restricting institutional hosts reinforces this concentration. However, increasing tax rates potentially helps redistribute welfare. SSRN working paper

  • Does Airbnb Reduce Matching Frictions in the Housing Market? (under review, with Nazanin Khazra and Peter Christensen)

    Abstract: There is growing concern that home-sharing markets can affect housing affordability. We use a theoretical model to provide key results on the mechanisms through which home-sharing can improve the quality of matches between buyers and sellers. We then test these predictions empirically using daily Airbnb data for the entire U.S. and a novel shift-share approach. We find that an increase in Airbnb increases house prices, reduces total sales, increases for-sales inventory, increases sellers’ time on the market, and reduces the probability of selling a house. The empirical evidence supports the hypothesis that Airbnb has reduced the matching frictions in the housing market. We then examine heterogeneous responses to Airbnb using Generalized Random Forest (GRF). Consistent with our theoretical model, results from the GRF model indicate that locations with a less elastic housing supply respond more to the Airbnb growth. SSRN working paper

Work in Progress

  • Introducing a Micro-Founded Index of Consumption Welfare: A Big-Data Approach

Research Experience

  • University of Toronto - Department of Economics: Assistant Professor (2020-current)
  • International Monetary Fund: Fund Internship Program (Summer, 2018)
  • University of Illinois: Big-Data in Environmental Economics and Policy Research Group (BDEEP, 2018-2020)
  • University of Illinois: Research Assistant to Dan Bernhardt, Yufeng Wu, Jorge Lemus (2016-2019)