University of Illinois Urbana-Champaign

Big data approaches to peer-to-peer markets and welfare estimation

Farhoodi, Abdollah

Content Files
FARHOODI-DISSERTATION-2020.pdf

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https://hdl.handle.net/2142/109563

Description

Title
Big data approaches to peer-to-peer markets and welfare estimation
Author(s)
Farhoodi, Abdollah
Issue Date
2020-09-15
Director of Research (if dissertation) or Advisor (if thesis)
Bernhardt, Mark Daniel
Doctoral Committee Chair(s)
Bernhardt, Mark Daniel
Committee Member(s)
  • Albouy, David
  • Deltas, George
  • Marshall, Guillermo
  • Lemus, Jorge
Department of Study
Economics
Discipline
Economics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2021-03-05T21:45:10Z
Keyword(s)
  • Peer-to-Peer Markets
  • Sharing Economy
  • Structural Estimation
  • Demand Estimation
  • Welfare Estimation
  • Machine Learning
  • Housing Market
Abstract
The first chapter provides a road-map to estimate agents well-being in Peer-to-Peer (P2P) markets. P2P markets allow small suppliers with limited capital to enter markets that were traditionally occupied by large firms. This feature provides a potential decentralized distribution of opportunities. I investigate the distribution of welfare and opportunities among agents in the Airbnb short-term rental market. I use daily panel data of Airbnb rentals in Chicago from August 2014 through April 2017 and develop an individual-level multinomial logit model to estimate the distribution of consumer and producer surpluses across differentiated agents and over time. I show that properties in less advantaged neighborhoods benefit the least from having access to the Airbnb market, even though these properties feature weaker competitive pressure and lower opportunity costs of renting. My results show a disproportionate concentration of welfare in neighborhoods with higher incomes and house prices. Importantly though, within neighborhoods, I show evidence of higher surpluses for low-income property owners, especially for those who live in high-demand areas. Second chapter, applies micro-data and big data tools to estimate households well-being. This paper introduces a micro-founded index of consumer welfare with household-level variations. It uses Albania's 2012 Living Standard Measurement Survey to estimate the index in two steps. In the first step, I apply machine learning to find a non-parametric relationship between household consumption and a large set of living conditions and characteristics indicators. In the second step, I estimate the distribution of households' marginal willingness to pay for each living conditions indicator, and estimate the index of welfare. I show that the index is highly correlated with households' consumption expenditures. However, unlike consumption as a naive measure of welfare, it accounts for the existing heterogeneity among their living conditions and preferences. Accounting for these heterogeneities assigns higher welfare to households who are younger and live in urban areas than the consumption expenditures. Finally, by looking at detailed households acquisitions this paper shows that the new index provides a better measure of welfare. Third chapter, is on the effect of P2P markets on related markets. We quantify the relationship between housing markets and P2P home-sharing markets using bookings and listings data from more than a million Airbnb listings across the United States and individual house sales. We use a new shift-share approach for identification and find that a one percent increase in Airbnb leads to a 0.06% increase in house prices, 0.14% decrease in total housing sales, and does not significantly change for-sales inventory or rental prices. We interpret fewer transactions, higher prices, and more houses for sales as evidence for improvement in matching quality. Home-owners can afford to hold on to their houses and wait to be well-matched. Also, buyers can live in a short-term rental property while looking for the best match. Moreover, we estimate Airbnb's effect on housing markets as a non-parametric function of zip code characteristics. We show that house prices increase more in locations with less elastic housing supply. The non-parametric results show that matching quality improvement is more pronounced in places with lower housing supply elasticity.
Graduation Semester
2020-12
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/109563
Copyright and License Information
Copyright 2020 Abdollah Farhoodi

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