{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/109563"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/109563","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Big data approaches to peer-to-peer markets and welfare estimation","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Farhoodi, Abdollah"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Economics","degree_department":null,"school":null,"contributors":["Bernhardt, Mark Daniel","Albouy, David","Deltas, George","Marshall, Guillermo","Lemus, Jorge"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-03-05T21:45:10Z","date_published":"2021-03-05T21:45:10Z","updated_at":"2026-07-22T22:24:50Z","subjects":["Peer-to-Peer Markets","Sharing Economy","Structural Estimation","Demand Estimation","Welfare Estimation","Machine Learning","Housing Market"],"languages":["en"],"rights":["Copyright 2020 Abdollah Farhoodi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/109563","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bernhardt, Mark Daniel","Albouy, David","Deltas, George","Marshall, Guillermo","Lemus, Jorge"]},{"key":"dc:creator","label":"Author","values":["Farhoodi, Abdollah"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-03-05T21:45:10Z","2023-03-05T21:47:41Z","2020-09-15","2020-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Peer-to-Peer Markets","Sharing Economy","Structural Estimation","Demand Estimation","Welfare Estimation","Machine Learning","Housing Market"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Abdollah Farhoodi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/109563"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01","The student, Abdollah Farhoodi, accepted the attached license on 2020-09-10 at 11:05.","The student, Abdollah Farhoodi, submitted this Dissertation for approval on 2020-09-10 at 11:22.","This Dissertation was approved for publication on 2020-09-15 at 08:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15806 on 2021-03-04 at 16:30:17","Made available in DSpace on 2021-03-05T21:45:10Z (GMT). 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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.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01","The student, Abdollah Farhoodi, accepted the attached license on 2020-09-10 at 11:05.","The student, Abdollah Farhoodi, submitted this Dissertation for approval on 2020-09-10 at 11:22.","This Dissertation was approved for publication on 2020-09-15 at 08:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15806 on 2021-03-04 at 16:30:17","Made available in DSpace on 2021-03-05T21:45:10Z (GMT). No. of bitstreams: 2 FARHOODI-DISSERTATION-2020.pdf: 10293416 bytes, checksum: e03af14c78ee874bd3b9625ab485d220 (MD5) LICENSE.txt: 4214 bytes, checksum: 74f838d50032f4ee04900973c5b0f69a (MD5) Previous issue date: 2020-09-15","Embargo set by: Seth Robbins for item 117268 Lift date: 2023-03-05T21:45:47Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 117268 Lift date: 2023-03-05T21:47:41Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/109563"],"dc:language":["en"],"dc:rights":["Copyright 2020 Abdollah Farhoodi"],"dc:subject":["Peer-to-Peer Markets","Sharing Economy","Structural Estimation","Demand Estimation","Welfare Estimation","Machine Learning","Housing Market"],"dc:title":["Big data approaches to peer-to-peer markets and welfare estimation"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Economics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:50Z"}