Essays on housing economics and household finance
type: CreativeWork
description: In chapter one, I provide new evidence on the house price elasticity of consumption by exploiting micro-level consumption data from the Nielsen consumer panel for 2004 through 2016. I estimate elasticity as a non-parametric function of household characteristics, locations and time using Generalized Random Forest (GRF), a causal machine learning model. At the county-level, the average elasticity ranges from 0.04 to 0.16 with some neighboring counties being up to eight standard deviations apart, while household elasticities range from 0.01 to 0.2. Among all characteristics, having a child, household size, and the age of a household head create substantial disparities. I find that locations with volatile housing markets are less elastic. This means that failing to account for local heterogeneities overestimates the magnitude of total consumption responses in booms and busts. Moreover, local heterogeneities in elasticity camouflage the existing asymmetry in responses. Looking within a county reveals that households, especially more financially-constrained households, are more elastic in busts than in booms. Policymakers should account for this individual and geographic heterogeneity in consumption responses to house price changes when formulating policy. In chapter two, we quantify the relationship between housing markets and peer-to-peer 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 r…
abstract: In chapter one, I provide new evidence on the house price elasticity of consumption by exploiting micro-level consumption data from the Nielsen consumer panel for 2004 through 2016. I estimate elasticity as a non-parametric function of household characteristics, locations and time using Generalized Random Forest (GRF), a causal machine learning model. At the county-level, the average elasticity ranges from 0.04 to 0.16 with some neighboring counties being up to eight standard deviations apart, while household elasticities range from 0.01 to 0.2. Among all characteristics, having a child, household size, and the age of a household head create substantial disparities. I find that locations with volatile housing markets are less elastic. This means that failing to account for local heterogeneities overestimates the magnitude of total consumption responses in booms and busts. Moreover, local heterogeneities in elasticity camouflage the existing asymmetry in responses. Looking within a county reveals that households, especially more financially-constrained households, are more elastic in busts than in booms. Policymakers should account for this individual and geographic heterogeneity in consumption responses to house price changes when formulating policy. In chapter two, we quantify the relationship between housing markets and peer-to-peer 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 r…
author: Khazra, Nazanin
language: en
keywords: House Price Elasticity, Machine Learning, Airbnb, Matching
date published: 2021-03-05T21:45:09Z
date modified: 2026-07-22T22:24:50Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/109562