Essays on housing economics and household finance
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 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. In chapter three, using the introduction of the Affordable Care Act (ACA) as a natural experiment, I test how for-profit (FP) and non-profit (NP) status affects the level of response to the created investment opportunity. In this research, I investigate FP and NP hospitals investment changes in response to the ACA. I implement a difference in difference methodology to test whether FP and NP hospitals responded differently to this legislation. The regression findings conclude that FP hospitals invested 1.6% more than NPs in the aftermath of the ACA. This paper suggests NPs' restricted financing options as a possible explanation for their different investment response.
House Price Elasticity, Machine Learning, Airbnb, Matching
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign