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.
Work in Progress:
- The Effect of Home Sharing on Local and Global House Prices: Evidence from Airbnb, with Peter Christensen
In this paper, we study the local and global effects of home sharing on house prices. We quantify the relationship between housing markets and peer-to-peer home sharing 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 0.04\% increase in house prices and 0.028\% increase in rents in each neighborhood.
Next, we estimate a decay function of the overall effect as a function of distance for the city of Los Angeles. Controlling for a rich set of location and time fixed effects we show that number of existing Airbnb listings within 500 meters of a property at the time of sales has a negative effect on its price. In sharp contrast, this effect becomes positive as we move further away (e.g., 2km from the house excluding the Airbnbs within 1km of the property). This finding underscores the positive ``global" effect of Airbnb on house prices, but the negative ``local" effect, which could be explained as negative externality associated with Airbnb neighbors, can provide insights for policymakers.
- Investment Decisions based on Profit Status: Evidence from Hospitals
I investigate how non-profit (NP) and for-profit(FP) firms respond to an investment opportunity. NP organizations in the US account for 5.3\% of its GDP in 2013 and paid 9.2\% of all wages and salaries in 2010. Despite their considerable size in the economy, we know far less about their corporate and economic behavior than we do about the FP sector.
I use the health care industry to study the investment patterns of FP and NP firms for three main reasons: first, balance sheets of both private and public FP and NP medicare-certified hospitals are publicly available. Second, the Affordable Care Act (ACA) provides a suitable environment to study the effect of a change in investment opportunities. Third, FP and NP hospitals compete with each other and are not separate entities with completely different objectives which makes the comparisons more meaningful. I use the introduction of the ACA as a natural experiment and use a difference in difference methodology to test how FP and NP status affects the level of response to the created investment opportunity. I find that FP hospitals invested 1.6\% more than NPs in the aftermath of the ACA, and uncover consistent evidence that NPs' restricted financing options underlie their different investment responses.