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.
Suggested citation: Khazra, Nazanin, Heterogeneities in the House Price Elasticity of Consumption (November 8, 2019). Available at SSRN: https://ssrn.com/abstract=3483279
Work in Progress:
"Emergence of Tech-Econ; Teaching Machine Learning and Big-Data Skills to Economics Students", working paper version 2021
"Does Airbnb Reduce Matching Frictions in the Housing Market?" with Abdollah Farhoodi and Peter Christensen, working paper version 2021
"House Price Induced Consumption Inequality" with Abdollah Farhoodi
"Investment Decisions based on Profit Status: Evidence from Hospitals, working paper version 2018
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.