Social Networks and Housing Markets

By Michael C. Bailey, Ruiqing Cao, T. Kuchler, Johannes Stroebel. Social Science Research Network, 2018

306 citations recorded by the source corpus.

We document that the recent house price experiences within an individual’s social network affect her perceptions of the attractiveness of property investments, and through this channel have large effects on her housing market activity. Our data combine anonymized social network information from Facebook with housing transaction data and a survey. We first show that in the survey, individuals whose geographically-distant friends experienced larger recent house price increases consider local property a more attractive investment, with bigger effects for individuals who regularly discuss such investments with their friends. Based on these findings, we introduce a new methodology to document large effects of housing market expectations on individual housing investment decisions and aggregate housing market outcomes. Our approach exploits plausibly-exogenous variation in the recent house price experiences of individuals’ geographically-distant friends as shifters of those individuals’ local housing market expectations. Individuals whose friends experienced a 5 percentage points larger house price increase over the previous 24 months (i) are 3.1 percentage points more likely to transition from renting to owning over a two-year period, (ii) buy a 1.7 percent larger house, (iii) pay 3.3 percent more for a given house, and (iv) make a 7% larger downpayment. Similarly, when homeowners’ friends experience less positive house price changes, these homeowners are more likely to become renters, and more likely to sell their property at a lower price. We also find that when individuals observe a higher dispersion of house price experiences across their friends, this has a negative effect on their housing investments. Finally, we show that these individual-level responses aggregate up to affect county-level house prices and trading volume. Our findings suggest that the house price experiences of geographically-distant friends might provide a valid instrument for local house price growth.

Subfields: Accounting, Economics and Econometrics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/23a40128657cf4a17b1c5daeccf929b59aec3917

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Citations in the corpus

House Prices and Consumer Spending (2015, unknown): “59Changes in expected house appreciation are consistent with recent empirical work by Case et al. (2012), Burnside et al. (2016) and Bailey et al. (2016).”
Peer Effects in Product Adoption (2019, unknown): “As a result, friendships on Facebook provide a good approximation of real-world friendship networks (see Bailey et al., 2018a, 2020a).”
Housing Wealth Effects: The Long View (2018, unknown): “Other factors that may play a role are that some regions are more “bubbly” due to social connections to inelastic cities (Bailey et al., 2018) or credit (Favara and Imbs, 2015).”
Five Facts about Beliefs and Portfolios (2019, background): “…and Thompson, 2012; Cheng, Raina and Xiong, 2014; Kuchler and Zafar, 2015; Burnside, Eichenbaum and Rebelo, 2016; Gao, Sockin and Xiong, 2016; Bailey et al., 2017, 2018; Glaeser and Nathanson, 2017; Adelino, Schoar and Severino, 2018a,b) as well as the role of firm expectations (e.g.,…”
Speculative dynamics of prices and volume (2017, unknown): “The two papers we are aware of that explicitly model how past price changes are extrapolated into expectations of future prices at varying horizons are Barberis et al. (2015) and Glaeser and Nathanson (2016).”
Game On: Social Networks and Markets (2021, background): “This specific prediction of the theory is confirmed by Bailey et al. (2018) who use Facebook data to show that people with friends who experienced recent house price gains increase their housing market expectations and “buy larger houses and pay more for a given house.” Further, investors’ local soc”
Narrative Economics (2019, unknown): “Geographic models of spread are increasingly complicated by worldwide social media connections (Bailey et al. 2016).”
House Price Beliefs And Mortgage Leverage Choice (2017, unknown): “Our empirical approach to overcome these challenges builds on Bailey et al. (2016), who document that the recent house price experiences in an individual’s geographically distant social network affect”
Why Don't We Agree? Evidence from a Social Network of Investors (2019, unknown): “Motivated partly by this observation, a growing literature evaluates the effects of investor disagreement on financial market outcomes (e.g., Varian, 1985; Nagel, 2005; Banerjee and Kremer, 2010; Carlin et al., 2014; Bailey et al., 2016).”
Are early stage investors biased against women? (2020, background): “See Bailey et al. (2017) for an example of this type of analysis.”