We show how data from online social networking services can help researchers better understand the effects of social interactions on economic decision making. We combine anonymized data from Facebook, the largest online social network, with housing transaction data and explore both the structure and the effects of social networks. Individuals whose geographically distant friends experienced larger recent house price increases are more likely to transition from renting to owning. They also buy larger houses and pay more for a given house. Survey data show that these relationships are driven by the effects of social interactions on individuals' housing market expectations. This paper was previously circulated as "Social Networks and Housing Markets." For helpful comments, we are grateful to

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.

Using data from a prominent online platform for launching new digital products, we document that the composition of the platform's 'beta testers' on the day a new product is launched has a systematic and persistent impact on success. Specifically, we use word embedding methods to classify products launched on this platform as more or less focused on the needs of female customers, and show that female-focused products launched on a typical day -when nine-in-ten users on the platform are men -experience 40% less growth and are 5 percentage points less likely to have an any users a year after launch. Using exogenous variation driven by the platform's daily newsletter, we find that that the product gender gap shrinks on days when women are more likely to engage with the platform. Conversely, entrepreneurs who happen to launch a female-focused product on an especially male-dominated day reduce their product development efforts by roughly 30% and are 4 percentage points less likely to raise venture funding. Overall, our findings suggest that sample bias can systematically corrupt signals of a startup's market potential, bias entrepreneurial strategy, and so lead to a dearth of innovations aimed at consumers who are underrepresented among early-users.

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Using data from a prominent online platform for launching new digital products, we document that the composition of the platform's `beta testers' on the day a new product is launched has a systematic and persistent impact on success. Specifically, we use word embedding methods to classify products launched on this platform as more or less focused on the needs of female customers, and show that female-focused products launched on a typical day—when nine-in-ten users on the platform are men—experience 40% less growth and are 5 percentage points less likely to have an any users a year after launch. Using exogenous variation driven by the platform's daily newsletter, we find that that the product gender gap shrinks on days when women are more likely to engage with the platform. Conversely, entrepreneurs who happen to launch a female-focused product on an especially male-dominated day reduce their product development efforts by roughly 30% and are 4 percentage points less likely to raise venture funding. Overall, our findings suggest that sample bias can systematically corrupt signals of a startup's market potential, bias entrepreneurial strategy, and so lead to a dearth of innovations aimed at consumers who are underrepresented among early-users.