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Musings of a FinTech – Actionable Insights from Social Media

Being able to effectively mine data produced via social media is very topical, with the emergence of companies such as Thinknum providing metrics from social media and other sources to provide new insights into company performance.

However, there is a wider question here of what data can actually be harnessed to provide genuine insight and tangible value to both companies and/or individuals – the classic problem of extracting the signal from the noise.

Thinknum provides company metrics such as Twitter/Facebook followers, employees on LinkedIn and web site traffic which arguably could be useful indicators of a company’s health for investors. A recent FT article provides a good run down of some of the current crop of investor-offerings in this space.

 

In the area of housing, a recently published piece of research from Harvard, Facebook, NYU and the Bureau for Economic Research provides one such insight using data from Facebook. Entitled “Social Networks and Housing Markets”, it looks at how social media influences an individuals perception of the attractiveness of property investment.

The key takeaway from the paper is “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, and (iii) pay 3.3 percent more for a given house. 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.”

It’s interesting to see how they combined the data sources for it – the model used Facebook user data along with market research data from Acxiom at its core to build rich demographic data.

One of the key uses of the Facebook friend data was the location of where an individual’s friends reside – specifically those that are within or outside of the Los Angeles county commuting zone (they surveyed homeowners all resided in LA county). This enabled the researchers to distinguish between local friend influences and biases, versus those further afield – the assumption being that house price movements experienced by friends outside the commuting zone would have been effected via social media channels (sec 1.4 p10).

This was supplemented with the relevant housing data, and a 4 question multiple-choice survey for testing the various hypotheses:

 

  1. How informed are you about house prices in your zip code?

[x] Not at all informed [x] Somewhat informed [x] Well informed [x] Very well informed

 

  1. How informed are you about house prices where your friends live?

[x] Not at all informed [x] Somewhat informed [x] Well informed [x] Very well informed

 

  1. How often do you talk to your friends about whether buying a house is a good investment?

[x] Never [x] Rarely [x] Sometimes [x] Often

 

  1. If someone had a large sum of money that they wanted to invest, would you say that relative to other possible financial investments, buying property in your zip code today is:

[x] A very good investment [x] A somewhat good investment [x] Neither good nor bad as an investment [x] A somewhat bad investment [x] A very bad investment

 

The ordering of the questions in 35% of the surveys was changed to avoid the framing effect with people’s responses, which was an interesting point to note (although they didn’t find participants were influenced by ordering of questions in this instance).

This survey & demographic data was then utilised alongside housing transaction data, and they created a number of regression models which supported the conclusions of the paper.

 

Given all of the talk about social media & mining this data, it’s a useful paper to be aware of and illustrates not only a potential use case for harnessing the power of social media to generate insights, but also how complex a task it is to do so.

Bearing in mind that the individuals in the survey were limited to those residing in LA county, and the measured impact of social media appears to influence individuals by up to ~3% which is pretty small in the grand scheme of things*, trying to apply a similar model to something similar like how social networks influence an individuals mortgage preference would be no small task!

 

*We are very excited overall that new data can not only lead to new ways of analysing risk but potentially be a strong leading indicator, allowing more time to rebalance portfolio risk. However making a judgment call on new data presents higher modelling risk.

 

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