10-Year Fixed Getting Cheaper

For those who are not tracking the developments in Australian yields, the following will be an interest as Huffle’s 10-year mortgage nears launch.

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Since July 2015, we have seen 2 cuts by the RBA to the cash rate. However, the yield curve has flattened a further 47bps on the 10-year. The 10-2 spread is now just 36bps.

It’s getting cheaper to borrow longer.

Hint hint home owners. Sign up at www.huffle.com.au.

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Musings of a Fintech: Australian House Affordability

Over the last few weeks we have been running an affordability analysis on Australian property. Our framework was as follows:

  1. Obtain Median Household Incomes (ABS), e.g. $51,896 in 2014.
  2. Define a reasonable expectation of a deposit
    • assuming households save 40% of income for 5 years
    • this is aggressive but caters for both those willing to save to own and might be living with parents and if they had other savings or hand-me-downs.
    • Result: a deposit equal to 2 years household net income
  3. Determine required mortgage from the above deposit and observed median house price (ABS), i.e. 2014: 100% less 18% deposit = 82% of median house price. Median House Price 2014: $571,700. 2014 Required Mortgage: $467,908.
  4. Consider achievable serviceable mortgage from observed variable rate mortgages (plus serviceability buffer) and household net income. Say 4.5x Household Net Income, but driven by a slightly more complex equation. 2014: $231,937.
  5. Look at how many multiples the required mortgage is above the achievable serviceable mortgage. 2014: 2.02x. Average: 1.69.

There are a few things to note:

  1. Median Income does not buy median house. Top earners will have multiple properties whilst lower earners will never own. However this analysis serves 2 purposes:
    • how has this relationship evolved
    • which percentile of household income can actually afford the median house
  2. Achievable mortgage is calculated from inputs and factors that we will not discuss but is roughly derived from:
    • interest rate + 3%
    • 40% of household net income to service that debt
    • hence a median current household income of $51,896 would have an achievable mortgage of $231,937 (about 4.5x net household income) if the current interest rate (SVR) is 5.95%

Resulting Observations:

A. Households cannot save as much of a deposit. In 1994, households could achieve a 29% deposit under our hypothesis. It has declined to 18%. Note: this is a major constraint for first time buyers whilst existing property stock holders can release equity in their property for further purchases.

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B. The ratio of required mortgage to achievable mortgage has been increasing, meaning the median household is further away from being able to afford the median house. Note: the dip into 1998 is a function of interest rates declining but house prices remaining relatively static. Screen Shot 2016-06-10 at 13.12.25

C. The Percentile of Household income required to buy the average house has trended heavily upwards and now you need to be in the 90th percentile of household income to buy the Median (50th percentile) house.

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We draw several conclusions from this:

  1. We can use #B to suggest Australian property is about 20% overvalued on an affordability basis. We note the risk of averaging here.
  2. The ability to raise deposits has become difficult (#A) but LMI and lower deposit requirements have solved this problem in some ways.
  3. If you need to be in the 90th percentile to buy the media house price, based on this simple analysis, we see 2 things:
    • The average person is leveraging too aggressively to buy property in such a way that they are acquiring a mortgage they may struggle to service (ie the 70-80th percentiles are still buying a median house that is only affordable by the 90th percentile). We note the average borrower is taking 5.3x net household income in Australia from Macquarie via Clancy Yeates, or
    • The 90th percentile and above are accumulating more property, possibly distorting household income with negative gearing, and that pocket of what is ultimately investment property, is deeply over leveraged.

Any potential solutions?

  1. 20% price downward correction, although this will impact those who are over leveraged but support those who are under-leveraged.
  2. If borrowers can lock in lower interest rates for longer periods (such as the Huffle 10-Year Fixed), then the serviceability issue may reduce and households with lower percentile incomes can confidently borrow more.
  3. There is potential need for the severe reduction of property investment if we want average Australians to be able to own the average house.

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FinTech Sandbox: My Words Introducing The Treasurer, The Hon. Scott Morrison

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Australian Treasurer, Scott Morrison, today made an announcement regarding the creation of a Regulatory Sandbox for Finance Start-ups. Below is my speech introducing the Treasurer at Tyro FinTech Hub.

Good regulatory frameworks are incredibly important for well-functioning financial systems and the Regulatory Sandbox is an important piece that helps start-ups, regulators and consumers work together to allow the formation of new products and services.

Attempting a finance start-up is a daunting experience. Not only do you need to develop significant advantages over existing well-resourced businesses, the layers of regulation make it almost impossible to get going and test hypotheses. The Sandbox should lower the very early stage constraints so that we can have a huge wave of innovative businesses delivering better outcomes for Australians and overseas.

Sandbox pictureFor those who don’t know me, my name is Damian Horton and I’m the co-founder of Huffle. We’re an early stage FinTech with the ambitious aim of introducing a new type of home loan to Australia and beyond.

As co-founder of a young finance start-up, we welcome the Government and Treasurer’s announcement of a FinTech Sandbox and I’d like to spend a few minutes explaining  its importance from our perspective.

Finance, as a sector, is now playing catch-up to the ever increasing influence of technology on our lives. Hence the recent growth in FinTechs.

The major underlying trend is that we are moving away from physical bank branches on our streets and into the mobile phones in our pockets. And the entire economy needs to keep up.

Up until today, we have only seen an initial wave of financial technology start-ups. And while many of these companies have been great trailblazers, we’d expect to see many more companies offering new products and services that haven’t even been considered yet. Much of this will occur as the cost of using technology continues to fall, talented and energetic people leave the comfort of the corporate world and consuming finance via your phone expands from about 10% towards 100% of the population.

And this is what brings us to the importance of the Fintech Sandbox.

In the case of my start-up, Huffle, where we are aiming to bring long-term fixed rate home loans to Australia which significantly reduce borrower risk, the complexity of being a start-up, primary lender, derivatives business and with a bank-like appearance all at once from day one creates a number of challenges.

As with all leading global financial centres, a strong regulatory framework is crucial, particularly for consumer protection, but these protections often present barriers for start-ups that are simply too expensive to overcome.

A start-up might not initially be able to tick all of the required boxes for regulatory approval. So they then can’t determine if a new business idea would work. The opportunity to create something new and potentially of major benefit has been lost as the cost of trying is too prohibitive.

In Huffle’s case, the initial barrier to ticking all of the boxes was driven by overseas founder experience not counting towards the required Australian experience under the consumer protection act.

Other fintechs face challenges such as the high cost of obtaining a financial services licence, which is required if they are offering investment and savings products.

Another challenge is the heavy reliance on incumbents to provide early stage services.

If there’s a market for a product and a start-up gains traction, this then becomes the right stage to invest in additional people to meet full regulatory approval.

The next step is nurturing.

The sandbox will provide a controlled environment in which start-ups, their customers and the regulators can explore and understand the new product and associated risks. Feedback from these multiple stakeholders can then help to refine and improve the initial products and nurture the new businesses to transform them into tomorrow’s banks and financial services businesses.

I congratulate the treasurer, the government, FinTech Australia and ASIC in the work they have done to put together the framework for this Sandbox and look forward to seeing many new fintech businesses and the jobs it will help create.

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Technical Notes: Dissecting P2P Securitizations

A couple of unrelated things popped up in the last few weeks that got the team at Huffle thinking. Firstly, the LendingClub issues over asset quality, which we believe is a specific case and not an industry risk.

The second, which I will discuss here, is regarding the securitization of peer-to-peer loans originated by Funding Circle in the UK.

TRanching

As with all securitization, Funding Circle’s has a pretty obscure name. The transaction is called SBOLT 2016-1, which stands for Small Business Origination Loan Trust. Having structured CLOs earlier in my career, the securitization on small business loans is not only interesting from a market demand perspective but also an intellectual one. How have unsecured assets been packaged and presented to the rating agencies?

Pricing:

securitization are commonly opaque in terms of pricing, often due to the discount on issuance that may increase the attainable yields or spreads. Luckily, the prospectus was available to me, so I can investigate a little further.

Pricing of CLOs is driven by the attainable credit ratings but also by an assessment of cashflow and scenario analysis. Here we have multiple tranches of increasing risk, with the senior piece getting a BBB rating and a spread of 2.2% over UK Libor.

It is interesting to see how the BBB has tightened in Europe. Locally to Australia, Commonwealth Bank priced AUD RMBS Medallion 2016-1 AAA at +140bps around the same time as SBOLT. Currency risk aside, I would certainly prefer to take on AAA RMBS risk and I also expect traditional CLO paper might also offer better value and liquidity.

Looking at this transaction against an underlying portfolio that is yielding 9.57%, it might be better to simply buy the underlying portfolio rather than the tranched transaction. Further, Class E seems to make no sense, particularly as Class D looks to have a higher IRR. Be aware that fee side-letters may exist and other mechanisms to make the transaction more attractive for investors.

collateral

Tranche Sizing:

Six tranches on a small deal appear to be a little tight: how much loss protection will the Class E offer the Class D in a stressed scenario?

Multiple tranching is possible as the underlying collateral is pretty granular, with over 2400 loans. For this quick assessment, Classes C to Z are of less interest to me. The B Class has obviously caused some discrepancy between Moody’s and S&P as Moody’s has given it a lower rating than the Class A.

So how secure is the Class A?

On inspection, the senior note is pretty secure. BBB rated assets have an annual default probability of around 0.2% and the 67% and 72% attachment points for the Class A and Class B fall comfortably inside the stressed scenario distributions required to meet the 99.8% pass rate (1 minus 0.2%).

A simpler way to recreate the credit rating agency analysis is to adapt the Advanced IRB framework for the given risk profile and asset class. The unexpected loss can be used as an indicator for senior tranching, although the final credit rating agency models are different.

Could we ever see this ever become AAA?

There is always potential to make AAA senior Classes. In this instance, the attachment point would need to be closer to 50%, which is too small and the transaction will struggle to sell. The reason for this is that the unsecured aspect of the underlying assets is a really high downside loss-given default (assume 80%-90%) and a probability of default of 5% to 10% (implied backwards given the high interest rate charged). The Advanced IRB framework can show you how much you can lose in downside scenarios needed to attain a AAA.

Different asset classes make the ability to create AAA rated securitization harder or easier. Secured assets, such as residential mortgages or auto-leases, are much easier for this asset class, which it ultimately about creating additional security rather than funding arbitrage. Unsecured loans are usually the hardest.

Verdict on SBOLT:

The senior tranching appears to make sense versus where the portfolio risk comes out. If we think the securitization mathematics are wrong, we should also assume the entire Basel framework on bank capital is wrong. As such, the transaction, structurally, is pretty well aligned to globally accepted risk frameworks and the securitization should be seen as a valid investment for regulated entities such as banks and insurance companies.

I am less concerned about the lower tranches as they are smaller fractions, speculative and more sensitive to the underlying portfolio for which I don’t have granular data.

How should we view this deal in Australia?

Overall, I am optimistic for the transaction. Wholesale funding is an important piece to peer-to-peer lending on the basis that not all investors want loan specific risk or equal risk that the borrower offers.

Caution should be taken that CLO securitization and subsequent layers of intermediation (such as fixed income portfolio managers, risk processes and rating agencies) add layers of costs that is worn, ultimately, in higher borrowing costs or reduced returns for investors. Direct lending by hedge fund-owned CLO platforms has been around for over decade. Can FinTech offer an advantage here?

In some cases they can, if they have a specialized team, but they need to ensure they have strong compliance procedures and the ability to perform the analysis and risk management process for risk transformation, which is where LendingClub recently faltered. Unlike vanilla funds without structuring overlays, the underlying collateral in securitization becomes ever more important in the resulting investment performance, particularly if there is a stressed market event.

As FinTechs evolve from new entrants and upstarts into more established businesses, these are the type of specific processes that are likely to be taken on.

Note: I have tried to simplify this blog so that more people can follow the analysis. Credit rating agency models have a number of different mechanisms and methodologies to the Basel II framework.

 

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Musings of a Fintech: Huffle 10 Year Fixed Target Pricing Drops to 4.49%

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The RBA cut the cash rate to 1.75% this week and further rate cuts are expected. This sent the team at Huffle back to the loan lab to tinker with our model. Adjustments are required in our modeling, not only in the yield curve but also the pricing of interest rate derivatives.

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The daily feed of Aussie government bonds show the recent decline in borrowing costs but also a slight increase in steepness in yield curve. This is indicated by the 2-10 Spread, which currently sits around 72bps. On a very simplistic level, this indicates that our expected pricing for a fixed rate, hedging and capital aside, is likely to be 72bs higher than where the variable rate home loan market is.

Updating our models and with a Return on Equity hurdle at 20%, we have reduced our expected pricing down from 4.99% to 4.49%. The main gains are due to the current derivative pricing and volatility.

Why is this the case?

As rates compress towards zero, the embedded call option in fixed rate prepayables has a reduced upside value to the borrower. The result is that this has a reduced sale value that would naturally be added into the price of the mortgage. The overall impact is a bigger reduction in the expected fixed rate loan price compared to the observed rate cut and decline in the yield curve.

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Musings of a Fintech: Single-Product Single-Brand Strategy

One of my guilty pleasures is watching Gordon Ramsay blow off steam. It recently occurred to me that his TV show, Kitchen Nightmares, has a nice insight into bank product offerings, marketing and product development.

In Gordon Ramsay’s Kitchen Nightmares there is one specific action he takes in almost every episode. He looks at their huge menus, usually multiple pages of every option possibly available, and culls it to a 1-page daily menu of the best items. He does this for a few reasons:

  1. Customers can’t decide, it is too hard, and then they pick something they don’t really want or the most popular default item.
  2. The restaurant needs to keep a lot more ingredients and perishables. This has a substantially higher cost in terms of wastage and inability to bulk buy.
  3. The restaurant doesn’t have a clear identity. Does it sell pasta, pizza, fish and chips, burgers or a sub-set of them.

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On the opposite end of the spectrum a great example is one of my favourite eateries in London, Burger & Lobster (no Ramsay link, as far as I know). As evidenced by their name, they only serve 2 things. This offers a great advantage:

  1. They clearly show what they do. Customers understand this. When they want burger or lobster, guess who is the first choice restaurant.
  2. They can buy in bulk. Lots of lobster and beef. This means they can offer more competitive pricing and their staff can become specialized at cooking specific dishes. Margins increase.

Pr. Mark Ritson of Melbourne Business School and advisor to many world leading brands, also believes killing brands or items is more important that creating them. It becomes harder to sell so many products under a house of brands or branded house and also to keep them all relevant. It is also a headache on stocking and matching demand and supply. Large businesses need to reduce first.

So what does this mean for banks and Fintech? We suggest a few key points:

  1. Whilst large banks should be culling SKUs (financial products), FinTechs should fill the void and be product specific. We call this “Single-Product Single-Brand” strategy.
  2. Single brand for a single product: the Fintech becomes specialist in a specific area and customers who want a product go to that fintech first. This creates marketing and sales efficiency as the brand becomes synonymous with that product.
  3. Navigating through most bank websites is like the old restaurant menus. See for yourself – try to find CUA’s credit card products from their home page (hint: you’ll probably have to use the search function as they aren’t there!).
  4. Lack of product differentiation between the banks means customers simply can’t decide. So in the case for home loans, they defer to a mortgage broker for fulfillment – creating an unnecessary expensive cost to acquire new customers.

So where does that leave us? If a bank had to pick what its single product would be, I expect it is going to be deposits. This is the item they are regulated on and have strong risk systems in place, particularly over capital and liquidity. One way this could go is the concept of “utility” banks, which sit as the underlying layer of financial services. On top of this core utility layer there could be a number of “single brand, single product” FinTechs acting as satellites with unique products and customer experience tailored to specific segments.

The major challenge to banks specializing in core deposits? This is the areas that Google, Apple and the large tech giants are likely to go after in coming years, but we’ll save that for another blog.

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Technical Notes: Behavioral Risk Frameworks

At Huffle we’re big fans of investigating behavioural risk models. Largely because improving the understanding behaviour is easier through Big Data gathered from social media than other types of risk.

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Behavioural risk can be split into separate areas: internal risk and external risk.

Internal risk is created by employees, where we start to see companies monitoring employee activity to estimate a behavioural risk that may be associated with fraud or processes that lead to poor customer outcomes.

External risk in the form of customer behaviour is also important. Marketing has always investigated behaviour of customer’s purchasing. In finance we need to look at customer behaviour over the life of transactions as there is a risk of default, churn, fraud or additional leveraging.

Many parts of behaviour can be understood through statistical modelling – and procedures to understand, segment, assign probabilities are well understood and continually investigated. However, non-linear dynamics and bifurcation theory may be a better avenue*.

Why bifurcation theory?

Bifurcation theory looks at system changes as certain assumptions or inputs change. We would look for indications that would adjust one of our expected outcomes, which may include willingness or ability to service debt or likelihood of finding and switching loan provider. The advantage over stochastic modeling is that we can use historical data but also forecast what could lead to changes beyond probability likelihoods. The obvious difficulty is developing a reliable non-linear dynamic model and testing it.

We also note that there are interactions in a system. Banks will be selling agents and create internal shifts in dynamics depending on their willingness to lower margins to gain market share. Ultimately, we would also be able to use these models to manage risk as well and measure it: understanding which reactions lead to optimal outcomes.

Starting Models: How Uber may change traffic jams

One of the easiest behavioral dynamic models is traffic. We’ve all sat on the freeway, stop-starting in a traffic jam only to eventually end up moving without any accident in sight. This is a result of human inefficiencies and behavior**. Traffic flow modeling has been investigated for decades but can be summarized in a few rules:

  1. A person wants to follow the car in front at the speed limit
  2. A person doesn’t to get too close. There is a minimum headroom between cars. If they get too close, they will hit the brakes
  3. Each person has a delay in reacting to the car in front

With these rules, we can start to build interactions between cars, with the simplest example being a single-lane freeway. Cars accelerate to meet the speed limit and brake when they get too close to the car in front.

If we build this (non-linear) system we can start to observe a few things:

  1. The system can converge to where all cars move smoothly at the speed limit
  2. If the first car stops, all cars behind it will eventually stop
  3. If the first car slows down, all cars behind it will eventually meet that speed

Perturbation Theory:

The next aspect is to add a small amount of noise. Here, noise would mean a single car suddenly brakes for a reason, perhaps the driver got too close to the car in front after looking at their Facebook feed on their smartphone.

This would lead to a sudden braking. The interesting aspect to take from here is that subsequent cars, if they were travelling at the speed limit and close enough to the car suddenly braking, will be forced to brake themselves. Furthermore, if they were at the headroom, they would encroach on the safety zone and need to brake more sharply to replenish that zone.

There is a potential knock-on impact that subsequent cars need to brake substantially more if they were all travelling close to the headroom distance behind the car in front and at the speed limit.

Without modeling this, we can understand a potential amplification of the initial noise, and subsequent cars can potentially be forced to stop. At this point, we can create the traffic jam.

What factors impact the potential for a traffic jam?

  1. Car density: more cars increase the likelihood of cars travelling closer to the headroom distance as they cram onto the road in rush hour.
  2. Speed: faster motion means the encroachment into headroom can be higher (the delay in human reactions is a fixed time but the distance travelled in that time will be higher)

There are other smaller impacts. However, the above two are controllable on a system wide basis.

What are the risks?

Car crashes are a potential risk if the stopping propagates too quickly. Time delays are also possible as cars also have a delay in returning to the speed limit after they stop, and this can lead to significantly longer delays in traffic.

How do we control them?

Reducing cars is an easy one. This can be done through toll roads or pricing strategies. Ultimately, this is one aspect that makes Uber incredibly interesting as it is adopted more widely. Rush hour will become more expensive to travel.

Speed is another. We often see temporary speed limits. This diminishes the human delay and reduced the likelihood of the traffic jam propagating. The obvious cost here is that the journey times are less than optimal and may be higher on average. But dispersion and risk is reduced.

Moving back to lending

Without detailing too much, the non-linear model becomes more complex as the forces driving it are very different in the world of finance. First of all, if we are building a default model, what defines a default? We could classify it in a similar way to a car stopping in the traffic model and with a speed limit taken as repayments on a mortgage.

Multiple agents would exist: borrowers wouldn’t react with each other, instead they would react around a set of banks, gravitating towards them as better mortgage offers appear. In this system, we would have a borrower acting as a metallic ball in a large financial system that acts as a pinball machine bouncing off different lenders.

Managing risk then becomes a similar trick:

The inputs that then cause reactions in the system (similar to the car braking above) can then be changed by:

  1. Adjusting the price of loans by lowering interest rates, making debt easier to service. This can be performed by central banks. This also relates to the velocity of money.
  2. Modify the loans, which can be performed by the lenders.
  3. Restricting the new loans made available by increasing interest rates. Banks would do this if they want to reduce the risk on their books (hope for churn of “risky” customers).

However, we can also identify other ways to manage risk and measure potential changes, particularly around macro-economic noise that may lead to significantly higher defaults or churn, depending on the conditions.

Internal Risks?

The thing to note here is that behavioural operational risk comes in various forms. In an ideal world, a pricing and cost mechanism can be established. To manage the risk and control temperament if it overheats, or to retrospectively adjust if poor behaviour is not picked up in time.

However, it is hard to reclaim salaries following poor behaviour and it is also hard to pay employees fully in deferred shares. In many ways, outsourcing and deferring a payment is easier here.

Another key aspect could potentially be keeping each product to separate teams, or where we expect things to go – with a separate company (FinTech). If a particular product emerges with a problem, the relationship or business unit can be replaced or removed.

Again, our modelling aims to track what would potentially lead to a change in behavior, most notably when behavior begins to focus on commission rather than customer outcomes.

Once we have achieved this, we may become significantly better at estimating our “through the crisis” revenue streams and understanding risk driven by both borrower and competitor behaviour, enabling us to proactively manage behavioural risks.

*We prefer to move away from stochastic modeling, instead preferring to build non-linear models based more on particle theory. We think this allows us to investigate potential behavioural shifts through bifurcation theory.

**The phenomenon is a sinusoidal compression wave travelling in the opposite direction to traffic flow.

Picture from www.fatvat.co.uk

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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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