Finding Early Stage Investment Options Through Thesis Development

Chris Langford
Mar 8, 2018 · 8 min read

After my last blog which was much more personal in nature, I was determined to write something more academic and professional. It was going to be a deep dive into a particular industry or trend that I have been monitoring and provide a glimpse into the macro and startup signals that have led me to believe in it. But every time I went to write it, I got bored. And I couldn’t shake the feeling that If I was bored reading my own thoughts then I can’t imagine how terrible that would be for someone else to read.

I began to wrack my brain and think “what do people ask me all the time” or “what do people genuinely seem interested in when we are speaking”. While being a VC can often mean lots of people pretend to think what you are saying is interesting, I knew there had to be something that fit these characteristics. That’s when the idea of how I create investment theses came to mind. I think I have crafted a relatively useful and thoughtful approach to doing so and most people who we speak to about our process (even well-established VCs) have said it is well done. So here goes.

Before we get too granular, I’d say the one overarching rule of thumb is to stay in your swim lane. There are lots of generalists in VC and I am always in awe of their ability to jump from major theme to major theme. I know that even still, many of them are rooted in specialization. To me, this specialization should be the basis of your outline. Did you develop SaaS products for years? Do you really understand what it takes to run a two-sided marketplace? What industry is your background in? What do you know extremely well as a starting off point?

Starting from this basis, I generally follow the same pattern: spot signals, develop insights, create thesis, and then develop options. I will use a current thesis of mine (Homes will become liquid assets) to show how this can work in practice. I’ll just caveat and say this is a data point of one developed over four years. Not the Bible on this and I can imagine that more established firms have their own methodology. However, I think this is a very practical approach.

Spotting Signals

Generally, this is the most time-consuming element of developing a thesis. It requires lots of reading and meetings. I try to meet with 25 to 50 founders each month. I listen to what they are working on, why they are working on it, the problems they describe, how they are approaching solutions, etc. All of this is extremely valuable information. At times the signal can be a cluster of people all working on the same problem. Other times, the signal is in the approach or in the rationale. It is super important to be a good listener in these conversation and probe. There is amazing information to be gathered in every coffee meeting that is hard to quantify. And while time consuming, it is definitely my favorite part of the job. This is why I almost never decline a meeting (I admit, I’m not a big deal so maybe this is easier for me than for others at more prominent firms).

In the case of my thesis on “Homes will become liquid assets”, the signals that I was able to spot throughout my conversations were:

· Selling a home is overly arduous and expensive

· Homebuyers and sellers do substantially more of the work these days and yet fees haven’t changed

· Home equity is often the largest asset on a person’s balance sheet but it is not accessible without taking on additional financial burden (doesn’t seem fair)

· Lifestyles are becoming more fluid but housing hasn’t adapted (this become a signal for another thesis as well)

· The total costs of a selling a home are opaque (repairs, time on market, time for showings)

Insights Generation

Armed with these signals, I became convinced that a movement was underway around changing the way home transactions were executed. So I began to dig in on the industry to get smart. I read about the history of home transactions, the power of the National Association of Realtors, trends in home equity, and the prevalence of digital tools in the homebuying and selling process. I also began looking at age cohorts and housing. Once all of this data was collected, I mashed it together with the signals that I had discovered in my conversations and started to look for linkage. Were they correlated? Were they mutually-exclusive?

The insights I was able to glean upon deeper inspection were:

· Millennials and GenZ are now the largest homebuying cohort. They have grown up in the age of digital and therefore more likely to use digital tools. They are also more likely to be cash-strapped and willing to look into alternatives that would save money (as long as it didn’t sacrifice confidence).

· Algorithmic, all-cash buyers were starting to gain traction in local markets signaling that people were willing to trade some level of return for simplicity. In addition, emerging single-family rental platforms were using similar tactics to purchase homes quickly and with no need for property repairs.

· More than half of all homebuyers find their home through a digital listing service prior to contacting an agent. Additionally, access to digital mortgage products and home services marketplaces made it simpler to take on more of the transaction process themselves.

· Large institutions (think pensions) saw residential real estate a great way to chase yield and hedge against inflation. Their long holding durations meant they were willing to explore partial ownership arrangements.

I knew something was there. It was time to develop a take.

Developing a Thesis

For me, thesis development always starts the same: what is the most outrageous yet plausible statement I could make with this data. Provocative statements make you really dig in because they are going to be challenged by people when you say them. They are also the most likely to reflect a major disruption in the status quo. My first attempt at this was focused on real estate agents and the likely elimination of that profession in the next decade. But I wasn’t 100% sold on this future and I also didn’t think it reflected all of the signals and insights. It seemed that the signals were telling me something about transaction costs (both monetary and time) and structures (full an partial sales). Harkening back on my days as an investment banker I realized that what was being challenged was the level of liquidity in housing. Some approaches were focused on time (I’ll buy your house quickly but it will cost you!) while others were focused on cost (full-service representation for a flat or highly reduced fee!). Still others were focused on getting access to the value of your home without having to move. All of these were elements of liquidity and I expected that as each segment matured that they would ultimately converge into a sweet spot where both costs and time would be reduced. Voila!

The final step in the thesis process for me is to then gauge my confidence in this future state. Confidence comes in many forms. There’s the “in my lifetime” confidence level where I can’t imagine a world where something doesn’t happen but I am so unsure of the timeframe I use my lifetime as a gauge. This would have applied to things like marijuana legalization or self-driving cars at some point. Then, market data and consumer sentiment start to provide a sense of timing. In this scenario, the traction of companies like OpenDoor and Offerpad made me feel confident that the algorithmic purchase segment could come quicker than I expected. Redfin and a plethora of other discount brokerage solutions showcased there was a market for reduced transaction costs but that consumer adoption still often required those reduced costs to come with full service. Finally, the emergence of equity structures for tapping into your home equity allowed us to see a future where partial liquidity wasn’t burdensome. However, the limited traction in these platforms to date and the unknown on how applicable these would be outside of major urban areas tempered our expectations here. The combination of these factors placed our thesis around 7 out of 10. I couldn’t imagine a world where homes weren’t considerably more liquid in my lifetime and I saw several elements that were either currently gaining traction or primed for scale. It was time to find the opportunities in the thesis.

Developing Opportunities

As a seed and series A stage investor, one of the challenges is how to find opportunities that haven’t scaled past your stage of investment, the market is large enough to support a “me too” approach, or where the incumbent has some vulnerability. For this thesis, I began to map out all of the major opportunities in a world where this future state were to occur. My thought process went something like this:

· Algorithmic home purchases was ruled out due to the current scale of major competitors and the quality of their management teams and investor bases. I was not going to throw my seed money into the ring against them.

· Alternative real estate brokerages was another model where I had some concerns but given the fragmentation in the market I felt it was still investible.

· Equity-based financing of real estate transaction was still nascent with no clear leader in the clubhouse. Also, the current products were exclusively focused on tapping in to home equity but there was a gap in the market when it came to origination.

· If homes become liquid assets, people will move more. It is generally a painful process but also a very lucrative moment in time for several industries. Developing a solution that took the pain out of moving and establishing that platform before liquidity increased dramatically could have “return the fund” economics”.

· Finally, algorithmic or institutional buyers of homes are traditionally institutions that grow by managing capital. However, part of the deal is they take on the burden of property repairs. Controlling the time and costs of these repairs will be paramount to their ability to generate IRR on their capital but managing a contractor network is painful and moving into a new market would require network development in each city. Why not develop a service to take this off their plate and allow them focus on sourcing and buying properties?

Conclusion

So this is a peek into how my mind works on opportunity identification. It’s important to note that this process is not as linear as it seems. In fact, it is a constant refresh of all of these steps. Keep collecting signals and turning them into insights. Take a provocative stance when there is one to be taken. Then map out all the ways to monetize this. So how’s it going? I’ll let you know in 5–7 years…

Chris Langford

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Venture Investor with Focus on the Home and Commerce