Why do mortgages matter?
At a high level, mortgages are one of the key levers in the real estate market and integral to the homeownership dream for Americans. Whenever the majority of Americans look to buy a home, they go through the mortgage application process and continue to interact with their mortgage servicer until they payoff their loan. It’s also the largest piece of debt and obligation that the majority of consumers take out in their lifetime. So at a minimum, you can say that one of the biggest decisions a consumer makes in his or her life is taking out a mortgage and thus, is core to the American Dream.
On a more macroeconomic level, if you view mortgages as somewhat synonymous with the real estate market, then it’s one of the largest drivers of the US economy. Construction and remodeling of homes (which is funded by mortgages) is ~5% of GDP and consumer spending on housing services is another ~12-13%. Unsurprisingly, this results in the US government having a massive vested interest in stabilizing and propping up the housing market (think of what happened in 2008!). It’s also hugely instrumental in promoting social stability through the creation of sustainable communities that many spend their entire lives in (Urban Institute). We can go on and on but a more efficient, cost-effective, and transparent mortgage experience is beneficial to society.
a16z's Guide does a fairly good job of going through how the ecosystem works today so I’ll skip through the process of getting a mortgage. Feel free to read through to get a quick overview. What should be readily apparent is that there are a number of heavily entrenched players and processes that are involved and it takes some real industry experience to unbundle the entire mess.
For our business, the key players that we care about day 1 are:
Types of Mortgages (QM / Non-QM / Conventional)
Conventional/Conforming mortgages are a category of loans with pretty standardized features. This makes it easy for investors to analyze and understand. In turn, consumers get a better rate and terms. You can read more about it here but you can largely think about them as highly regulated mortgages that Fannie/Freddie can buy and hence is cheaper. You cannot service these without a Fannie/Freddie ticket. This does not mean that non-conforming mortgages are much riskier - they’re just not as standard and therefore harder to relatively evaluate. For example, jumbo (larger size) mortgages can be non-conforming if they’ve exceeded the conforming loan limits. Similarly, FHA/VA loans can often be non-conforming. This is often used interchangeably with the term “qualified mortgage” or QM. Qualified mortgages are ones where the lender has done an ATR or Ability-to-Repay analysis. By doing this work, the government gives what’s called “safe-harbor” to the lender, thereby absolving them of liabilities if the borrower defaults. Technically, any loan that has a DTI (debt-to-income) less than 43% is a QM or if it is GSE-eligible (Fannie/Freddie/FHA/VA). On the other hand, Non-Qualified Mortgages do not have these standardized features or borrower profiles and hence, are outside of the bounds (and requirements) of the GSEs. Back pre-’08, these were the crazy loans that blew the US housing market up. Today, these are generally given to individuals who don’t fit the tight box that is QM (i.e. you are a non-salaried worker / contractor) and have low loss rates because investors have become very cautious. They also are easier to work with in the sense that the only qualifications that are necessary are state licenses and you only have to follow state and federal regulations.
The Problem We Are Trying to Solve
While many companies focus on the mortgage origination side of the business, we want to focus on the servicing side of the equation which happens to also be the long term customer relationship management component. Like we explained earlier, this is the part of business where the primary roles are two fold: 1) collecting payments (P&I, taxes, insurance, HoA) and 2) handling delinquencies and foreclosures. While there are a ton of problems with mortgage servicers, the net result is that they have gone from being 20-30% net income margin business to 0% margin business.
Why did this happen? When people look at the left chart only, they sometimes just attribute it to delinquent loans being more expensive to service and after the Great Financial Crisis, there were a lot of delinquent loans.
This chart suggests that yes there was a large increase but it has since come back down. Moreover, the Loan Servicing Costs chart clearly tells a different story - performing loans had a similarly large increase in costs. Moreover, if you look at the proportional decrease in efficiency in the left chart, it matches the inverse of loan servicing cost increase of performing loans. This strongly suggests that there is more to the story than just simply mortgage delinquencies increasing.
Our belief is that the fundamental problem that arose was one of poor data schema and service abstraction. And it’s a problem we think is easy to sympathize with. Imagine if you were to build this from scratch without knowing much about the future regulation or really much about the mortgage market. You would hard code all of the mortgage rules and regulations and once a loan has been instantiated, it wouldn’t be turned back. This wasn’t a problem pre-crisis when the market did well and most loans that went delinquent were just foreclosed and sold perhaps even for a profit. However, post-crisis, all of these regulations started to come into play. Some were pending and some were ambiguous but failing to comply would definitely result in large penalties. So, ad-hoc solutions were layered on top, like hard coding new rules into the system. Well, maybe that handles new loans going forward, but what do you do with old loans? Does the history change? Were the borrowers considered delinquent in the past based on the new rules? If you keep just the history and change the code, how do you go back and know why the loan was considered delinquent? Clearly, the old data models couldn’t handle this!
Here are some more example situations:
Now, there are other technology problems like missing features, lack of full integration, and lack of consumer transparency. All of these are independent but improvable pieces of the puzzle but as we pointed out in this section, the root is bad (or lack of focus on) technology.
What’s the simplistic result of bad technology? More people/overhead and hence, massive scale in order to get somewhere close to profitability
Also, it generally means there’s a terrible consumer experience. Like no mobile and some clunky website.
For such a poorly tackled space, you would expect that the market just isn’t large enough to justify the resource and investment. Fortunately for us, this is definitely not the case.
The rough math works like the following. There are $10.3T UPB (unpaid principal balance) for residential mortgages in the United States. The minimum fee is 25bps annualized, which means we’re really talking about $25+ billion paid in fees to servicers yearly. This is a market that grows as real estate values grow and more housing is needed.
We’re not even adding on core servicing businesses of recapture and refinancing. There are $500 billion in refinancings a year and you can earn an additional 1-3% on top of that.
You can imagine how limitless the opportunity starts to look when we start layering on other value add services. In short, the opportunity in this sector is absolutely massive.
CEO of Valon
Ex-investment and mortgage professional at Soros