Real estate: to own, or not to own

I was first exposed to the real estate business through my grandpa and grandma. While I was in my early teens, my grandparents both worked as real estate agents in Long Island, New York. They were locally known as “The Dream Team.” One day after school, my grandpa took me out for a slice of pizza and I asked him to explain his work as a real estate agent.

Grandpa Jerry explained, “A real estate agent helps people buy and sell homes. In exchange for their help, the real estate agent keeps a small percent of the transaction price when a home is bought or sold. To put this “small percentage” in perspective, imagine you receive a small slice of a normal sized pizza, you can eat for a day. But if you receive a slice of pizza the size of a house, you can eat for a year.” My grandpa and I both love to eat pizza, and his explanation stuck with me.

For the last eight years, I’ve participated in the residential real estate market as a renter in Shenzhen, New York, and Cambridge. When you agree to be a renter, you agree to pay a fixed amount of money each month to live on your landlord's property. In exchange, the landlord needs to provide the renter with selected amenities and agrees to cover any capital expenditures required to mitigate wear and tear.

From a theoretical perspective, paying rent is very similar to entering into an interest rate swap contract.  In this contract, as the renter, I agree to pay fixed, and my landlord receives floating.  The landlord collects a fixed rent, pays variable expenses, and most importantly receives a floating rate of return equal to the capital gains or losses that come from changes in the property's market value.  More recently, I've been re-thinking my decision to pay fixed and have started to think about taking the other side of the swap.

Of course, there is an academic answer to the rent/buy decision. The challenge is that the answer entirely depends on your assumptions.  While these assumptions are uncertain, they can be estimated with varying degrees of precision. My friend Ben Willinksy recently passed me a model that the NYT created that helps you easily evaluate the theoretical approach to the rent/buy decision.  The drawback to this tool is that it has 20 input assumptions.  Like all assumption driven valuation models, slight changes to some of these assumptions can lead to a wide dispersion of outcomes.

I’ve always felt this theoretical approach to answering the rent/buy decision to be unsatisfying. A more satisfying approach would help me answer questions like:

• How good of an investment has buying a home been historically?
• What is the relationship of housing investment returns to other asset returns (stocks, bonds, cash, etc.)?
• What is the relationship between housing and inflation?

I wanted to approach the rent/buy decision empirically rather than theoretically.  Remarkably, there was little long term research on housing returns until quite recently. In November of 2017, a team of five economists working in Europe and the US published a paper that comes a long way to bringing an empirical perspective to the rent/buy decision.  The paper wonderfully titled, “The Rate of Return on Everything: 1870-2015” asks the question, what is the rate of return on the four core investible assets in the economy?* The researchers analyzed the real and nominal rates of return for Treasury Bills, Treasury Bonds, Public Equities, and for the first time, residential real estate for 16 countries from 1870 to 2015.  Collecting and cleaning this data alone was a huge achievement.

From this data, we can look at the historical returns, volatility, and correlations for each of the four core asset classes.  I extracted the charts below from the paper and they help to provide a sense of the shape of the return distribution for each of the four asset classes.  I pay special attention to housing below.

As is clear from the charts above, the average public equity market total return for this period was about 7% real and the average housing return was also about 7% real.**  Even more intriguingly, housing returns appear to be lowly correlated with public equity markets and considerably less volatile. Housing also appears lowly correlated with inflation while equities are negatively correlated with inflation.  Modern portfolio theory suggests if there are two assets with similar returns but low correlation with one another, then it is a strong argument for owning both assets.

The critical risks which could cause total capital loss to a housing investment are tail events including political risks, local economic decline, natural disasters and a sudden need for liquidity at a time when there are few interested buyers. Assuming these risks do not materialize, my instinct is that over long periods of time you are likely to receive a real return comparable to the returns described above.

The real housing returns described above include both rental income and capital gains.  However, a landlord receives rental income only if they have a renter living on the property.  If a home is owner occupied, then the owner only receives the capital gain return and the rental income is instead enjoyed as the benefit of living in the home. The chart below details the nominal returns for housing, decomposing it into capital gain and rental income.  Looking only at the capital gain return gives us a good sense of what outcome we can expect should we become a landlord and live on our property.

So what should you expect as a homeowner? Nominal returns of 3.5% to 8.5% with an average of 5.7% over the sample period with half the volatility of equities. If the rest of your portfolio is composed of bills, bonds, or equities, these characteristics are even more intriguing.  If you have the desire to become a landlord and manage renters, your returns can be considerably higher and the rental income comes with a volatility profile considerably lower than treasury bills which are often described as a risk-free asset.

After reading this paper, I am reasonably confident that taking the floating side of the swap is the optimal long-term decision.

End Notes:

*The paper was written by Oscar Jorda (Federal Reserve Bank of San Francisco and the University of California Davis), Katharina Knoll (Deutsche Bundesbank), Dmitry Kuvshinov (University of Bonn), Moritz Schularick (University of Bonn), Alan Taylor (University of California Davis): Link to the paper is here. 

**For reference, real returns are defined as nominal returns minus inflation.

Book Review: A Man for All Markets

I first learned about Edward O. Thorp while reading "Fortune's Formula" (Poundstone 2006).  In FF, Thorp is portrayed as a brilliant mathematician who decided to focus his attention on games of chance (roulette) and games of probability (blackjack) to gamble against Las Vegas casinos with an edge.  Poundstone wrote a fine book, but much of Ed's humanity, personality and research approach are not well explained. Published just a few months ago, Ed tells his life story from growing up poor during the great depression, to becoming a gambler, mathematics professor, investor and ultimately a philanthropist. 

Thorp's story begins in the 1930s, his family felt the ravages of the depression and they were often short on money.  From an early age, a number of unusual personality traits and mental abilities became clear. He enjoyed reading and had an outstanding memory.  For example, he was once challenged by a passerby who noticed that Ed was holding a tome on British history that looked much too complicated for him, and asked Ed if he knew anything about the British monarchy.  To his adversary's surprise, Thorp went on to name all British monarchs from Alfred the Great to George VI.  In addition to reading comprehension, Thorp's interest in experimentation was evident from his younger years as well. He enjoyed building radios, model airplanes and using tools to improve his surroundings. This theme would become a defining characteristic of Thorp's life.

From an investment perspective, Thorp's book contains a wealth of wisdom. Common consensus on roulette was that it was a game of pure probability, and given the house's slight edge, unbeatable.  Thorp, knew full well that in theory roulette might be unbeatable, but he wanted to explore it for himself to see if the path of the ball was random as theory suggests or if there might be some patterns/path dependency present on the table.  Thorp decided to tackle the roulette wheel by approaching the problem as an experimentalist.

Thorp bought a few old roulette wheels and began exploring for patterns recording where the ball ended up after a normal spin.  He then learned Fortran to run computer simulations on the data he obtained.  These experiments implied that there were patterns to the balls, but he'd need a computer to help him identify them.  This finding resulted in Thorp inventing the world's first known wearable computer which helped him make real-time calculations and gave him an edge betting at the roulette table.  Thorp developed new techniques and equipment as needed to recognize patterns in data and used theory to codify his results into a betting strategy that could be carried out in real casino conditions.  This involved practicing with his friends and in casinos with small amounts of money to see if he could execute his strategy in a risk controlled manner.  Once satisfied with his results, he slowly increased his bet size. 

Later, Thorp would apply an open minded experimentalist approach to investing in securities, commodities and other financial products on Wall Street.  The prevailing wisdom among academic economists and financial theorists at the time was that market prices were efficient, that is to say, that all prices were right.  One of the implications of this theory of securities prices was that there are no patterns in securities prices which could lead to sustained arbitrage profit. As Thorp admits, this is a fine theory, but to test it you need to examine the data and see if there are in fact any patterns.  Thorp shows again and again throughout his story that this theory is false.  From investing in closed-end funds trading at a discount, statistical and fundamental arbitrage techniques and other well-known techniques in the vein of Benjamin Graham are tradable and produce a positive return.  Thorp's several decades of success should give any proponent of the efficient market hypothesis pause.  Thorp's success inspired many other mathematician run investment partnerships that were able to invest in systematically mispriced securities.  Several of these groups were likely trading on the so-called "Black-Scholes" model for more than a decade before Black and Scholes published their result.

In addition to investing on his own, he was one of the first to run a fund of funds.  He invested with many others including Warren Buffett (early in Buffett's career), believing that detecting skill in pricing securities is ex-ante observable, but requires extensive due diligence.  When contemplating an investment in another investor's partnership, Thorp would visit the fund's office and in some cases ask the investor to work from his office for several months.  His process for reviewing an investment manager included reviewing trade ideas in detail and assessing their thinking for rigor and creativity.  This kind of due diligence approach led him to recognize Bernie Madoff as a Ponzi scheme decades prior to the scheme's collapse.

Thorp's book ends with some thoughts on the importance of science.  One of Thorp's central concerns is that the US may be losing its future edge in science, mathematics, and technology because of a pattern he's observed in labor markets. Thorp notes that America which during the 20th century was viewed as the place which caused brain-drain in other parts of the world, such that brilliant people would leave their home countries and go to America to seek their fortune.  Thorp notes that today, the opportunities for brilliant scientists (given the US-visa system and lack of funding) may be better in China than the US.  He's observed a concerning pattern where many top talents are being educated in the US and then returning to their home countries.  Ultimately, this lost potential could result in the decline of America's role in the economy as a leading innovator.  Thorp notes that while Rome was not built in a day and did not fall in a day, a slow decline due to reverse brain-drain is deeply concerning.

In summary, I'd strongly recommend Ed's book if you are interested in games of chance, strategy, investing, seeing applications of the scientific method to human problems and spend time with a man who cares deeply about education and acting morally.  Thank you for taking the time to write "A Man For All Markets" Ed, your experimental approach rooted in your tendency to not believe what others tell you unless you check it for yourself is inspiring to me. Perhaps this is why Seth Klarman named his horse "Read the Footnotes."

Please pick up a copy of Ed's book today.