A Different House Hedge

Where do stock market winners buy houses?

There are many ways to predict how the price of an asset will change in the future. For stocks, one approach is based on fundamental analysis and another approach uses portfolio diversification theory. A third approach to predicting stock movement is so-called “technical analysis,” which is too silly for more than a mention. There are also statistical arbitrageurs in the high-frequency market-making and trading arms race, who make minute predictions thousands of times per day. If we pretend real estate acts as a stock, we can stretch the analogy into a new mathematical tool for hedging house prices.

Fundamentalism

Fundamental analysis is usually what people think about when picking stocks. This is the Benjamin Graham philosophy of digging into a company’s internals and financial statements, and then guessing whether or not the current stock price is correct. The successful stock picker can also profit from an overpriced share by temporarily borrowing the stock, selling it, and then later buying it back on the cheap. This is your classic “short,” which may or may not be unethical depending on your politics. Do short trades profit from misery, or reallocate wasted capital?

Fundamental analysis is notoriously difficult and time-consuming, yet it is the most obvious way to make money in the stock market. Fundamental analysis is also what private equity and venture capitalists do, but perhaps covering an unlisted company or even two guys in a garage in Menlo Park. When you overhear bankers talking about a “long/short equity fund” they probably mean fundamental analysis done across many stocks and then managing (trading) a portfolio that is short one dollar for every dollar it is long. This gives some insulation against moves in a whole sector, or even moves in the overall economy. If you are long $100 of Chevron and short $100 of BP, the discovery of cheap cold fusion will not trash your portfolio since that BP short will do quite well. However for conservative investors like insurance companies and pension funds, government policy restricts how much capital can be used to sell assets short. These investors are less concerned about fundamental analysis, and more about portfolio diversification and the business cycle.

Highly Sensitive Stuff

If a long-only fund holds just automobile company stocks, the fund should be very concerned about the automobile sector failing as a whole. The fund is toast if the world stops driving, even if their money is invested in the slickest, most profitable car companies today. Perfect diversification could occur if an investor bought a small stake in every asset in the world. Though huge international indices try to get close, with so many illiquid assets around, perfect diversification remains just a theory. How can an investor buy a small piece of every condominium in the world? How could I buy a slice of a brand like Starbucks? Even worse, as time goes by companies recognize more types of illiquid assets on their balance sheets. Modern companies value intellectual property and human capital, but these assets are difficult to measure and highly illiquid. What currently unaccounted-for asset will turn up on balance sheets in 2050?

Smart fund managers understand that perfect diversification is impossible, and so they think in terms of a benchmark. A fund benchmark is usually a published blend of asset prices, like MSCI’s agricultural indices. The fund manager’s clients may not even want broad diversification, and may be happy to pay fund management fees for partial diversification across a single industry or country. Thinking back to our auto sector fund, they are concerned with how the fortune’s of one car company are impacted by the automobile industry as a whole. An edgy upstart like Tesla Motors is more sensitive to the automobile industry than a stalwart like Ford, which does more tangential business like auto loans and servicing.

Mathematically we calculate the sensitivity of a company to a benchmark by running a simple linear regression of historic stock returns against changes in the benchmark. If a company’s sensitivity to the benchmark is 2.5, then a $10 stock will increase to $12.50 when the benchmark goes up by one point. A sensitivity of 0.25 means the stock would just edge up to $10.25 in the same scenario. A company can have negative sensitivity, especially against a benchmark in another related industry. Tesla probably has a negative sensitivity to changes in an electricity price index, since more expensive electricity would hurt Tesla’s business. No sensitivity (zero) would turn up against a totally unrelated benchmark. Sensitivity has a lot in common with correlation, another mathematical measure of co-movement.

One type of sensitivity is talked about more than any other. “Beta” is the sensitivity of a stock to the theoretical benchmark containing every asset in the world. Data providers like Bloomberg and Reuters probably estimate beta by regressing stock returns against one of those huge, international asset indices. An important model in finance and economics is called the Capital Asset Pricing Model, which earned a Nobel Prize for theorizing that higher beta means higher returns, since sensitivity to the world portfolio is the only sort of risk that cannot be diversified away. Though the CAPM beta is a poor model for real-life inefficient markets, sensitivities in general are a simple way to think about how a portfolio behaves over time. For instance, it turns out that sensitivities are additive. So $100 in a 0.25 sensitive stock and $50 in two different -0.25 sensitive stocks should be hedged against moves in the index and in the industry the index measures.

Back to Real Estate

Prices in certain local real estate markets are bolstered by a rally in the stock market. The recent murmurings of another IPO bubble suggest that newly minted paper millionaires will soon be shopping for homes in Los Altos Hills and Cupertino. We can put numbers behind this story by calculating real estate price sensitivity to a stock market benchmark. If we choose the S&P 500 as the benchmark, the sensitivity number will be a sort of real estate beta. Since real estate is far less liquid than most stocks, I regressed quarterly changes in our Altos Research median ask price against the previous quarter’s change in the S&P 500. Historically speaking, those real estate markets with a high beta have gotten a boost in prices after a good quarter in the stock market. Those markets with a low, negative beta are not “immune” to the stock market, but tend to be depressed by a stock market rally.

Below is a map of the Bay Area’s real estate betas. These numbers were calculated using prices from Altos Research and benchmark levels from Yahoo! Finance. The darker red a zipcode, the greater an increase in the market’s home prices after a quarterly stock market rally. As we might expect, the betas in Silicon Valley are above average. However there are also some surprises in Visalia and Wine Country.

Our hypothesis for positive real estate beta is easy: those IPO millionaires. But what could cause a real estate market to tank after a good run in the stocks? Perhaps negative real estate betas are in more mobile labor markets, where stock market wealth triggers a move away from home ownership. Or maybe negative real estate betas turn up in markets where the condo stock is higher quality than single-family homes, like in some college towns. Remember the betas mapped above are based on only single-family home prices.

Real estate remains a difficult asset to hedge, an asset almost impossible to short by non-institutions. This is unfortunate, because a short hedge would be a convenient way for people with their wealth tied up in real estate to ride out a depressed market cycle. However like long-only fund managers, real estate investors could benefit from thinking in terms of benchmark sensitivity. If we choose a benchmark that represents the broader real estate market, we could hedge real estate buy purchasing non-property assets that have negative real estate betas. You would want your value-weighted real estate beta to net out to about zero. Now there is a plethora of problems and assumptions around making investment decisions with a crude linear sensitivity number, but at least real estate beta gives us another tool for thinking about housing risk.

(An abbreviated version of this post can found be at http://blog.altosresearch.com/a-different-house-hedge/ on Altos Research’s blog)