In 1988 Michael Sklarz and I published a paper called “It’s Time for Some Options in Real Estate.” In 1995 David Geltner and I suggested that we needed a fourth asset class, Housing Equity Investment Trusts (HEITs), whereby an owner could sell part of their home price appreciation to investors and these units might be traded. This selling of potential future appreciation could be used to improve affordability by providing immediate capital assistance in less affordable housing markets. Now, some 35 years later, we once again broach the question of whether housing might become a more transparent and efficient market through the use of indices and real time valuation models?
What has changed since 1995?
While there are still some laggards among public agencies, housing price data is now widely available online. It has been aggregated by several large data vendors, like Black Knight, Core Logic, Attom Data, Warren, Zillow, Redfin, Trulia, CoStar, and many others. Such vendors have not only aggregated the public transaction data, but also mortgage data, property tax data with physical attributes, multiple listing data, and HOA data. All of this has been geocoded to locational addresses, parcels, and maps. Such data goes back in time anywhere from twenty to fifty years and some vendors update their data daily. The data is now available for near real time price trend indices.
Housing price trends remain uncertain, which creates more opportunity for trading future interests. As of late 2023, we are not certain if interest rates have peaked out and whether home prices will be going up or down, with a combination of pressures in both directions.
It is precisely when prices are uncertain that the market for hedging instruments and price insurance products is highest. The inability to sell short in the housing market is a major impediment to a more efficient market. Home price indices can overcome this impediment.
What is wrong with Case-Shiller?
Nothing is wrong with the monthly Case-Shiller indices. The late Karl “Chip” Case and Nobel prize winning Robert Shiller have provided valuable insights into the housing market with their repeat sales method applied to 20 major cities. Similar methods are used by CoStar and MSCI for commercial real estate. At the same time, most housing analysts wish that Case-Shiller was produced with less of a lag, which runs about two months and more with data adjustments. Most concerning is the aggregation of an entire metro into one index, which might not be very representative of the idiosyncratic nature of the underlying geographically local markets.
For example, in June of 2011 when the housing market was in decline in much of the US, the Case-Shiller index reported San Diego as down 37% from June of 2005 prices. But 59% of the zip codes declined less than this, and some less than 10%, while a few zip codes in very distressed housing markets were down over 50%. A few local markets were greatly influencing the overall Case-Shiller index for San Diego, not unlike other metros.
Bill Wheaton of MIT would always say “No one lives in the median house” and yet media headlines suggest otherwise. North county and south county markets might be going in opposite directions on price trends, but blending them is akin to the old economist joke about a man with one hand in the freezer and one in the oven. This may explain why more housing market participants have not, to date, been very interested in broad housing price futures.
Housing indices work just like options
For those less familiar with option trading, there is an actual index, a future strike price, a bid and an ask for each. So, if the San Francisco index is now 100, and the three-year future strike is 95, and the bid is three, then the total in price for a three-year index is 98 which is less than the current index. This tells the market that prices are expected to fall, and yet, allows someone who thinks prices will be flat or rise to lock in a total price less than the current price. An owner, exposed to this index, might sell a put, the right to sell at a given price and lock in a sold price at some point in the future. Naked calls and puts are sold in the majority of cases, without the need for ever taking ownership of the assets in question.
There are several possible housing indices which could utilize all housing sales, not just existing repeats. Filters would be necessary to reduce the level of tolerable noise in the data. Among these a simple transparent hedonic regression model that utilizes age, size, location, and a few other variables, would be possible in near real time for most urban markets. The model would be revisited and updated annually but not phased in for three years, always providing historical trends. It would be very similar to a highly filtered price per square foot result, but with a few more controls.
How granular can we get?
There are over 41,000 zip codes in the US. For those with housing, the average is about 200 residential sales a year. Filtered zip codes where sales activity exceeds 100 sales per quarter, would provide several hundred eligible zip codes. At the very least, we could start with the 100 to 200 most active zip codes or census block groups.
Even 100 zip code level indices will be a 500% increase on what is available now with Case-Shiller indices. An advisory board will need to be organized to review filters and decisions on granularity, as well as approving the hedonic control model used. Output can be generated literally a few days after the close of any month or quarter.
Investors would be able to buy or sell each of the indices, and new combination products might also be offered such as the tech group or oil belt regional indices. Price insurance products could be sold to homeowners that would shield them from price declines or allow them to reduce the cost of home ownership by selling off some of the potential appreciation.
HEITs might come into play as well. Homeowners that want to hedge home price risk or trade shared appreciation units for other markets could do so. For example, I might buy a home in San Francisco and trade 50% of my future appreciation for equivalent value units in another market. Such trading would allow homeowners to consume housing, but diversify home price risks, without owning a larger portfolio. Selling off future appreciation would allow buyers to put this towards a down payment making the high appreciation markets more affordable.
Near real time data exists for the housing market allowing for instant valuation, disclosure, and the development of home price indices. It is possible to do this for several times the number of indices provided now at the metro level by Case-Shiller. Real estate tokenization has shown us a process by which we might trade indexed units of shared appreciation units from specific local markets. Morphing the real estate market into a more efficient market with the possibility of both long and short positions could also help mitigate some of the existing price volatility risk and allow for home ownership and mortgage lender risk reduction via diversification and hedging. Is the market finally ready for such products?
