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Resi Wrap - Outliers
July 2023

Friends of ResiShares:

Not everyone writes the June edition of their newsletter on July 3, but I’d like to think that we’re in good company.

Anyway, some of the sportsball fans and political junkies in our audience may be familiar with a statistician named Nate Silver, who, among other things, founded statistics site FiveThirtyEight. He recently wrote this substack article about how the Denver Nuggets broke his model.

His model, called RAPTOR, compares contemporary players in their current season to historical analogs. About Nuggets star Nicola Jokic, Silver writes:

With Jokic, on the other hand, RAPTOR has no idea what to do. It can’t find any other players like him…

…So in the entire history of the NBA, all but four players are more dissimilar to Jokic than similar to him, and the other four are just barely on the other side of the line.

There is a perception that statistical modelling is meant to deliver insights that human reasoning cannot - finding hidden patterns in high-order interactions of big data beyond our comprehension. This is mostly inaccurate. Most quant research is about systematizing and automating persistently useful human-style insights, so that they can be applied to arbitrarily large data sets without having to hire lots of expensive human analysts.

Models are meant to deliver predictably precise forecasts within a tolerable error band. The genius of RAPTOR is not necessarily that it has a more accurate opinion on the Steph versus Lebron debate than Stephen A. Smith, but that it can stack rank Onyeka Okongwu (A young center on the Atlanta Hawks, for those asking) at zero marginal cost, which is not worth Stephen A’s valuable time (especially given the extra hours he’ll have to pull after those ESPN layoffs).

So there are two possible reactions when RAPTOR chokes on one of the NBA’s most high-profile and dynamic players:

1) Pffffft….this model sucks. How am I supposed to trust its confidence in Jokic when it has such low and prima facie absurd similarity scores?

2) The model has identified an edge case. I should spend some time trying to understand what the model does not get and figuring out if that is advantageous or disadvantageous.

Number 2 is the correct answer. Silver’s a smart guy - he bet on the Nuggets to win at rather favorable odds.

This brings us to another dinosaur-inspired-comparison-score-model.

(Sorry Nate - T-Recs beats Raptor, as demonstrated in the documentary, Jurassic Park).

Can we find the Nicola Jokic of real estate markets? It may be Miami.

T-RECS likes Miami, ranking it #45 out of the top 200 markets for home price growth over the next 3 years. But it can’t tell us why.

  • Its closest comparison cities, such as Detroit and Chicago in the middle of the last decade, were underperformers.

  • It had a huge, tech-driven boom during COVID, just like Austin, Boise, and Salt Lake City, which T-RECS has (correctly) hated.

  • It’s population shrinking fast, and, unlike oft-maligned NY, San Francisco, and Chicago, that shrink is accelerating, rather than decelerating.

None of that sounds great, especially that last point about negative population growth. And yet, Miami’s home prices continue to confound naysayers by marching higher. How can price go higher when demand goes lower? This doesn’t just break T-RECS. This breaks freshman year economics class!

This substack from housing economist Kevin Erdman lays out how this works. Erdman writes, with regards to the similarly-situated Los Angeles housing market of the last 15 years,

Where there is a lack of adequate housing, moderate population growth leads to a process of “musical chairs” in which some families must be displaced from the area. That choice—whether to stay or leave—is moderated through the financial burden of increasing housing costs, which naturally falls more heavily on households with fewer financial resources. This leads to a self-selection of households out of the expensive metropolitan areas based on how much they are willing to choose excessive housing costs over displacement.

In other words, housing prices are not going up IN SPITE OF net out-migration. Instead, net out-migration is CAUSED BY housing prices going up. The basic mechanism is as follows:

  • A desirable location has constraints, either geographic (Miami coastline and swamp) or regulatory (California NIMBYism), on supply.

  • A paradigm shift enables wealthier people to move into the region (California TMT boom. COVID freeing up New Yorkers to move to Florida).

  • This drives up price, displacing the less affluent.

If we were talking about cars, sneakers, or iPhones, this displacement would cause price to then subsequently fall. But the housing market is not the widget market. A Miami with more money and fewer people is MORE attractive to prospective wealthy in-migrants, not less.

It appears that is precisely what is happening in Miami, just as it had done in San Francisco in the past, according to this Bloomberg analysis.

The Rest Of Resi

Cubano sandwiches, mojitos, and rising home prices.