Rents, Credit, Divide, Data

Rents Softening + Global Inflation Issues

The SF Fed recently produced a study on the potential for rent inflation to slow and even possibly turn negative by next year. Real estate owners are patient by nature and rents/shelter have been some of the slowest moving inflation metrics. Shelter represents around 40% of the official inflation statistics (CPI), so any movement downwards in shelter inflation will have a significant impact. The study can be found here.

Argentina’s government hiked interest rates to 118% last week, compared to 5.5% in the US. US investors have incredible advantages that should not be under appreciated. Home country bias is a real phenomenon and we are lucky in the US to have access to incredible abundance, independence and self reliance. Approximately 120 million people globally are living with triple digit inflation, and 1.58 billion are living with double digit inflation. Inflation in the US is around 3%.

Crypto is still largely considered a toxic financial topic with too many issues to be discussed in this paragraph, but ETH and USDC does give people global access to hold their assets in USD regardless of their home country or state of their local currency. In 2017 banks in Argentina blocked locals from converting their holdings to USD to reduce selling pressure on their local currency, and as a result many citizens discovered ETH as a way to solve their local currency problems and get their assets out of the control of the local government. If I were living in Argentina today I would be buying ETH and swapping it into USDC (the USD stable coin) to avoid the triple digit local inflation. That would be a no brainer in my opinion. 

Private Credit Activity

Regular readers know that I am a fan of the emergence of the private credit system. There is a great FT opinion article that addresses the nuances of this asset class which emerged as a result of the ‘08 financial crisis. I recommend reading it if you are interested in this topic. Link here.

The private credit ecosystem allows investors to actively lend their capital for a yield. The ecosystem is diverse and constantly expanding into new areas. It’s a more honest social contract than traditional banking which surreptitiously lends out checking account balances, pays little to no interest, and places liquidity tail risk on government bailouts. 

Instead, investors knowingly participate in the lending activity by locking their capital up to better match the duration of the underlying loans and receive compensation for providing their capital to loan officers. 75% of the $1 trillion dollar private credit market involves lending to private equity managers, which theoretically adds sophistication to financial behavior of both the borrowers and credit managers. We’ll see how that holds in practice over the next few years in the higher rate environment.

There will be inevitable liquidity and credit issues in private credit and this FT opinion article points out that most credit risk managers spent the past decade in a goldilocks period without needing to deal with sweeping defaults or bankruptcies. 

KRUZ vs NANC

There are two new ETFs that track all holdings of Democratic and Republican members of Congress. “KRUZ” loosely stands for Ted Cruz and “NANC” is named after Nancy Pelosi. With Congress roughly evenly split, the asset mix represent 222 and 212 public market portfolios. What’s notable is that the asset mix could not be more different, which is not surprising given the divided nature of our country.

Democratic portfolios heavily favor large concentrations in information technology, cloud computing, semiconductor and other technology assets. Republican portfolios are dominated by oil and gas, financials, tobacco, and are far more broadly diversified. The portfolios are based on public filings updated on a weekly basis, making it a fairly reliable way to evaluate the differences between the two sides over time.

AI Needs Data

Corporations are still in the early stages of training AI models on their vast internal and proprietary data sets. Current LLMs like ChatGPT are trained on publicly available data. The Economist has a great article on this topic. Investors should consider the companies that play a direct role in collecting, organizing and feeding data to AI models for use behind closed doors within the corporate realm.