Cash Flow Champs Real Estate Podcast

Prashant Kumar- I understand your point, Stefan, but one thing I don’t quite grasp is whether the data produced is reliable. Has the data accurately predicted past events? Have the predictions made based on the data been proven to hold true, so that they can be taken at face value? What are your thoughts on this?

Stefan Tsvetkov- Yes, that’s a great question and a very important consideration. It wouldn’t make sense to rely on data that doesn’t have any predictive power. For instance, if the population trends aren’t indicative of any trends in the market cycle, there’s no point in looking at them. It all boils down to the predictive power of the data. Interested individuals can find more information in the articles posted on realtyquant.com.

To determine the best predictor of downside risk, I examined every recession in the past 45 years. I evaluated factors such as foreclosure rates, volatility, risk-adjusted returns, and other price metrics, including the code sharp ratio in finance. Ultimately, I found that the strongest predictor of downside risk was the deviation from fundamentals, which included income, population, and housing supply. Any price deviation from what prices ought to be based on these fundamentals proved to be a reliable predictor of downside risk during the three recessions in the past 40 years that had any significant impact. Using our data at RealtyQuant, a passive investor can achieve almost scientific-like results with a 90% correlation by investing in metro areas using simple deviation from fundamentals. This was evident during the global financial crisis, where markets that were invested ahead of the crisis using this approach had a 90% correlation with our data.

I have developed a method to identify the best predictor of downside risk by analyzing data from every recession in the past 45 years. I looked at foreclosure rates, volatility, risk-adjusted returns, and price metrics to find the leading indicators with the strongest predictive power. I discovered that the deviation from fundamentals – income, population, and housing supply – proved to be the most reliable predictor of downside risk in three of the past 40 years of recession.

Using our data at RealtyQuant.com, we can identify undervalued metro areas that have not declined much and overvalued metro areas that have declined significantly. At the state level, the correlation is around 85%, but it starts declining as you go to smaller geographies like counties, where it’s around 75%. Although it’s harder to predict at the county level, a 75% correlation is still useful for investing, especially for portfolio diversification. The correlation drops further at the zip code level, and it’s difficult to compute for every geography.

In short, predictive power is crucial in investing, and my method has proved to be a reliable indicator of downside risk.

The key takeaway is that picking metro areas based on this data has extremely high predictive power. However, there is currently a unique event happening that was not present during previous recessions – the suburban trend. This trend is similar to the internationalization of labor in the 50s, where outsourcing to China led to job losses and population outflow in certain markets. The suburban trend is having a similar effect, but with high-paying “white core” jobs. As a result, several markets that are undervalued are actually declining. It is important to factor in this trend and track valuation and population outflow when making real estate investment decisions.

However, the suburban trend is a unique event happening now that was not present in previous recessions. It is essentially having the same effect as the internationalization of labor in the 50s, where cheap labor in China led to job outsourcing and population outflow in certain markets. But now, it’s impacting high-paid white-collar jobs, and we’re seeing several markets like San Jose and San Francisco experiencing a significant outflow of their white core high-income earning population. Even though these markets are undervalued compared to fundamentals, their declining fundamentals are crushing. Therefore, it’s important for everyone to look at valuations and track population outflow due to suburban trends. The main value of realty content and the data I brought at the beginning of 2020 was to calibrate it to previous recessions and demonstrate its predictive power.

Prashant Kumar- Stefan, I understand that you possess the raw data and have created it yourself. Please correct me if I’m mistaken. I propose that we assemble a team to develop algorithms and calibration techniques for this data, which can be presented in a commercial format and made available to passive investors, retail investors, hedge funds, or any other interested parties at a larger scale. With your data and our efforts, we can make a powerful impact in the field. I believe there is a high demand for this type of analytics and information among many passive investors and hedge funds. Other sources may exist with similar information, but we can make our product stand out with precision and reliability.

Stefan Tsvetkov- There are indeed other sources of similar information available in the market. It’s an interesting point you’re making, and it’s worth noting who these sources are catering to and where they are situated. For instance, when we look at syndicators and investors, are they aware of the overwhelmed markets? For instance, let’s consider Rod Cliff, who hosts a podcast on losing money following the global financial crisis by investing in 1000 single-family homes in Florida. On the other hand, we have Viney Chopra, who invested in Texas during the same crisis and managed to come out on top. However, it’s not typical for syndicators to utilize market valuation data. While we have a few who do it through our company, it’s not usually their focus. Having said that, similar data is produced by Moody’s Analytics.

There are various versions of this data available at institutions such as Core Wardrich, Bloomberg Economics, and a study at Florida Atlantic University. This data is primarily being used in the lending industry, where institutions like Moody’s use it to make value adjustments. They can then decide not to buy loans in certain states or neighborhoods if they are deemed overvalued. However, the data is not typically utilized by syndicators or investors, although a few do use it through our company.

To answer your question, the institutional level is where it’s done, and it’s now being consumed by the lending industry. I recently spoke to a company that buys portfolios worth around $9 billion, one of the larger players, and found that their usage of it is still relatively limited. They have some metrics for a few markets, but they haven’t delved too deeply yet. However, they are using it, and while hardly any investors are using it, I find it strange that they are not. Even though investors may be geographically focused, they should be aware of it and use it, as I don’t want to invest in something that I know is doomed for the next ten years, only because it’s overvalued by 50%. This is an excellent question. From my perspective, the real future market is in institutions within the lending industry space.

Prashant Kumar- Awesome.