“Small changes often appear to make no difference until you cross a critical threshold. The most powerful outcomes of any compounding process are delayed."
We often hear from Haystack readers looking for a forecasting edge. They ask (and we write) about more sophisticated approaches to key assumptions like cap rates and rent growth, or even submarket performance. Others push further into harder questions about forecasting returns across entire portfolios and funds. Recently a reader asked a great question: For the most liquid real estate investments (REITs), how much of their returns is actually forecastable?
That question opened a surprisingly productive rabbit hole. We dug into new machine learning research that quantifies just how predictable REIT returns actually are — and how much edge investors might extract from that insight. Below, we unpack the findings, why they matter more than they seem, and how these models might shape real-world portfolio construction
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