Abstract
We propose a technique to infer cash flow yields for investment assets whose trades are infrequent, but for which cash flow data is available. We construct a Self-Propagating Rolling-Window Panel VAR framework, adapted from a Dynamic Gordon Growth Model setup. We use this framework to estimate yields and volatility in yields for untraded commercial properties as out-of-sample predictions from our VAR based on these properties’ cash flow data. We find that our predicted cash flow yields closely resemble ex-post realized transaction yields, and that these predicted yields even outperform appraisals in this respect. We find that this paradigm provides a good representation of commercial real estate yields, and propose that investors can readily apply this algorithm to infer values of untraded investment assets.
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