The SNL Real Estate database, now an offering of S&P Global Market Intelligence, contains property level and geographical market-based demographic information that can be difficult for investors to obtain. These unique data points are valuable to investors seeking an understanding of the relationship between property level information and future stock price movement.

In this report, we demonstrate how investors can use these data points as alpha strategies. Our back-tests suggest that metrics constructed from property level information may provide insights about future price direction not captured by fundamental or estimates data. Investors may want to consider incorporating information on a REIT’s property portfolio when building a robust REIT strategy.

This paper is a continuation of our work on the efficacy of stock selection signals within the REIT industry (REIT Paper - Part 1)

Key findings include:

• The ability of a REIT to cover both interest and preferred dividend payments is important, especially if investors forecast a tightening in monetary policy.
• Metrics constructed using property level data have a low correlation with metrics constructed using fundamental and estimates data.
• A strategy that combines both property level and fundamental data has an information ratio (long-only active return) that is at least 15% higher than a strategy that uses only property level or fundamental data.

We introduce a new class of signals derived by overlaying demographic information on REIT property level data. These new signals enable investors to compare the average demographic characteristics of real estate portfolio across REITs.