Myth 2 intro
This is the second myth in a series debunking five common real estate myths. By challenging these myths we hope to provide clarity to enable you to make better real estate investment decisions and contextualize performance and risk characteristics against other asset classes.
We’re not suggesting your instinct doesn’t matter – it will be informed by years of experience. Listen to your instinct and then test your assumptions using our unique market data and analytics to make data-driven decisions.
This is where data can support your instinct across the investing cycle:
Myth 2 tabs
To help you decide on your allocation to private assets and in particular real estate, you can find transparent insights in our Real Estate Market Size Report published on a yearly basis
When making strategic decisions at times of financial turbulence looking at the dispersion of returns across under-performing sectors is key. Cross-sectional dispersion analysis run in Global Intel highlights the importance of asset selection and takes a more granular analysis of performance within segments and sectors.
Often individual investment decisions are based on broad market-level information but determining underwriting assumptions for individual properties requires greater specificity. By doing this, investors can scale adjustments from market forecasts to asset-specific, point-in-time underwriting assumptions. This approach can be applied consistently to various metrics across global markets and bring previously absent quantitative evidence to bear on a complex real estate investment problem.
To help investors understand and communicate their relative performance as part of their reporting cycles, attribution analysis can be extremely useful. In a recent blog on Why Real Estate Asset selection matter – especially in a crisis we used Global Intel’s attribution analysis to separate the impact of allocation differences from the impact of asset selection within those allocations, which in turn positions performance within the context of wider market trends.
These are just some of the many examples of how data-driven decision making can be implemented across the entire investment process.