For almost a decade, the multifamily industry has experienced unprecedented growth. Occupancies have been as high as ever while the Bull Run on rents continues to surprise even the most seasoned industry observers. Capital has followed this growth, with new supply arriving to absorb historic demand in most markets. The surge in both demand and supply has created a new problem for multifamily investors and asset managers: it has become harder than ever to find value in the market.
To be more precise, it has become hard to find and underwrite properties that offer upside at this stage in the cycle. There are plenty of available properties – investors today receive more broker packets on deals than they have time to evaluate. But risk profiles have changed. While markets continue to grow the rate of growth has slowed, increasing the levels of risk and placing even greater onus on investors to make good predictions about multifamily asset performance.
Market conditions demand quick decisions, so investors have to work quickly to identify worthwhile opportunities from the pack, matching to their investment criteria, while ensuring that they do not overlook potential gems.
At the same time, there is an ever-escalating supply of different data sources, each providing a different perspective on the state of the apartment market, sub-markets and individual properties. But with scores of information sources vying for our attention, our challenge is to cut through the noise to discern the truth about the value of a property and its value-add potential.
Why current approaches aren’t always the best
Investors and analysts all have their favored analytical approaches. They usually include some combination of analysis of a property’s rent roll, some blend of third-party market surveys and forecasts, sales comps and the best available census information. While these data elements in concert provide a lot of useful insight into the asset and its potential, each data source has its shortcomings and biases.
Savvy analysts have to do a lot of work to evaluate data sources. Listing data provides a good example, as data from ILS’s and property websites is abundant and relatively easy to source, making it a convenient resource for multifamily underwriting. But the data is designed for marketing, so while it can help to understand a property’s leasing strategy, there are many ways the data can be misleading to investors. Imagine, for example, a property that is advertising its cheapest unit as a way to lure more traffic. While that strategy may be effective, the pricing information from the site is not at all representative of the rents that the property is achieving.
Census data is another touchstone of underwriting analysis that is often less instructive than people realize. Renter income, for example, may make intuitive sense, especially for understanding rent growth potential, but the swathes of population it covers are so broad that the information is sometimes barely relevant. This is particularly problematic for investment grade multifamily assets, which attract highly specific segments of the market. Screening data, for example, offers far more reliable insight into renter income.
Innovation leads to better, faster answers
The challenge and the opportunity is for underwriters to forecast performance of assets that they don’t yet own, using localized, street-level data on supply and demand. Analysts work to accomplish this today, typically using sub-market data. But this does not always yield the right answer. Imagine a property in Alpharetta, Georgia. The traditional approach would be to pull the forecast for that sub-market. But the Alpharetta trend may not be the most relevant data point for a property on the periphery of that submarket or an asset that is otherwise atypical of the submarket.
A better approach is to identify a direct set of comps for that individual property and create a forecast at the comp set level. This, of course, requires a more granular level of data than is readily available at the levels of accuracy that this type of analysis warrants. Further, we must factor in local considerations, like local employment, drive times, etc., and understand how they impact the analysis.
The good news is that help is at hand. At RealWorld 2018, RealPage’s annual conference held this July, we will be offering our most ambitious ever program of Asset Optimization sessions.
As part of this program, we will be devoting multiple sessions to discussing some exciting new developments in underwriting analysis. We will show how companies today are taking this complex analysis and using new and innovative data sources and tools to cut through the complexity and deliver better, faster answers.
Register now and join us in Las Vegas to see how you can benefit!