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Multifamily – Which is the most common data set that operators use to calculate their GPR from month to month (e.g. Zillow, in-house market surveys, local brokers, etc.)?
Industrial – Total leasing cost reserve – Does the total leasing cost reserve fluctuate year over year as the market rent/SF, average leasing commissions, etc. increase or decrease year over year?
Great questions.
Sources for multifamily rent data to support underwriting assumptions.
If the property is stabilized and has operating history, there’s no better place to look for rent data than in the actual rent roll at the subject property. For instance, at a 300-unit complex with 95% occupancy, you have 285 in-place rent comps that give you a very good idea about how much you could lease up those 15 vacant units. Especially new leases signed at the property within the last three months.
However, if you’re modeling potential rent not previously achieved at the property such as is the case with a value-add deal or a ground-up development, you’ll need to look elsewhere for rent comps. The best source is always your own personal database. If you’re regularly in the market, you’ll have visibility to the actual rent rolls for competing properties. That is raw source data, far superior than any of the data providers out there. The key is to retain and organize that data as you get it so you can use it when you need it.
Besides in-house data, the brokerage community is probably your next best resource. Assuming you trust your broker contacts, they too will have direct knowledge of and access to actual data from competing properties. And so long as you have a good relationship with those brokers, they’re usually quite happy to share information; even if they’re not directly involved in that particular deal.
Next in order of quality would be the data aggregators. These would be CoStar (i.e. owner of Apartments.com, Loopnet.com, etc), Axiometrics, REIS, and a few smaller data providers specific to certain markets. The quality of this data depends on the source and method for collecting the data. In my experience this data is used as additional support, not primary support, as you can usually cherry pick the data to fit your narrative.
Does the total leasing cost reserve fluctuate year over year?
Generally yes. There’s an expectation that leasing costs will grow over time, whether due to expense inflation (i.e. TI costs) and/or rent growth (i.e leasing commissions).
Thanks for the clarification.
While looking at 4D – Multifamily, the T12 income statement’s GPR shows the average rent in the market and helps calculate gain/loss to lease. Are these the data sources that drive the month to month GPR fluctuations?
This is an interesting observation and good follow up question. Let me first make a point that I made in a separate thread earlier this week but that bears repeating here.
It’s important to make the distinction between accounting (i.e. reporting the past) and finance (i.e. modeling the future). In real estate financial modeling, we’re modeling the future. Sure we might look to the past to make assumptions about the future, but largely speaking our job is to make educated predictions about what might happen in the future rather than simply reporting what did happen in the past. And we’re compensated quite well for developing that skill.
A T12 income statement is an accounting document. It reports what happened in the last 12 months.
So to your question. Most often the gross potential rent in a T12 is simply the actual asking rent in each of those reported T12 months. The actual rent per unit is multiplied by the total number of units to arrive at a gross potential rent. The reason for the loss-to-lease line is that in any given month, a good share of the rent roll is occupied at rates that are less than (or sometimes more than) the asking rent in that month.
However, to predict a gross potential rent going forward or to estimate a current gross potential rent for a property not yet stabilized, we make assumptions. And those assumptions must be supported. We use support such as past performance and existing performance at the subject and at comparable properties, market forecasts created by professional prognosticators, micro and macro trends underway, location specifics, and a host of other data and arguments. That data, whether past, present, or future most often comes from the sources I listed above.
A long-winded answer to a basic question, I admit! But it was an opportunity to help you recognize the difference between accounting tasks (e.g. T12) and finance tasks (e.g. DCF).
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