Short Term Rental Arbitrage analysis discussion

[Clint Harris]

Short Term Rental Arbitrage analysis

This topic keeps coming up recently recently and I feel like a broken record, so thought I would post about it and share how I run an analysis on a potential arbitrage deal, and hopefully spark some discussion and learn how everyone else is doing it. I very much believe that every person knows something that I don’t especially in this group, so thought I would take the opportunity to try and learn from you all. I like using real numbers, so I’ll go through a real analysis that we completed recently.

Side by side duplex in our beach town, within 2 blocks of the ocean, one unit available for rent now at $1200 per month, the other available next October, so a while to wait. The unit in question is a 2/1.5, some recent updates, good location, and nice shape. I located this unit by going to Craigslist homes for rent, and turning on the “Map” function, and then looking at the parts of the island that I know do really well just based on our other units. I put together a list, cold called a few owners, and this guy and I hit it off. I’ve written other posts about that messaging, but this is just for the numbers. So before I called, here’s the analysis, let me know how y’all run yours.

First off, I alway run an AirDNA Rentalizer analysis. This property is 2 beds, 1.5 baths, and I ran the analysis at 4 guests (data has proven against the idea of max beds/heads here, and it’s not worth the hassle) AirDNA shows Gross Annual Revenue of $47,116. (Just ran it again to confirm, because it does fluctuate a little). Now this metric is as a Median performer in the market, meaning that it uses average occupancy, daily rate, seasonality, etc. We always use median metrics, and have so far always beaten those metrics soundly by trying to be a top performer and optimizing and streamlining our listings. PS, there can be a 7-10% standard deviation on this metric if you run the analysis just at the end of peak season versus off-season because it uses the previous 365 days of data, so account for that. The analysis on this unit shows Gross Annual Revenue of roughly 47k, average daily rate of $205, occupancy of 63%, and as expected, a seasonal Revenue Forecast. So here’s the analysis part.

We spend 25% of our gross rents on cleaning fees and airbnb fees after starting our own cleaning company, but I still run my analysis at 30%, which is what it was when we started. So 47k, multiplied by 0.7 is $32,900. Then I subtract my monthly rent, and utilities provided by previous tenants, which is $300 because our water is expensive. Our utilities will usually be less in STR than long term tenants, but thats the number we use. So that's $32,900 minus $18,000 (rent and utilities) which leaves $14,900. Divide that by 12, and we're looking at $1,240 per month profit over a yearly average. Our barrier to entry is that we need to make at least $1,000 per month per listing, so this hits that metric. A couple other things that are important though, we spend $4,000 to stage a 1/1 unit, an extra $500 for porch furniture if needed, and then another $1,000 per additional bedroom, so we'll stage this unit for $5,000. We use that number as a bargaining chip when speaking with the own to justify why we need a minimum of a 2 year lease.

Of course there are always some small Cap Ex, and we build most of that into our cleaning fee. We know that on average we spend $5.40 per booking on cleaning supplies, restocking supplies, coffee grounds, filters, and the occasional broken dish or stained towel, etc. Depending on the size of the unit, we charge $80, or $100 cleaning fees, and we pay $60, and $80 respectively, including laundry. That extra $20 covers that Cap Ex and restocking fees, and a little extra builds up for any larger Cap Ex that ever may occur. On some of our units, we are pet friendly and charge $50 pet fee per pet with a limit of 2. Over the year, those fees stack up and usually create another 3-5, depending on the property, some allowing 3 pets.

So even though we run our analysis using median metrics, between the small extra margins from our cleaning and pet fees, and the fact that the average occupancy for our town is 63%, but out occupancy over the past year and a half has been 88%, as long as we originally line ourselves up to hit that $1,000 a month profit, we set our selves up for fun surprises when it comes in higher than what the median prediction is.

OK, whew, long-winded. What do y’all do differently? Are there any other factors that have proven to help predict value? As our listings grow, it’s easier to lean on that performance more than AirDNA, but so far that still proven to be a solid benchmark for us. Anyone else have a better way?