Do you own a few rental properties or a multi-family unit? Do you know your best tenant? Is it the tenant that pays slightly lower than market rent but always pays on time? Is it the tenant that pays above market rent but seems to have some sort of maintenance request every other month? Maybe it’s the tenant that you haven’t heard from in years, always pays a few days late but you know the check will be in house before the first week of the month has passed? Let’s take a look at your Real Estate Data Analytics and Tenant Analytics
The answer can be any one of those or a multitude of other scenarios. The trick is to get a good understanding of your customer, and in this case your customer is your tenant. The retail industry has been doing this for many years under the name Customer Analytics. They are engaging their customers via marketing promotions, collecting data via loyalty cards or simply capturing and storing demographic information to make better decisions around their customer base. All this to be sure they are selling the right items to the right people, ensuring the cross-sell is always in play.
Similar strategies can be leveraged when looking at your tenants, which we I call tenant analytics. Whether you’re using a large platform like Yardi, another smaller software as a service offering or just plain old Excel, you are already gathering a large amount of valuable information organically. You may not be gathering demographics and other slightly more abstract details on your tenants, but we will revisit how to close that gap in a later post. Let’s focus on what you should already have in place and how to do this once you have your Yardi data extracted into your data warehouse.
Let’s start with some basics for this post:
- How profitable is a current tenant?
- How much revenue do they generate over the lives of all their leases vs. how much do they cost you over that same time period?
Every month you record revenue and expenses at the unit level for a single lease. As the years pass this data accumulates but most people only look at the current month or the current year, not the whole picture.
First let’s start by pulling data from Accounting. This data comes primarily from the tables Total, GLTotal, Trans and Detail. This is a great way to obtain all financial information needed for the first part of the picture at the unit level. Remember, it is valuable to look at marketing cost and turn cost leading up to the lease, as that initial investment really can help provide the baseline for your investment at the unit level.
Next, by extracting the lease, tenant and collection information you now have the more “human” side of the equation. Most systems like Yardi, will capture specific work order information, as well. This will allow you to deep dive what items were basic wear and tear maintenance vs. what could have been prevented with some planning or better tenant placement.
Simple analysis of just these data elements will give you a base understanding of a tenant. Does it make sense to cut a quiet tenant lose if you can recoup the vacancy cost with a rent maximized tenant in place. Maybe you have a tenant that always pays just a few days late. They aren’t a risk for non-payment leading to eviction and net you a few hundred dollars in late charges. How do they compare to the tenant that always pays on time, but complains about everything and hassles you for endless minor repair items. Although reviewing each tenant on a case by case basis can be difficult and time consuming, it’s a good start and significantly better than flying in the dark.
In your data warehouse, you can use these metrics, as well as others, to derive a Tenant score. The score itself gets smarter and more accurate with every passing day as it continues to collect, store and analyze the information coming out of the aforementioned data points. This score can be used to sort, scan and make decisions very quickly on how you want to proceed with your tenants.
Let’s say you have a unit that cost $100,000 with a monthly rent of $1000. For the sake of simplicity we will assume there are no expenses. Having a forgetful tenant continue to pay $50 of late fees every month on a lease increases the cap rate from 12% to 12.6%. Although this may not seem like a lot, the changes at the micro will drive the changes at the macro level of the property or investment which you start multiplying this value add out across a portfolio.
Of course, all of this is only doable if you’re capturing your data! For more information on some of the best practices, check out our blog post from the previous series; “Getting your data from Yardi”. In the next post we will deep dive some more cool analytics around your custom…I mean tenants.
If the contents of this post are outside of your wheelhouse, don’t worry, this is why I started CREXchange.io to help clients get up and off the ground with Yardi Data Integration and Advanced Power BI Custom Reports to help run your CRE business. Contact us there for more details or a demo!