Scholastic Capital Update #17 | The Use of Data in Single Family Real Estate

Scholastic Capital is an evergreen real estate fund that buys single family homes in elite school districts. We then rent those homes on long term leases to highly qualified tenants interested in being there for the school district.

We do this on behalf of our investors looking for a “high floor, bond-like” real estate fund. Our investors, and new investors who may join us, are typically interested in our monthly cashflow distributions, equity appreciation, and depreciation tax benefits.

Hello Friends of Scholastic Capital,

If done right, data can dramatically improve the operations of the Scholastic Capital portfolio and our investor’s returns. 

In today’s update, we’re going to talk about that data: what it is & what it isn’t, what data we track, how it benefits us, and why it’s difficult to get right.

This should be fun to write because it’s a significant part of how we spend our time on a day-to-day basis at Scholastic.

 

What the data is (what it isn’t):

Our data is focused internally vs externally.

For example: we measure how many prospective tenants reach out per day per property. We measure how that changes between a weekend and week day. 

We track how different descriptions on the listing on Zillow leads to more or less prospective tenants reaching out.

In my mind, that data is operational data. We have control over this data. We can track it with a high degree of confidence.

It’s pretty black and white if we had 2 maintenance requests last week or 3 maintenance requests.

What our data does not track is market based data. 

For example, we aren’t trying to build some algorithm that can identify mis-priced homes for sale. That has been tried, repeatedly, by companies like Zillow Offers. Billions of dollars have been lost trying to outsmart market data.

In my mind, trying to outsmart the market is a losing gambit. 

Trying to use existing internal data to improve operations? That is 100% do-able, and that’s what we use data for. 

What kind of data do we track 

Since our data is operationally focused, all data eventually fits into two main categories:

  • Maintenance

  • Leasing

On the maintenance side, we track the make/model/serial number of every single piece of equipment in every house.

On the leasing side, we track every single lead, time to respond to lead, and conversion rate through our leasing funnel.

How it benefits us

Let’s talk about how that data benefits us.

Improved Maintenance Operations

Let’s say we get a maintenance request from the tenant at redacted address-only investors get addresses.

The heat isn’t working as well as it should and the tenant wants someone to fix it.

With our data, we know:

  • The furnace is a Carrier model number 58TPOA090V171316

  • It’s serial number 0223A15473

  • It was installed 2.04 years ago and based on this exact model we expect 21.56 years of useful life left

  • We know that this furnace has never had a service call 

Unsatisfactory maintenance is the number one cause of tenant unhappiness and tenants not renewing leases. 

Traditional maintenance calls typically go like this:

  1. HVAC technician gets a call to go out to the house

  2. The HVAC technician gets there, figures out detail about the furnace and diagnoses the issue

  3. The HVAC technician gives a very high quote (commonly, “you need to replace the whole furnace!!”) while they drive back to their shop to get the parts or a brand new furnace

  4. The HVAC technician schedules a time a few days later to complete the repair

The whole time, the tenant is cold and increasingly unhappy.

For us, we can streamline the process now. Our call to the HVAC technician goes like this:

“We need you to go out to redacted address. While there, you are going to fix a Carrier model number 58TPOA090V171316, serial number 0223A15473. It has had no service calls to date but is putting lower than expected heat.

Please make sure to bring the parts you need to fix this exact model in one trip.

This is a two year old system. We will therefore not be replacing it so please ensure your quote focuses on the repair to get the system working.”

This outcome is a win-win-win for everyone:

  • We are the easiest customer the HVAC customer has to work with. They can get our service call done quickly and get another job in during that day 

  • The tenant is thrilled, because the heat is back on ASAP

  • Scholastic has a happy tenant likely to renew their lease. We also didn’t have an HVAC technician attempt to rip us off and try to push us to buy a new furnace we didn’t need

Improved Financial Forecasting

The biggest variable in our month-to-month spend is maintenance.

Property taxes don’t change monthly and insurance is fixed for the year.

The biggest variable spend  we have therefore is maintenance. 

With our data, we can be extremely precise. Let’s use that example again from redacted address.

That furnace has an expected useful life left of 21.56 years. We have a high degree of confidence in that forecast; it comes from a software called Source7 that is the clear industry standard here.

That particular model of furnace sells for $5,400. Labor to install it will cost at least $1,600, so total $8,000 to replace.

Based on that data, we need to escrow $30.92 per month for the inevitable furnace replacement for this home.

This allows us to be dramatically more precise. Many real estate operators use generic formulas, like “5% of asset value should be allocated to maintenance reserves” or similar.

Since we have the ability to be precise, we can also therefore be precise on how much money we distribute back out to our investors.

Investors can have confidence that we aren’t “over-escrowing” and withholding money from investors that they should otherwise get.

Investors can likewise also have confidence that we aren’t “under-escrowing” and are going to get hit with big maintenance events that we weren’t planning on either. 

Improved ability to sell the portfolio

When you build an SFR portfolio, there are only three options:

  • Keep the portfolio forever

  • Sell the homes individually

  • Sell the homes as a portfolio

I believe the last option can generate the highest return for our investors.

That also means that we are selling to highly sophisticated operators: Blackstone, Tricon, Invitation Homes, etc.

They will not overpay for homes, especially if they are unsure about the quality of those homes.

That’s why it’s in our best interest to make sure that the quality of the homes is easily underwriteable.

For example, what home would Blackstone have more confidence in their ability to operate at a profit?

Home A, which has no data beyond a P&L for the past decade.

Home B, which has:

  • A list of make/model/serial number/year for everything in the house

  • A detailed list of every maintenance call over the past decade

  • Detailed documentation of preventative maintenance completed at all of these houses (e.g: furnace filter replacement).

House B certainly provides more confidence here in the sale. It probably sells for more money too.

We need all of our houses to be “House B’s.”

Why it’s difficult to get right

The challenge with data is it’s very difficult to do this retro-actively.
If we decide to become a data-driven organization down the line, it’s nearly impossible to do.

For example, lets say we decide 3 years from now we want to start tracking make/model/serial number for our homes.

Well, those homes are leased. We legally cannot access them without the tenant requesting us to come in to fix a maintenance issue. 

We could wait until we get a maintenance issue, but we sometimes have homes that go a year without a maintenance issue. It can take a long time to retro-actively collect all of this data!

Get to know us

As always, we love getting to meet more folks interested in niche real estate thesis.

For that reason, we have an open Calendly link with 15 minute slots.

If you’d like to chat niche thesis like Scholastic, real estate, or even joining us as an investor, feel free to grab time HERE.

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