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Same street, different home

How data and tech fueled SFR growth…

As discussed in last week’s newsletter, as Auben Realty began to grow, we created rental guidelines and policies to help further define our leasing standards. I wanted the business to grow primarily because I saw a massive void in the market for intentional, investor-focused SFR property management. Daily, I also saw the impact our investment was having in our communities.

Others also saw the opportunity to own SFR. And while we began to grow the framework of our property management company, the beginnings of institutional SFR ownership also began to grow. No one called it SFR (single family rental) in those days, but you began to see large buckets of institutional capital enter the SFR asset class for the first time. Starting initially on the West Coast in San Francisco and other cities in California, companies  like Waypoint, Invitation Homes, American Homes 4 Rent and Tricon began to enter the single family rental asset class with lots of cash and an appetite for disruption. 

Early movers into the space had a lot of assumptions about SFR’s similarities to multifamily that largely proved to be incorrect. But it really didn’t matter how wrong (or how right) they were when they were consistently buying at a fraction of home replacement cost. These large buyers targeted primary sunbelt and growth market cities like Phoenix, Dallas, Austin, Atlanta, and Charlotte and built strict buy-boxes around the number of beds, baths, square footage, distance from city center, etc. The acceptable vintage of a home constantly migrated to newer: for some aggregators starting at post 1950/1960 moving post 1980 to post 2000. 

All of these restrictions and modifications of acquisition standards were attempts at controlling the variability of ownership of an extremely variable product: scatter site SFR. These large buyers/owners would discover that no matter how much they controlled their processes, it was extremely common to have multiple houses in the same neighborhood (or even on the same street) with dramatically different floor plans, mechanicals, and finishes. Even with access to the best data and risk modeling tools, trying to determine the projected amount of maintenance or capital expenditures proved to be very challenging. 

As messy as the operations were, many of these large buyers realized they could essentially build machines to eat the equity. And the operations for owning would simply be a byproduct of a future sale. Available inventory and cheap money made for exponential full-throttle growth.  For a great companion read, check out The Big Long, written by Waypoint founders Colin Wiel and Doug Brien. It details the challenge most operators were facing of figuring it out at 100 miles an hour trying to buy hundreds (or thousands) of homes a month. 

Along with an abundance of capital coming into the asset class, the data and technology also began to quickly evolve. Zillow, Trulia and other real estate data platforms began to break down the closed doors of fragmented local market multiple listing services (MLS). This allowed an analyst sitting in Charleston to feel confident about suggesting an offer on a home in Kansas City, site unseen. Most offers were contingent only on a renovation manager or inspector’s field inspection to ensure the house matched the photos and there were no major red flags. 

A potential homeowner—with multiple financing and inspection contingencies—didn’t have a chance of winning the contract to buy a home when competing against the no-contingency cash offer of a fund. 

A hedge fund owner who I worked for called it “a data revolution” and referred to data as being the new oil. SFR was a vastly different asset class from the one I entered only a couple of years earlier. However even as the data became more accessible and available, data integrity was always in question,  mainly due to home condition variability. Rental comparisons were also nearly impossible to obtain with any reliability. The truth was (and still is) that most SFR owners were small local mom and pops who intentionally kept low profiles and built systems not to scale but to keep their sanity as single operators. 

In order to combat variability of product, the hedge fund I worked for tried to create rubber bands of expected renovations around the age of the homes. The older the home, the more you were going to spend. Pictures became really important.

If you would like to learn how Auben handles home variability for our investors, or why we believe so intently in build-for-rent, come to one of our upcoming events!