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🚨🚨Just absolutely thrilled to see my first dissertation paper in print with Annals of the AAG🎉🎉 "Where the Landlords Are: A Network Approach to Landlord-Rental Locations" 🧵... doi.org/10.1080/2469...

There is still so much that we don't know about landlords in the US. What's the best way to differentiate among corporate landlords? Where are landlords located, relative to their properties? How do different landlords differ? I find that:

1) Most landlords are local. Approximately 80% of landlords have a residential mailing address (even corporate landlords) & 80%+ of those have mailing addresses in the same MSA. Landlords w/ a residential mailing address are located closer to their rentals than other landlords.

2) Landlords live in neighborhoods that are on average, richer, whiter, and more educated than the neighborhoods where they own property. To test for statistical significance, I create a simulated randomized network of landlord and rental locations. I run 1,000 simulations.

3) How should we conceptualize the locations of landlords? While "most" are local, what does that tell us? To try and drive deeper here, I introduce the concept of a "landlord market area," which is the area where a significant share of landlords can be found.

Conversely, it's a way to think about where capital is located that searches for real estate investment. While (pre-pandemic, at least) most workers were local, capital is global — it searches for returns and is enabled by new technologies.

Here's what it looks like locally. While commuting backbones (below) are mostly constrained by the MSA boundaries, landlord market areas extend nationally.

A note on the data: I use rental registries to identify landlords here, rather than tax assessment data. I have another paper in review as to why I dislike tax assessment data, but here are some benefits of rental registries:

1) They represent an actual sample of rentals, depending on their enforcement. 2) They often ask for owner name & address, manager name & address, and differentiate between those two. 3) They provide a bunch of other information to make data cleaning easier, like phone numbers.