The US is home to more than 100 million renters, and approximately 11 million landlords, yet these two sides to the rental market are rarely studied in tandem. This study uses a multiscalar network-based approach to identify landlord market areas. Building on administrative data of rental properties’ and landlords’ locations, I define a landlord-property network as a spatial bipartite network, where landlords’ addresses are connected to their properties’ addresses, and vice versa. I first examine the location of landlords relative to their properties. I then compare the differences in socioeconomic characteristics in landlord and rental tracts. I simplify this network by extracting its backbone, defining a core component of a landlord market. I compare these networks to Metropolitan Statistical Areas and commuting networks, in order to evaluate the performance of the backbone extraction method. I find that most landlords are local, and, perhaps unsurprisingly, that landlord neighborhoods are richer, whiter, and more expensive than where their properties are located. Extracting the backbone of the commuting network results in a network that mirrors a regional definition, while the landlord market area is much more national in scope. These two networks differ geographically, and also with regards to their network statistics. While renters and homeowners search within a region for new housing, landlords and capital can search nationally for locations in which to invest. This paper provides a new, robust foundation to understand rental market investor dynamics and the relationship between owner, renter, and property.