The Rise of the ADU

Should YOU build an ADU in your backyard?

On my ongoing job search, I have come across many different start-ups with wide ranging ideas. One of the the ideas that really piqued my interest was the business model put forward by “Rent the Backyard”. They, and other companies like them, propose to build an accessory dwelling unit (ADU) in a homeowner’s backyard and then split the rent with them until the unit is paid off. I’m intrigued by this idea because of the power it has for social change. ADUs (sometimes called granny flats) can be both a smart investment and are a potentially key part of the solution to the lack of affordable housing seen in most large American cities.

But how would a company like this, or even a motivated individual homeowner, decide that building an ADU in their backyard is a good option? Intuitively we might expect that a yes or no recommendation would be based on data like home value, mortgage value and equity, rental price, and rental market saturation in that particular area, etc. In this, the first article on the topic, we’ll be taking a look at some of these (publicly available) factors to see if we can come up with some recommendations for enterprising homeowners looking for prime ADU building areas. We’ll be using data from the city of Portland, Oregon in making our recommendations for reasons described hereafter.

Portland, Oregon, and ADUs

Portland is currently in a self-described State of Emergency on Housing and Homelessness. This gives the city leeway to expedite construction of affordable housing and change zoning codes that stand in the way of progress, among other measures. In this case, “other measures” includes incentivizing ADU construction. Portland has the least stringent ADU standards in the country and this has led to an explosion in the number of permitted units in the city. Still, the percentage of lots that currently host an ADU is less than 5% of lots that are “ADU friendly.”

Clearly we haven’t missed the boat here. There is still plenty of incentive to jump into the game in Portland. So let’s start by looking at a map of Portland. Here we can get a sense of its size and the various areas (for those unfamiliar with its layout). Portland is divided nicely into various neighborhoods. There is a river that runs directly through the middle of the city, which, while tough to see on this map, divides Portland into the West-side and the East-side. The downtown area is located on the West-side and includes neighborhoods like the Northwest District, the Pearl, and of course, Downtown. Northwest Portland is very hilly and is home to one of the largest urban forest reserves in the United States, Forest Park. Most of the neighborhoods that will be ideal for building an ADU will be on the East-side and in the Southwest.

Sourced from here.

The first thing we’ll want to look at in determining the best places to build is the current geographical distribution of ADUs in the city. To accomplish this we’ll have to download and work with GeoJSON files that encode geographical features. We will use these features to visualize all our neighborhoods and points of interest. The Python libraries most helpful here, and the ones I used, are GeoPandas and Shapely. Shapely gives us the geometries that will allow us to visually explore our data, here points and polygons. GeoPandas extends the data types allowed by Pandas to include geo-spatial data. This simplifies plotting immensely. We won’t be diving into the widespread functionality of these libraries here, but if you’re curious check out this article by Tom MacWright which details most of what you’d need to know. If you are interested in the code that makes the following plots happen, please follow along in my GitHub.

Of interest here is the functionality of polygons and points. Polygons, when graphed using the Shapely library will give us a visual representation of all the Portland neighborhoods, which we can then plot points over. Let’s do this now. Utilizing PortlandMaps Open Data for neighborhood boundaries we can plot an spatially empty map of Portland neighborhoods.