I was recently asked: “how might someone figure out the local daytime population density across the Bay Area from public data?” My answer, in short, was that you really couldn’t accurately. But you could at least produce a coarse, biased estimate. Here’s how.
I examined the Bay Area’s tract-level daytime population density using three input data products: the 2010 TIGER/Line census tracts shapefile with DP1 attributes, the 2010 California LEHD LODES data, and the census bureau’s 2010 US states shapefile. I preferred the 2010 census demographic data to (more recent) ACS data because the ACS tract-level variables are five-year rolling averages. Given this, I preferred not to compare 2014 LODES data to 2010-2014 ACS data as the Bay Area experienced substantial housing, economic, and demographic upheaval over this interval – patterns obscured in the ACS rolling average. To avoid inconsistent comparison, I opted for more stale – but more accurate and comparable – data.
Our article “New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings” is finally appearing in print in the Journal of Planning Education and Research‘s forthcoming winter issue. We collected, validated, and analyzed 11 million Craigslist rental listings to discover fine-grained patterns across metropolitan housing markets in the United States.
I co-authored a chapter in the new bookUntapped: Exploring the Cultural Dimensions of Craft Beer with the estimable Jesus Barajas and Julie Wartell. Our chapter is titled “Neighborhood Change, One Pint at a Time” and it explores the relationship between craft breweries, urban planning and policy, and gentrification.
We found that many cities have changed their zoning codes recently (and even offer subsidies) to make it easier to establish craft breweries and brewpubs, with the goal of economic development. There’s a strong narrative (and anecdotal evidence) of craft breweries “revitalizing” neglected neighborhoods. New breweries often seek inexpensive industrial spaces in close proximity to urban centers and residential districts. In turn, they serve as anchor institutions that appeal to whiter, wealthier, and more educated demographic groups. Many craft brewers explicitly see themselves as agents of neighborhood revitalization and “committed urbanists.” The brewers we interviewed uniformly stated that neighborhood character was an important or even primary reason for their location choice, and many referred to themselves as pioneers and catalysts in neglected historic neighborhoods.
This is a summary of our JPER journal article (available here) about Craigslist rental listings’ insights into U.S. housing markets.
Rentals make up a significant portion of the U.S. housing market, but much of this market activity is poorly understood due to its informal characteristics and historically minimal data trail. The UC Berkeley Urban Analytics Lab collected, validated, and analyzed 11 million Craigslist rental listings to discover fine-grained patterns across metropolitan housing markets in the United States. I’ll summarize our findings below and explain the methodology at the bottom.
But first, 4 key takeaways:
There are incredibly few rental units below fair market rent in the hottest housing markets. Some metro areas like New York and Boston have only single-digit percentages of Craigslist rental listings below fair market rent. That’s really low.
This problem doesn’t exclusively affect the poor: the share of its income that the typical household would spend on the typical rent in cities like New York and San Francisco exceeds the threshold for “rent burden.”
Rents are more “compressed” in soft markets. For example, in Detroit, most of the listed units are concentrated within a very narrow band of rent/ft² values, but in San Francisco rents are much more dispersed. Housing vouchers may end up working very differently in high-cost vs low-cost areas.
Craigslist listings correspond reasonably well with Dept of Housing and Urban Development (HUD) estimates, but provide up-to-date data including unit characteristics, from neighborhood to national scales. For example, we can see how rents are changing, neighborhood by neighborhood, in San Francisco in a given month.
Which U.S. cities are the most expensive for rental housing? Where are rents rising the fastest? The American Community Survey (ACS) recently released its latest batch of 1-year data and I analyzed, mapped, and visualized it. My methodology is below, and my code and data are in this GitHub repo.
This interactive map shows median rents across the U.S. for every metro/micropolitan area. Click any one for details on population, rent, and change over time. Click “switch” to re-draw the map to visualize how median rents have risen since 2010:
Our paper on collecting and analyzing U.S. housing rental markets through Craigslist rental listings has been accepted for publication by the Journal of Planning Education and Research. Check out the article here. This map of rental listings in the contiguous U.S. is divided into quintiles by rent per square foot: