Tag: livability

Categories
Planning

Craigslist and U.S. Rental Housing Markets

This is a summary of our JPER journal article (available here) about Craigslist rental listings’ insights into U.S. housing markets.

Small map of 1.5 million Craigslist rental listings in the contiguous U.S., divided into quintiles by each listing's rent per square footRentals 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:

  1. 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.
  2. 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.”
  3. 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.
  4. 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.
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Categories
Planning

How to Visualize Urban Accessibility and Walkability

Tools like WalkScore visualize how “walkable” a neighborhood is in terms of access to different amenities like parks, schools, or restaurants. It’s easy to create accessibility visualizations like these ad hoc with Python and its pandana library. Pandana (pandas for network analysis – developed by Fletcher Foti during his dissertation research here at UC Berkeley) performs fast accessibility queries over a network. I’ll demonstrate how to use it to visualize urban walkability. My code is in these IPython notebooks in this urban data science course GitHub repo.

First I give pandana a bounding box around Berkeley/Oakland in the East Bay of the San Francisco Bay Area. Then I load the street network and amenities from OpenStreetMap. In this example I’ll look at accessibility to restaurants, bars, and schools. But, you can create any basket of amenities that you are interested in – basically visualizing a personalized “AnythingScore” instead of a generic WalkScore for everyone. Finally I calculate and plot the distance from each node in the network to the nearest amenity:

Berkeley Oakland California street network walking accessibility and walkability

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Categories
Planning

LEED-ND and Neighborhood Livability

I recently co-authored a journal article titled LEED-ND and Livability Revisited, which won the Kaye Bock award. LEED-ND is a system for evaluating neighborhood design that was developed by CNU, USGBC, and NRDC. Many of its criteria, particularly site location and neighborhood pattern, accordingly reflect New Urbanist and Smart Growth principles and are inspired by traditional neighborhood design.

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Categories
Academia

The Inside Field Exam and Urban Complexity

I recently completed my inside field exam, one of the many steps involved in advancing to candidacy. The three professors on your inside field committee send you six questions – a pair per professor – and you are given 72 hours total to answer one question from each pair. The answers are to be in the form of a scholarly article with thorough citations. Long story short, you’ve got to write 30 pages of academic scholarship in three days.

The exam questions themselves are very interesting. The professors construct them based on their reading of your inside field statement, trying to probe areas that might be particularly rich or a bit weak in the statement. Here are the questions I answered:

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Categories
Planning

Urban Complexity and the March Toward Qualifying Exams

The Department of City and Regional Planning at UC Berkeley has a rather arduous process for advancing to candidacy in the PhD program. It essentially consists of 6 parts:

  1. Take all the required courses
  2. Produce an inside field statement – a sort of literature review and synthesis explaining the niche within urban planning in which you will be positioning your dissertation research
  3. Complete an outside field – sort of like what a minor was in college
  4. Take an inside field written exam
  5. Produce a defensible dissertation prospectus
  6. Take an oral comprehensive exam covering your inside field, your outside field, general planning theory and history, and finally presenting your prospectus.

Whew. Lots to do this year. The good news is I am currently wrapping up my inside field statement and preparing to take the inside field exam. My topic is generally around complexity theory in urban planning. Here is the working abstract from my statement:

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