How Not to Search for Housing

Nate Silver and New York Magazine have posted a neighborhood ranking article and interactive widget that make some awful assumptions and miss a huge opportunity.

From the article:

Our goal was to take advantage of this wealth of data and apply a little bit of science to the question. If there was anything that could plausibly affect one’s quality of life in a particular neighborhood, we tried to incorporate it. We sorted the dozens and dozens of statistics we compiled into twelve broad categories: housing cost (as measured on a price-per-square-foot basis, for both renters and buyers), housing quality (historic districts, code violations, cockroaches), transit and proximity (commute times to lower Manhattan and midtown, the density of subway coverage), safety (as measured by violent- and nonviolent-crime rates), public schools (test scores and parent satisfaction), shopping and services (the number of neighborhood amenities, especially supermarkets), food and restaurants (judged by density and quality of options), bars and nightlife (ditto), creative capital (arts venues as well as the number of residents engaged in the arts), diversity (in terms of both race and income), green space (park and waterfront access, street trees), and health and environment (noise, air quality, overall cleanliness).

Silver goes on to rank 50 of New York’s neighborhoods and includes an interactive Livability Calculator. The article rankings and the calculator, with its preset options of  “Young, Single, and Cash-Strapped”, “Double Income, No Kids”, “Married with Children”, and “Empty-Nested and Retired” as well as a customizable version, suffer from a number of flaws:

  • assuming that these profiles represent the best way to understand neighborhoods;
  • making some absolutely awful assumptions about what these profiles value and devalue (see next bullet list);
  • providing only a partial methodology, no justification for the assumptions of the parts of their methodology described, and no listing of source data;
  • assuming that everyone works in midtown or Lower Manhattan;
  • and, by ignoring the role that race plays in housing choice, perpetuating segregation.

Specifically, the index makes the following faulty assumptions:

  • the “Empty-Nested and Retired” aren’t interested in diversity,
  • the “Married with Children” are more interested in shopping and ‘creative capital’ than diversity;
  • the “Double Income, No Kids” care more about shopping than safety/crime and green space;
  • and the “Young, Single, and Cash-Strapped” don’t care at all about schools and barely care about safety/crime or housing quality.

These profiles are grossly over-simplified and, such as in the case of “Young, Single, and Cash-Strapped”, make implicit assumptions about folks within that profile – there are many young, poor, single parents that would benefit from some help finding a better neighborhood. Despite our apparent fascination with lists of this type, they provide little help when it comes to actually making a choice about neighborhoods. Every neighborhood search metric should be unique, tailored to each of our families’ needs and the resources we have to share.

But the worst part is that articles like this don’t take advantage of available technologies.  Whereas for decades the mainstream press was limited by the printed word to one-size-fits-all-lists, the barriers that formerly existed to unleashing the long tail of opportunity-based housing search are now nearly gone. Rather than crunch all of the data into rankings and sliders, Silver and NYM should have empowered their readers to search and explore the source data in an intuitive way, identifying specific neighborhoods that meet their specific needs and providing details as to how they might take advantage of those opportunities. Moving a slider along a bar with no units of analysis and then providing no maps, amenity listings, nor other visualization is far from helpful (although I’ll admit it is kind of fun and interesting).

Especially in New York, where the NYC Data Mine provides journalists with a great resource of data, stories and widgets like this do little more than drive speculative real estate investments and spread stereotypes about neighborhoods.

Where we live has an enormous impact on our lives and far, far too many make that decision based on shoddy information.

Notes:

  • I am the co-founder of MoveSmart.org, an opportunity-based neighborhood search system.
  • I’ve only visited NYC a handful of times and have never fully explored its diverse and amazing neighborhoods, so this post intentionally leaves out any comment on the actual list created. That said, that their top choice is near the bottom of the pack in affordability and diversity says a great deal about their assumptions and intended audience.

Where 2.0 workshop submission

Just submitted the following application for a 30-minute session for 2010’s Where 2.0 conference. Keep your fingers crossed.

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title:
Digital Segregation? How Offline Inequalities and Online Behavior Will Impact the Geospatial Web

description:
As it grows beyond points of interest to include social networks and user-generated content, the Geospatial Web  increasingly reflects the realities of segregation and inequality that exist in today’s physical world. Without targeted interventions, the Geospatial Web will become separate and unequal. This session will explore if and how offline realities are impacting digital segregation.

abstract:
While our workplaces are more integrated than ever, most Americans’ personal networks and neighborhoods remain largely segregated by race and income. Organizations and companies layering user-created content or social networking on top of maps should consider the implications of offline inequality on the digital environment, and craft online content that will bridge gaps and transcend the barriers of race and income.

This session will trace a line through the Kozmo.com delivery discrimination lawsuit, the Pew Internet and American Life Project research on the changing nature of digital divide and online behavior, danah boyd’s groundbreaking research on social networking segregation, new research on neighborhood ‘racial blind spots’ and race-based perception by Prof. Maria Krysan, and case studies of user-generated information disparities and will draw upon the enormous body of research on the disparities wrought by racial, ethnic, and economic segregation to establish the extent and nature of how offline segregation is manifested online. It will conclude with an exploration of how the Geospatial Web might not just avoid perpetuating offline inequality but actually play a role in advancing equity.

Despite the election of Barack Obama and public proclamations that America has become a ‘post racial’ society, severe inequalities by race, ethnicity, and income continue to affect families and neighborhoods. The Geospatial Web’s impact on the physical world will only grow in coming years, but the nature of that impact has yet to be determined. The GIS industry has a unique opportunity to build value through strategies that expand opportunity for all users, such as partnering with organizations in impacted communities to fill gaps in data or connecting diverse users to expand social capital.