This is the first in a series of four posts about how to navigate the realm of data, big and small, to connect low-income people to the jobs and amenities they need.
We live in the era of big data. Every week there’s another story about how data is revolutionizing industries by revealing insightful information about human behavior and decision-making. At LIIF, Enterprise and Living Cities, we spend our days thinking about how data might inform another important issue for us: how to connect low-income people to the jobs and amenities they need to improve their economic well-being. This is particularly relevant now as low-income households are increasingly displaced from transit-accessible, amenity-rich areas. In thinking about how to prevent the displacement of low-income families from gentrifying neighborhoods, we sought to use big data to better understand the characteristics of the housing stock in high-opportunity areas near transit, which could help us develop better strategies to keep it affordable for low-income families.
Transit-oriented development (TOD) can sometimes be a catch-22; it’s great because it means an investment in transit and other community services, but it also increases property-values, which can lead to the displacement of low-income families. Through our Connect work, LIIF, Enterprise and Living Cities have sought ways to make TOD equitable (eTOD), and ensure that high-opportunity neighborhoods remain inclusive. We decided to take a deeper look into four cities we work in—the Bay Area, Denver, Los Angeles and Seattle—to better understand barriers to eTOD implementation. Through our work, we identified cross-cutting themes such as the desire to preserve market-rate affordable housing—what we call “naturally occurring” affordable housing—near transit. Before we could devise a solution to address the issue, however, we found that we needed to learn more about the nature and characteristics of the situation.
This is where the data comes in. Or does it? The goal of our research is to better describe the characteristics of the naturally occurring affordable housing stock in order to assist developers in assembling deal portfolios and devising solutions to acquire and preserve these units. Over the last six months, we’ve navigated a complex web of data and learned about various challenges of working with different datasets, how best to use data to our advantage, and how to scope a research project reliant on (sometimes coveted or expensive) data sources for community development. So far, we’ve concluded:
- 1) There is a big difference between the ideal data, available data and useful data;
- 2) Some of the best data is stored in people; and
- 3) We need a central place to store our research and build field knowledge.
Over the next few days, we will share three more blog posts describing each lesson in more detail. Read the next blog and follow along on social media with #ConnectUS!
Special thanks to Erin Austin for her contributions to this blog post.