For the past three years, Newburgh-based Hudson Valley Pattern for Progress has been doing various things to improve a focus on regional revitalization. Its Urban Action Agenda, which provides comparative demographic and other data on eleven small Hudson Valley cities and 14 villages, is a takeoff point for community insights.
Seeing what the region’s urbanized communities have in common and what they can learn from each other “is the best way to properly address the issues facing these communities,” Pattern CEO Jonathan Drapkin argued in his introduction of this year’s UAA update. “With their existing infrastructure, access to transit, and traditions of denser development, these communities are well positioned to accommodate the region’s growth. By looking regionally, we can support individual cities and villages with targeted strategies to help them revitalize.”
The 25 selected UAA communities, containing slightly more than a quarter of the Hudson Valley’s population, Pattern said, have urban characteristics and can benefit from outside assistance. There’s at least one UAA jurisdiction in each of the nine counties stretching northward from Westchester and Rockland counties to Columbia and Greene counties.
This year’s list revisions have adjusted the study area to include only Saugerties village rather than the entire community, the remainder of which is less urban. Kingston city and Ellenville are the other UAA communities in Ulster County. The UAA list of 25 Hudson Valley urban communities also includes Catskill in Greene County, Hudson in Columbia County, Poughkeepsie, Wappingers Falls and Beacon in Dutchess County, and Walden, Newburgh, Middletown, Port Jervis and Highland Falls in Orange County.
There may be no single best knowledge base by which to accelerate community revitalization. Comparing the same variables among different communities at the same time (“latitudinal studies”), as suggested by Pattern, can provide valuable policy insights. But comparing the same variables in single communities over time (“longitudinal studies”) can also be insightful, especially when updated by ingenious means.
Federal census data for small cities and villages is both out-of-date and woefully unreliable. The annual American Community Survey (ACS), which Pattern uses for demographics in its “Community Profiles,” is derived from a sample of two percent of the local population — statistically useless. By blending five years of results, reliability is increased, but at the cost of six-year-old data being the best we have. Pattern supplements this year’s expanded community profiles with rudimentary additional data from sources specializing in education, health, housing, taxes etc.
Researchers could add extra value by incorporating additional information to accelerate local revitalization. State income-tax figures, local real-estate sales, data on building permits, employment and occupational data, sales-tax information and expenditures by non-profits come to mind as readily available sources that I don’t think Pattern has yet tapped. Many of these numbers need to be interpreted and contextualized. It’s a ponderous process.
Census data is so yesterday. Drapkin told me he found that characterization “unnecessarily demeaning,” given that there was no better source of demographics. He acknowledged that the researchers at Pattern “have frequently been asked to do a ‘deeper dive’ beyond the ACS,” and that Pattern has used other sources as well.
How local and neighborhood economies are changing is being increasingly explored by nowcasting digital platforms that bring data closer to real time and add additional context. That’s where the greater promise is.
Last month a National Bureau of Economic Research working paper was published that combined Yelp and census data to predict urban social gentrification at the neighborhood level. Certain changes in the local business landscape proved a leading indicator of housing price changes. Most highly correlated was an increase in the number of grocery stores, cafes, restaurants and bars.
The entry of coffee shops into a neighborhood accurately predicts the arrival of more well-educated young people. Each additional Starbucks that enters a zip code is associated with a one-half of one percent increase in housing prices. An increase in Yelp listings is a leading indicator of change to come, and perhaps even itself accelerates the process of neighborhood change.
“While the intended purpose of Yelp data is to help customers identify local businesses,” wrote Edward Glaeser, Hyunjin Kim and Michael Luca in Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change, “the same data is a valuable resource for policymakers and researchers looking to better understand economic activity.”
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