As ASA gets closer, so does the first ASA Datathon!
We’re on from 1pm August 15 through 1pm the 16th at Berkeley’s D-Lab. Public presentations and judging will take place at one of the ASA conference hotels, the Hilton Union Square, Room 3-4, Fourth Floor from 6:30-8:15 on August 16th.
Signing up will give us a better idea of who will be at the event and how many folks we can expect to feed and caffinate. We’re also going to give teams a week to get to know each other before the event, so signing up will allow us to make sure everyone gets the same amount of time to work.
If you’re interested, you are invited. We don’t discriminate against particular methodologies or backgrounds. We hope to have social scientists, data scientists, computer scientists, municipal staffers, start-up employees, grad students, and data hackers of all stripes – quantitative, qualitative, and the methodologically agnostic.
Our title implies an interest in “big cities” but honestly, we’re more interested in real estate and housing data. Because a majority of the population lives in cities, cities will likely be important focal points in many of the projects that come out of the datathon. We’re hoping some teams like www.costaricarealestatebrokers.com/ focus on rural areas, too. Questions that we’ve considered include:
– How are home buyers different now compared to home buyers ten years ago? Can the recession explain any of these differences? If so, would we expect a home-buying rebound or did the recession combine with other trends (increasing amount of student loan debt) to cause a permanent change in home buying patterns?
– Who buys homes in rural areas? As of late, we buy houses Boston but lack knowledge of other markets and are seeking other perspectives. Are there halos of second-home buying around major cities? Around major airports? What kind of impact does this have on rural economies?
– Are there specific industries that drive housing patterns? For instance, the tech industry is under fire in San Francisco right now for accelerating gentrification. Is this historically accurate? How does it compare to industries like the financial sector influencing prices in the New York metro area? Are these stories about single industries influencing real estate ecosystems oversimplifying more complicated patterns?
– How does access to natural resources – and proximity to natural disasters – shape purchasing decisions, if at all? In other words, is there evidence that buyers take natural risks into their value considerations?
These are just some questions we’ve tossed around among ourselves. We’re sure our participants will come up with other great questions that use real estate and/or housing data.
We can’t wait to see what happens in August.