(Joint with Nathan Lauster and cross-posted at HomeFreeSociology) The “real estate has swallowed Vancouver’s economy” zombie is back, with wild claims by a City Councillor that “If you look at the long-form census data going back to 1986 every 5 years, […] we went from selling logs to selling real estate […], major shift from resource extraction to real estate property development and construction as the primary driver in the local economy.
We have previously look at T1FF tax data which is an extremely rich annual administrative data source. The cansim tables have a range of variables to inform about incomes of individuals, families (sliced by number of children, including zero children), low income statistics, and just statistics about the number of taxfilers and dependants by age. It’s available on cansim for Canada overall, the provinces and CMAs/CAs. That’s great, but sometimes it’s nice to have finer geographic detail.
(Joint with Nathan Lauster and cross-posted at HomeFreeSociology) Empty Homes Taxes are back in the news! In a very short time period, we’ve got Vancouver raising its Empty Homes Tax rate from 1% to 3%, based in part on a report from CMHC about a sharp rise in condos on the rental market, we’ve got Toronto eyeing its own Empty Homes Tax, and now reports suggest that even Ottawa is considering getting in on the game.
The tongfen R package is now on CRAN, so it’s time for an overview post. Tongfen has changed a bit since it’s inception and is now a lot more flexible but slightly more abstract to use. What is tongfen? Tongfen, 通分 in Chinese, generally denotes the process of bringing two fractions onto the least common denominator. This is akin to the problem of making data on different but congruent geographies comparable by finding a least common geography.
At the end of June StatCan released an interesting census tract level metric, dubbed the D-index, measuring how much the income distribution in each census tract differs from the metro-wide distribution, and we decided to take it for a test drive. We are a bit of a sucker for this kind of fine-geography index. Condensing our wealth of information into a single number is an interesting exercise that involves lots of attention to detail.