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Here's a chart from Knight Frank's 2019 Global Affordability Monitor that I think you'll find interesting:
It compares real home price growth and real household income growth (after tax) over the last 5 years for 32 world cities. The bolded percentages represent the former and the non-bolded percentages represent the latter.
Consider the variations here.
Amsterdam saw a real home price change of 63.6%, but a household income change of only 4.4% (although the circle looks to be in the wrong spot if this number is correct).
Moscow, on the other hand, saw flat home prices (0.1%) and a 22.7% increase in household income.
Though San Francisco is the star in terms of income growth.
Sao Paulo, unfortunately, saw a dramatic decline in both home prices and incomes. It's in the bottom left corner.
When I look at this chart, I don't see a strong correlation between household incomes and home prices. And the proportions of the chart tell you that the y-axis is moving more than the x-axis.
But if the top number exceeds the bottom number, then you could come to the conclusion that housing affordability has gotten worse over the last 5 years.
I just came across this chart from Axios, which relies on data from the Federal Reserve Bank of St. Louis and the Kaiser Family Foundation. It compares median household income against the average cost of employer health insurance (in the United States).
What it is saying is that, after adjusting for inflation, the median household income has only increased by 2% from 1999 to 2017, whereas employer health insurance costs have increased by some 121% over this same time period.
The takeaway: Rising healthcare costs are believed to be eating away at take-home pay in the US. As of 2017, health insurance costs were estimated to represent about 30% of the average household income. That feels like a big number to me.
A recent study and research paper by the MIT Senseable City Lab -- called, Tasty Data -- has discovered that restaurant data alone can be used to accurately predict location-based factors such as daytime population, nighttime population, number of businesses, and overall consumer spending within a specific geography.
They started by pulling restaurant data from Dianping (Chinese equivalent of Yelp) for 9 Chinese cities: Baoding, Beijing, Chengdu, Hengyang, Kunming, Shenyang, Shenzen, Yueyang, and Zhengzhou. They then paired their Dianping data with other available data (such as aggregated mobile phone data) and used machine learning to search for any correlations.
Below is a diagram of "nighttime population" in Beijing. They are using a 3 km2 grid.
If you're a regular reader of this blog, you'll know that I like these kinds of studies. By 2020, it is estimated that 1.7MB of data will be created every second by every person on earth. The numbers are staggering. And yet, "official" data sources, such as census data, remain slow and fairly limited. Studies like this one continue to show us what's next.
Image: MIT Senseable City Lab
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