Since the 1940s, the US has been adding roughly 9 million new homeowning households about every 10 years. This, after all, is a fundamental component of the American Dream. But Aziz Sunderji -- who writes over at Home Economics -- has recently been arguing that this 80-year boom is now at an inflection point. And it is largely because the rate of population growth in the US is now declining. Here's his chart, which uses data from the US Census Bureau and the World Bank:
In fact, for the first time ever, the Census Bureau is now forecasting the US population to start declining. The current forecast has its population reaching a high of 370 million in 2080 and then declining to 366 million by 2100. But even before these far off dates, organic growth is expected to turn negative in less than 15 years (see above). So yeah, it makes sense that this would impact the real estate sector.
For more on the future of homeownership, check out Aziz's Home Economics.
This recent post by Sam Karam at NewGeography illustrates the relationship between female literacy and total fertility rates in Sub-Saharan Africa, India, and China. The overarching argument, which won’t surprise any of you, is that, “higher female literacy is a reliable predictor of lower fertility and improved prosperity.”
The following graph uses data from populyst, the UN Population Division and UNESCO. The time period for the dataset varies by country but approximately corresponds to the latest 2000′s. All Sub-Saharan countries are represented, except for the Congo, Somolia, and South Sudan.
Facebook, as part of their internet.org initiative, is working on bringing internet access to people in rural areas all across the world. For obvious socioeconomic reasons, this is an important initiative. From a business standpoint, it also grows the base of potential Facebook users at a time where that top line number is starting to plateau.
In order to understand how people are settling and aggregating throughout the world, Facebook has been using high-resolution population maps and then creating models to drill down and analyze individual buildings.
This is interesting because: how else could you track informal settlements? So far this seems to be the most effective method. In fact, in architecture school I worked on a project in Dhaka, Bangladesh and I remember relying heavily on aerial photography because I simply couldn’t find the data I was looking for.
Here’s an excerpt from a recent World Bank post. They, along with Columbia University, are collaborating with Facebook.
Facebook’s computer vision approach is a very fast method to produce spatially-explicit country-wide population estimates. Using their method, Facebook successfully generated at-scale, high-resolution insights on the distribution of buildings, unmatched by any other remote sensing effort to date. These maps demonstrate the value of artificial intelligence for filling data gaps and creating new datasets, and they could provide a promising complement to household surveys and censuses.
And here’s an excerpt from a recent Facebook post on the same topic:
From this preliminary analysis, we’ve determined that slightly less than 50% of the population lives in cities. However, 99% of the population lives within 63 km of the nearest city. Hence, if we are able to develop communication technologies that can bridge 63 km with sufficiently high data rates, we should be able to connect 99% of the population in these 23 countries. [”These 23 countries” represents about 1/3 of the world’s population.]
I would imagine that this type of model works better in sparsely populated / low-rise areas. Still, I am very interested in thinking about ways in which our current survey and census methods could be improved. Here in Canada we conduct our national census every 5 years. In today’s world that feels like eons. One day, I am sure, it will be real-time.
Image: World Bank
Since the 1940s, the US has been adding roughly 9 million new homeowning households about every 10 years. This, after all, is a fundamental component of the American Dream. But Aziz Sunderji -- who writes over at Home Economics -- has recently been arguing that this 80-year boom is now at an inflection point. And it is largely because the rate of population growth in the US is now declining. Here's his chart, which uses data from the US Census Bureau and the World Bank:
In fact, for the first time ever, the Census Bureau is now forecasting the US population to start declining. The current forecast has its population reaching a high of 370 million in 2080 and then declining to 366 million by 2100. But even before these far off dates, organic growth is expected to turn negative in less than 15 years (see above). So yeah, it makes sense that this would impact the real estate sector.
For more on the future of homeownership, check out Aziz's Home Economics.
This recent post by Sam Karam at NewGeography illustrates the relationship between female literacy and total fertility rates in Sub-Saharan Africa, India, and China. The overarching argument, which won’t surprise any of you, is that, “higher female literacy is a reliable predictor of lower fertility and improved prosperity.”
The following graph uses data from populyst, the UN Population Division and UNESCO. The time period for the dataset varies by country but approximately corresponds to the latest 2000′s. All Sub-Saharan countries are represented, except for the Congo, Somolia, and South Sudan.
Facebook, as part of their internet.org initiative, is working on bringing internet access to people in rural areas all across the world. For obvious socioeconomic reasons, this is an important initiative. From a business standpoint, it also grows the base of potential Facebook users at a time where that top line number is starting to plateau.
In order to understand how people are settling and aggregating throughout the world, Facebook has been using high-resolution population maps and then creating models to drill down and analyze individual buildings.
This is interesting because: how else could you track informal settlements? So far this seems to be the most effective method. In fact, in architecture school I worked on a project in Dhaka, Bangladesh and I remember relying heavily on aerial photography because I simply couldn’t find the data I was looking for.
Here’s an excerpt from a recent World Bank post. They, along with Columbia University, are collaborating with Facebook.
Facebook’s computer vision approach is a very fast method to produce spatially-explicit country-wide population estimates. Using their method, Facebook successfully generated at-scale, high-resolution insights on the distribution of buildings, unmatched by any other remote sensing effort to date. These maps demonstrate the value of artificial intelligence for filling data gaps and creating new datasets, and they could provide a promising complement to household surveys and censuses.
And here’s an excerpt from a recent Facebook post on the same topic:
From this preliminary analysis, we’ve determined that slightly less than 50% of the population lives in cities. However, 99% of the population lives within 63 km of the nearest city. Hence, if we are able to develop communication technologies that can bridge 63 km with sufficiently high data rates, we should be able to connect 99% of the population in these 23 countries. [”These 23 countries” represents about 1/3 of the world’s population.]
I would imagine that this type of model works better in sparsely populated / low-rise areas. Still, I am very interested in thinking about ways in which our current survey and census methods could be improved. Here in Canada we conduct our national census every 5 years. In today’s world that feels like eons. One day, I am sure, it will be real-time.
Image: World Bank
Noteworthy about this dataset is that the biggest decline in the total fertility rate happens precipitously after female literacy reaches and exceeds 80%. What is also interesting, but not surprising, if that the countries with the lowest gender equality rankings tend to also have high fertility rates. And that’s because low gender equality tends to translate into lower female literacy rates.
According to populyst, the above phenomenon – precipitous decline in TFR with rising female literacy – has already proved itself out in China.
Based on data from the World Bank, China’s total fertility rate dropped from 6.38 in 1966 to 2.75 in 1979. And since the one-child policy was only enacted in 1979, it doesn’t appear to be driven by that. (I would have initially expected some sort of surge in births prior to that policy.) From 1982 to 2000, the female literacy rate in China rose from 51% to 87%. Today it is 99.6%, which is basically the same as it is for males.
For a more detailed look at the above data, check out this populyst post.
Noteworthy about this dataset is that the biggest decline in the total fertility rate happens precipitously after female literacy reaches and exceeds 80%. What is also interesting, but not surprising, if that the countries with the lowest gender equality rankings tend to also have high fertility rates. And that’s because low gender equality tends to translate into lower female literacy rates.
According to populyst, the above phenomenon – precipitous decline in TFR with rising female literacy – has already proved itself out in China.
Based on data from the World Bank, China’s total fertility rate dropped from 6.38 in 1966 to 2.75 in 1979. And since the one-child policy was only enacted in 1979, it doesn’t appear to be driven by that. (I would have initially expected some sort of surge in births prior to that policy.) From 1982 to 2000, the female literacy rate in China rose from 51% to 87%. Today it is 99.6%, which is basically the same as it is for males.
For a more detailed look at the above data, check out this populyst post.
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