This is a poorly executed study that is factually incorrect.
Here are the facts:
* Median (household) gross rent in 2000 was $602 (1) or $7224 per annum
* Median (household) gross rent in 2019 was $1097 (2) or $13164 per annum
That is an increase of 182% for rents over the period.
* Median household income in 2000 was $42,148 (3)
* Median household income in 2019 was $68,703 (4)
That is an increase of 163% for incomes over the period.
So while its true that rents have increased faster than incomes, saying that rents increased 175% faster than incomes is false. Rents increased roughly 20% faster than incomes over the same period.
More importantly, affordability (e.g. median income/median rent) decreased from 5.8 to 5.2, which is a decrease of 10%.
I've noticed a lot of these poverty-porn pieces that wildly exaggerate how terrible things are, and at this point I'm not at all surprised to see this type of misinformation in Academia, which has long ago crossed into crusading territory. If you want to know what the various tricks are that advocates use to inflate these figures, here is a brief listing:
* confusing asking rents with rents paid. Particularly in cities with rent control, there is a huge gap, which is why you need to use census data on rents actually paid. I suspect this is the trick used in the current study. In addition to rent control factors, note that there is always a difference between price paid and asking price when you look at boards like Zillow or craigslist because cheap goods are taken off the board quickly while more expensive goods linger.
* Household versus personal income. E.g. look at personal wages and compare that to household rents.
* Playing games with boundaries. E.g. is a city the legal limit or is it the MSA? You should always use MSA (that is why they are statistical areas) to get closer to an apple to apple comparison. The 50 largest MSAs would be 180 million people and would not have median affordability ratios much different than the nation as a whole. But looking at craigslist adds for downtown areas would yield a different story.
* Making inappropriate comparisons. E.g. comparing a large MSA to a small town.
* Games with real and nominal incomes. E.g. I've seen people compare real to nominal incomes, or talk about "real" rates without adjusting for inflation.
* mixing average with median. Median should be used in distributions with fat tails or if you are interested in middle class questions. I've seen this abused a lot.
* Doing things like taking percentages of percentages when the base is small. E.g. the number of blind people electrocuted in their swimming pools is up by %1000!
* Strategically chosen start and end-dates. It's important to use peak-to-peak business cycle comparisons rather than trough to peak. Especially as we know that things like rents and home prices are more volatile than incomes (which are famous for being more downward sticky). Always ask yourself what the economy was doing at the start and end points.
* Ambiguously wording statements to get a big number. E.g. the rate of increase of X is %175 faster than Y! What does that mean? That the compounded growth rate of X is 1.75 that of the growth rate of Y? Or that the total increase of X is 1.75 times the total increase of Y? This type of strategic ambiguity is used to make false claims that are hard to pin down.
Here are the facts:
* Median (household) gross rent in 2000 was $602 (1) or $7224 per annum
* Median (household) gross rent in 2019 was $1097 (2) or $13164 per annum
That is an increase of 182% for rents over the period.
* Median household income in 2000 was $42,148 (3)
* Median household income in 2019 was $68,703 (4)
That is an increase of 163% for incomes over the period.
So while its true that rents have increased faster than incomes, saying that rents increased 175% faster than incomes is false. Rents increased roughly 20% faster than incomes over the same period.
More importantly, affordability (e.g. median income/median rent) decreased from 5.8 to 5.2, which is a decrease of 10%.
(1) https://www2.census.gov/programs-surveys/decennial/tables/ti...
(2) https://www.deptofnumbers.com/rent/us/#:~:text=Average%20gro...
(3) https://www.census.gov/library/publications/2001/demo/p60-21...
(4) https://www.census.gov/library/stories/2020/09/was-household....
I've noticed a lot of these poverty-porn pieces that wildly exaggerate how terrible things are, and at this point I'm not at all surprised to see this type of misinformation in Academia, which has long ago crossed into crusading territory. If you want to know what the various tricks are that advocates use to inflate these figures, here is a brief listing:
* confusing asking rents with rents paid. Particularly in cities with rent control, there is a huge gap, which is why you need to use census data on rents actually paid. I suspect this is the trick used in the current study. In addition to rent control factors, note that there is always a difference between price paid and asking price when you look at boards like Zillow or craigslist because cheap goods are taken off the board quickly while more expensive goods linger.
* Household versus personal income. E.g. look at personal wages and compare that to household rents.
* Playing games with boundaries. E.g. is a city the legal limit or is it the MSA? You should always use MSA (that is why they are statistical areas) to get closer to an apple to apple comparison. The 50 largest MSAs would be 180 million people and would not have median affordability ratios much different than the nation as a whole. But looking at craigslist adds for downtown areas would yield a different story.
* Making inappropriate comparisons. E.g. comparing a large MSA to a small town.
* Games with real and nominal incomes. E.g. I've seen people compare real to nominal incomes, or talk about "real" rates without adjusting for inflation.
* mixing average with median. Median should be used in distributions with fat tails or if you are interested in middle class questions. I've seen this abused a lot.
* Doing things like taking percentages of percentages when the base is small. E.g. the number of blind people electrocuted in their swimming pools is up by %1000!
* Strategically chosen start and end-dates. It's important to use peak-to-peak business cycle comparisons rather than trough to peak. Especially as we know that things like rents and home prices are more volatile than incomes (which are famous for being more downward sticky). Always ask yourself what the economy was doing at the start and end points.
* Ambiguously wording statements to get a big number. E.g. the rate of increase of X is %175 faster than Y! What does that mean? That the compounded growth rate of X is 1.75 that of the growth rate of Y? Or that the total increase of X is 1.75 times the total increase of Y? This type of strategic ambiguity is used to make false claims that are hard to pin down.