Does the data presented in that article really suggest that the apartments are actually empty or is it simply that it takes a while to sell out a new development? It's going to take a while to move a hundred units, especially if you want to increase the profits.
The StreetEasy report the article is inspired by actually shows that in 2019 there were more condos sold than completed.
Are there really that many empty apartments? I am sure they exist, but are there really enough of them to make a dent in the market? Having lived in the city for years, I've never actually seen one.
I also wonder if you have enough cash to buy and hold NYC apartments, wouldn't your money do better in some sort of a REIT?
There is a section that addresses that specific point:
> The Landmarks Preservation Board protects the facades of historic buildings, but not their interiors. Thus landmarking led townhouses once divided into multiple apartments to be converted into single-family mansions for the incoming super-rich—serving only to further inflame the speculative energies that skyrocketed the prices of the buildings they preserved.
Speaking of distinguishing subtle differences in blues, there is actually a linguistic component.
English and Russian color terms divide the color spectrum differently. Unlike English, Russian makes an obligatory distinction between lighter blues (“goluboy”) and darker blues (“siniy”). We investigated whether this linguistic difference leads to differences in color discrimination. We tested English and Russian speakers in a speeded color discrimination task using blue stimuli that spanned the siniy/goluboy border. We found that Russian speakers were faster to discriminate two colors when they fell into different linguistic categories in Russian (one siniy and the other goluboy) than when they were from the same linguistic category (both siniy or both goluboy). Moreover, this category advantage was eliminated by a verbal, but not a spatial, dual task. These effects were stronger for difficult discriminations (i.e., when the colors were perceptually close) than for easy discriminations (i.e., when the colors were further apart). English speakers tested on the identical stimuli did not show a category advantage in any of the conditions. These results demonstrate that (i) categories in language affect performance on simple perceptual color tasks and (ii) the effect of language is online (and can be disrupted by verbal interference).
I on the other hand, find most R packages provide barely readable documentation. I can just hope that the vignette exists and actually explains the inputs/outputs.
By that theory, you should diversify away from those that are not going to be "superstocks" (in the post, Dell grew 550x and pushed up the entire index). For example, HP or Exxon or UPS will probably do okay, but will not outperform the rest of S&P by a huge margin. Therefore, they will only weigh down your big earners.
> you can research growth stocks (e.g. pharmaceutical companies undergoing FDA trials) and pick the promising ones
Those FDA trials are expensive. Those companies have many experts in the field that believe those trials have a reasonable chance of success. Otherwise, they would not proceed with them. Even the FDA itself believes those trials have a good chance of success. There are plenty of Wall Street analysts who are also evaluating these trials. Regular Joe is not going to outsmart all those people.
Each doctor offers hundreds of services/procedures. Each one has a different price. The insurance company needs a certain number of doctors in the network to be competitive and they can't really have hard cutoffs for every single item. It's not that surprising that there are outliers.
Additionally, lots of doctors often bill incorrectly on purpose so that a particular services/procedure gets covered, so I am not sure we should keep "justified" and "validated" in these discussions.
"If you know ggplot2"... but you need to make a lot of plots to get the hang of ggplot2. The "+" syntax (not sure what the proper name for that is) alone is completely foreign and intimidating.
If you want to make great graphs in R, you will need to learn ggplot2. If you just want to learn R, why not keep it simple at first?
Without experiencing base R, you won't appreciate the tidyverse packages, which tend to have more of a learning curve.
For example, you can just run boxplot(x) in base R and it will make you a plot. Only after trying to make any modifications to it that you will see the benefit ggplot2.
As you mention yourself, "I almost quit R completely in frustration". I believe that is exactly why you appreciate the other packages you mention.
Housing generally underperforms the overall market.