Reverse mortgages are fine, but I think a lot of people are cashing out their equity earlier with no means to make their mortgage payments once their income is reduced.
One worrisome trend is the fact that more homeowners are retiring with large mortgages. The days of the mortgage burning party are over. Banks seem happy to do cash out refis on homeowners soon to retire.
This will be hosted at either Brookhaven National Lab (NY) or Jefferson Lab (Virginia). Unfortunately, National Labs need to find new large construction projects to justify their continued existence, and there aren't enough big science projects to go around. The European model of sharing a single facility (CERN) is much more sensible in that it decouples scientific prioritization and the issue of local jobs.
The three largest OTT providers (Sling, YouTube TV and DirectTV Now) all raised their prices by $5 recently. This seems similar to how airlines all manage to raise their fares at the same time without explicit coordination (one airline does it, and the others quickly match). I don't think it has anything to do with the merger.
To be fair, the people who have massive housing equity generally bought their houses when interest rates were sky high. In the 1970's, when you could buy a decent house in Palo Alto for $50,000, the interests rates were 15-20%. As rates have gone down, the prices have gone up. Mortgage payments as a fraction of income have gone up much less dramatically than the sales prices.
Another goal of particle and nuclear physics funding from the 60's through 90's was to produce a steady stream of highly qualified people to work at the defense labs (Los Alamos, Livermore, Lincoln Labs, etc). My colleagues would get prestigious fellowships to do research at these places, and eventually get sucked into the classified research area. Unfortunately, I don't think the best and brightest U.S. students are going into physics these days, so this rationale for research investment is no longer there.
The odd thing about the current market, compared to the previous periods of rapid appreciation in the Bay Area, is that few of the current owners seem to be cashing out and leaving. This is causing the inventory issues.
Music is fundamentally different the video. Video is typically consumed and monetized once. A piece of music is consumed over and over again by the same individual. From an artist perspective, they want their music to be widely distributed so that they can monetize as many times as possible, and they also want to drive consumers towards their other lines of business (live performance, merchandise).
This is very common in California because under this scenario your house is not reassessed for prop 13 property tax purposes. Your cost basis only goes up by the dollar value of the improvements (which you can easily understate). If you do a complete teardown, the county reassesses at current market value. A lot of people leave the chimney up because you get the added benefit of getting grandfathered on the air quality ordinances.
There is allegedly one study from the Rhode Island blizzard of 1978 showing an increase in deaths during the storm and abnormally low rates after, so the deaths were moved forward by a couple of weeks. Similar things have been observed with runners (die during the race, but lower mortality aftewrards)
Another second order effect is the reduced supply of family sized units. Many 3+ bedroom units are occupied by older couples and single people who raised their kids there and no longer need the space but would find it much more expensive to downsize. This drives families out of the city (among other things). Prop. 13 solves this by allowing people older than 55 to transfer their property tax break to a smaller house.
If robots.txt allows Google and Bing but nobody else, it should be ignored. If it blocks everyone, then I agree. We need to make sure that the next Google has a chance to succeed.
I've always tried to sit in on a few sales calls at any place I've worked and have encouraged people working for me to do the same. It's valuable to see your products from the Sales team and customer perspective. The surprising thing I've observed is how great sales people spend most of the call listening to problems, not offering solutions.
What happens if these cars get in a situation where there is no legal way to move? For instance, the other day I was driving down the street and it was closed because of a Farmers market. Will they just sit there for hours? In gridlock, there is often no way to move without "blocking the box."
We are already seeing that Uber/Lyft are causing declines in the usage of public transit, and there could actually be decreasing appetite for public transit investment. Compared to the rest of the US, the Bay Area is actually already pretty good when it comes to fraction of workers commuting by transit (#2 or #3 after New York City and possibly Washington, D.C.). Unfortunately, I find that most people are all in favor of other people using public transportation so that they can drive where they want to go faster. Transit only gains massive adoption for particular commutes where it is substantially faster than driving.
glmnet, one of the core packages for machine learning is written in Fortran. So are many of the linear algebra packages commonly used. It's not just physicists that still rely on this stuff.
A couple of years ago (in Oakland) we bid on some houses that were priced at the market rate or slightly over. In one case, we bid a token amount over the asking price, and were the only bidder. In the other case, we were slightly below asking. In both cases, the sellers had expected to get a bidding war and pulled the house from the market. In the first scenario (we bid over asking), the sellers are supposed to pay the selling agent the commission the agent should have received had the deal gone through (that is generally the rule when the agent delivers a full price offer). In the Bay Area market, however, demanding the commission would be fatal to the agent's reputation. The sellers of both houses re-listed about 6 months later and got a much higher price that what we had offered, so they won in the end.
The research mentioned from 2007 fascinating. Ticketmaster auctioned off a limited number of prime seats. The authors showed that the auction price for these seats was equal to the secondary market "scalper" price for these seats. It's possible to have a hybrid where only the prime seats go to auction. Ticketmaster does this sometimes already by directly listing some seats on StubHub.
There is a growing gap in life expectancy based on income, and many of the people collecting private pensions qualify as upper income. I doubt that companies will get much of break once that is factored in.
They likely want to implement a pay to link scheme for news articles. This would be similar to the way that every songwriter gets a small royalty when their song is played on the radio or on a streaming service. In the case of the songwriters, it is government sanctioned and therefore not an illegal cartel.
I'm surprised he didn't call out title insurance as a place for potential innovation. This insurance pays out at something like 1% of premiums collected. Historically, the customer acquisition was handled by making a large kick back to the real estate agent. The research involved (look for contractors liens and such) seems like something that could be automated using NLP.
The paper fails to discuss the fact that the data for a given patient is biased by the preliminary (likely unrecorded) diagnoses of the doctor. The doctors are trained to systematically order certain sets of tests depending on their preliminary diagnoses. If some doctors have a pretty good idea of the final diagnosis, the deep learning classifier is simply learning what doctors do when they suspect a particular disease. It is not learning to predict a disease. A truer test of the deep learning method would also require that the deep learning algorithm orders the tests, or that we randomly select tests for a patient -- both of these possibilities are highly unethical.
The one place where the University and the Professor are perfectly aligned is the summer salary. Most professors are paid for only 9 months by the University. They need to pay the remaining portion out of their grant. This gives the Professor and the University a mutual incentive to secure one or more grants. The Professor gets paid and the University gets their overhead tax on the grant.