The stock market is going down

Not the stocks, mind you, but the market itself. There are less than 4,000 companies listed, and new companies have less and less appetite for going public. Conversely, there are over 8,000 private-equity-backed companies in the US, and growing.

There are a couple of problems with the stock market mouldering. One is that this is where most of America keeps their retirement. Common wisdom is that if you invest in a nice index fund, then your money should grow apace to the US economy. This works until the economy grows without the market.

Suppose the public market continues to dwindle. New companies stay private, so the companies on the market get older and older. Old companies have a tendency to grow more slowly and, eventually, go out-of-business. Without new blood, I’d guess the market would ossify around a few large surviving stocks.

Without the possibility of explosive growth in the public markets, any money that could go into private equity would (and is). Unfortunately, at the moment, private equity is only open to accredited investors (i.e., the already well-off) so most of America won’t be able to participate at all.

It’s important that everyone in the US:

  1. Has the opportunity to benefit from economic growth and
  2. Able to participate in a way where they won’t get fleeced.

As I understand it, the SEC doesn’t allow the middle nor lower class to invest in private companies because they don’t want people who are barely making ends meet to be hoodwinked out of all of their money.

Why is investing private equity riskier than public companies? Small companies tend to be riskier, but there are plenty of private companies that are larger than public ones. If I understand correctly, most of it is related public companies being better-regulated than private ones.

My knee-jerk reaction is, then: how can we get more companies to go public? And maybe that’s a good goal. However, why are we in the pocket of big-market here?

The goal is to allow more people to benefit from private companies, not bail out banks/NYSE/NASDAQ. So one possible solution is making the private market more liquid while adding protections for investors.

Making the private markets more liquid is easy: just let anyone buy and sell shares without being accredited and without needing the company’s approval.

However, given that something like 50% of VC firms don’t even return the capital they invest and (almost) everyone there is doing this professionally and on the up-and-up, making sure people aren’t fleeced seems more difficult. My strawman proposal is to tighten up regulations on private companies based on top-of-line revenue. Making $1M/year? Great, you need some sort of S-1-like prospectus for investors. Making $10M/year? Great, you can’t just spout off about “funding secured” on Twitter. Making $50M/year? Great, you basically have to follow all of the rules public companies do. I dunno, it’s a first draft. But I think it’s important the SEC gets on making private equity more accessible before the middle and lower classes are left in the dust.

Losing money sucks – the mathematics of loss aversion

There’s a lot of research on loss aversion: how bad people feel when they lose $50 vs. how good they feel good about gaining $50. This research is kind of taking an absolute value of emotion, positive or negative. We can represent this with emoji equations:

  • 😀 == 😫 (large-magnitude happiness or sadness)
  • 🙂 == 🙁 (small-magnitude happiness or sadness)
  • 😫 > 🙂 > 😐 (ordering magnitude of emotion)

What research has shown is that, when someone loses $X and feels 😫 (large-magnitude), if they gain $X they feel 🙂 (small-magnitude). Gaining money doesn’t make them as happy as losing money makes them sad!

My hypothesis is that people have an intuitive feeling about the math behind these experiments and the results make a lot of sense if you look at percentages instead of values.

For example, suppose you invest $100 and your investment goes up to $150. Holding that money in your hand, $100 is a distant memory, when you had 33% less than you currently have. ($150 – (33%*$150) = $100)

Now let’s say your investment doesn’t do well and you’re standing there with $50, feeling sad. Now you have 50% of what you started with.

My hypothesis is that loss aversion is really an intuition about the difference between 33% and 50%. My guess is that the emoji magnitude would be (roughly) equal if you gained and lost the same percentage. E.g., these would have equal magnitudes of satisfaction:

LoserWinner
33% plan$67 🙁$150 🙂
50% plan$50 😫$200 😀

If we extend this out to an infinite number of plans, we can see it breaks down at the ends. I don’t think losing 80% would feel the same magnitude as making 4x, but it also feels hard to imagine. Losing 80% of a meaningful amount of money would feel terrible, but would it feel as terrible as 4x-ing my money feels good? 10x-ing? 10x feels more significant than 4x, but 4x-5x? I’m not sure I’d be materially cockier.

Anyway, I assume the ratios are a bit different for different people. But it always struck me with the loss aversion studies that most people understand that going from $1 to $2 doubles your money, but going from $2 to $3 does less.

The surprisingly complex math behind startup equity and taxes

Taxes for employees at startups are weird and can vastly change the amount you make.  To illustrate why, let’s take a simple example.

Suppose we have a group of early employees at a startup, we’ll call them the Unicorn Inc. Mafia.  They’re all fresh out of college and managed to get through it without any debt, so they each have a net worth of $0. They all join the same day and are given the same equity package: $10k in stock options with a strike prices of $1 (so, 10,000 common stock). We’ll take four members of the mafia, each with a different strategy.

Name Description Net worth
Alice Exercise very early, before price changes $0
Bob Exercise somewhat early $0
Carol Wait until liquid to exercise, wait until long-term capital gains apply $0
David Wait until liquid to exercise, sell immediately $0

From here on out, keep in mind that this could be the end of the story. The company could always fold, leaving everyone with zero or, if they’ve exercised, a negative net worth.

However, suppose the company is doing well and lets the employees know that they’re going out to raise a $40M round.  Alice exercises her options before the round happens.  This means that she has to pay for them, so she’s in the hole $10k.  Now if anything goes wrong, she’s out $10,000.

Once the company raises the round, the stock is worth $5/share.  Unfortunately, Bob’s significant other got a job across the country, so he has to find a new job. He feels like the company is going places, though, so he wants to collect his equity before he goes. He exercises his options. Because he is buying his stock for $1 and it is now worth $5, the IRS says that he just “made” $4. So he has to pay normal income taxes on that $40,000. To keep things simple, let’s say everyone’s tax rate is 25%. So now he’s paid $10k for the stock and $10k for taxes:

Name Description Exercise Income taxes Net worth
Alice Exercise very early, before price changes ($10,000) 0 ($10,000)
Bob Exercise somewhat early ($10,000) 25%*$40,000 -> ($10,000) ($20,000)
Carol Wait until liquid to exercise, wait until long-term capital gains apply $0 $0 $0
David Wait until liquid to exercise, sell immediately $0 $0 $0

So Bob’s out $20k if the company goes under (ouch!).

However, luckily for Alice & Bob, over the next several years, the company continues to grow and raise money. Finally, the company goes public for $100/share. Wow! Once the lockup period expires, everyone eventually sells (somehow it’s still exactly at the IPO price) and makes $1M. Our final shakeout looks like:

Name Description Exercise Income taxes Short-term capital gains Long-term capital gains Sell price Net worth
Alice Exercise very early, before price changes ($10,000) 0 0 0 $1,000,000 $990,000
Bob Exercise somewhat early ($10,000) 25%*$40,000 -> ($10,000) 0 20%*$990,000 -> ($198,000) $1,000,000 $782,000
Carol Wait until liquid to exercise, wait until long-term capital gains apply ($10,000) 25%*$990,000 -> ($247,500) 0 20%*$990,000 -> ($198,000) $1,000,000 $544,500
David Wait until liquid to exercise, sell immediately ($10,000) 25%*$990,000 -> ($247,500) 25%*$990,000 -> ($247,500) 0 $1,000,000 $495,000

There are, uh, a couple of different outcomes. Alice obviously has an accountant in the family: she avoided paying any taxes at all! How is this possible? First, she exercised his options before the price changed, so she didn’t have to pay any taxes on exercise. Then she held them long enough to qualify for long-term capital gains. However, she didn’t even have to pay those! It turns out that, if you own stock in a startup before it has $50M in assets, long-term capital gains up to $10M are tax-free (Google “QSBS” for details). However, Alice is also taking on more risk for longer than anyone else: most startups don’t have outcomes like this and she’d have just been out $10,000 if they had gone out of business.

Obviously there are a ton of simplifying assumptions (stock prices never change! Everyone has the same tax rate, which happens to be one that make numbers easy!). However, I wish someone had told me about all this ~10 years ago, so putting this out there in the hopes that it’ll help someone else.

Goals for 2019

Followup from last year’s post:

Programming: I really dug into Pandas and not I feel pretty comfy with Python & Pandas. This year, I’d like to focus a bit more on stats and machine learning.

Work-life balance: I am freakin obsessed with my job, which is terrific. But I also feel comfy taking days off and leaving at a reasonable time to walk Domino. So, solid win on that front. I’d like to keep that going this year.

Had many fun adventures with Andrew. We’ve kind of made that a priority this year and I’d like to continue doing that.

Baking: made all sorts of cool things. Highlights were Georgian cheese bread (which tasted like crack but sat like a bag of cement), apple cider donut muffins (they’re muffins, so they’re healthy!), and cinnamon pull-apart bread (I killed the yeast and they were still delicious).

Boxing: started going to sparring regularly, which is incredibly fun and hard.  However, one of the women I spar with is several inches taller than me, solid muscle, and just got down to 30lbs lighter than me for a fight. While I don’t feel the need to be that skinny, I’m thinking maybe I should lose a couple pounds next year. Speaking of:

Eating better: we ate weekly salads this year (thanks to Andrew!) but I wouldn’t say we ate more healthily overall. I think we have a solid plan for next year, though.

Finances: not sure if I saved more than usual from my paychecks, but the MongoDB IPO was a nice bump! Dropping this from goals for next year.

Travel: failed miserably, had to go to California many times for work. However, I think I’ve figured out the basics of air travel to the extent that I no longer feel totally screwed over every time I fly.  I even got a free upgrade last trip! Dropping this from goals for next year, I’m going to have to continue to travel a bunch. And playing Assassin’s Creed: Odyssey is making me want to visit Greece.

Read a couple of books: success! My favorite books this year were Spinning Silver and Bad Blood. Dropping this from goals for next year.

Writing: despite feeling like I wasn’t writing anything, actually did manage to hit my goals.  Also, started writing on Medium, since it’s a GV investment.  I’d like to continue with attempting 6 posts next year.

Went camping and hiking a couple times and it was very nice, aside from the one time we got caught in the rain several miles from the trailhead.  But that was fun too, just not at the moment.

Home improvement: fixed most of our toilet’s innards and it works so much better now.  Brings me joy with every flush!

Finally, it was fun to think about this list and what I had done so far all year, looking forward to following up around New Year’s.  So, hopefully it’ll be the same next year.  Happy 2019!

AST Financial: the dumpster fire of a company

As an employee at MongoDB for several years, I had a bunch of shares that MongoDB was holding for me when it went public which I had to get to my brokerage account to actually sell. It turns out that the company can’t just hand you your shares, they have to go through a transfer agent who keeps the record of who owns what shares of a company. Then the transfer agent transfers those shares to a brokerage.

So several months ago, I got an email that MongoDB would be transferring my shares to AST Financial (a transfer agent). I got an email from them with some forms to return. I heard from international friends that the email address they gave to people outside the US didn’t actually work, but luckily mine worked fine.

I received a somewhat confusing snail mail from AST with an account summary, which added together my first and last stock grants and broke those out into a separate section than the total, for reasons that are completely unclear to me.

I figured that maybe their website would have a more detailed breakdown, so I tried to activate my account. Their password form was pretty badly done but whatever, banks always have crappy logins. Then we got to the “security questions,” which either didn’t actually apply to me (‘What is your oldest sibling’s middle name?’) or might as well have been yes/no questions (‘What color was your first dog?’ Black. It’s the most common damn color for dogs in the world.) Then I agreed to their terms and services and… got an error page. I tried going back to the homepage, saying “Sign up” again, and it took me directly to the terms & services. I nervously accepted, again. My account appeared!

Note that every time I have logged in subsequently, the site has presented an error page. Then I go back to the home page, click “Log in” again, and it takes me to my account.

I decided it was time to transfer my shares to my usual brokerage account. So I called AST, navigated their phone maze, and waited for someone to pick up. And waited, and waited. Eventually, the robot said that they were experiencing heavy call volumes and asked me to leave a message.

I left a message and a support person called a few hours later. Yay! I explained what I wanted and she asked to put me on hold while she looked up my account. Then she disconnected me.

After I finished raging, I called them back. After an hour of waiting for someone to pick up, I gave up and hung up. I sent the original person who had responded to emailed-in forms, asking her to please have someone contact me.

I received a response from help@astfinancial.com: the original email they sent with the forms to fill out and send back.

Luckily, the MongoDB alumni had a group where everyone was bitching and, to a lesser extent, offering advice. Apparently AST actually had an online conversion process: under Account Holdings -> Account Profile, which of course takes you to the General Account Information page, which (of course) is where you transfer shares. I clicked on the button to convert my shares and… got a page that said “ERROR. An error occurred while processing your request.” I tried going to the previous page (which of course didn’t work, back button just took me to the error page again and history was unhelpfully a zillion pages with the title “AST” so I manually put in the URL. This got me to the conversion confirmation page, where it asked if I wanted to submit to convert all shares… with a universal “go back” symbol on the button.

Playing with fire, I entered my total number of shares and pressed submit. It gave me a confirmation page… with a submit button. So I submitted again, and finally got to the real confirmation page.

I didn’t want to publish this before I got extricated from ever having to deal with them again, but now that all my shares are safely out of their hands: AST is the worst. Anyone working on a blockchain-backed transfer agent?

Thinking with Pandas

You can see and run all of my work for this blog post at Colabratory.

Pandas is a Python library for manipulating data. Wrapping my head around it took a while: I’ve been using it for ~6 months and I’m still learning how to use it “right.” It uses all kinds of syntactic sugar to optimize working with vectors and matrices instead of scalars. This makes working with Pandas very different than working with “vanilla” Python.

For example, let’s say you wanted to get a bunch of random dice rolls for playing Dungeons and Dragons. D&D uses 20-sided dice, so in normal Python, you’d probably do something like:

rolls = [random(1, 21) for i in range(0, 10000)]

In Pandas, it would be something like:

rolls = pd.DataFrame(np.random.randint(1, 21, size=(10000, 1)), columns=['roll'])

In D&D, rolling a 20 on the die is special and called a “critical hit.” It usually does good things for the player. If we iterate through rolls seeing how many critical hits we have in vanilla Python, it’s pretty fast:

%%timeit
count = 0
for roll in rolls:
  if roll == 20:
    count += 1

# Output:
1000 loops, best of 3: 267 µs per loop

If we do the same naive approach with Pandas, it’s, uh, slower:

%%timeit
count = 0
for roll in rolls.iterrows():
  count += (roll[1] == 20)
count

# Output:
1 loop, best of 3: 3.12 s per loop

That’s over 10,000x slower (267 µs -> 3.12 seconds). Like, they-must-have-put-a-sleep()-in-iterrows()-to-discourage-you-from-using-it slow.

So why use Pandas? Because it isn’t slow if you do it the “Pandas way”:

%%timeit
(rolls.roll == 20).sum()

# Output:
1000 loops, best of 3: 341 µs per loop

Nice! Only 1.3x slower than vanilla Python. Also, notice the syntactic sugar: you can pretend that the vector is a single number, comparing it to a scalar. If you look at (rolls.roll == 20), it’s a series of booleans:

(rolls.roll == 20).head()
0    False
1    False
2    False
3    True
4    False

When you take the sum() of that series, False is converted to 0 and True to 1, so the sum is the number of True elements.

Modifying some elements of a vector

If you’re attacking and your roll (as calculated above) is greater than the defender’s armor class (say, 14), then you roll for damage. Suppose you do 2d6 damage on a hit. If your attack roll is greater than or equal to 14, then you do 2d6 damage, otherwise the blow glances off their armor and you do 0 damage.

With vanilla Python, this would look something like:

for roll in rolls:
  if roll >= 14: 
    damage = roll_damage()
  else: 
    damage = 0

However, with Pandas, we shouldn’t loop through the rows. Instead, we’ll use a mask. Like a theater mask, a Pandas mask is an opaque structure that you “punch holes” in wherever you want operations to happen. Then you apply the mask to your Pandas dataframe and apply the operation: only the entries where there are “holes” will get the operation applied.

First we create the mask with the criteria we want to update:

mask = rolls.roll >= 14
mask.head()
0    False
1    False
2    False
3    True 
4    True 
Name: roll, dtype: bool

Now we want to:

  1. Grab all the hits (wherever the mask is True).
  2. Generate random numbers for each hit (equivalent to rolling 2d6).
  3. Merge those hits back into the rolls dataframe.

First we’ll create a series that tracks all of the hits. We want to be able to merge that back into our original dataframe at the end, so we want to preserve the index of each hit from the original dataframe. Based on the mask above, this would be [3, 4, ...] and so on (wherever the mask is True). We get this with mask[mask], which is a series of all the True values with their associated index. Then we set every element of this series to a randomly generate 2d6 roll:

hits = pd.Series(index=mask[mask].index)
hits.loc[:] = np.random.randint(1, 7, size=(len(hits),)) + np.random.randint(1, 7, size=(len(hits),))
hits.head()
3     6
4     3
9     8
10    3
14    3
dtype: int64

Note that we’re generating a “1D” random int (randint(len(hits),)) for the damage instead of the 2D one above (randint(10000,1)) because this is a series (1D), not a dataframe (2D).

Then we can combine that damage back into the rolls dataframe using our original mask:

rolls.loc[mask, 'damage'] = hits
rolls.loc[~mask, 'damage'] = 0
rolls.head()
        roll	damage
0	8	0
1	8	0
2	6	0
3	17	7
4	15	7

This lets you quickly and selectively update data.

Also, I’m still learning! Let me know in the comments if there’s a better way to do any of this.

Staying out of trouble on Elastic Beanstalk

Editor’s note: devops always makes me cranky and Domino is in the doggy hospital (he’s okay), so have a rant about Elastic Beanstalk.

This is my life:

If you’re unfamiliar with Elastic Beanstalk: healthy is green, unhealthy is red, and grey is unreachable. Grey is the worst, because it’s inescapable: you can’t change the configuration once it’s grey and if it’s your configuration at fault, none of these options will actually help you:

Theoretically, if you had a good configuration, you could load it from this list. But obviously I don’t. Amazon is just being prissy here: “Well, you really should have made a known-good configuration before mucking around with your only config, shouldn’t you have?”

Shut up, Amazon.

Here is some hard-won advice I have on configuring an Elastic Beanstalk environment:

If you’ve never used Elastic Beanstalk before, start your journey by filing an allocation request. By default, Amazon lets you start up 0 instances. This was insufficient for my needs of, you know, running something. You can check your limits at EC2 -> Limits section (for your region).

Set up SSH key and upload it to the Key Pairs section of EC2. Then, when you’re first creating your Elastic Beanstalk environment, go to “More options” and add it as an SSH key. That way you can actually log into the machine when things inevitably go wrong.

Add a user. Give it access to AWSElasticBeanstalkWhatever (honestly, I’d go with FullAccess for development, but obviously I have no idea what I’m doing). Create an access key for it. Copy the security key! It’s the last time Amazon will ever let you see it. Put it in your ~/.aws/credentials file.

The documentation on credentials files assumes that you were born knowing how a multi-profile credentials file should be formatted. In case you weren’t, here’s how it’s supposed to look:

[default]
aws_access_key_id = HEREISTHEKEYID
aws_secret_access_key = shhItsASecretToEveryone
[personal]
aws_access_key_id = ANOTHERKEYID
aws_secret_access_key = moreSecrets

Not exactly mind-blowing, but I hate it when documentation does that.

personal is the profile I use for my personal projects. Every time I run eb whatever on a personal project, I have to remember to specify eb whatever --profile personal. This is not a thing I am good at remembering: I have uploaded personal projects to my work EB account ~40 times. But, it beats the alternative of uploading my work projects to my personal account.

For my fellow absent-minded readers, I recommend setting the environment variable AWS_PROFILE=personal to default to personal when needed. However, if I do that, I will inevitably forget to unset it when I’m done. So, I set my prompt to show the variable–prominently–whenever it’s set:

PS1='$(date +%X) w$(__git_ps1)${AWS_PROFILE+ e[41m${AWS_PROFILE}}e[0m$ '

This looks like:

Honestly, I could still miss that. But it’s less likely.

Once you get all that done, you’re probably ready to actually create your “zero configuration required” Elastic Beanstalk app.

The thing about renting an apartment in NYC

There’s a lot of weird stuff about NYC real estate and this post attempts to cover some of the things that I’ve experienced. I’ve spent years renting apartments in NYC, mostly from rather small-time landlords with pre-war, rent stabilized buildings. If you’re looking for luxury rentals in new construction, a lot of this advice probably doesn’t apply.

The Mysterious Other Renter

When I’ve found a place I like, invariably the real estate agent calls me a couple hours later telling me that someone else is willing to take the apartment for $100-$200 more per month. Luckily, I’ve never been in a position where I need that particular shitty rental, so I’ve always said, “That’s too bad, I guess I’ll keep looking.” Then a couple hours later the real estate agent calls back and tells me that the other deal miraculously fell through, would I still like the apartment?

This is, I assume, a scummy trick on the part of real estate agent to get an extra $100 commission for a few minutes of extra work (and ingratiate themselves with the landlord). If you have options, don’t fall into a bidding war over a rental.

“Legal rent” vs. what you’re paying

This can actually works out in your favor (at least in the short term). If you are looking for a cheap place, you’re likely to end up with a rent-stabilized apartment. Often, when you sign the lease, there will be an alarmingly high rent listed on the lease (e.g., if you agreed to pay $1,800/month, the lease lists $2,600).

How this works: for rent-stabilized apartments, the landlord is only allowed to raise the rent by a certain city-determined amount each year. For example, if you’re paying $2000/month and the city says landlords can raise rent-stabilized rents by 2%, the landlord can ask you to pay $2200 next year. However, let’s say you’re a good tenant: you always pay your rent on time. You threaten* to move unless the landlord keeps the rent at $2000. So they do: they’d rather have another “guaranteed” $24,000 than risk months of vacancy, a bad tenant, etc. for an extra $2,400.

The city also sometimes puts the rent increase at 0%. Up until last year, there were ~5 years of 0% rent increases. If a landlord had been bumping the legal rent by the max allowable amount before that, though, they could keep bumping the actually rent (up to the legal rent’s ceiling).

Gentrification also is an issue: if you are renting a rent-stabilized apartment and the landlord wants to renovate it into luxury apartments, they have to get rid of their current tenants, first. A great way to do this is to suddenly bump your rent to the max legal rent. If you balked at paying an extra $200/month for a crappy studio with no view, how would you feel about a $1k/month bump? Only the old tenants are gone, the landlord is free to renovate the apartment. Renovations increase the allowable rent they can charge for the apartment, often bumping it out of the range that the city will consider rent-stabilized… allowing the landlord to charge the new tenants absolutely anything.

The migratory patterns of renters

Most people move in the warmer months. Once it starts to get cold, people seem to have a nesting instinct and just don’t want to go out and find a place. This means that landlords are more likely to make concessions and give discounts in the winter months, if you can move then. And they’re more likely to be friendly about letting you out of your lease in the summer months.

Rental agents: the crème de la crap

Real estate agents work on commission: they literally make $0 salary. Like strippers, they generally pay the brokerage for getting to use their space. Because brokerages can charge agents to work for them, they’ll often hire literally anyone that can pass the licensing exam.

Now guess which gives a better commission: signing a lease on a rent-stabilized apartment or closing a $2.5 million sale. The lease gives the agent one month’s rent (maybe a thousand). The sale gives them a few percent of the listing price (maybe $30,000 for the example above).* There are more expenses with selling a listing, but still. Thus, the real estate agents you’ll be working with will generally be the least-experienced agents at the brokerage. A broker once told me, “If a new agent has a deep network and skills, I’m not going to waste them doing rentals.” Putting together rental deals is the latrine duty of real estate.

Thus, if you can avoid it, don’t work with an agent. They’re unlikely to be good at their job and are generally a huge waste of money. However, landlords of big buildings (especially) will sometimes only work with real estate agents. This is because the real estate agents will do some pre-screening (if you make $30k/year they’re not going to waste anyone’s time showing you a $3k/month place) and they will steer tenants to the buildings that they work with (which works out well for everyone, pretty much: the landlords get tenants, the tenants find a place, the real estate agent gets a commission fairly easily).

If you are trying to avoid a broker’s fee, though, you’ll want to avoid those buildings.

Leases

In Manhattan, you’re almost certainly going to have standard year-long leases. The landlord sends you a new lease within 90 days of your lease expiring and you either sign it, ask for modifications, or decline and move out. If you need to move out before that, try to find someone to take over your lease (hey, I know a startup that can help with that) or your landlord is likely to fine you at least a month’s rent (good bye security deposit) for breaking the lease.

A perk of Queens and Brooklyn is that leases often turn into month-to-month leases after then initial year is up (and occasionally will start as month-to-month leases). This gives you a lot more flexibility: often you only have to give 30 days notice before moving out. (I don’t know much about renting in the Bronx or Staten Island.)

Wrapping up

When dealing with landlords: remember that they really just want someone who will reliably pay rent. And, I mean, not burn the place down or turn it into a drug den, but hopefully you won’t have to work very hard to convince them of that side of it.

When working with agents: remember that they are in it for the commission and you are probably more skilled at literally everything than they are, but they have connections and (some) experience. But do your own research and don’t trust anything they say. And since I’m a real estate agent… and I wrote this post… well, I guess this was a huge waste of your time 🙂

I’d love to hear what your experiences have been, let me know in the comments.

* Threatening your landlord: I use the word “threaten” loosely. I’ve always had good luck with just calling the landlord and saying, “I’d really like to stay, is there any way you can see your way to keeping the rent at $2k?” So I’m not going all First Blood on them.
* Commission on sales and rentals: I’m assuming that a brokerage is likely to take at least 50% of the commission for either, although this varies widely.

A self-indulgent post for a self-indulgent day

I think how I feel around holidays is a good barometer for how my life is going. For example, last year over the winter vacation, I just wanted to stay there forever and dreaded returning to “real life.” By contrast, year’s winter vacation I had a lot of fun, but I was looking forward to coming back to work.

Similarly, today is my birthday and I did exactly what I wanted to do: went to work and got dumplings for lunch (which I do a couple times a week). I got a bonus sesame pancake sandwich, which I don’t usually get and was delicious, but I was so full I could only eat half of it. And got a piece of my favorite kind of cake (tres leches from Whole Foods), which I don’t have on a weekly basis. At work I was actually doing work that I don’t enjoy very much, but I knew it was important and related directly to my company’s success. And I found and fixed the bug I was looking for, pushed the fix, and got to see my fix working in production, which was satisfying.

Not actually mine, but basically my lunch.

Now I am drinking whiskey and finishing out the day coding up some more enjoyable stuff with two dogs at my feet (both had some of my leftover sandwich). Life is good.

I liked the name. It’s pretty good!