Presidential “Pop”


An upset worthy of hyperbole: Donald Trump will be our next president.

Almost immediately, the markets started reacting.  US Futures dropped like a rock.  Foreign markets dropped for real and then rebounded.  Now, almost a week later, lots of folks are starting to interpret what a Trump presidency means.  I wondered what typically happens shortly after an election.  Let’s just stick with the S&P 500 and look at how it closed on November 1 (before an election) vs. December 1 (after an election).  Maybe we can see how panicked or calm we should be…

Here’s a table showing the 1 month closing values and the % changes since 1900.  If you’re wondering why that particular window, it covers a pretty significant span of US history and a range of tumultuous to halcyon periods.  By the way, the S&P Data is from here.   Special thanks to Robert Shiller for making it freely available.  And, the Electoral College data is from here.

One month S & P 500 changes since 1900.

One month S & P 500 changes since 1900.


For all years since 1900 without a presidential election, the average one month change was a positive increase of 0.5% (standard deviation of 0035).  For all election years in the same period, the one month change was a 0.3% increase (standard deviation of 0033).  Here’s a histogram to help visualize things.  See the overlap?  The stats seem to suggest that for this time window, there’s no net benefit to either jumping into or out of the stock market in response to election results.



But, wait a minute.  Aren’t Republican presidential candidates better for business and the stock market?  In the days since The Donald was elected, the US stock market hit record highs.   See!  Well, let’s look at the data.  For all election years in our data set, we can split the one month returns by the winning party.  When Republicans win the White House or Democrats win the White House, there is no difference compared to non election years.

So, forget about non election years: let’s pit Democrats against Republicans directly.  Since 1900, there have been 15 Republican presidential terms and 14 Democratic ones.  Here’s the histogram of the one month change in the S & P 500.  Visually, it looks like there might be something there: a couple more high returning years in red vs. blue.  Again, the stats don’t show any meaningful difference between the two parties.



So, what’s the take-away?  Warren Buffet’s sentiment: “For 240 years it’s been a terrible mistake to bet against America, and now is no time to start.”  Despite all the rhetoric, punditry, and discord, America is a great nation to belong to. 


Regardless of your political affiliations, invest.  Invest for the long haul.  Plant that seedling as soon as you can.  It will grow into your own money tree.

Rental Operations: 10 Months Of “Normal”


Last post, I talked about the time we invested to get our first rental property up and running.  As landlords, my wife and I may have an odd perspective on our tenants.  They are, in fact, our customers.  Our goal is to keep them happy enough to continue to occupy our property while paying the rent on time and causing as little wear and tear on the unit itself.  We’ve had 100% occupancy for 10 whole months (yes, I know that’s hardly a world record).  Starting up a rental, I knew we would be spending time and energy to keep our customers happy.  I didn’t know exactly how much it would take, so I kept track of our hours.  Let’s take a closer look at what we’ve done and how much time it took…

Our first tenant came to us on the recommendation of a great friend.  Even better, the prospective tenant was in a pinch and needed a place quickly.  It was November 15, and our place was almost ready. Despite a few missed milestones from Home Depot in delivering appliances and counter tops, we thought we could accommodate this tenant if she went through our application process.   Things checked out, and we settled on a 6 month lease with the intent of signing a new 12 month lease in the late spring during peak rental season in our area.  Because our tenant came to us, we didn’t spend much time or effort to “sell” our unit.  Most of our time was spent developing our lease and application process.  All in all, we spent about 40 hours preparing our lease, showing the unit, and signing the lease with our tenant.  I should mention that we used this e-book, The Everything Lease Addendum: How-to For Landlords by Elizabeth Collective over at to help us write our lease, and it was one of the most helpful resources we’ve found as new landlords.


We spent about 40 hours in November of 2015 preparing our lease and application process.

Our first tenant moved in while the unit was still being renovated.  We never took photos of the empty interior space.

Doh! <Smacks forehead>

We remedied this oversight in August 2016.  But, it was one of the more frustrating parts of trying to rent our unit.  Note to aspiring landlords.  Take photos.  Take great photos. Take lots of great photos.   If you don’t have a decent camera (by the way, your cellphone is not a decent camera) and a basic understanding of photography, hire a real estate photographer.  It’s almost impossible to get web traffic without photos.  And, great photos will set your rental unit apart.  Hopefully, the images will serve you for many  years to come.

Our first tenant left after about 8 months; she needed a larger space to accommodate her kids who were moving back in with her after finishing college.  So in August, we needed to go through all the tenant screening steps in addition to creating the marketing materials for our unit.  In total, we spent 33 hours taking photos, building a virtual tour, and updating our lease. We showed the apartment 4 separate times (about 1/2 to 1 hour each time).  So, our total to get the unit re-rented in July/August was just under 40 hours.


About 40 hours to re-rent the unit

So, what happened in between?

Remember how everyone who told you not to become a landlord cited not wanting to fix toilets!?  I can tell you that we didn’t fix the toilet ourselves.  Within two months of leasing the unit, we had a pipe inside our floor/the unit below ours start leaking. We spent 7.5 hours coordinating/supervising/figuring out who would pay for the repair in January of 2016.

Book keeping and taxes occupied a bunch of time in 2016: about 14 hours in April went to tax preparation & research.

Remember how I said we’re making about $20/hour when you include all the searching and renovations?  Let’s take away those startup costs.  We’ll remove the ~200 hours we spent searching for a rental property.  And, let’s remove the 400 hours we spent renovating.  Now, for some fun math: $17,000 divided by 200 hours of operations.

Wait for it


Not bad for a side hustle.  That works out to the hourly wage of a hand or foot model!

Hands.  Photo Credit: Alex Tran

Hands. Photo Credit: Alex Tran

Stats and Personal Finance: Oh My!


Lions and Tigers and Bears… Oh my! Wikipedia user: Wulfstan; Wikipedia user: Dibyendu Ash

Last time, I wrote about volatility in one’s expenses.  I kept it pretty generic.  On purpose.

Today, I’m going full nerd.  We’re going to look at expenses and how much volatility there really is.  And, we’ll quantify that volatility.  I’ll do my best to keep it accessible, but it will involve a smidge of math.

If you’re still trying to get started tracking your expenses, is a great way to start.  You can probably get everything set up in about an hour.  After that, it’s almost entirely automatic.  You get all the charts and graphs that most normal humans would ever want to look at.  And, for us nerds, everything is downloadable so you can do extra analysis when no one else is looking.  OK, back to the task at hand.

Before we get too deep in the proverbial muck, a question: why would you want to look at expense (or income) volatility!?

Answer: so you can plan better for long term things like financial freedom, retirement, house purchases or pay-offs, and budget risk.

In my last post, I showed a time series plot of our monthly expenses:

And because I'm a nerd, I also track spending within each pay period to ensure I don't go over budget.

And because I’m a nerd, I also track spending within each pay period to ensure I don’t go over budget.

So that I can work with our actual data in the public domain of the internet, I’m going to convert all of the expenses to a standard normal distribution so I’m not sharing the actual values.  You’ll still be able to see each monthly value expressed as a z-score relative to the average of all the others.  (That translates into the following: expensive months will have big numbers.  Cheap months will have small, possibly negative numbers.)   If that leaves you confused or angry, you can watch Psy dancing Gangnam Style again and feel a little bit better.   Or, you can just skip the conversion when (if!) you try this at home with your real data (and not post it on the internet).

std_expense-varianceA couple of things jump out at me based on these two views.  Lets start with the time series plot:

  • There’s a whole lot more volatility in the past 3 years than in the first 3 years.  Lifestyle inflation, buying and renovating a rental property, and having a little one will do that.
  • Baseline expenses are going up.  And, we probably have more increases to look forward to in the future.  That’s even after accounting for the “unusual” expenses of starting a rental property
  • The histogram shows two humps: the main one centered on 0, and a second one centered on 3 sigma.  Given the relative sizes, that says to me that we’re pretty good about sticking to our budget.  But, when we overspend, things really come off the rails.
  • As with so many things in life, “Average” doesn’t tell the whole story.  That’s the crux of this post, so I’m going to break out of bullets and expound.

A lot of conventional wisdom is centered around the idea of working to an average.  Average home prices, sales figures, time to commute to work, heights, salaries, etc.  The list goes on and on.  We’re taught from a very young age that in order to summarize a data set, we should add up the numbers and divide by the number of numbers.  That’s the average.  It’s a useful statistic to start with, but it doesn’t tell the whole story.  Check it out.  Each of these lines (H, S, and P) all have the same average.  What’s different?  The spread.


There’s more to data than just the average. If your head is in the freezer but your feet are in the fire, on average you’re fine! Right?



Enough stats for a minute.

Back to personal finance.  If I were to plan my 2017 monthly budget around last month’s expenses, I might pick up an outlier where we paid all the fees for an au-pair (and ate nothing but peanut butter and jelly to compensate.)  If I take a 6 year average, I might overlook the fact that my recent baseline expenses seem to be increasing.  So, let’s only look at the past 3 years.  That might have a more representative sample.

The past 3 years of standardized expenses

The past 3 years of our standardized expenses

Now I have a range of expenses to plan for in the future.  Picking the average and planning for that level of spending would result in having enough money allocated to cover 54% of months!  I’m dipping into my savings or emergency funds almost half the time.  If I want to be more confident that I’ll be allocating my budget appropriately, I should plan for a higher level of spending: enough to cover 80% of months.  That means I need to be budgeting at the 1 z-score level and not the average  (Or, we could slash our expenses by eating nothing but Ramen noodles).

Even if I upped our budget to cover an 80% level of spending, we’ll still be short in approximately 2 months of each year!  I have more confidence that the financial plan I’m building will get us where we want to go.  Is it relevant for 20 years from now? Probably not.  But, for the next couple of years, yes.  This model is probably relevant and can give me a good idea of what life will cost, including some pretty major surprises.  With this new information, I can set new thresholds for my emergency funds to ensure that we’re as prepared as we can be if/when disaster strikes.

Has anyone else looked at their spending (or income) with an eye towards variation?  What insight did you draw from the analysis?

Even When You Track Every Penny, You Still Have Uncertainty!

Source: US Mint. Do you know where all of your Lincolns are?

My friend and I were discussing, Your Money Or Your Life the other day.

There’s this cool graph where they extrapolate current earned income, current expenses, and investment income.  At some point, investment income surpasses (hopefully!) expenses.  At that instant, you’re financially free.  It’s a profound idea.

Now, don’t get me wrong.  I think tracking your expenses is a crucial start to to developing financial freedom.  But, I also don’t think it is a silver bullet.

As someone who has tracked expenses for almost 6 years, I’ve really struggled to apply the Your Money or Your Life view for myself. Maybe it’s because we’re 35 and have a new daughter.  I can tell you firsthand, she has injected more financial uncertainty into our lives than I thought possible.  Maybe it’s simply because financial freedom seems so far away.  Or, maybe it’s because there is lots of volatility in day to day, week to week, month to month, or even year to year financial ins and outs.  Let me show you what I mean.

Here’s our tracked expenses from since 2010:

Mint's auto expense tracking is super convenient. Here's our chart of monthly expenses since 2010

Mint’s auto expense tracking is super convenient. Here’s our chart of monthly expenses since 2010

And here’s, my spreadsheet version…yes, I track things in 2 spots.  Ugh, and I’m admitting that publicly.

And because I'm a nerd, I also track spending within each pay period to ensure I don't go over budget.

And because I’m a nerd, I also track spending within each pay period to ensure I don’t go over budget.

First, they provide different results. automatically bucketizes things.  It’s super convenient, and I use it less for monthly tracking than for long term trends.  I also use a modified envelope method to keep savings funds for things like home improvement/repairs, rental property expenses, insurance premiums, etc.  My goal is to take big periodic cash outflows and bake small amounts of savings into my budget so that when the expense hits, I’ve already saved for it.  Unfortunately, Mint doesn’t do such a good job of managing these type of periodic expenses.

My Excel version is completely manual and is really used tactically.  How much do I have left in my budget for this pay period?  Can I go out to lunch with the work crew tomorrow, or am I brown-bagging it?  So, the bucket granularity is much different.

The first thing that jumps out at me is the upward slope of the trend-lines.  Yes, our real expenses are increasing.  That much is clear from both charts.  So, is it time to finally cut the cable TV cord!?  Should we start eating Ramen?  Probably not.  In this case, we’ve purchased our first rental property.  That’s the big set of spikes at the end of 2015.  And, our little girl is costs us way more than our two dogs do!  So, while the upward trend is disconcerting, it’s also understandable given everything that’s been going on in our lives.  To me, this is the power of tracking every penny you spend (twice!).

The real reason that I think tracking every penny still leaves you with uncertainty is the volatility.  I put trend-lines into each plot to also illustrate how far from “average” our expenses are in any given month.  Some months, they’re high.  Some months, they’re low.  This volatility also makes me pause when I try to extrapolate a trend-line 20 – 30 years from now.

I would like to explore this topic further in future posts.  I think there is great wisdom in tracking one’s spending.  I also think with thoughtful analysis, using income and expense trend-line extrapolation can reveal deep insight about one’s financial freedom trajectory.

Has anyone else tracked their expenses manually vs. using an automated tool like  Have you tried to forecast your financial freedom point? What did you learn?

Wealth to Lifetime Earnings Ratio: Part 2

This is Part 2 in a short series exploring the concept of lifetime earnings vs. net worth.  It’s pretty heady stuff so I’m breaking it up into a couple of linked posts.  You can find Part 1 here.

Today, we’re going to look at the lifetime earnings part of the wealth to lifetime earnings ratio:

1. How to calculate it
2. What you can learn from it
3. How yours compares to others

Step 1: calculate your lifetime earnings
As of 2016, if your income is from the U.S. and you make less than $118,500, your task is easy. Go to the social security website and logon (create your profile if needed). Once you’re in, find the “Your Earnings Record.”. Download your earned income history so we can do some math (yes, math… unless you’d prefer to go back to watching cat videos).

If you’ve ever earned more than $118,500, you could take the extra step of finding out your actual earnings for those years. (Americans are only taxed on the first $118,500 of earned income). Every earned dollar above $118,500 is exempt from social security taxes. If you want to find your real wages for years when you made more earned income than $118,500, tax returns, old pay stubs, or the spreadsheet you started this exercise on when you first read Your Money Or Your Life (affiliate link) in the 90’s are your best bets to find this information.

Step 2: what can you do with it?

The first thing to do once you have your earned income history is plot it over time. Here’s the median US household income plotted over time.  Depending on your personal situation, your actual income may be close to the median or far from it.  But, it’s a consistent yard stick that you can measure yourself against other Americans.

US Median Income

Median US household income in 2014 dollars, from

Has your earned income grown over your career? What kind of disruptions can you see (e.g., Dotcom era, Great Recession)?

If you take each year’s % change, you can see your annual earned income growth/decline.  Here’s a view for % change from year to year for the US Median Household.  Not terribly exciting is it?

US Median Income Percent Change

Year/Year % Change in median US incomes.  Source:

The long-run average annual % change: 1.0.  Not a 1% increase.  Flat.  It took me longer than I care to admit to extract the data from the census website.  I kept thinking there had to be a better way than just re-typing.  (There probably is, but I gave up).  So, I made a Google spreadsheet that has the raw data, the percentages, and space for you to put your values in and see how you’re comparing to the US median over time.

Please, please download the sheet to your local system first!

Here’s a snapshot of what the spreadsheet would look like for someone starting with $60,000 of earned income in 2000 and making a 3% raise year over year for 10 years.  Let’s hear it for compounding growth!



So, how does your income compare to the US median?  Did your earned income keep up with the median year/year rate changes?  What insight did you get from plotting your data this way?