NC3 Scenario Variables

Yesterday I showed the variables you can use related to Accounts on the Nomad Calculator 3™. With these variables you can modify things like account balance and rate of return on the Accounts in each Scenario.

Today, I’ve added the ability for people to create Rules for modifying any of these variables at different points in your Scenarios for the Scenario itself.

While there are not that many, there are a few variables (like the ones shown below) that you may want to modify while modeling your investment portfolio. Eventually, I do plan on making additional capabilities for the calculator to allow you to use scenario-wide values to modify Accounts and Houses. Features like what I’ve added today and what I plan to add in the future will continue to improve your ability to model your own real estate and stock investing portfolios holistically plus it allows me to do even more sophisticated monte carlo simulations for the forthcoming videos, podcasts, blog posts and books I am writing.

Here are the variables you can modify Nomad Calculator 3™ for Scenarios.

Effective Income Tax Rate

This is your Effective Income Tax Rate for calculating how much tax you pay. If you enter in 19.4 you are saying you're paying 19.4% in income tax.

Inflation Rate

This is the rate of inflation for the entire Scenario. We use this to show what a dollar in the future is worth in today's dollars. We expect a value of 3.000 for 3% inflation rate.

Just liked with the Houses and Accounts, it is important to realize that these are primarily input variables and not all the variables that we store based on the output from Scenarios.

NC3 Account Variables

Last month, I showed you the variables you can use related to Houses on the Nomad Calculator 3™.

Since then, we’ve added the ability for people to create Rules for modifying any of these variables at different points in your Scenarios.

In quarter 2, 2018 I have been working on (and will continue to work on) features that allows us to set these types of variables to random numbers or quasi-random numbers based on a normal distribution so we can do monte carlo simulations of real estate investing portfolios. To be 100% clear, while users can become subscribers of the Nomad Calculator 3™ and use it model both simple and sophisticated Scenarios by changing values, the Monte Carlo features are only available to me since they take up so much time and computer resources to run.

Today, I would like to also announce that you can also modify the following variables for Accounts.

Account Balance

This is the amount of money you have in this Account for this month. Expecting a dollar amount in the format of 123000.00 for $123,000.

CumulativeDeposits

CumulativeReturns

CumulativeWithdrawals

NetCumulativeDeposits

TotalAccountBalanceBeforeReturnThisMonth

TotalReturnThisMonth

Yearly Rate of Return

This is the yearly rate of return on the money you have in this Account. The return is calculated monthly even though the return is shown as a yearly return. Expecting an interest rate in the format 11.3 for 11.3% return per year.

Just liked with the House, it is important to realize that these are primarily input variables and not all the variables that we store based on the output from Accounts.

Real Estate Appreciation Rate using Percent Change from Normal Distribution Curve

Good morning and good news! I woke up this morning and started working on adding in some additional ways to randomize variables in the Nomad Calculator 3.

Earlier this month, I added the ability to set a House Variable to a random number between a high and low. With the way I previous coded it, for example, I could say the house price appreciation is somewhere between -5% per year and 5% per year (calculated monthly). That meant that each month you decided to run this Rule, the real estate portfolio modeling software, would pick a random number between -5% and 5% and set that appreciation rate to be equal to that.

As some of you might imagine, that meant that the results were erratic. You could have a month where you’re property went up in value at a rate of -5% per year (since we calculate it monthly though… it means it went up 1/12 of that for that month) but the very next month we could, if the random number gods made it so, have it go up 5%. For modeling reality, I found this highly unlikely when talking about appreciation. For things like modeling maintenance costs on a property, a truly random number between a range might be an acceptable way to model it. Of course, you can choose the range of random numbers, but still, it was completely random.

Back to appreciation rate though… when we are modeling appreciation rates, I think appreciate rates tend to trend. For example, if a property has been going up at about 3% per year, it will tend to continue to up about 3% per year the next month. Maybe it is going up 2.9% or maybe 3.1% but not likely be -5% the next month. To model this, I decided to add an additional way to model these which will be especially helpful when we do monte carlo modeling of real estate investing portfolios.

Now, with the new code, I pick a percentage change for appreciation rate based on a normal distribution curve. So, I might say the appreciation rate can change anywhere from -25% to 25% in any given month with the average being that it does not change at all. So, if it was at 4% and it changed -25%, that means the next month it could be as low as 3%. But, that is not super likely. It could also go up 25% so that the yearly appreciation rate was 5%. Again, that’s not super likely either. It is most likely to remain at 4% (a change of 0%). It is also relatively likely it will drop something like 3.9% or go up to 4.1%.

I ran it on a Scenario I had been playing around with for modeling Nomad and here’s what the appreciation rates for a number of houses ended up being.

As you may be able to see in the chart above, this shows appreciation rates that trend. Again, this is because we are adjusting the appreciation rate a percentage of what it was the previous month based on a normal distribution curve.