The **Real Estate Financial Planner**™ software allows you to model how a portfolio of **Accounts** and **Properties** perform over time.

If you’re modeling your portfolio using static values for variables, you might not see any variation in the results if you rerun a **Scenario**. Obviously this is not a good model of reality over long periods of time for inputs such as:

- Appreciation rates on
**Property**values and rents - Interest rates as you buy
**Properties**over time - Rates of return on your stocks, bonds or other investments
- Maintenance and capital expenses costs
- Vacancy rates

We’re likely to see variation in how quickly or slowly a **Property** goes up or down in value.

In other words, if we plot the appreciation rate of a **Property** we own in our portfolio over time, it is not likely to look like this:

It is just not likely that a **Property** will go up in value each month at a rate of 3% per year.

With the **Real Estate Financial Planner**™ software we can use **Rules** to set the **Appreciation Rate** of the **Property** to be, on average, 3% per year. If we measure the increase for a particular month it might range from +13% to -10%… or really whatever we set the **Rule** to be. For this example, I used the following settings for the **Rule** to vary the **Appreciation Rate**.

That **Rule** means that the average **Appreciation Rate** will be about the same 3%. It should average out to be the same, but there are months when the **Property** **Property**

The following **Chart** shows the range of values we could have for the **Appreciation Rate** using this new **Rule**.

When we look at the **Chart** of **Appreciation Rate** again, this is what it looks like this time:

Does this really even matter? Maybe, maybe not.

Let’s compare the **Net Worth** of someone who buys just one **Property**. In one **Scenario**, they have a fixed **Appreciation Rate** of 3% and in the other **Scenario**, they have a variable **Appreciation Rate** that averages 3%. Here’s a **Chart** of **Net Worth**.

The difference on a single **Property** over 40 years is about $10,000. It could just as easily have been the other way around, but in this comparison, the variable **Appreciation Rate** did $10,000 better than the static **Appreciation Rate**. If we run the variable one again, we’re very likely to get slightly different results. Let’s do that.

Running it again, the **Net Worth** of the variable one was about $14,000 lower this time. Here’s the **Chart**.

What if we make a copy of the **Scenario** with the variable **Appreciation Rate** as a third run to the **Chart**? Here’s a **Chart** comparing the 3.

As you run each one, there is some variation in how much the **Property** is worth and therefore what the ultimate **Net Worth** will be just buying the one **Property**.

I won’t do it here, but you can imagine what would happen if we were to vary more inputs like rent appreciation, taxes, insurance, maintenance, capital expenses, vacancy, stock market rates of return and more… all in the same **Scenario**. It is likely that the range of **Net Worth** would be wider.

I’m going to skip ahead and show you what running 10 variable **Scenarios** looks like with just varying the **Appreciation Rate** on a single **Property**.

And here is what it looks like in month 480 so you can more easily see the difference between the 11 different **Scenarios** (10 variable and 1 fixed). The 3% fixed **Appreciation Rate** happens to be the yellow one, the 3rd from the right.

If we were to summarize these into ranges of values, we might get a **Chart** like the one below. It shows **Net Worth** for 10 **Monte Carlo** runs summarized.

It is difficult to see what is going on in the **Chart** above so here is the same **Chart** zoomed in on the last 30 months (months 450 through 480).

Each different shade represents percentile ranks. Shown in the image above are the 1-99% percentile, 5-95%, 10-90%, 25-75%, 40-60% and the 50th percentile (median) as a line.

We can also choose to look at the **Appreciation Rate** for **Monte Carlo** **Scenarios** to see the range of values that we have for **Appreciation Rate**. Here is a **Chart** showing the same 10 **Monte Carlo** runs that we were showing you above. I have shown you the full 480 months.

If we want to look at the actual dollars in **Appreciation** that we’re seeing for each month summarized for the 10 **Monte Carlo** runs, we can also look at the **Appreciation** **Chart**.

What if we bump up the number of **Monte Carlo** runs that we do. For example, let’s say we do 25 total runs instead of just 10? Here’s a **Chart** showing **Net Worth** for 25 **Monte Carlo** runs.

If we zoom into this one as well to see only months 450-480 like we did above, the **Chart** looks like the following.

For the 10 **Monte Carlo** runs, the values for the 1-99% ranged from $762,569.75 to $805,441.24. For 25 runs, the same 1-99% range varied between $757,458.15 and $803,737.34.

What if we look at the **Appreciation Rate** for 25 **Monte Carlo** runs? It looks like the following **Chart**.

If we look at the amount, in dollars, that we see in **Appreciation** per month for the 25 **Monte Carlo** runs, the **Chart** looks like the following.

If we were going to do a full on **Monte Carlo** model of a **Property**, I would recommend that we create separate **Rules** to vary the following variables that are related to **Properties**:

- Appreciation Rate
- CapEx Appreciation Rate
- HOA Appreciation Rate
- Maintenance Rate
- Rent Appreciation Rate
- Utilities Appreciation Rate
- Vacancy Rate
- Other Income Appreciation Rate if there are any
- And, Other Expenses Appreciation Rates if there are any

I’ve gone ahead and created **Rules** to modify these variables. The following screenshot shows the **Rules** that we set up to model this.

Here are the results for **Net Worth** on 25 **Monte Carlo** runs.

If we compare that to the 25 **Monte Carlo** runs for just varying the **Appreciation Rate** you can see there is a lot more variation in **Net Worth** when we vary more of the options. In fact, the entire band is inside the band for the **Scenarios** with more variation.

We can see this a little more clearly if we zoom in to look at just months 450 to 480.

Also, you can see I included a line showing the difference between the averages of the two **Monte Carlo** **Scenarios**. That’s the dashed line and the labels for the values of the difference are shown on the right hand side of the **Chart**. The dotted “average” line in the middle of each band is the **Expected Value** from all the **Monte Carlo** runs. I will talk more about what that is and how to use it to make numbers-backed investing decisions in a future blog post.