With all the volatility in today’s real estate market, it’s more important than ever to understand the numbers before you buy. As my grandmother used to say, know how deep the pool is before you jump in! The right software can do just that (metaphorically speaking, of course), turning an often confusing and intimidating analysis into a straight-forward (and dare I say “fun”) exercise. The following case study looks at real estate investment analysis software, and how it can be used by investors—novice and professional, alike—to assess the financial viability and risk of a property.
This case study analyzes real world properties using real estate investment analysis software by GreyStone Analytics (full disclosure: I’m the founder and CEO of GreyStone Analytics, so please forgive any bias). The goal of the study is not to promote a particular software product, but to show how such analysis tools can help investors make profitable decisions.
The scenario: You have $25,000 you want to invest in real estate. You want to maximize your financial return while minimizing risk.
The problem: There are several properties to choose from in your area. How do you select the right one? You need an effective way to evaluate and compare investment financials.
The solution: Use real estate investment analysis software to quickly, simply and effectively evaluate the financial viability and risk of all your options.
About the Property
As the basis for this case study, I looked through real estate listings in my hometown of York, PA. There were several listings that met the criteria for the investor scenario described above. Let’s start by looking at one property in particular:
Property A (Codorus Manor):
Purchase Price: $159,900
Number of Units: 4
Total Monthly Rent: $2,125
Property Tax: $3,150
Other Operating Costs: $580/month (property management) + $100/month (maintenance)
Closing Costs: $3,000
Estimated Cost of Resale: 7.0% of sales price
Loan: 30-year fixed rate @ 6.000%
Of the $25,000 the investor has to invest, we assume $3,000 goes to closing costs and the remaining $22,000 is used as a down payment. That translates into a loan of $137,900 ($159,900 purchase price MINUS $22,000 down payment).
Before diving into the analysis, we need to make a few more assumptions regarding future revenue, costs and resale valuation. While it’s difficult to accurately predict future market trends, the GreyStone software automatically displays historical zip code-level market data to provide context for these assumptions. For instance, Property A is in the 17404 zip code. According to the software, the median rent for this zip code increased by an average annual rate of 2.7% (from 1990 to 2000). The user can modify the assumption based on his/her knowledge of the market, but for purposes of this analysis we’ll use the historical rate of 2.7%. We assume operating costs and taxes increase by 2.0% annually, and the property has an average vacancy rate of 3.0%.
Similarly, we must make assumptions regarding the property’s resale value. Most software packages allow you to choose from a few different valuation methodologies. The GreyStone software, for instance, allows you to estimate the property’s future value using any one of the following methods:
- Annual Appreciation
- Cap Rate (based on current year Net Operating Income)
- Cap Rate (based on next year Net Operating Income)
- Gross Rent Multiplier
For purposes of this example, we’ll use the annual appreciation method. Again, we’ll apply the historical market data from the software, which shows a 3.5% annual increase in median home value for the zip code.
While the software lets you go into much more detail (e.g., capital expenses, variable interest rates, interest only/balloon loans, refinancing, passive loss assumptions, unit level revenue/vacancy assumptions, reimbursable expenses, etc.), we’ll keep this example relatively simple.
The software walks the user through a set of input screens: Property, Loan, Revenue, and Costs. An example of the Property input screen is shown below (note, the Market Data in the lower-left corner is automatically generated by the software, based on the zip code entered by the user).
Looking at the Numbers
Once all the inputs are entered, the financial analysis is automatically generated by the software. So rather than trying to calculate the financials yourself, you can put the power of the software to work for you. Let’s first look at cash flow.
Figure 2 shows the investment property generates a cash flow (after tax) of $3,326 in year 1, growing to $6,280 in year 10. Annual cash flow continues to rise over the course of the analysis as rent increases outpace expected operating expense increases. If you’re interested in looking at the detail behind these figures, the investor can select the Operating Cash Flow table (shown in Figure 3).
While cash flow is helpful in understanding annual cash generated by the property, we need to look at other metrics to answer the fundamental question: Is this property a good investment? To answer this, let’s look at the investment return metrics (Figures 4 and 5).
There are several worthwhile metrics to look at here, but let’s start with Net Return on Investment (ROI). In this case, Net ROI is defined as the total profit generated by the investment (after tax). For instance, in the above example the investor would enjoy a profit of $35,150 if the property were sold at the end of year 5. For many investors, this is the most straight-forward way of analyzing an investment. I like to think of it as the “show me the money” approach. And if the investor were to hold onto the property for 10 years, he would enjoy a profit of $98,215. On an initial investment of $25,000, these returns look pretty good.
Another powerful metric at the investor’s disposal is the Internal Rate of Return (IRR). In the above example, the IRR (after tax) in year 5 is 23.1%. In general, if the IRR is greater than your expected return from an alternate investment (e.g., stocks, money market account), then it’s a good investment. There are definite pros and cons to using IRR, which deserves a more thorough discussion than the one provided here, but suffice it to say an IRR of 23.1% is better than most investments (certainly beats the 0.05% annual interest on my Wells Fargo checking account).
Based on the analysis so far, the property looks like an excellent investment candidate. However, before making a “go/no-go” decision, we need to look at the risk analysis and compare the financials to other investment opportunities.
Suppose the investor was confident he could buy the property at the assumed purchase price ($159,900), but was less certain about some of the other assumptions—such as resale value, operating income and costs. The GreyStone software allows the investor to set a “risk level” for each of these input assumptions, then uses this information to run 10,000 simulations and displays a range of financial outcomes with associated probabilities. (This methodology is known as Monte Carlo simulation, which is used extensively in scientific and financial analyses to address uncertainty.)
For instance, in Figure 6, the investor has set the risk factor for individual input assumptions and run the analysis for Net ROI in year 5. The analysis reveals there is an 80% chance the year 5 ROI will fall between $11,474 and $59,304, with a 10% chance ROI will be below $11,474 and a 10% chance ROI will be above $59,304. The risk analysis gives the investor a sense of the potential upside and downside to the investment property financials. So while the initial financial analysis may look good to an investor, the risk analysis may show a downside exposure that is greater than the investor can tolerate. This is the power of risk analysis.
Now suppose the investor did a similar analysis for a different property he was considering (I won’t bore you with the details). The software allows the investor to compare the financials for the two properties side-by-side. Figure 7 shows such a comparison. Based on the example in Figure 7, our initial property (Codorus Manor) has a superior ROI compared to the second property (Yorktowne Condo) over the entire 30-year analysis period. Similar side-by-side financial comparisons can be made for other key metrics, such as IRR and NPV (Net Present Value).
While individual investor criteria will vary, Property A (Codorus Manor) looks like a good investment opportunity with a relatively low risk profile.
One other useful component of real estate investment software is the Help tool, which provides on-demand descriptions and formulas for the inputs and outputs used in the analysis. This is a valuable aid while using the software, and also helps the investor gain a greater understanding of real estate investment analysis in general. For instance, if you were unfamiliar with the term Cap Rate, the Help tool provides a description of the term and a formula for how it is calculated (see Figure 8).
There you have it. You probably need a few extra shots of caffeine if you made it this far! There’s a lot more we could cover, but this case study provides a high level example of how real estate software can be used to evaluate an investment opportunity. You don’t need a Ph.D. in Finance to thoroughly analyze the potential value of an investment property, but you do need the right tools. I certainly encourage any interested investors to take a look at GreyStone’s website: http://www.greystoneanalytics.com. There’s a free trial version if you want to test it out.
And even if our product isn’t your cup of tea, I would strongly recommend looking at other real estate investment analysis software packages (a quick search on Google should do the trick). It’s a very small price to pay compared to the potential value of your real estate investment.
Good luck to everyone and happy investing!
About the Author: Bill Christensen is the founder and CEO of GreyStone Analytics, LLC. If you have any questions or comments, you can contact Bill directly at billc [at] greystoneanalytics [dot] com.