1. First, we identify the most statistically similar properties to the subject, using physical distance, year built, number of units, GPR per unit to assess similarity for properties in the same MSA. This ensures our benchmarks are based on the most similar properties for which we have data.

2. Next, we average the last 90-days worth of recently closed listings (taking the last listed price before each unit was taken off the market, which is within $5-10 of the actual rents based on our comparisons of our data to our clients' rent rolls) to get an average rent per unit for the property. We then multiply this by the number of units in the building to get an estimate of GPR. We don't estimate loss-to-lease yet, but this approach helps determine the most accurate current market rents to use in your analysis.

3. Next, we use the 10 most similar expense comps (again, determined by similarity of year built, number of units, location and GPR per unit) to provide a benchmark for each income line item as a % of GPR.

4. We then multiply the estimated GPR by the computed % of GPR benchmarks to generate estimates for the remaining income line items.

5. With the remaining income line items predicted, we calculate an estimated EGI for the property.

6. After we have the estimated EGI, we use the expense comps again to generate benchmarks for each expense line item as a % of EGI.

7. We then multiply the computed EGI by the benchmarked % of EGI values to estimate the amount of each expense line item.

All of this is done automatically in a few seconds when you enter an address for the subject property, so no manual calculation is required.