In a previous article on the new Advisory Opinion 37 (AO-37), I reviewed the requirement to scrub the data. Specifically, AO-37 states, “Regardless of the tool chosen, the appraiser is responsible for the entire analysis including controlling the input, the calculations, and the resulting output”.
Part of controlling the input is excluding erroneous data. As indicated in Part 1 of this series, an appraiser’s first step is to confirm whether the data is correct. Often this is obvious. For example, a parcel may be listed with a lot size of 192,000 acres instead of 1.92 acres. In a market with no parcels exceeding 20 acres, a 192,000 acre parcel is an immediate red flag, though knowing this would require some familiarity with the market or geographic competence.
But, finding erroneous data is not always that obvious. If an appraisal assignment comprises a 5,000 square-foot parcel, should the comparative data set include only sales of 4,000 to 6,000 square-foot parcels? Should the search be broadened to 2,000 to 10,000 square-foot parcels? Up to a half acre?
Before the advent of easily accessible data and the tools to understand that information, we would cast a wide net and then narrow the search down to three to six properties. Perhaps that was the best we could do in a data poor environment. But now, in suburban or urban environments, that approach will limit the number of sales to such a point that you cannot understand (model) what is happening in the market place. In an AO-37 world, “controlling the input,” requires including the transactions that affect the subject’s value, and excluding the transactions (outliers) that do not have a bearing on the subject’s value. Only then are you ready for the next step: analyzing the data or “the calculations.” There are many considerations here, among them, date of sale, view, topography, neighborhood boundaries, and other influences. In most cases, this makes a paired sale useless because there are too many variables. Furthermore, using two sales, almost by definition, limits your understanding of the marketplace. Compounding the problem, if they fail to conform to the clear directives of AO-37, appraisers face possible career-ending scrutiny from tools like Fannie Mae’s Collateral Underwriter (CU).
What is the solution? There are several. Many appraisers are going back to school to learn applied mathematics or statistics and then employ a tool like “R” (which is free) or Excel (which is nearly free). But that option could take years! Other appraisers have started looking for tools that will transparently analyze real estate data, empowering the appraiser to make defensible valuation decisions. HouseCanary Appraiser is such a tool. It allows the appraiser to see vast amounts of information in an understandable form.
Here is one example: when you begin an appraisal in HouseCanary Appraiser, the first screen you see is a map. This allows the appraiser to decide if the neighborhood is best understood in terms of census tracts, schools, values, or some other combination of inputs. Metaphorically, we are no longer limited to the visible spectrum, but can now see in something akin to the infrared.
HouseCanary Appraiser automatically fills in fields such as owner, borrower, and legal description and takes you directly to data that is foundational to understanding the neighborhood, or as AO-37 puts it, “... controlling input, the calculations, and the resulting output.” This saves time and gives you the power to make informed decisions about the data. The time-consuming information that was previously available to the GSE’s and underwriters is now made available to appraisers.