This year has felt like riding a roller coaster…in the dark…facing backwards. Some of the changes are probably specific to my market, but others will be familiar to most of you. The shifts in supply and demand have been rapid and not always easy to predict.
I wrote previously about the impact of increasing interest rates and inflation on the residential real estate market. In that article I demonstrated the decrease in buying power from rising interest rates. That decrease in buying power puts downward pressure on demand, which in turn should put downward pressure on equilibrium prices. What actually happened was a short term spike in pricing.
The explanation? Expectations.
As the interest rates rose from 3% to 5%, the impact on the decrease in buying power is a mathematical certainty; however, the market reaction is not merely based on current or historical events in a vacuum. In this case, buyers expected interest rate increases to continue. For the sake of clarity, let us assume that the market as a whole expected interest rates to rise from 5% to 7%. This expectation creates an incentive for some potential buyers (who could still afford the higher payments) to purchase now. The potential buyer’s mindset is not focused on the 2 percentage points they have already ‘lost’ by not acting sooner. Instead, the mindset is focused on the 2 more percentage points they might lose by continuing to delay. This actually resulted in a spike in pricing even after a dramatic rise in interest rates.
As you can see from the graph, the phenomenon was short-lived, with the longer term downward pressure in demand finally catching up. As appraisers, this phenomenon creates an interesting situation.
While several questions may arise in considering this phenomenon, today we will just deal with one. What is the best way to apply adjustments for market conditions changes when changes in the market over a given time period are not only non-linear, but also multi-directional? In other words, if I am making adjustments to comparables during the past year, there is no single adjustment formula that makes sense. The data from the graph above suggests that when considering a year-over-year price change, the market has increased by approximately 9%. While that may be true, if a 9% annualized rate is applied to all of the sales the results would not be reflective of the actual changes in the market. In fact, it is clear from the graph that sales which occurred during the spike in pricing would need a downward adjustment for market conditions, not upward.
This is where utilizing an index or benchmark is useful. A benchmark is a point of reference by which comparison can be made with other points. In the case of the graph above, the monthly average price per unit can be used to create a benchmark from the most recent period available relative to different time periods. In the chart below, a composite average of the 3 areas, or searches, was utilized as an index.
June 2022 was considered the benchmark, as the most recent time period for which data was available. Each prior month was compared to the benchmark index to derive the percentage change in the index price relative to the benchmark. Assuming you have chosen data relevant to your subject’s sub-market, the percentage change could be applied as an adjustment. Of course there are other considerations needed before choosing to apply that adjustment. Pricing of current listings would be one. If the listings suggest an ongoing erosion in pricing, then a different benchmark could be supported.
Benchmarking and indexing are just tools. As always, it is up to the appraiser to analyze and report market conditions accurately. While this is a useful tool to that end, it is not the only tool available to appraisers.
By the way, the table above can be created in about 15 minutes in Google Sheets or Excel. You may want to keep an eye out for ways to export CSV files from your data sources. These can be easily imported into a spreadsheet which makes it easier to gather the information for your analysis. Keep in mind that the quality and quantity of data is critical for the applicability of any statistical tool, so choose your search criteria wisely.
We can’t stop the roller coaster, but maybe we can at least keep the lights on.