The health of the real estate markets is on everyone’s minds. As we all continue to be inundated with housing data, here are some tips in actually making sense of it. You will note that our comments below serve to ask more questions than they seem to answer. This is because data crunching is rarely black or white. The key is in understanding the gray and avoid taking others’ conclusions at face value.

Seasonal adjustments: The real estate industry is highly seasonal. This means buyers buy in the fall, renters lease in the summer and most activity is dead in peak winter months, each and every year. In order to then figure out if “up” is really up in terms of data, you need to compare numbers to the same “season” last year. To avoid waiting a whole year to compare data as it arises, and smooth out seasonal trends, researchers “seasonally adjust” data to make it more useful and relevant. You will often see seasonally adjusted data reported as “SA”, and not seasonally adjusted data as “NSA”. The trick is knowing which is which and how to read it. In a period of seasonally high volume, the adjusted numbers will be lower than the not adjusted, and vice versa, precisely due to this smoothing out process. Reading that SA housing starts are up by 20%, for example, doesn’t mean that starts themselves are up by that much; rather that they beat the expectations of the smoothed out numbers we would have seen if we ignored seasonal influences. Tip: be aware of the nature of the numbers you are reading, SA or NSA, and read analyses through those respective lens.

Margin of error: Data on new home sales is released each month, only to be revised up or down weeks later (same goes for unemployment figures, jobless claims, home prices, etc.). Needless to say, when the margin of error % is greater than the actual reported change in sales, the released figure becomes meaningless. Since the markets are forward looking, few people actually look back to see the revised numbers, relying purely on the first-reported estimates. Tip: compare the margin of error with the degree of change being reported to gauge how meaningful the data really is, and don’t neglect revisions.

The issue with trends: Trend numbers imply a linearity of sorts. One could look at prices in February versus May, for example, draw a straight line and conclude the degree of movement (assuming the same 1-bedroom that rented for $2500 in February is now renting for $3000 in May). What such trends neglect is the actual shift in inventory from month to month or quarter to quarter, which is particularly poignant on the rental front. The key question is: What does the seasonal difference in actual market inventory look like and how significant is it? Tip: observe the changing inventory of what you are comparing as a backdrop against which to analyze the data.

Quarter on quarter mistakes: Take quarter-on-quarter data analysis with a grain of salt, as it neglects the seasonality of real estate markets. Of course Q2 will be busier than Q1, for example; this happens every year. This is why researchers primarily use seasonally adjusted numbers versus not seasonally adjusted data. Year on year comparisons (y-o-y) provide a more accurate perspective on market activity. Tip: do not make decisions or enter negotiations relying solely on quarter-on-quarter data.

Year on year imperfections: While Y-o-Y data is the gold standard, even it is imperfect. Great examples can be found on the Lower East Side and Midtown East, where a plethora of new condo developments have significantly skewed year-on-year sales numbers upwards based on luxury inventory which previously did not exist. Neighborhoods have evolved significantly over the last few years and will continue to change over time. Tip: analyze year on year data with an understanding of neighborhood-specific developments.