You’ve probably heard the words “Case-Shiller” mentioned hundreds of times by now … it’s not surprising. The Case-Shiller Index (CSI) is now touted as one of the most respected and reliable means of tracking the health of the housing market. Headlines are littered with references to this magical index, so we thought we would take a peek at how relevant the CSI is as it relates to our Home Sweet Home: New York City.
The purpose of the index is to solve for the basic dilemma faced in tracking home values over time: the particular mix of properties sold at any point in time can create the illusion of a change in property values. In other words, if larger, more expensive homes sell in one month, values appear to increase, even if the price of every home in the market has actually remained flat. The same dynamic applies in the opposite direction. Welcome to the fun, fun world of reading housing data.
So let’s look a bit at the methodology employed by the index. First, it uses the repeat sales method of tracking changing home values. In order to capture the true appreciated value of a property, it only tracks data on those units that have sold at least twice. The index looks to sales pairs to determine the price change for same property. Further, it tries to discount for changing sizes or quality by down-weighting price change observations that are dramatically out of whack with surrounding area comps. Lastly, and importantly, its focus is single-family homes. This means that the index (reporting on the Metropolitan Statistical Area of NYC, or MSA) does NOT include co-ops and condos.
What are the pros and cons of the CSI as it relates to NYC? Let’s start with the cons, first (numbers crunched from StreetEasy, Miller Samuel and Property Shark data).
- Does not include co-ops: Among purchase properties, co-ops represent approximately 65% of Manhattan inventory, yet represented 45% of sales that took place in 2009.
- Does not include condos: Among purchase properties, condos represent approximately 30% of Manhattan inventory, yet represented over 50% of sales in 2009. [Note: Shiller has created a condo-specific index recently, though these numbers are not incorporated in the CSI, itself.]
- One large chunk of data omitted: When we combine co-ops and condos, as a percentage of 2009 activity, they represent 99% of sales in Manhattan, 46% in Brooklyn, 42% in the Bronx and 40% in Queens. That’s quite a significant amount of data that’s in no way included in the CSI.
- Does not include new developments: New development sales (all of which are condos or condops) made up fewer than 20% of overall sales in Manhattan. Therefore, new construction in established markets or emerging market activity is not captured.
- Does not include multi-family dwellings: While less of a factor for Manhattan, multi-family dwellings are certainly material in number throughout the outer boroughs.
- Skewed towards volume: The theory goes that smaller homes tend to have more turns per period. Therefore, since larger homes turn less often, data will be skewed in either direction by when these larger homes do move.
- Excessively large footprint: The counties included in the NYC metro area are many. Should conclusions about Manhattan trends be made off a single-family home sold in Passaic, NJ?
- Connecticut: Fairfield and New Haven
- New Jersey: Bergen Essex, Hudson, Hunterdon, Ocean, Passaic, Morris, Monmouth, Middlesex, Somerset, Sussex, Union, and Warren
- New York: Bronx, Dutchess, Kings, Nassau, New York, Orange, Putnam, Queens, Richmond, Rockland, Suffolk, and Westchester
- Pennsylvania: Pike
- Same to same: It actually tracks changes in value of the same home over time. Average and median prices usually published on a quarterly basis are difficult to interpret based on the size and quality of what sold in that particular time period.
- Not including new construction in the mix is a good thing: Overall, new development units tend to be larger and more expensive, thereby skewing trends upwards the greater percentage of sales they make up. (The same goes for remodeling and house upgrade trends, nationwide.) Analyzing average sales prices alone may therefore provide a false market high because it does not fully reflect the cost of improvement. Furthermore, the fact that contracts were signed on average 12-18 months prior to closing makes it an excessively lagging market indicator.
- It bypasses the size issue altogether: Other methodologies of tracking housing values divide house prices by the number of square feet to create even comparison of homes by size. Yet, in NYC, as we’ve recently read, square feet are far from static and can vary quite significantly.
- A Manhattan market reflection it is not: The mere fact that 99% of the sales in Manhattan are excluded, along with about half of the sales in the outer boroughs, makes the CSI tangential, at best, in analyzing NYC trends. What is interesting to ponder upon, however, is the relationship between the single-family home sales and condo/co-op activity. To the extent that anomalous times require a more macro, national perspective, for as local as real estate is, the CSI serves as a valuable yet limited starting point for overlaying national trends on top of local NYC dynamics.
- Best for anticipating longer-term swings: While there is arguably little predictive value of this or any index for near-term conclusions, the index becomes much more useful for analyzing long-term trends. This is particularly the case during times when we are far away from historical norms and equilibrium levels, as we have been.
- Far from perfect yet still the most relied upon: Making sense of housing statistics is no easy feat. As such, every methodology will have its pitfalls, as does the CSI. That said, as imperfect as it is, the repeat sales method continues to be regarded as the gold standard. The key is to understand its strengths and limitations as you read the conclusions drawn from headline numbers.
- Laudable intentions: We must not forget the intentions behind the tracking and analysis of such data: adding transparency into the system and empowering all end-users to make better decisions, whether it’s buyers, sellers, agents or even the government as it looks to create policy. The great news is that the trend is heading towards more and better data, and we must recognize and encourage that … it’s just not perfect quite yet.