Tuesday, July 3, 2018

Housing Market vs. Condo Market – A Good Champ-Challenger Starting/Learning Point

-- Intended for Start-up Analysts and Researchers --

1. Champ-Challenger analysis is an excellent way to provide a validation of your primary research. Let's assume you are analyzing the local single family housing market. Instead of combining single family residences (SFRs), townhomes (PUD/HOA) and condos, provide separate analysis of each category as their underlying demand characteristics are usually different, often resulting in vastly variant market behavior. 

2. Aggregate demand is not necessarily the best way to present a particular market, especially when the components do not move in tandem. For example, the Condo market generally leads the housing market - on the way up, as well as on the way down. So, in your analysis make the SFR market your Champ and the Condo market your Challenger. 

3. Remember, the Challenger analysis is nothing but a validation exercise. When the Champ is meaningfully challenged (validated), the study becomes inherently more meaningful and statistically more significant, considering they are mined off the mutually exclusive and competing market segments. The same concept applies to the other major markets, e.g., challenging a sector Mutual Fund with a competing ETF or a country analysis in emerging Europe with BRICS, etc. 

4. An unchallenged univariate analysis like month-over-month median sale price analysis is inadequate (necessary but not sufficient) to make informed business decisions. It needs to be challenged "intra" or "inter." The intra challenger is generally the normalized median sale price per sf. Conversely, the ideal "inter" could be the analysis of the condo market as it is a competing component (sub-market) of the overall housing market, thus leading to the highest and best analytical use of the condo market. See the links below to my prior posts for more detailed analysis.  

5. Normally, the SFR and condo markets remain in sync. When they diverge, you need to investigate as to why. Since the condo market often takes the lead - either way - it could be tell-tale, pointing to the beginning of a new market swing. For example, if the condo market starts to trend up, SFRs and Townhomes won't be far behind. When they diverge for a long time, you need to run the normalized tests to find out if the market internals are diverging as well. If not, it could be the "monthly" aberration. The 2-Month Moving Average helps reduce the monthly noise. These are the basic tools you must apply as part of your investigation. If the basic tools are unhelpful, a step-by-step Regression model could point to more precise reasons.

6. If you are forced to build a Regression model for the condo market, you must remember that the condo modeling is different from the SFR modeling. Condo modeling can be top-down or bottom-up. Avoid top-down modeling as it involves income modeling requiring hard-to-find complex-level income-expense data. Since condo sales are at the unit level, the bottom-up market modeling is more common. In addition to the unit-level condo sales data, market modeling does require data related to the unit-level property attributes, complex-level amenities and general location, which are generally available on county assessment sites.   

7. Remember, the SFR market tends to be more homogeneous than the condo market. While there are Waterfront Mansions, French Tudors, Brownstones, etc. in the SFR market, they are not the norm. On the other hand, condo markets routinely comprise low-rise, mid-rise, high-rise, skyscrapers, etc. with significantly different amenities. So you need to know the apples-to-apples comparison. For example, in NYC, only the low-rise condos are grouped with the SFRs in the same tax class, easing the comparison. In suburban markets, it is prudent to remove the high-rise/skyscraper condos. Of course, if you are using the median sale price or median price/sf, a handful of high-rise condo unit sales will not skew the results. 

8. If you are learning on your own, you need to collect the data externally. Now-a-days, a vast majority of counties (where the population level data originates) make the sales data available on their sites (as a customer service so the property owners can develop their own comps and validate the market values on the tax roll). Try to choose a county that makes the property data elements like Bldg SF, Land SF, Year Built, etc. available, in case you need to develop the normalized test or the Regression model. Of course, if you are undertaking the project for your institution, you are better off buying the data from a national data vendor. They will give you a 10-case sample to evaluate the data quality and the variables they warehouse.

9. Last but not least, compare your market results with S&P Case-Shiller's. The Case-Shiller monthly housing indices are available for 20 major markets (MSAs), both seasonally adjusted and not. Since yours is seasonally unadjusted, compare with their unadjusted index. Add this comparison in your report, but not on the presentation. This is the 3rd party work so you do not want to promote theirs; instead you should promote your own solution. For instance, a smart real estate broker will always advice his/her salespeople to try to sell in-house inventory as it costs the brokerage a lot of money and time to acquire exclusive listings. Similarly, you have poured your heart into creating this solution, so promote it to the max.

Good Luck! 

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