Single-Family Housing Market vs. Condo Market – A Good Champ-Challenger Analysis

Champ-Challenger analysis is an excellent way to provide a validation of one's primary research. If the local housing market is the primary research focus, some competing stats from the condo market could offer an excellent challenge in the form of validation. This comparative approach from the same collective market also provides readers with a context to better understand the primary stats. In valuation analysis, unchallenged stats leave a void that technical valuation experts like the valuation modelers often fail to understand. Here are some specifics:    

1. Presenting the Components – While analyzing the single-family residence (SFR) market, one should analyze and present it separately from townhomes (including PUD/HOA), condos and, coops. Instead of combining them as one category and averaging the results, the component-level analysis would make more sense, as their demand characteristics are usually different. The alternative approach could be (value) weighted averages. 

2. Diverging Components – Aggregate demand is not necessarily the best way to present a particular market, especially when the components do not move in tandem or diverge significantly. For example, the Condo market generally leads the housing market – on the way up and on the way down. In presenting a residential market analysis where the growth is at variance, it's better to explain the SFR market as the Champ while the condo market serves as the challenger, thus clearly portraying the divergence. A combined picture would musk the on-going reality -- a classic mistake many local reporters tend to make.   

3. Power of Challenger – 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. That is why the Property Tax Appeals consultants often hire well-known AVM consultants to develop a challenger AVM to unearth the over-valued parcels on the tax roll. 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. 

4. Single Parameter Champ – An unchallenged single parameter champ like the month-over-month median SFR sale price analysis is inadequate (it is necessary but not sufficient) to make informed business decisions. It needs to be challenged both "intra" and "inter." The intra challenger (from within the group) is generally the normalized Median Sale Price per SF. Builders often challenge the market approach with a market-adjusted cost approach. 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 overall market.   

5. Reducing Market Noise – Normally, the SFR and condo markets remain in sync. When they diverge, one needs to investigate the reason. 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, one must run the normalized tests to determine if the market internals are diverging. If not, it could be the "monthly" aberration. The 2-Month Moving Average helps reduce the monthly noise. These are the primary tools one must initially apply in diagnosing the reason for market divergence. If those tools are unhelpful, a step-by-step regression model could point to more precise reasons.

6. Challenger Condo Model – If one is forced to build a challenger (regression) model for the condo market, one must remember that the condo modeling is different from the SFR modeling. Condo modeling can be top-down or bottom-up. It's good to avoid top-down modeling as it involves income modeling requiring hard-to-find condo 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 available on county assessment sites. Under severe time constraints or If the condo data are not easily accessible, a condo sales ratio study could provide a stop-gap challenge.    

7. Apples-to-apples comparison – The SFR market tends to be more homogeneous than the condo market. Though there are Waterfront Mansions, French Tudors, Brownstones, etc. in the SFR market, they do not necessarily form the norm. Conversely, condo markets routinely comprise low-rise, mid-rise, high-rise, skyscrapers, etc. with significantly different amenities. So, one needs 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 and skyscraper condos from the sample. Of course, if one uses the Median Sale Price or Median SP/SF, a handful of high-rise condo unit sales would not skew the results. 

8. Data for External Analysts – While collecting the data, the external analyst must know that, nowadays, a vast majority of counties (where the population-level data originates) make at least the sales data available on their sites (as customer service so the property owners can develop their own comparables analysis and validate the market values on the tax roll). Additionally, it's prudent to choose a county that makes the property data elements like Bldg SF, Land SF, Year Built, etc. available to develop the normalized tests or the regression model. Of course, when one has ample time for the project and is undertaking it for the institution, one would be better off buying the data from a national data vendor with many more data variables. Most data vendors offer a small data sample to evaluate the quality of data and the variables they warehouse.

9. The External Challenger – Last but not least, it's good to compare the internal results with S&P Case-Shiller's indices. The Case-Shiller monthly housing indices are available for the 20 major markets (MSAs), both seasonally adjusted and unadjusted. Since the internal analysis is generally seasonally unadjusted, the comparison must be made with Case-Shiller's unadjusted indices. Since the 3rd party data comes with many copyright restrictions, the comparison should be shown in the report with full disclaimers, but not in the presentation. Moreover, considering this is the 3rd party work, it does not make much economic sense to promote theirs; instead, one must always learn to encourage one's own/internal work as the solution. For instance, smart real estate brokers always advise their salespeople to sell in-house inventory as it costs the brokerage a lot of money and time to acquire exclusive listings.

Again, a good champ-challenger analysis is self-selling and convincing as the challenger does most of the selling.

-Sid Som, MBA, MIM

homequant@gmail.com

Link to the Book

Coronavirus Pandemic – How the Pandemic has Impacted the Major Housing Markets

(Click on the image to enlarge)

The Phoenix housing market continues to be the standout leader with 12% growth since Jan-2019. Boston, Los Angeles, and Miami have also outperformed the national average. On the other hand, New York and Chicago remain the laggards, flatlining for the last 18 months. 

Despite the statutory forbearance (in place until 12-31-2020) and contrary to the massive media hype, the national average has shown marginal growth, inching up a mere 1.3% in 2020. FYI -- this analysis is based on Case-Shiller monthly indices (published today), which are the most widely-watched and followed housing metrics in the analytics world today.

(Click on the image to enlarge)

The above correlations matrix nicely summarizes the market interactions. Since Phoenix, Boston, LA, and Miami have been moving up in tandem, they share very high colinearity (correlation coefficients above 0.90) among themselves. In contrast, they have much lower correlations with Chicago and New York as the latter have stagnated. 

Likewise, considering Chicago and New York have flatlined, they have the highest collinearity (0.9187) between them. 

(Click on the image to enlarge)

The above graph portrays the competition between Phoenix and New York -- the best and worst-performing markets. While the Phoenix market has produced near-perfect linear growth, surging steadily from 188 to 210, the New York market has moved sideways, remaining range-bound between 200 and 205. 

Given the most recent trend, New York must stay above the 198-200 support level, breaching which it may quickly spiral down to 190.

Stay safe!

Data Source: 

Case-Shiller Housing index (as of 09-29-2020)

-Sid Som

homequant@gmail.com

Coronavirus Pandemic – How the Pandemic has Impacted the US Housing Market

(Click on the image to enlarge)

1. The above Case-Shiller Composite-10 (the ten largest housing markets) table shows that the composite grew 2.3% in 2019, followed by a growth of 0.9% in the first half of 2020. San Diego has been the frontrunner in both periods. Though Boston performed very well in 2019, its growth retraced a bit this year. While Denver has been a consistent performer throughout these 18 months, Miami has also picked up some momentum this year. On the other hand, the growth rates of Chicago and New York have been anemic, while lately, San Francisco has been flatlining.

2. The Composite-20, which additionally includes major markets like Atlanta, Dallas, Phoenix, and Seattle, produced slightly better growth rates than the Composite-10. The Composite-20 grew at 2.7% in 2019 but has moderated to 1.2% this year. Phoenix has been the standout winner in both time segments, with 6.1% and 4.1% growth rates, followed by Tampa, Charlotte, Cleveland, and Seattle. Overall, the slopes of the curves are very similar, meaning they have moved sideways this year, especially in Q2.

(Click on the image to enlarge)

3. The regression (7/19 thru 6/20) between the strongest and the weakest markets is quite impressive. After moving from 200 to 204 in the second half of 2019, New York flatlined around 205 in the first half of 2020; in contrast, San Diego steadily moved up from 261 to 272 during these 12 months, contributing significantly to indices' growth. The asymmetric growth rates have lowered the r-squared values. 

(Click on the image to enlarge)

4. Though Phoenix outperformed San Diego 8.5% vs. 4.5%, these two markets have been the hottest between 7/19 and 6/20. Therefore, the regression between them is also more telling, perhaps more apparent. They not only share a near-perfect linear trendline, with a high r-squared value of 0.98, but have also much helped the two composites remain positive this year. 

In a nutshell, contrary to the heaps of market reports praising the monthly growth of the housing market, the Case-Shiller indices, which serve as the de facto data standards of the US housing markets, paint a very different growth picture -- one that is anemic at best, save a few pockets here and there.

Stay safe!

Data Source: Case Shiller Housing Indices

Sid Som

homequant@gmail.com

Link to the Book

How did the Housing Market Fare in 2019?

** Intended for New Graduates / Analysts **

(Click on the image to enlarge)

Michael, an Econ graduate with three years of experience as a Housing Analyst, is interviewing for Senior Research Analyst with a major consulting firm.

Question # 1
Interviewer: Would you have combined these two graphs into one? 

Michael: Yes, since they both represent the same data period and considering the value ranges are not too far apart, I would have used one graph, stacking up the lines.  

Question # 2
Interviewer: Why not Y1 and Y2 instead of stacking up the lines on one Y axis?

Michael: As I said, the value ranges are not too far apart to justify the use of Y1 and Y2 axes. 

Question # 3
Interviewer: As far as this graphic presentation is concerned, please name two issues that positively appeal to you. 

Michael: No. 1 -- The Underlying data as it represents Case Shiller indices. No. 2 -- Combining the data tables with the graphs.

Question # 4
Interviewer: Explain the two market trends.

Michael: The trends are very similar. Between January and July, they remained sideways. Since then, they have been trending up in tandem.

Question # 5
Interviewer: Despite having moved in tandem, did one index outperform the other? 

Michael: Yes, the Composite-20 did slightly outperform the Composite-10. While the Composite-10 moved up roughly 5 points, the Composite-20 climbed roughly 6 points, thereby marginally outperforming its counterpart. 

Question # 6
Interviewer: Is there a "hare and tortoise" parallel here?

Michael: Yes, the Composite-10 took a nap in June and July, thus losing the race. In other words, the Composite-10 dozed off in June and July, while the Composite-20 slowly but steadily kept inching higher.

Question # 7
Interviewer: Based on this latest trend how would you characterize the current housing market as an investment vehicle?

Michael: In view of the current reversal to positive trend, I would say this market could be a great investment vehicle for seasoned flippers looking for an arbitrage on fixer uppers.

Question # 8
Interviewer: Are you saying that this is not the right time for a random homebuyer looking for a primary residence?

Michael: Not at all. Anyone with a good time horizon may buy a primary residence at any point of the business cycle. Of course, since the housing market has eclipsed the pre-recession highs, I would caution random homebuyers who have limited or uncertain time horizon. 

Question # 9
Interviewer: How would you advise our community bank clients?

Michael: I would urge them to closely follow the developments in the secondary market, as well as any emerging shifts in securitization practices. Considering the significant run-up in the housing market in last 8-9 years, I would definitely urge them to practice prudent risk management.

Data Source: Case Shiller Seasonally-adjusted Housing Indices as published on 02-25-2020

-- Sid Som, MBA, MIM
homequant@gmail.com

Link to the Book

Single Family Housing Market vs. Condo Market – A Good Champ-Challenger Starting Point

Champ-Challenger analysis is an excellent way to provide a validation of one's primary research. If the local housing market is the primary research focus, some competing stats from the condo market could offer an excellent challenge in the form of validation. This comparative approach from the same collective market also provides readers with a context to better understand the primary stats. In valuation analysis, unchallenged stats leave a void that technical valuation experts like the valuation modelers often fail to understand. Here are some specifics:    

1. Presenting the Components – While analyzing the single-family residence (SFR) market, one should analyze and present it separately from townhomes (including PUD/HOA), condos and, coops. Instead of combining them as one category and averaging the results, the component-level analysis would make more sense, as their demand characteristics are usually different. The alternative approach could be (value) weighted averages. 

2. Diverging Components – Aggregate demand is not necessarily the best way to present a particular market, especially when the components do not move in tandem or diverge significantly. For example, the Condo market generally leads the housing market – on the way up and on the way down. In presenting a residential market analysis where the growth is at variance, it's better to explain the SFR market as the Champ while the condo market serves as the challenger, thus clearly portraying the divergence. A combined picture would musk the on-going reality -- a classic mistake many local reporters tend to make.   

3. Power of Challenger – 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. That is why the Property Tax Appeals consultants often hire well-known AVM consultants to develop a challenger AVM to unearth the over-valued parcels on the tax roll. 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. 

4. Single Parameter Champ – An unchallenged single parameter champ like the month-over-month median SFR sale price analysis is inadequate (it is necessary but not sufficient) to make informed business decisions. It needs to be challenged both "intra" and "inter." The intra challenger (from within the group) is generally the normalized Median Sale Price per SF. Builders often challenge the market approach with a market-adjusted cost approach. 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 overall market.   

5. Reducing Market Noise – Normally, the SFR and condo markets remain in sync. When they diverge, one needs to investigate the reason. 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, one must run the normalized tests to determine if the market internals are diverging. If not, it could be the "monthly" aberration. The 2-Month Moving Average helps reduce the monthly noise. These are the primary tools one must initially apply in diagnosing the reason for market divergence. If those tools are unhelpful, a step-by-step regression model could point to more precise reasons.

6. Challenger Condo Model – If one is forced to build a challenger (regression) model for the condo market, one must remember that the condo modeling is different from the SFR modeling. Condo modeling can be top-down or bottom-up. It's good to avoid top-down modeling as it involves income modeling requiring hard-to-find condo 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 available on county assessment sites. Under severe time constraints or If the condo data are not easily accessible, a condo sales ratio study could provide a stop-gap challenge.    

7. Apples-to-apples comparison – The SFR market tends to be more homogeneous than the condo market. Though there are Waterfront Mansions, French Tudors, Brownstones, etc. in the SFR market, they do not necessarily form the norm. Conversely, condo markets routinely comprise low-rise, mid-rise, high-rise, skyscrapers, etc. with significantly different amenities. So, one needs 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 and skyscraper condos from the sample. Of course, if one uses the Median Sale Price or Median SP/SF, a handful of high-rise condo unit sales would not skew the results. 

8. Data for External Analysts – While collecting the data, the external analyst must know that, nowadays, a vast majority of counties (where the population-level data originates) make at least the sales data available on their sites (as customer service so the property owners can develop their own comparables analysis and validate the market values on the tax roll). Additionally, it's prudent to choose a county that makes the property data elements like Bldg SF, Land SF, Year Built, etc. available to develop the normalized tests or the regression model. Of course, when one has ample time for the project and is undertaking it for the institution, one would be better off buying the data from a national data vendor with many more data variables. Most data vendors offer a small data sample to evaluate the quality of data and the variables they warehouse.

9. The External Challenger – Last but not least, it's good to compare the internal results with S&P Case-Shiller's indices. The Case-Shiller monthly housing indices are available for the 20 major markets (MSAs), both seasonally adjusted and unadjusted. Since the internal analysis is generally seasonally unadjusted, the comparison must be made with Case-Shiller's unadjusted indices. Since the 3rd party data comes with many copyright restrictions, the comparison should be shown in the report with full disclaimers, but not in the presentation. Moreover, considering this is the 3rd party work, it does not make much economic sense to promote theirs; instead, one must always learn to encourage one's own/internal work as the solution. For instance, smart real estate brokers always advise their salespeople to sell in-house inventory as it costs the brokerage a lot of money and time to acquire exclusive listings.

Again, a good champ-challenger analysis is self-selling and convincing as the challenger does most of the selling.

-Sid Som, MBA, MIM

homequant@gmail.com

Link to the Book

Use Tiered Prices to Understand Housing Market – The Boston Case Study

** Intended for New Graduates/Analysts **

(Click on the image to enlarge)

As indicated in prior chapters, not all price segments of the same housing market necessarily move in tandem. In a well-distributed and liquid market, the price escalation generally starts at the low price tier and graduates up as the underlying market fundamentals strengthen. Therefore, in the world of research and analytics, Case-Shiller price tiered indices are highly sought after.

The above table demonstrates that while the Low tier (under $395,499) registered an excellent overall growth (between January 2017 and July 2019) of 17.90%, the two upper tiers returned much lower growth rates of 13.35% and 10.59%, respectively, and stayed significantly above the aggregate growth rate of 12.55%. Similarly, while the Middle tier did not perform as good as the Low tier, it did return a better growth rate

than the High tier, remaining above the aggregate growth rate as well. Thus, the segment-wise growth rates prove that one-size-fits-all growth rate does significant injustice to both ends of the price curve.

So what are the primary uses of the Case-Shiller price tier indices? Here are some:

1. Time-adjust Prior Year Tax Roll Values – When analysts and appeals consultants do not have the time or resources to develop new market values to challenge (validate) the current Tax Roll (market) values, the Case-Shiller tiered growth factors could be an ideal independent alternative. Given its independence, it would be a much easier sell than some internally developed heuristic rates. Of course, the counter case could be compelling too: Since the Case-Shiller markets are defined at the MSA level, the County Assessor could make  a case that such time factors are too broad-based to be meaningful at the County (small subset) level.

2.   Challenge Internal AVM Time Adjustments – AVM modelers can use the tiered time factors to challenge the internal AVM time factors. The Case-Shiller factors should be in line with the large subsets; for instance, LA County time factors should be very similar to those of Case-Shiller’s. Therefore, the internal QC supervisors, both private and public, should additionally use these independent factors to test the metallurgy of the internal models. Needless to say, those who develop models at the MSA level would be the big beneficiaries of the Case-Shiller tiered time factors.     

3.   To Periodically Update Mortgage Portfolios – Mortgage portfolio analysts can use these factors to periodically update the portfolio values, without having to develop challenger AVMs. These factors are more meaningful when the mortgage portfolios are rolled up at the MSA or regional level. Conversely, one must be careful in (over) using these factors at the small subset level, unless prior studies show that those subset factors tend to align well with the MSA’s.

4.   Update a Not-so-recent Comparable Sales Pool – When an analyst or a loan officer must work out of a not-so-recent comparable sales pool, these Case-Shiller factors could be used to time-adjust at least the older sales. Of course, it would be a quick fix, but not a real valuation solution per se. This method is especially helpful in some bigger environments (e.g., AMCs) where the time-adjusted comps are often used in batch modes, in place of the 3^rd party AVM values.   

5.  Enhance Shelf-life of AVM Values (sell side) – AVM houses sell their values to wide range of end-users like banks, mortgage companies, assessment jurisdictions, SFR rentals, REITs, large tax appeal lawyers and consultants, hedge funds, etc. Many such AVM houses outsource the development of the modeling and value generation to 3^rd party research outfits, professors, etc. Case-Shiller tier indices will help them to apply time adjustments and thus enhance the shelf-life of those AVM values, easily up to a year. 

6.   Enhance Life Expectancy of AVM Values (buy side) – Even the 3^rd party non-custom AVM values could be quite expensive, e.g., $5 to $10 per parcel. Given that, many end-users can use the Case-Shiller tier indices to enhance the usefulness of the AVM values for several quarters, thus saving a ton of money. In fact, those internally time-adjusted AVM values, oftentimes, are very similar to the new values that the originating AVM houses sell. Anecdotally, some tax appeal consultants use smart college students to have their old AVM values adjusted up to the new target date, meaning the new Tax Roll (valuation/status) date. 

  

7. Develop Analysis for Investors – When researchers are required to develop inter-market (across markets) comparisons for investors, the Case-Shiller Price Tiered indices are more useful than the un-tiered composites as these indices allow true apples-to-apples analysis. Therefore, in order to compare two competing markets, one should compare by the tiers rather than the overall markets, allowing investors to understand the current valuations of each market segment; for instance, when the Low tier makes a significant upward run, investors might shy away from that market segment (and perhaps vice versa). So, the one-size-fits-all market analysis does not work well for the investors.

8.  Flight to Quality in Financial Markets – The three tiered index helps smart investors and traders to swap investments back and forth between the housing market and the equity market as the latter comprises primarily of three price segments as well, i.e., small cap, mid cap and large cap segments. While rotating investments, the smart investors and traders would naturally prefer studying the competing markets by price tiers, to avoid having to rotate from one over-valued market segment to another similarly over-valued market segment (thus defeating the basic purpose).      

9.  Understand True Volatility – When the volatility is important to the organization, developing the tiered price volatility is critical as it does not mask the two ends of the price curve. The above volatility table shows that the Low tier has been lot more volatile than the upper price tiers. The reason is quite simple: the high growth (translating to expanded price range) segment comes with higher volatility while the low growth (resulting in more compact price range) paves the way for lower volatility. Case in point: If the aggregate rate of 3.71% is used across a portfolio, the volatility of the low price tier of the portfolio would be understated while the high price tier would be overstated, thus distorting both price segments. Of course, the middle tier would be in line with the aggregate rate.

When the new values are not immediately available, the Case-Shiller tiered price indices come in very handy while updating the older portfolios, rotating investments across financial markets, and understanding the true underlying volatility of the housing market.   

P.S. These are Case-Shiller’s seasonally-adjusted indices so the month-over-month comparison is fine. While using Case-Shiller’s seasonally unadjusted indices, one should compare July- 019 with July 2018 and July 2017, etc. 

-Sid Som, MBA, MIM

President, Homequant, Inc.

homequant@gmail.com

Link to the Book

How to Use Case-Shiller Indices to Predict Housing Trends

(Click on the image to enlarge)

Sonya, a fresh college graduate with a major in Economics, is interviewing for an Economic Analyst position with a major economic consulting firm.

Question # 1
Interviewer: Is there any difference between these two Case-Shiller* housing trends?

Sonya: They are very similar trends. In fact, even the monthly growth rates are almost in lockstep. By the way, am I looking at the seasonally adjusted data here?

Question # 2
Interviewer: Yes, you are. Are these month-over-month data comparable?

Sonya: Yes, since they are seasonally adjusted; otherwise, we would be comparing April, 2019 with April, 2018, etc. 

Question # 3

Interviewer: Why do you think the top-20 markets are moving in tandem with the top-10?

Sonya: Because the US housing market, overall, has returned to normalcy. Right after the last recession, known as the Great Recession, a number of major Wall Street companies started buying up the inventory, in large volume, creating a highly asymmetric market around the country. For the last 2-3 years that trend has significantly subsided, paving the way for a more normal market.

Question # 4
Interviewer: How did their involvement create an asymmetric market?

Sonya: Because they concentrated primarily on Sunbelt markets and as a result the growth in prices in those markets far exceeded the other markets.  

Question # 5
Interviewer: How would you characterize the health of the current market?

Sonya: It's still a healthy market considering 4.0% to 5.0% annual growth rates. Of course, these are muted rates compared to the prior run-up rates when the Wall Street investors were active. 

Question # 6
Interviewer: In terms of the index components, are there any duplications?

Sonya: Yes. The Composite-20 has all of the Composite-10 components, plus 10 more markets.

Question # 7
Interviewer: Can you name a few components that are mutually exclusive?

Sonya: Atlanta, Charlotte, Cleveland, Dallas, Detroit, Minneapolis, Phoenix, Portland, Seattle and Tampa.

Question # 8
Interviewer: Wow! That was sensational. Would you have graphed the data differently?

Sonya: Yes. Since these are seasonally-adjusted data, I would have bar-graphed the month-over-month (percent) changes.  

Question # 9
Interviewer: Would you use the NYC Case-Shiller data to show the Brooklyn trend? 

Sonya: Yes, to perform a quick-and-dirty trend analysis. If I were performing a trend analysis for a client, I would not use this MSA-level data which is quite broad in nature. I would start by collecting the parcel-level sales data from the Borough of Brooklyn itself.

* Case Shiller is a registered trademark of S&P CoreLogic.

Link to the Book