Coronavirus Pandemic – France, Spain, and the UK Face Massive Resurgence

France, Spain, and the UK -- the hard-hit countries in Europe initially -- are facing a massive resurgence since early August. Unfortunately, the other hard-hit countries like Belgium, Germany, Italy, and the Netherlands are not too far behind.

Source: https://www.worldometers.info/coronavirus/
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France -- As the above graph shows, while the daily cases averaged around 5K during the initial outbreak in the last March-April, it has been averaging 10K lately. Today's (9/30) cases amounted to 12,845. The 7-day moving average confirms the massive resurgence.

Source: https://www.worldometers.info/coronavirus/
Click on the image to enlarge

Spain -- During the initial outbreak in March-April, Spain's daily cases used to average around 7K. Lately, the same average has been hovering around 10K. For example, Spain reported 11,016 cases today. The 7-day moving average confirms rapid resurgence.

Source: https://www.worldometers.info/coronavirus/
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The United Kingdom -- At the peak during the initial outbreak, the UK's daily cases used to be around 5K. Lately, it has been averaging nearly 7K. The 7-day moving average shows the current resurgence as being the beginning stage. Today, it has hit 7,108 cases. 

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The silver lining has been that the death toll has remained extraordinarily low during this resurgence, due primarily to proven medications (like Gilead's Remdesivir) and the ventilators' better availability. Though the UK's death rate remains elevated (residual impact from the initial jolt), its positivity rate has been low. Spain and the UK have been recording meaningful testing credentials.
  
The timing of the resurgence concerns considering that the winter is around the corner, and the flu season is in the offing. Hopefully, the vaccines are around the corner too.

Stay safe!

Data Sources: https://www.worldometers.info/coronavirus/

-Sid Som
homequant@gmail.com

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

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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.

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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. 

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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

How to Analyze and Present a Complex Dataset – in 60 Minutes

        For New Graduates/Analysts

We talked about analyzing and presenting a large and complex dataset in 30 minutes in the prior blog post. Would one handle it differently if one had 60 minutes? Here is one approach one might like to consider:

1. While starting out, many young folks tend to underestimate themselves. The very fact that one has been tasked with this critical presentation speaks volumes, so one must learn to take full advantage of this visibility in narrowing the (internal) competition down. These meetings are often frequented by other department heads and high-level client representatives, leading to significant loss of time in unrelated (business) discussions. The best way to prepare for such contingencies is to split the presentation into a two-phase solution where phase-1 leads seamlessly to phase-2. 

2. In a business environment, it's never a good idea to start with a complicated stat/econ model; instead, one must start a bit slow but use one's analytical acumen and presentation skill to gradually force people to converge on the same page, retaining maximum control over the presentation in terms of both time and theme). Therefore, the phase-1 solution should be the same as the full 30-minute solution we detailed in a prior blog post (including the sub-market analysis). Even if the meeting leads to unrelated business chit-chat, off and on, the presenter will still be able to squeeze in the phase-1 solution, thus offering at least a baseline solution. Alternatively, if one has an all-encompassing solution, one could end up offering virtually nothing. 

3. Now that the phase-1 presentation, establishing a meaningful baseline is over, one should be ready to transition to the higher-up phase-2 solution. In other words, it's time to show off one's modeling knowledge. The phase-1 presentation comprised a baseline Champ-Challenger analysis, where the Champ was the Monthly Median Sale Price, and the Challenger was the Monthly Median SP/SF. The presenter used the "Median" to avoid having to clean up the dataset for significant outliers. Here is the caveat of sales analysis though: Sales, individually, are mostly judgment calls; for example, someone bent on buying a pink house would overpay; an investor would underpay by luring a seller with a cash offer, etc. In the middle (middle 68% of the bell curve), the so-called informed buyers would use five comps, usually hand-picked by the salespeople, to value the subjects – not an exact science either.  

4. Now, let's envision where the presenter would be at this stage – 30 minutes on hand and brimming with confidence. But it's not enough time to try to develop and present an accurate multi-stage, multi-cycle AVM. So, it's good to settle for a straight-forward regression-based modeling solution, allowing time for a few new slides to the original presentation. Ideally, the model should be built as one log equation with a limited number of variables (though covering all three major categories). The variables one might like to choose are: Living Area, Age, Bldg Style, Grade, Condition, and School/Assessing District, avoiding the 2nd tier variables (e.g., Garage SF, View, Site Elevation, etc.).

5. One should use Time Adjusted Sale Price (ASP) as the dependent variable in the Regression model, explaining the connection between the presentations (meaning phase-1 and 2) so the audience (including the big bosses like the SVP, EVP, etc.) understands that the two phases are not mutually exclusive, rather one is the stepping stone to the other. At this point, the presenter could face this question "Why did you split it up into two?" The answer must be short and truthful: "It's a time-based contingency plan."

6. At this point, the presenter must keep the regression output handy without inserting it into the main presentation, though, considering it is a log model (the audience may not relate to the log parameter estimates). If the issue comes up, the presenter should talk about the three critical aspects of the model: (a) the variable selection (how all of the three categories were represented), (b) the most vital variables as judged by the model (walking down on the t-stat and p-value), and (c) overall accuracy of the model (zeroing on the primary stats like r-squared, f-statistics, confidence, etc.).   

7. The presenter must explain the model results in three simple steps: (a) Value Step: ASPs vs. Regression values, showing the entire percentile curve, 1st to 99th percentile rather than the median values only, and also pointing out the inherent smoothness of the Regression values vis-a-vis the ASPs; (b) Regression Step: How some arms-length sales could be somewhat irrational on both ends of the curve (<=5th and >=95th) and why the standard deviation of the Regression values was so much lower than ASP'; and (c) Ratio Step: Stats on the Regression Ratio (Regression Value to ASP) as it's easier to explain the Regression Ratios than the natural numbers so spending more time on the ratios would make the presentation more effective.   

8. The presenter should explain the outlier ranges -- the ratios below the 5th and above the 95th percentile, or below 70 and above 143. Considering this is the outlier-free output, it's good to display Std Dev, COV, COD, etc. The outlier-free stats would be significantly better than the prior (with outliers) ones. Another common outlier question is: "Why no waterfront in your model?" The answer is simple: Generally, waterfront parcels comprise less than 5% of the population, hence challenging to test representativeness. (In an actual AVM, if sold waterfront parcels properly represent the waterfront population, it could be tried in the model, as long as it clears the multi-collinearity test as well).  

9. Last but least, one must be prepared to face an obvious question: "What is the point of developing this model?" Here is the answer: "A sale price is more than a handful of top-line comps. It comprises an array of important variables like size, age, land, building characteristics, fixed and micro-locations, etc. so only a multivariate model can do justice to sell prices by properly capturing and representing all of these variables. The output from this Regression model is the statistically significant market replica of the sales population. Moreover, this model can be applied to the unsold population to generate significant market values. Simply put, this Regression model is an econometric market solution. Granted, the unsold population could be comp'd, but that's a very time-consuming and subjective process."

-Sid Som

homequant@gmail.com

Link to the Book

How to Analyze and Present a Complex Dataset – in 30 Minutes

For New Graduates/Analysts

Often, with minimal time on hand – say 30 minutes – to summarize and present a relatively large and complex home sales dataset, comprising 18 months of data, with 30K rows, and ten variables, here is one approach worth considering:

1. Given the limited time, instead of trying to crunch the data in a spreadsheet, it's better to one's your favorite statistical software like SAS, SPSS, etc. What SAS will do in four short statements (Proc Means, Var, Class, and Output), and a matter of minutes, will need much longer to accomplish the same in spreadsheets. When one is starting out, it's good to take full advantage of these types of highly visible, often gratifying challenges to narrow the potential competition down.

2. It's good to have a realistic game plan. Instead of shooting for an array of parameters, it's better to start with the most significant one, i.e., Monthly Median Sale Price (and the normalized Sale Price per SF). Since the median is not prone to outliers, the dataset doesn't have to be edited for outliers, saving a significant amount of time.  

3. Now that the monthly median prices are there, one should be ready to create graphs for the presentation. While one graph depicting both prices (Y1 and Y2) against months (X-axis) may be created, it's prudent to keep them separated for ease of presentation. 

4. Since basic graphing is more straightforward in Excel (in fairness to the remaining time), it's better to transfer the output from SAS to Excel, ensuring that the graphs are adequately annotated and dressed up with the axis titles, legends, gridlines, etc. One must also remember that just doing things the right is not good enough, one must learn to present things elegantly as well. 

5. Since so much of the data have been summarized and rolled up behind one or two graphs, one must make sure they not only tell the overall story but also convey enough business intelligence to make the presentation look like a well-thought-out business solution in front of the attending EVP, SVP, etc. In the presence of clients, it enhances the bosses' image as well. So, it's smart to add trendlines alongside the data trend, selecting the primary trendline by eyeballing the data trend (linear, logarithmic, polynomial, etc.). Adding a 2 to 3-month moving average (depending on the time series) trendline to iron out any monthly aberrations could enhance the presentation.

6. It's also smart to keep the reporting verbiage clear and concise, explaining the makeup of the dataset, methodology including monthly medians, and how the normalized prices add value and help validate the primary. It's also important to explain the use of the trendline and its statistical significance and the other statistical measures like r-squared, slopes, etc. one might display on the graphs (avoiding the printing of equations on the graphs). 

7. It's good to add some business intelligence to the talking points, sticking to the market being presented but proving the depth of knowledge of that market by highlighting possible headwinds and tailwinds and how they would react to an inverted yield curve. Also, one should address other issues: If there is an on-going structural shift in demand for homes (are more millennial showing interest in that market); what the NAR's prediction of the summer inventory there is; if the inventory of affordable homes on the rise there; and how any expected change to the FHA rules would help first-time homebuyers in general, etc. 

8. One must try to control the conversation by sticking to what one is presenting, rather than what one does not have. For example, out of the ten variables, if only three are used (e.g., Sale Price, Sale Date, and Bldg SF), one should not start a conversation about the other important variables – Lot size, Age, Bldg Characteristics, and Location – that had to be left out ('If I had 30 more minutes' would be unnecessary). If that question comes up, one must answer it intelligently and truthfully, emphasizing, of course, the added utility of the three variables being used.

9. Now, let's assume that one has managed to complete the first cycle (as indicated above) in 20 minutes. In that case, one must go back to SAS and crunch the sales analysis by the sub-markets (Remember: Location! Location! Location!). In other words, one must understand how to walk down on the analysis curve. 

Of course, it's good to have these printouts handy. Just remember, one complete solution is always better than the more aspiring one but 95% complete.

-Sid Som, MBA, MIM

homequant@gmail.com

Link to the Book

Coronavirus Pandemic – Another Grim Milestone as Global Death Toll Reaches One Million

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The pandemic that has caused unimaginable physical and economic misery reaches a new milestone today: The global death toll reaches 1 million. The top three countries -- the US, Brazil, and India -- account for 45% of all deaths.

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The graph of daily cases shows how the three lines meet at the beginning of August, after which the US and Brazil have steadily trended down in tandem, while India has trended up almost linearly. Sadly, India has been registering, on average, 90K cases and 1K deaths daily. Of course, the graph demonstrates the volatility in Brazil and the US's reporting, primarily at weekends. India's reporting has been a lot more consistent.

The correlation matrix also nicely summarizes the trend. Since halfway through the curve, the US and Brazil have been trending down in tandem, the correlation coefficient has been moderately positive at 0.53. On the other hand, since India has diverged on both sides of the curve, it has very low collinearity with Brazil (0.10) and the US (0.17).

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The daily death trends have been very similar, including the reporting volatility. Brazil and the US have been moving in lockstep, whereas India continues to surge. Since 9/1, India has reported at least 1K cases daily, with a daily average of 1,124, while Brazil and the US have reported 770 and 789, respectively. Conversely, in June, India's average stood at 345, compared to Brazil's 1,023 and the US's 641. 

Therefore, the correlations matrix shows a high 0.66 correlation between Brazil and the US, a negative -0.23 correlation between Brazil and India, and a moderately low 0.33 correlation between India and the US.  

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As indicated above, the US, Brazil, and India have accounted for 45% of all deaths, despite owning 54% of the cases. Russia and Mexico have been the distant fourth runners in total cases and deaths. Despite the rapid surge in cases and deaths, India's death rates -- both actual and population-wise -- have remained low. The high positivity rates in Latin America have been well-known. Mexico's high death and positivity rates are tragic, while its testing record remains abysmal. 

Until the vaccines are widely available, the US, Russia, and Spain must maintain the testing momentum; other hard-hit countries -- especially Brazil, Mexico, and India -- must intensify their testing campaigns.

Stay safe!

Data Sources:

https://www.worldometers.info/coronavirus/

https://en.wikipedia.org/wiki/COVID-19_pandemic_in_the_United_States

https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Brazil

https://en.wikipedia.org/wiki/COVID-19_pandemic_in_India

-Sid Som

homequant@gmail.com

  

How to Pre-launch a B2B Start-up

"I have been toying with a B2B concept for a while. I think it has great potential. How do I take it forward?"

As an entrepreneur, I often get this question from budding entrepreneurs.

First off, a B2B Service is one of the most challenging segments to penetrate. Consider these steps to pre-launch a B2B start-up:

1. Conducting a Pilot – If you have a good job, do not jump ship. Instead, take some time off and try out a pilot "live." If your concept/invention pertains to the same industry you are currently employed, have an attorney review your employment contract for "conflict of interest" and "no compete" clauses. Since start-ups do not qualify for SBA loans, hire a qualified consultant to review your financials (both business and household), type of business formation (S, LLC, C, etc.), liability insurance, etc.  

2. Implementing Marketing Plan – Make sure you implement your marketing plan (from the actual business plan) to promote the pilot (as if it were the real launch!). It's better to have an average concept backed by a super-duper marketing plan (recipe for success) than a super-duper concept supported by an average marketing plan. Therefore, a significant amount of time and effort must be paid to developing the marketing plan. Ideally, it should also be reviewed by a marketing expert or a social media consultant, thus ensuring that the bases are amply covered.

3. In the Case of Local Service – If it is a local service, some meaning networking is critically coupled with several live campaigns (with real money) to get a good reaction for the future clients' actual outcome. When campaigns are launched or conducted without real money, they could lack the kind of intensity that is generally needed to get the right feel for the market. For instance, if the product or service relates to the real estate valuation market, it is critical to network with the local appraisers, assessors, realtors, social media consultants, etc.

4. In Case of National Service – If it is a national service, it's essential to mobilize the marketing Rolodex (LinkedIn, FB, Instagram, etc.), with an announcement that you are open for business. Before promoting national service, it's essential to understand the industry trend, especially any emerging trend. It's good to visit one or two seminars or conferences where national vendors display their products at the exhibit hall. While attending such conferences could be expensive and time-consuming, the resulting rewards generally far exceed the associated costs.  

5. Campaigning on Twitter – Campaigning on Twitter is more specialized than other social vehicles, so it's crucial to simultaneously implement the marketing campaigns. The campaigns need to fine-tune, and rerun (or re-implemented) based on Twitter Analytic, which could often be an iterative process to optimize the marketing plan, and short-cut could lead to an inefficient strategy. It might be a good idea to even consult with a well-known Twitter expert to iron out any hidden inconsistencies. The point is, the marketing plan must virtually back the product or service being promoted.

6. Advice from the like-minded – Seek advice from the like-minded B2B entrepreneurs – both successful and struggling – to avoid reinventing the wheel. It will save you many trips to the ER, so to say. Locally, it complements networking and, nationally, it saves a ton by not having to attend some vital industry seminars. As long as the product or service is not directly complete with theirs, most would welcome and satisfy your curiosity by sharing their road to success, critical in developing self-confidence.

7. Publishing the Underlying Concept – If you have already written a book highlighting the invention's underlying concept, it might be a good idea to join the Amazon Marketing Service to beef up its sale, bolstering "indirect" marketing before the actual pre-launch. The Kindle version alone is not enough; the Paperback is equally essential. Additionally, Twitter and other social campaigns need to be developed with direct links to the book. Ideally, the book's publication should coincide with the pre-launch of the actual product or service to intensify the marketing efforts without having to split the advertising and marketing costs.

8. Business IT Concept – If it is a Business IT concept, it's imperative to copyright it, leading to patenting; otherwise, the market protection would be virtually absent. While it's costly to patent it in a host of other countries at the outset, it is prudent to start the process here, gradually followed by the nations as they would be penetrated. The filing of the US copyright and provisional patenting will, at least, prevent the foreign companies from doing business here from directly infringing on yours. The provisional patent application will buy you 12 months to prepare for and submit the actual application (during which time "patent pending" could be added).

9. Analyze the Pilot Results – Analyze the results from the pilot as they come in, preferably in direct collaboration with a well-known marketing consultant, and seeking analytical help from a consulting data scientist could make sense as well. If you find that the results far exceeded your (and your consultant's) expectations, work on initiating a much larger pilot with the updated service coupled with a vastly upgraded marketing plan, adequately factoring in the initial pilot's inputs. If the follow-up growth curve is exponential (at this point, linear growth is not good enough!), you are "on to something."  

As indicated above, a pre-launch is a critical interim stage that must not be ignored. Far too many budding entrepreneurs make the mistake of launching the product/service without a meaningful pilot, thereby depriving them of the market knowledge, a priori, to face the competition.

-Sid Som, MBA, MIM

homequant@gmail.com

Link to the Book