How to read the Coronavirus Stats between the lines

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

Highlights...

1. As of 3/18, the worldwide cases have escalated to 215K, with China still leading at 81K, followed by Italy at 36K, Iran at 17K, Spain at 14K and Germany at 12K. After a meteoric rise through the end of February, China has been flatlining without any significant rise in cases or deaths.

2. The death toll has risen to nearly 8,885, with China and Itlay owning the lion's share of 3,237 and 2,978, respectively. The death toll of 8,885 represents 10% of the closed cases, which has seen a steady rise since the middle of the last month when it had registered at 6%.

3. Italy and Iran have been registering very high death rates of 8.34% and 6.54%, respectively. Since China has totally tapered off, its death rate has been holding steady at 4%, though the death rates of Spain, France, the Netherlands and the UK are rising rapidly. South Korea has done a great job in keeping the death rate extremely low.

4. The fact that China's infection and death rates have been holding steady is further evidenced by its exceptionally high recovery rate of 86%. Iran's recovery has gotten better lately. Conversely, the hard-hit European countries have so far very low recovery rates, proving that their drugs and therapies are still at the experimental stages.

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

5. Last week, New York had eclipsed Washington in total cases. Today, it is closing in on 2,500 cases. Of course, it has still fewer deaths than that of Washington. California's cases are also on the rise, while Florida has been inching up, along with New Jersey, Louisiana and Massachusetts.

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

Also read:
The Initial Interview
Universal Basic Income     
Phasing out Income Tax

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

How to Steadily Reduce Dependence on H-1B Foreign Workers

Studies show that almost 2 million H-1B visas have been distributed between 2000 and 2018. Here are some basic facts about the H-1B visa program:

a. The program was created by the Immigration Act of 1990

b. It allows employers to hire foreigners to work on a temporary basis, for up to 6 years, with two 3-year back-to-back stints

c. It allows foreigners to work in jobs that require highly specialized knowledge and a bachelor’s degree or higher.

d. Visas are awarded to employers on a first-come, first-served basis, with applications accepted each year beginning in April

e. If the number of applications exceeds the annual cap set by Congress (currently at 85,000) during the first five business days of April, visas are awarded through a lottery system

g. Though it's a temporary non-immigrant visa, many workers have been allowed to adjust to permanent status with green cards (adjustment data are unavailable).  

How to Steadily Reduce Dependence on H-1B Skilled Foreign Workers – A 5-Point Solution

1. Provide Corporations Significant Tax Benefits to Hire Local STEM Graduates – Instead of incentivizing the US corporations to hire more of H-1B workers, the federal government should allow them significant tax incentives to hire the local STEM (Science, Technology, Engineering and Math) graduates at the prevailing rate. This special tax incentive should last, say, up to five years (or the longevity of the employee, whichever comes first), thus vastly negating the incentive to hire foreign workers at a reduced rate. Of course, in order to neutralize the arbitrage (lower hiring rate vs. additional tax advantage), it must remain effective for the proposed tax incentive period (could be more or less). This tax incentive will also encourage the future STEM students, foreseeing a fast leveling playing field. Without this assurance, it would be difficult to entice local students to venture into the STEM field. Today, the qualified American workers are training their far-less qualified foreign counterparts to take their jobs. Hopefully, the tax incentive would force corporations to hire local STEMs while a renewed interest among future students would reinvigorate the field, the sum of which would reduce the dependence on H-1Bs.      

2. Introduce Higher Educational Qualifications for H-1B Applicants –

According to the 2018 Congressional mandate, 65,000 H-1B applicants need only bachelor's degrees while another 20,000 require master's or higher. Unfortunately, a bachelor's degree in SE Asia (which accounts for 80%+ applicants) is not equivalent to an US bachelor's. In order to effectively meet the US standard, Congress should consider transposing the degree requirements, meaning 65,000 applicants with master's+ and 20,000 with bachelor's. It makes no sense to displace a truly qualified American degree-holder with a much lesser qualified foreign degree-holder. That is why the replacement wages tend to be much lesser for foreign workers. Since H-1B is meant for the highly skilled foreign workers, Congress should gradually move to an all-master's+ requirement, at least leveling the playing field.

3. Until Higher Educational Requirements are Established, Congress must Insist on Degree Evaluation by ETS (and Other Entities) – While Congress debates on upping the ante on degree requirements, they must require that the foreign degrees are properly vetted and evaluated, a priori, by well-known education evaluation organizations like Educational Testing Service (ETS), thus forcing the sponsoring organizations to prove that their selected candidates, at least, satisfy the basic educational requirements. This simple yet independent step will surgically (identify and) disqualify many applicants from the export-oriented private schools as they will not meet the US degree requirements. Ideally, Congress must additionally require all applicants to pass a US-administered standardized test (good for 3 years), along the lines of Foreign Medical Graduates Exam (FMGE). Conversely, these requirements will work to the advantage of the truly qualified candidates as they will pre-qualify themselves (by getting their degrees evaluated and passing the exam in advance). Needless to say, the rouge employers will not be able to abuse the truly qualified workers either (by forcing them to work outside of the US labor laws, etc.).

4. Let the Sponsoring Companies Recruit Foreign Students Graduating from the Major US Universities First – Foreign students graduating from the major US Colleges and Universities are more valuable candidates for these unfilled jobs than their all-foreign counterparts. There are other advantages to this hiring approach too:

(a) No need for the equivalency assessment;

(b) Since the vast majority of them undergo internship or practical training in the US, they are already used to the requirements of the American workplace and work ethics;

(c) Graduates from the major US schools are at least as good as the best and brightest from foreign nations;

(d) They will command the prevailing wages, negating the aforesaid arbitrage that many sponsors have been trading on;

(e) Rouge sponsors will be discouraged;

(f) Will foster the enrollment of foreign student population, benefiting the US Schools;

(g) Better English proficiency (both verbal and written) and so forth.

5. Let the Annual H-1B Quotas Steadily Decline as we Promote STEM Education – If we switch to a merit-based immigration, H-1B will be a thing of the past. Whether that comes to pass or not, the rapid and aggressive promotion of STEM education here will help lower the quotas steadily. Hopefully, the current 85,000 level would decline by 10,000 annually, leading to a total phase-out in 8-9 years. In fact, if we are able to promote STEM education in keeping with the needs of the labor force, this phase-out could take place even sooner. Of course, the promotion (of the positives) of STEM education must start early in high school so the students are always in the know of the unrestricted domain of opportunity the STEM universe offers. 

The advantages of lessening the dependence on H-1B are numerous:

a) Producing thousands more of home-grown engineers, scientists and technologists (by far, the best on earth!) every year;

b) No need for a debate everyday whether the spouses of the H-1Bs must be given work permits or not;

c) Our politicians won't be able to convince us of the need to admit 85,000 (yes, per year!) foreign engineers and scientists at the expense of our own;

d) The Housing boom and the list can go on and on.

- Sid Som, MBA, MIM

President, Homequant, Inc.

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

Median-based Trend Analysis – Despite being Industry Standard – Must be Challenged

(Click on the image to enlarge)

Ryan is interviewing for a Supervising Analyst position with a major think-tank.

Question # 1
Interviewer: These two graphics are derived from the same housing market and reflect the same period. Why do they look so dissimilar? To keep the conversation more professional, let's address the Monthly Median Sale Price as the Champion or Champ and the Monthly Median Sale Price per Square Feet as the Challenger.

Ryan: This dissimilarity proves that an industry-standard Champ needs to be validated or challenged. A prudent market analyst must not take a set of established assumptions for granted; instead, the analyst should subject such assumptions to frequent tests and validations.

Question # 2
Interviewer: Why do you think that is important? What's wrong with a time-tested Champ? Why do you need to introduce an untested Challenger?

Ryan: The Challenger helps identify any on-going shifts in the market; for example, when the prospective buyers are gradually moving towards smaller homes, the demand pattern shifts. The Champ will not capture and reflect that shift in the demand pattern, but the Challenger definitely will. That is why analysts should meaningfully challenge the Champ.

Question # 3
Interviewer: Why are the double top and double bottom formations bearish and bullish, respectively?

Ryan: A double top is bearish because it fails to break out of the congestion, generally with a downward trend. On the other hand, a double bottom is bullish as it's a breakout event with an up-sloping linear trend, often making new highs. 

Question # 4
Interviewer: Explain the difference between the two in more quantitative terms.

Ryan: After peaking at $320,000, the Champ remained sideways, congesting between $300,000 and $310,000. The Challenger trend was almost diametrically opposite with an extremely bullish up-sloping double bottom, eclipsing the prior high of $180/SF. Even the moving average has confirmed the breakout.

Question # 5
Interviewer: If you have to show one of the two graphs to our clients, which one would you choose and why?

Ryan: I would choose the Challenger graph, as it captures and depicts the market's underlying fundamentals. 

Question # 6
Interviewer: Is there a missing piece in this presentation explaining why these two solutions are diverging? If so, how would you present that data?

Ryan: Yes, the Monthly Median Home Size (SF) is missing. SF would explain why they are diverging. I would use a simple table showing all three monthly data variables without showing these two-dimensional graphs.  

Question # 7
Interviewer: Why do you think the bullish R-squared is so much higher than its bearish counterpart?

Ryan: Because that's the right trendline for the slope of the curve. The bearish one does not demonstrate a linear trend, so the resulting R-squared is low.

Question # 8
Interviewer: In that case, what type of trendline would you fit, and how much difference would that make?

Ryan: I would fit a polynomial trendline of the 6th order, expecting reasonably similar results. 

Interviewer: Give me a minute and let me check it out. Yes, you are right; it's 0.794. That's excellent data visualization. Congrats!

Question # 9
Interviewer: Would you use the median-based analysis in business decision-making? If not, how would you improve upon it?

Ryan: The median-based analysis is necessary for quick and dirty analysis but isn't sufficient in making business decisions. I would use an extended percentile data curve like 5th to 95th, without the outliers.  

-Sid Som, MBA, MIM

homequant@gmail.com

Link to the Book

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