Monday, October 21, 2019

Comparable Sales Analysis – The Learning Curve for New Analysts and Appraisers

Like any other body of knowledge, Comparable Sales Analysis, a.k.a. Comp Sales, comes with a graded learning curve so the new analysts and appraisers are expected to take the time and go through the process. The graded learning process also helps them understand its professional superiority over the other market-based run-of-the-mill valuation methods like Comparative Market Analysis (CMA), Broker Price Opinion (BPO), etc. that are generally used by the real estate salespeople to arrive at the initial listing prices.

Broadly, it’s a two step learning process. The initial step involves understanding and experimenting with various adjustments and selection methods to arrive at a set of median adjustments for the overall market. The final step involves actual valuation in line with the market tier the subject is derived from, leading to meaningful value conclusions.

The median adjustments alone are not adequate. Since subjects may come from different tiers of the same market, i.e., low-price tier, mid-price tier and upscale tier, tier-wise starting adjustments are also needed. Often, sub-tier adjustments are also compiled; for instance, if the upscale tier is defined as the 75th percentile and above, subjects from the 95th percentile may require special sub-tier adjustment factors. 

Step 1

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While the starting adjustment matrix comes with adjustment ranges, it tends to have null adjustment coefficients and time factor. If one were to start with this matrix, one would create the initial pool with all comps coming from 25% physical attribute ranges and one mile search radius.

Assuming that the underlying sales universe comprises all recent and arms length sales, the pool would thus return a set of fairly comparable sales, nonetheless without any quantitative adjustments for physical differences and market growth (time adjustment). If a 54-year old subject with 2,310 SF of building area on 6,600 SF lot needs to be valued, one has to accept all comps, unadjusted, between 41 and 68 years of age, 1,733 and 2,888 SF of building area and 4,950 and 8,250 SF of land area. Worse yet, if the sales are older in a dynamic market, they would produce a grossly incorrect subject value. Last but not least, accepting the final comps without proper spatial validation could spell trouble as well.   


Step 2

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Holding the ranges constant, one needs to graduate to the next level. After having experimented with the local market parameters, one might come up with the above median adjustments. Then, if these adjustments are applied on to the prior subject, the following pool would be created.


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The above table shows that the five best comps based on the distance method have been picked. While the first two comps are excellent, including time (with a valuation date of 12-31-2017), the other comps have much smaller improvements requiring significant dollar adjustments. The building age has not been an issue. Given the unusually low sale price of comp # 1, despite having met the distance criterion, one has to be careful of its selection as it might come from an incompatible neighborhood altogether or might not be an arms-length sale after all. The spatial view shows that the comp is located at the farthest distance but the lack of additional data prevents one from reaching any conclusive decision.


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As expected, the above comps grid shows sizable total adjustments for comps 3, 4 and 5, despite being in close proximity of the subject. The most probable value (median) is $329,300 with a probable range (25th to 75th percentile) of $280,770 to $400,800 and a wider 5th to 95th percentile range of $251,250 to $401,925.

This wide range points to the need for tier-wise adjustments; for instance, a subject from the lower tier (5th to 25th) must be valued differently than a subject from the higher tier (75th to 95th), with different set of pertinent adjustments.


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The above table is organized on least adjustment, meaning the comps requiring smaller adjustments are preferred. Though comp # 1 (third on the lineup) has showed up as one of the five best, it has been ignored paving the way for the sixth as the substitution. The vast majority of analysts and appraisers prefer the least adjustment method over the distance and sales recency as they are already baked into the total adjustment, thereby significantly reducing the element of subjectivity or personal judgment from the process. Of course, spatial verification is extremely important in desktop appraisal.


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The above (least adjustment) comps grid shows a much lower probable value of $280,770 with a very tight probable value range (25th to 75th) of $269,850 to $328,800 and a much narrower 5th to 95th range of $251,250 to $329,300. And, all of these have been achieved without sacrificing the distance (still the same 0.03 to 0.18-mile range) and the recency of sales (04/2017 to 11/2017). The sales recency method has been ignored as the local market was totally stable and all of the sales came from the same valuation year. 


Though the comparable sales analysis is inherently subjective, the new analysts and appraisers who take the time to learn the process systematically and methodically tend to be more intuitive with the logic and the mechanics, rather than just the mechanics.

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

Coming Soon: Sid's New Book on Comparable Sales Analysis -- A Spatial Approach

Saturday, October 19, 2019

AVM is a Market Solution, Comparable Sales Analysis isn't

Part 1 – Comparable Sales Analysis

If a subject and a sales population are provided to a group of concerned parties – from an Assessor to a Bank Appraiser to a Listing Agent offering buyback guarantee to a traditional Listing Agent to a Buyer's Agent to an Appeals Consultant – one would be unpleasantly surprised by the outcome.

They will pick different sets of comps based on their professional requirements and objectives, leading to different, often very conflicting, valuations. For instance, Assessors may not have the taxpayers’ best interest at heart as they have to meet budgetary requirements, paving the way for counterparties like Appeals consultants. A Listing Agent looking to get an "exclusive" may not do well with a set of middle-of-the-road comps which a Buyer's Agent might be interested in. In other words, the selection of comps is a function of the hat the party wears, making the entire process highly subjective. AVM, on the other hand is a fairly scientific exercise. All variables interact with one another in an econometric equation and produce the resulting values. Therefore, all other factors remaining constant, two identical homes will have identical values – but not so in the world of the comparable sales analysis (aka, comp sales) as it is very party-specific.
 Once the pool of sales, that closely represents the subject are properly scored and quantitatively adjusted, becomes comps. Generally, five best comps are then selected to value a subject. Appraisers tend to use one of the three common methods – distance, least adjustments and sales recency – to narrow their choices down to the five contributing comps.    

In this analysis, the attributes of the subject home are: Bldg SF=2,360 Lot SF=6,098 and Bldg Age=19. From a large sales population, an optimal pool of 10 comps has been algorithmically produced to demonstrate how subjectivity plays a key role in this valuation process.

In order to make location more or less irrelevant, not only all comps have been pooled from the subject’s Zip Code of 89144 within the town of Las Vegas, but also from a limited one mile radius. Also, the comps are quantitatively adjusted for land and building sizes, depreciation and time of sale, with a forward (as of this writing) valuation date of 12-31-2019


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The above table with spatial view represents the distance method, meaning the five closest (to the subject) comps are considered to be the best comps, producing a probable value range of $439,245 to $479,175, with the most probable (median) subject value of $463,390.

Since least adjustments and recency of sales are ignored here, obviously several comps needing larger adjustments or of older originations have managed to creep in, making the process sub-optimal. The 5th to 95th percentile range of $430,700 to $511,040 is quite wide as well. Interestingly, the comp-1 (closest) is the most expensive one (95th percentile value) due to high building size and time adjustment.




The above table represents the least adjustment method, meaning the comps that required the least adjustments were considered the best comps.


The least adjustment is nothing but a balancing act. In other words, larger lots are often compensated in value by smaller building sizes while lesser time adjustments are compensated by higher depreciation, etc. In this example, the first and second least adjusted comps (comp # 8 and # 9) are being corrected by larger building sizes and older sales. It also sacrifices the closest comp (comp # 5). The above least adjustment grid produces the most probable value of $430,700 with a very compact range of $425,125 to $439,245 and an equally compact 5th to 95th percentile range of $409,839 to $463,390.




The above table represents the sales recency method, meaning the most recent five comps (in terms of sale dates) are the best ones.

Though all four 2018 sales have showed up on the final lineup, it has also sacrificed some of the closest and least adjusted comps. Interestingly, the two smallest size comps with highest values (resulting from size adjustments) have hit the list of the final five.

The sales recency method shows the most probable value of $479,175 with a probable value range of $463,390 to $511,040 and the 5th to 95th range of $439,245 to $571,595. This method therefore produced the highest value, having been influenced by the two highest values.

To sum up, the sales recency produced the highest value, followed by the distance and least adjustment methods respectively. Least adjustment also produced the most compact value ranges. This subject is currently listed (as of this writing on 10-20-2019) for $425,900 which is also closest to $430,700 produced by the least adjustment method.

Therefore, if this comparable sales analysis were to be used to cater to the aforesaid target audience, this is how the game would be played out

1. Assessors and Listing Agents (traditional) will be given the "sales recency" value (highest value).

2. Bank Appraisers and Listing Agents (buyback) will be given the "distance" value (middle-of-the-road value).

3. Appeals Consultants and Buyer's Agents will be given the "least adjustment" value (lowest value) which happens to be closest to the currently listed price.

Despite the fact that the subject and the comps have been picked from a very homogeneous area, the three comparables methods produced fairly different values and ranges, indicating how subjective the world of comparables analysis is. 

  
Part 2 – Automated Valuation Model (AVM)


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The same sales population - derived from a single Zip Code - has been used across all three graphs (you may use other fixed locations like Census Tract, School District, etc.). Considering all sales from the same Zip, it helped minimize the impact of location (of course, you can never make location totally irrelevant as each block has a different appeal).
The above graph shows the noisy relationship between the uncorrected (raw) Sale Price and Bldg Size (Heated Living Area). The reason is very simple: Each sale is directly related to a buyer's judgment, hence is highly subjective. For instance, buyers are paying between $100K and $250K for a 1,500 SF home. While the investors would target the lower end of the range, the informed buyers would be in the middle and the uninformed buyers (someone who is bent on buying a pink house!) would succumb to the higher end of the range. The R-squared is therefore extremely low (0.189), explaining very little of the variations in sale prices.  




The Regression Value-1 graph proves that even a rudimentary regression model (with only three independent variables - Land SF, Bldg SF and Bldg Age) is capable of producing a decent market solution. The fit is significantly tighter, especially in the long end of the curve. The R-squared jumps from 0.19 to 0.91, accounting for 90% of the variations in sale prices. But this model has clearly bi-modal issues between 1,000 and 2,200 SF as the regression values are forked. 

FYI – If you see such stacked values, you have to investigate the underlying reason(s). The simple way to identify the issue is to scatter the normalized regression values against the other independent variables as well and look for an explanation.




The above investigation guided me to the solution. As the normalized regression values from the first model were scattered against the Bldg Age variable (above graph), it was evident that many buyers were paying a premium for the younger homes, causing the stack. In fact, a sizeable portion of those buyers were willing to pay over $130/SF for the younger homes, while very few offered such premium for the older ones. More precisely, none paid over $160/SF for the older stock.



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So the Bldg Age variable had to be transformed from continuous to binary (younger homes vs. the rest). The re-run of the regression model with the transformed Bldg Age produced the above (Regression Value-2) graph. Consequently, the value fork has disappeared, translating to a much tighter fit, with improved R-squared, lower intercept and a steeper slope approaching 45 degrees.  

To conclude, this analysis proves the superiority of AVM over comparable sales analysis as a market solution. Of course, they are not necessarily apples-to-apples as AVM provides a top down solution (one top-line equation values an entire unsold population), while comparable sales analysis is bottom up, valuing one subject at a time. Batch comp is the connection in between, providing algorithm-driven (automated) subject-level comp solution but without the human interface.

- Sid Som, MBA, MIM
President, Homequant, Inc.

Coming Soon: Sid's New Book on Comparable Sales Analysis -- A Spatial Approach

Thursday, October 17, 2019

Valuing Waterfront Homes with Comparables Sales Analysis

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Waterfront is often the most significant variable in comparable sales analysis. Generally, a waterfront home costs 10's of thousands to 10's of millions more than a non-waterfront home. In other words, just move a home one block from the non-waterfront block to the waterfront block and its value could jump many, many fold. 

Given the above axiom, one has to be very careful in relying on the automated comps to value a true waterfront subject, with unobstructed view and access. Unless the selected comps are very similar, the valuation would be off, perhaps way off. 
That is why an all-algorithm approach (without the professional and spatial verification) could be a hit-or-miss. Once the algorithm scores and returns the optimal pool, one needs to spatially verify the pool and select the final five comps contributing to the subject valuation. 

A good comparable sales solution should, therefore, urge its users to "experience" the environment by turning on the spatial view or at least the Street View* and strolling around the neighborhood before selecting the final five comps. If the scored pool comprises very similar and clustered comps, the use of the distance method, as opposed to the more powerful least adjustment method would be preferred to zero in on the final ones, ensuring that the comps are pooled from subject’s immediate waterfront.

Using the above graphic once the algorithm scores and returns the pool of ten best comps, the final five comps should be manually selected after the aforesaid data research and spatial experience.While valuation of waterfront subjects generally calls for site visit, any all-desktop valuation must require the simultaneous view of spatial comps or a generic GIS package. Often, the use of spatial comps or GIS does not necessarily explain certain valuations of waterfront comps; for instance, the high valuation of comp #7 (3rd on the lineup) could not be explained by the standard desktop spatial analysis – only a visit to the actual location might help explain it. 

Of course, one must always expect much wider variations in values in prime waterfronts due to different demand criteria for lifestyle living, often causing paucity of supply leading to irrational valuations. Therefore, a site visit may not always point to the reason for awkward valuations (might not be economic reasons, after all).   





While an extra large non-waterfront lot (assuming not buildable two per local zoning) in the middle of a block may not fetch much higher price, the larger waterfront lots (land area as well as the frontage) generally attract significantly higher prices as the rich and famous are willing to pay that premium for lifestyle living; for instance, the comp-2 with land area of 20,909 SF sold for $463,000 on 08/21/2017, though comp-5, a much younger property with very similar building size on 10,890 SF of land (and assuming all other factors remaining equal), fetched $254,000 on 11/20/2017 – an astounding 82% premium for the oversized lot.

Again, the valuation of waterfront subjects must not be an all-algorithm hit-or-miss comparables event; it must be properly supported by spatial analysis along with proper manual and professional interventions. 

Sid Som, MBA, MIM
President, Homequant, Inc.

Coming Soon: Sid's New Book on Comparable Sales Analysis -- A Spatial Approach

Sunday, October 13, 2019

LA Housing vs. San Diego Housing – Who Wins?




Though Los Angeles and San Diego, the two major Southern California housing markets, tend to move in tandem, they have been diverging since January 2019. While the San Diego market has been on a rapid upswing, the Los Angeles market has been inching up, at best.

In terms of the overall growth between August 2016 and July 2019, San Diego led the way, registering a growth of 15.56% while Los Angeles returned a lower 13.61%. 

While the growth rates in both markets in 2016-17 were robust, SD consistently outperformed LA, though not by a wide margin. Even when the time series is split in two halves between August 2016 – December 2017 and January 2018 – July 2019, SD grew faster at 10.25% and 2.72% versus LA’s 9.23% and 0.54%, respectively.

Though the LA market has built a decent support at 278-282 level, it needs to stay above it and any significant breach could quickly force it down to the 268-272 level. On the other hand, given SD's upward sloping trend with higher highs, it can easily break out of the next congestion of 264-266 in the near future, rapidly approaching towards 270. 

Of course, the recent green-shoots could be attributed to the interest rates, meaning the fast decline in interest rates in recent quarters might have contributed to an enhanced activity leading to a short and perhaps temporary upswing in these markets. The reason LA has responded less convincingly than SD is that the former is generally more prone to foreign cash buying which has tumbled in recent months, resulting primarily from the on-going Sino-American trade and tariff concerns. SD, on the other hand, is a more natural market driven by its own market fundamentals.

Of course, the activity of the most recent month, July 2019, could be an aberration due to the lack of (data) liquidity; as the new data points come in, the sudden decline (LA) and spike (SD) might get moderated out.



When two markets are compared and contrasted with the comparable data, the regression line could be quite telling. It shows a near-perfect linear trend. Needless to say, the combination of the high r-squared (0.9814) and the correlation coefficient (0.9906) further confirms the fact that these two SoCal market constituents move in lockstep.




In terms of volatility, the COVs are in close proximity too. (FYI – the higher Standard Deviation does not necessarily point to higher market volatility; the COV is a better indicator of volatility than the Standard Deviation as it is normalized by the Average).                                                 
The SD market is the clear winner here, considering its consistently higher growth rates, better markets internals and recent trend reversal after a good consolidation.             

The above examples are based on Case-Shiller’s seasonally-adjusted indices so the month-over-month comparison is fine. While using the seasonally unadjusted Case-Shiller indices, one should compare July 2019 with July 2018 and July 2017, etc. 

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


Saturday, October 12, 2019

Making US College Education and Student Loans more Labor-force friendly

According to the most recent (2015) PISA scores which measure the basic skills (reading, math and science) of 15-year-olds, the US ranked 30th in Math among the 38 OECD countries – nothing to write home about, right? We need a sea change in the way our colleges work. Also, we need to rethink the qualification criteria for student loans. Here are some remedies: 

     1. College Accreditation must require Local and Regional Business Participation – One of the perennial complaints of the US college education is that it’s too theoretical. Despite the rising trend of internships, only a small percentage of the graduating students are blessed with this fortune, mostly in highly-sought-after disciplines like the STEM. College accreditation must require local and regional business participation (including representation on the board), allowing meaningful access to the business, science and technology community. Ideally, college charters must stipulate that at least 33% of all credit courses be taught by external experts so the students get to learn how the theories are actually being implemented in ‘live’ environments. Of course, it must be a simultaneous process, meaning teaching theories and practice must take place during the same quarter or semester. For example, students specializing in real estate finance must learn from the top mortgage professionals as to how the various mortgages are originated, including the full array of the paperwork involved (industry standard forms, etc.). Likewise, the STEM students who are considering a career in technical trading must learn from the renowned hedge fund managers and (program trading) algorithm scientists. Colleges and universities must therefore offer majors in line with the availability of the aforesaid local and regional industry experts. Needless to say, there will be no dearth of successful industry people who would be more than willing to teach such classes. This joint venture is a necessity today.

     2. New Professors must have at least 3-5 years of Verifiable Business Experience – Colleges must look for qualified professors (US PhD) with actual hands-on business experience. They will rise above the “canned” case studies as they are often antiquated and out of sync with the marketplace. These new crop of professors will also make better liaison with the industry experts, thus vetting and selecting the most fitting ones (with outstanding technical expertise) to teach applications. These technical experts will be able to explain and demonstrate the pieces that comprise the black box. In other words, these professors would know how to avoid walking into the old trap – inviting generalists. Exposing the young students to such generalists tends to be futile as the missing link becomes more elusive. On the other hand, all professors – new and existing – must be allowed and encouraged to work as consultants so they remain thoroughly conversant with the ever-changing industry standards and practices.

     3. Interest Rates on Student Loans must be tied to SAT Scores and APs – Obtaining student loans should be no different than obtaining home loans. Let’s face it: Two prospective homebuyers (mortgage applicants) with 600 and 800 FICO scores, respectively, will be offered vastly different mortgage interest rates, down payment requirements and origination fees (points) by the same bank. Similarly, interest rates on student loans (to pay for college education) should be a function of the SAT and AP scores (these are comparative metrics while the general academic records aren’t). For example, the student who scores 1,580 (out of 1,600) in SAT and completes six APs with all 5’s must be eligible for a much lower interest rate than his/her counterpart who scores 1,300 in SAT and completes three APs with all 3’s. This merit-based system will incentivize everyone to do well academically from the get-go. By the same token, those who fail to do well in SAT and AP may consider other avenues: community colleges, vocational schools, etc. Simply put, we need an incentive-based school system where performers are greatly rewarded. The current system is backward-bending and requires significant overhaul.   

    4Sallie Mae must publish SAT/AP-based Student Loan Rates to provide Transparency – Sallie Mae, the largest student loan provider, and other large providers like Citi, Nelnet, Wells, etc. must develop and publish SAT/AP-based rates to educate and entice students of the advantages of the high scores. If the high school students (starting in sophomore) are taught that high score equals low rates, they would be working harder, thus gradually bumping up the curve making the system globally more competitive.

     Of course, unlike mortgage rates that change daily, the proposed SAT/AP-based student loan rates would be revised annually on the basis of the new data trends (i.e., changing scores). Hopefully, the rate chart would be prominently displayed in all high school cafeterias as a constant reminder that a little extra push would go a long way. Here is an example. Actual rates must be derived from the recent loan data from Sallie Mae and other major lenders in the field.


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     5. Interest Rates on Student Loans must be Significantly Higher for Lateral education (education for the sake of education) – When students stay back in schools and continue to take unrelated courses aimlessly (e.g., 2nd/3rd major or 2nd Master’s, etc.), lenders must discourage such loans by charging significantly higher interest rates related to those credits. If students plan on co-concentrating (e.g., business and economics; social science and statistics; applied economics and math; finance and applied math, etc.), they must declare their intention right at the outset while applying for loans, thus locking in their preferred rates throughout the period, as well as to avoid having to pay a significantly higher rate down the road for the “co” in the form of a second major. Oftentimes, the meaningful co-concentrations help job-seekers narrow the competition down. Likewise, many employers prefer those graduates as they bring in truly complementary knowledge.

     6. Interest-free Student Loans must be provided to All STEM Candidates – Instead of enticing foreign STEM graduates with visa adjustments, we must learn to nurture our own. And, it must start with an awareness movement at the middle and high school. At the core of this movement lies the marketing of the awareness to the female students in that they have “equal access” to this career domain. Until and unless our young ladies are convinced of the equal access, we will have no choice but to depend on the foreign employees. In promoting STEM education, teachers and counselors must also explain to the students that 10’s of thousands of STEM jobs remain unfilled and, as a result, our “volume” employers are forced to hire foreign employees to fill in those slots. Interest-free student loans could be a big incentive to entice more students to look into this colossal and unrestricted career domain. Obviously, once accepted, the qualified yet economically disadvantaged students, irrespective of ethnicity, must continue to receive (full) free STEM education, at both public and private institutions. 
    
     7. STEM Students in State Schools must qualify for Financial Aids ahead of all others – In addition to interest-free student loans, STEM students must receive financial aids ahead of their counterparts. Given the urgent need for STEM graduates in our economy, it does not make much sense anymore to treat all economic needs equally. At this point, college education must be compared with and treated like government services, meaning essential education (like essential government services) must always receive higher weights and protections than the not-so-essential education (like non-essential government services). Simply put, STEM education must be declared, protected and promoted as essential education. Ceteris paribus, the qualified STEM student population must get the first shot at the pool of financial aids and the residual will then be distributed to the other disciplines depending on the needs of the labor force. Of course, it has been assumed that the health and mental care education – another market area with critical shortages here – is part and parcel of the STEM, specifically part of ‘S.’   

     8. Ideally, a Moratorium on Student Loans is needed for Business and Humanities Majors – Due to the easy access to student loans, far too many students – relative to the aggregate market demand – continue to major in business and humanities, resulting in significant disguised unemployment all across the country, arguably reaching a point of moral hazard. In order to reduce the incidence of such disguised unemployment, we need a moratorium on such student loans for a period of time, at least 5 to 7 years, thus allowing enough time to get the excess market supply meaningfully absorbed while the wage level rises back up to the point of equilibrium. This pause will allow Sallie Mae to re-evaluate its existing debt load, meaning if they could use a meaningful stress test to evaluate if they might be approaching the "too big to fail" threshold. Meanwhile, a good chunk of the potential fallout population (business and humanities majors) would be redirected to the STEM universe. Sadly, if this decline is not arrested, the possibility of a bailout would be on the horizon in not too distant future (considering the student loan portfolio in the US has recently eclipsed $1.5T). Absent student loans for business and humanities, only a small percentage of the future student population – mostly from the well-to-do families and foreigners – will opt for these over-subscribed majors. Obviously, neither group would pose any renewed threat to the US labor force or contribute to the accentuation of the aforesaid bailout scenario.     

     9. Encourage Ivy League and other Renowned Schools to Eradicate "Legacy" Admission – The legacy admission system is nothing but a "privileged" quota system. Any quota system is detrimental to the overall growth and equality. Yes, applicants from the poorer families must not be discriminated against, but that financial hand-holding must come in the form of added financial aids. Therefore, the better way to handle that event is to increase the family income limit from $60K to $100K for full free-ships. Even a geo-indexed multiplier could be experimented with (Case in point: The purchasing power of $100K family income in NYC is significantly lower than that of Wichita, KS, so to say). In a free society, merit must never be compromised. For instance, if a particular ethnic group qualifies for 60% of all admissions at Harvard, they must be admitted as such, unconditionally. Of course, to promote STEM education, Ivys and other major schools should offer financial aids to qualified STEM applicants ahead of the other disciplines, for a period of time, until the home-grown STEMs are well-represented on the labor force. 

     10. Last but not least, Professors must be Apolitical in classrooms, leaving their ideology, affiliations and agendas outside (the classrooms) – Most American students take on huge loans for college education so they deserve the highest quality education in preparation for successful careers. Unfortunately, too many professors bring their political rhetoric and viewpoints to the classroom, in an effort to brainwash and indoctrinate students to their personal political agenda. This is totally unacceptable. We must keep our great educational institutions free from such partisan politics. Yes, the professors are entitled to their political viewpoints, without commingling with the education inside the classrooms. Going forward, all new hires must be independently vetted (including all of their social media accounts, going back at least ten years) and any political bias must be seriously investigated. Our labor force needs future industry leaders, not political activists. When our institutions become nonaligned and professors’ non-partisans, our labor force will regain its old glory, becoming the envy of the world, again! Of course, in order to weed out politics from our colleges, we must consider one final option: All professors, including the departmental chairpersons, should be hired and placed on 4-year contracts, instead of career tracks. Competition is the cure-all medicine!   

Again, it’s high time that we make our college education and student loans more labor-force friendly. Our students deserve better!

-Sid Som, MBA, MIM
President, Homequant, Inc.

Link to the Book

Wednesday, October 9, 2019

The Great Income Disparity – The US Case

According to Forbes, “In 1965, America's top 1% controlled about 10% of the nation's after-tax income. That number has now grown to over 15%. The average CEO-to-worker pay ratio has increased from 20-1 in 1965 to a whopping 312-1 in 2017. And middle-class real wage growth has been stagnant for decades.”

Presidential candidates are also weighing in on the fast-growing income inequality in the US. On September 24, 2019 Senator Bernie Sanders announced his “Tax on Extreme Wealth” with a proposal for ultra-wealth tax ranging between 2% and 8% depending on the net worth. Presidential hopeful Andrew Yang’s campaign website Yang2020.com states, “Andrew would implement the Freedom Dividend, a universal basic income of $1,000/month, $12,000 a year, for every American adult over the age of 18. This is independent of one’s work status or any other factor.”

Given this widening income gap between rich and poor and stagnant wages for the middle class, we need some serious socio-economic re-engineering. Here are some:

1. Implement Laureate Yunus’ Microcredit Model to Create Economic Opportunities in Inner Cities Most inner cities in the US lack proper economic opportunities resulting in poverty, often rampant poverty. Thousands of bright people are stuck in poverty in inner cities due to state and local governments’ inability to create any meaningful economic opportunities. One size fits all economic model does not work there; instead, the local governments should try Laureate Yunus’ Microcredit Economic Model, thus financially empowering the local entrepreneurs (who “are too poor to qualify for traditional bank loans”) to turn their neighborhoods around. Though this bottom-up economic model was developed for poor villages in third world countries, it has tremendous potential for our inner cities. In order to maintain the uniformity of the program, it needs to be federally (HUD) funded or insured, with a dedicated chain of private financial institutions operating and managing it, in line with the existing SBA program. Again, for the program to successfully work, governments must not be involved in running it.
   
     2. Proclaim all small and mid-size Downtowns as Enterprise Zones –
     Downtowns of many small and mid-size towns around the US suffered heavily with the out-migration of population to the suburbs. While the theme of revival and revitalization of downtowns has been gaining momentum, it needs to accelerate and become more widespread. In fact, all such downtowns must be proclaimed as Enterprise Zones, enticing businesses and builders to return to take advantage of the long-term income and property tax abatements. Sales tax subsidies could be offered to entice consumers to return to shop in revitalized downtowns as well. Public parks could be privatized in an effort to convert them to esthetically-decent yet income-producing family-oriented amusement and entertainment centers. A well-planned nationwide downtown revival initiative will create enormous economic opportunities and jobs; in fact, it could complement the much-talked about trillion dollar Federal Infrastructure Plan, creating much better synergy than approaching them mutually exclusively.

     3. Build Water and Sewage Treatment Plants – Clean water along with effective sewage system is life’s basic necessity. In fact, providing clean water to citizens is as important as the basic education and preventive healthcare. Therefore, investing in water and sewage treatment plants must also be viewed as preventive healthcare, helping people avoid unnecessary trips to health centers and emergency rooms due to easily avoidable water-borne diseases and lack of sanitation. Private companies must be enticed to build and run these plants in exchange for long-term tax-free revenue. Upon expiration of the initial contracts, governments must auction off the maintenance and revenue rights for lump-sum and upfront revenue. This could be one of the best investments in keeping people healthy while reducing overcrowding at the ER, thus freeing up doctors and nurses to provide more critical medical services. This rising tide will incentivize private companies to make bigger and better (AI and robotics) investments in water treatment and recycling technology, striving to lower the overall development and maintenance costs.

    4. Let the Private Sector Develop a Fair and Equitable Property Tax Assessment System – Property tax is often the major source of revenue for Cities and Towns. The poorly built or haphazard assessment systems tend to be highly regressive, thus heavily favoring the rich. Under such a biased system, the poor and middle-class homeowners subsidize the upscale and high-end properties. The young and prospering cities and towns around the country must therefore consider outsourcing this important public task to the private sector or at least develop it in collaboration with the private sector so it becomes truly fair and equitable. Ideally, the development and managing of this task must be entrusted to the private sector. Obviously, an unfair system discourages home ownership at the rank and file level, uproots seniors and minorities and often pushes the middle class off the cliff. On the other hand, a fair and equitable system spontaneously entices property developers, both residential and commercial, to explore those markets. Likewise, the major developers tend to avoid cities and town with unfair and/or unpredictable assessment systems.

     5. Develop a Competitive yet Investment-friendly Business Climate –
     States down to cities and towns must develop an investment-friendly business climate and learn to compete with one another in order to entice significant domestic and foreign investments, leading to persistent and long-term economic prosperity and an ever-expanding job base. Political leaders must also realize that a marketable local economy requires a marketable labor force along with an attractive business climate comprising lower corporate taxes, growth-friendly corporate and environmental regulations, separation of church and state, developed financial institutions, low crime, cooperative and functional government, etc. Furthermore, in order to attract major corporations to help take the city/town to the next level and reshape the economic landscape, local governments must be as forthcoming and accommodative as is economically possible, considering such an event could bring about epoch-making economic impact locally; Case in point: In 2018, we noticed the absolutely astounding reactions from many cities and towns across the country to the proposed development of Amazon’s HQ2 and the regional centers.

    6. Build more Long-term Care Facilities, not Jails and Prisons – People committing the so-called “serious crimes” must be sent to high-security long-term care centers under the care of qualified psychologists and psychiatrists. If we decide to move to a “merit-based immigration,” the top-notch psychologists and psychiatrists from around the world must top the merit list alongside the STEM professors and highly qualified researchers. This humane approach will help save a ton of taxpayer dollars, finding ways back into those poorer communities. The young and reinvented cities around the country should rethink and redefine crime and punishment from a moral high ground. The lack (perhaps absence of) of economic opportunities often forces poor people to commit petty offences, resulting in unnecessary jail terms. Instead of sending them to jails, they should be assigned to the local clergies, rabbis and imams to perform community service. Similarly, in a civilized world, the building of juvenile detention centers does not pass the muster of moral hazards. Those kids should also be supervised by the local spiritual leaders. This holistic approach will be a much better deterrent than the traditional jail terms. They will thus remain as normal and productive citizens without the useless stigma of jails and detentions. In return, the participating religious institutions must receive government aids and grants for maintenance and conservation of their facilities.

    7. Make College Education Free for STEM Students – This country needs to emphasize science and technology education to maintain global championship. Government colleges must provide free STEM education to all qualified poor students. In order to get into the free STEM programs students must compete and qualify for the available seats, ensuring the acceptance of the best and brightest. Students pursuing other essential disciplines like nursing, teaching, etc. must receive tuition subsidies as well. All other majors (e.g., business, humanities, etc.) irrespective of the students’ financial needs must pay full tuition, thereby forcing the otherwise needy to pursue vocational education in line with the market demand. Again, vocational education must carry full financial aid for the needy. While the local governments must always ensure that the financially disadvantaged students are never left behind, they must simultaneously understand the marketability of the labor force. Taxpayer dollars must never be squandered on education that is contrary to the market demand.
     
     8. Richest 1% Needs to Accept the Generational Reset – The richest 1% now owns 50.1% of the world’s wealth. Given this absurd concentration of wealth, we need this 1% to be self-convinced (like Mr. Warren Buffett) that they are just temporary custodians of their wealth. They must therefore come to terms with the generational reset meaning, at the end of their lives, they must return a sizable portion, if not all, of their wealth back to the society, pulling tens of thousands out of abject poverty each year. In other words, the success or failure of this country is now largely dependent on them. If they are honest and honorable enough to accept this harsh reality, the advancement of the citizenry will gleefully continue; absent which, millions more will continue to drift away in utter poverty. Hopefully, this voluntary return of wealth – and not forced redistribution of wealth – will become a self-fulfilling prophecy in arresting the ever-widening income disparity and mitigating poverty. We just want them to be more humane in feeling the pain and anguish of millions of mothers watching their children go to sleep hungry.

    9. Apply the Same High Moral Standard to the other 99% – We must learn to put the interest of the country ahead of our own. So, the rich and poor alike must also come to terms with the generational reset, voluntarily returning a big part, if not all, of our wealth back to the society. Perhaps, we need a universal ring-tone ‘Mom, I am hungry, I can’t sleep’ which will constantly remind us that millions of children are hungry and that their mothers are starving. That nightly cry is the Via Dolorosa for those mothers – that just never ends. We must never forget that these are our children and their mothers are our daughters and sisters. They are inseparably part of us!

   Sid Som, MBA, MIM
   President, Homequant, Inc.