To Analyze a Sales Population, Consider the Expanded Percentile Curve

 

(Click on the image to enlarge)

As we know, not all segments of the market move in tandem. When the market starts to move up, it generally begins at the bottom of the value strata (start-up homes) and graduates up the value ladder. Therefore, while analyzing a large sales population, it is prudent to use the entire percentile curve (as shown in the above Miami graphic) rather than just the Median as it may musk the actual pictures on both ends of the curve, say below the 25th percentile and above the 75th percentile and more precisely below the 10th and above the 90th.

How to Analyze a Sales Population

1. Single Parameter – Instead of just one parameter (like the Median), it's better to consider the expanded percentile curve, preferably 1st percentile to 99th percentile, avoiding minimum and maximum as they may skew the picture as well.

2. Sample Selection – When confronted with all sales meaning both arms-length and non-arms length and virtually no time to validate the sales, the 5th to 95th percentile sample is more meaningful, without having to spend time on manual validations. Conversely, if the sample comprises only the arms-length sales, the 1st to 99th percentile range could be more meaningful.

3. Outlier Analysis – Therefore, while studying outliers of a sample lacking validation, it's better to consider only the cases below the 5th and above the 95th percentile. Likewise, below 1st and above the 99th could be a better starting point for a validated sample, gradually extending out to the outliers on both ends of the percentile curve (as time permits).

4. Sales Timeframe – When the timeframe is extended (9 to 24 months), sales must be time-adjusted, preferably at the monthly level (deriving monthly time factors). If the sample comprises 3-4 years' of sales, quarterly adjustments will make more statistical sense. When an extended series (e.g., 10+ years) is analyzed, annual factors would be appropriate. Most extended series analyses are performed to detect seasonality in the data.

5. Growth Factors – As we all know, the residential market is as local as it gets. Therefore, a good sales analysis must additionally be broken down to the sub-market level as long as those sub-markets are well-established and accepted. Since the growth rates vary by the market, time adjustment factors must be derived at the sub-market level (e.g., 12% in our example for the City of Miami). Applying national or even regional factors could result in flawed and indefensible results. Time adjustment in AVM is generally different (will be discussed later).

6. Use of Median – Due to time constraints, If one has to choose one parameter to ascertain time's impact, it must be the Median, as it is less prone to outliers (outliers heavily influence average, often distorting the analysis). In an even like that, the sale's Median must be compared with the normalized (by Bldg SF) Median, ensuring they are close to each other.

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7. Spatial Distribution – As part of the sales sampling, one must also ensure that the sales are spatially distributed in line with the population, so a meaningful spatial chart is in order alongside the data tables. In the above example, one must understand that the Median ASP and Median Bldg SF are mutually exclusive, but they may be connected to get a general idea of the ASP/SF, but not for any serious analysis. To analyze the normalized ASP/SF, one needs to create the organic variable (row-wise ASP/SF) and the run percentile stats.

8. Creating Sales Ratios – In the above example, in addition to the percentile analysis of sales, the distribution of sales ratios (ratio of County Market Values to Time-adjusted Sales or ASP) is shown, thus connecting the apples-to-apples dots. The spatial chart additionally depicts the stratified sales ratios. Caution: While creating the sales ratios, one must time adjust the sales to the valuation date (in this case, 01-01-2019) as the tax roll values are as of that date, or else it would be an apples-to-oranges.

9. Regression Values – Ideally, ASP should be modeled (using multiple regressions or any other industry-accepted methodology). The resulting sales ratios of the regression values (which are smoother and statistically more significant) used all analyses, Including defining and removal of the model outliers. Regression values could also be used to challenge the tax roll market values additionally. When there is a paucity of comps, such regression values could also be used to proxy actual comps in a comparables grid.

In a nutshell, to get a better picture of the overall market, an expanded percentile distribution analysis of sales is significantly more meaningful than a simplistic median-based sales analysis. Additionally, normalized values and spatial sales ratios could provide better insight into the building blocks.

-Sid Som, MBA, MIM

homequant@gmail.com

Link to the Book

These 9 Enhancements will make most Assessment Rolls Fair and Equitable

Despite statistical modeling, the vast majority of assessment rolls remain moderate to highly regressive, meaning the middle-class homeowners often subsidize the upscale homes. In other words, the homes in the lower-to-mid value range tend to be over-assessed while the higher value segment gets a significant break. The use of the antiquated CAMA modeling cannot fix that problem in major jurisdictions with highly complex housing stock, but setting and adhering to the following issues would take those jurisdictions in the right direction, leading to more equitable tax rolls.

1.  Backward-bending Status/Valuation Date: Since the Taxable Status Date (or the Valuation date) is often a futuristic date, the available market information and data tend to be quite inadequate to develop proper predictive (mass appraisal) models that, in turn, generate the assessment roll. Case in point: Many taxing jurisdictions utilizing the CAMA (mass appraisal) modeling generally build their models in August/September with the available data, targeting the next January as the status date, creating an enormous predictive (data) gap. A vast majority of those modelable sales would have been contracted in the first and early second quarter of the year (the gap is direr!). Therefore, the status/valuation date must be set back in line with the availability of the modelable data, to avoid having to produce (or gamble with) a futuristic roll. 

2.  Short-term Sales: When the holding period of the property is less than two years, those sale prices must be anchored as market values, thus forcing the potential gamers (institutions, flippers, etc.) to pay higher taxes. The point is that the housing market's rapid institutionalization needs to be evaluated separately, without having to redistribute their share (of the burden) on to the rest. Therefore, assessors must aggressively lobby to remove the application of any state/charter restrictions (e.g., annual growth capped at 5%, etc.) from short-term sales, so they stand on their own at the market level, without any pseudo protection. Exclusion: non-arms length sales (inter-company, distressed sales, etc.).

3.  AVM/CAMA Application: Since Automated Valuation Modeling (AVM is a top-down econometric solution encompassing the vast majority of residential properties on the roll) is inherently more equitable, it must be applied to the rest of the tax roll, including the non-arms length from #1. An independently developed challenger model should point to the outliers at the population level, requiring them to be separately hand-worked (using the analysts' traditional comparable sales approach). Depending on each roll's complexity, these models generally work well between the 10th and 90th percentile of the value curve, so the rest should be scrutinized and perhaps hand-worked. Unfortunately, failing to pay proper attention to the aforesaid outer bounds often degrade the entire roll. The point is, AVM/CAMA should be applied where it is applicable.          

4.  Unique Properties: Again, due to the paucity of arms-length sales, AVM/CAMA values are not meant for this group (trophies, mansions, large waterfronts, tiny oceanfront bungalows, etc.), so they must be hand-worked by the assessing staff. The sale of unique or vastly atypical properties must not enter the modeling (AVM) sales sample. Right at the outset of modeling, separation of such categories must be discussed and isolated from the modeling spectrum altogether. Such decisions help enhance the marketability of the roll. Still, when the modelers arrogantly apply models to these strata, the values are generally indefensible, forcing taxpayers and other external agencies, including media, to question the entire roll value.

5. 2-to-4 Family Properties: Although assessment and mortgage statues generally lump these properties with the single-family residences (SFR), they must not co-share the SFR roll, considering these are inherently income-producing properties. Therefore, they must be modeled, valued, and taxed as a separate group or as a sub-group (small cap) under the multifamily group. Depending on the liquidity of this group, they could even be market modeled standalone or hand-worked. If the market approach, rather than the income approach, is used to value them, that approach must not change from year to year as that could introduce significant inequity. 

6.  Outsource AVM: It's much cheaper to outsource residential AVMs to a financial consulting/research firm (but not an appraisal or CAMA outfit) than maintaining a dedicated group of in-house modelers. It will also be more effective as those firms are non-political, and as such, their feet would be held to the fire for proper performance following the performance metrics contained in the contract. For example, one such metric could be the level of appeals meaning if the appeals go up due to flawed assessments, they could be forced to make the necessary corrections promptly without any political interventions or cover-ups. Conversely, partisan politics plays a significant role when the values are internally generated, especially when the Assessor is not an independently elected official.   

7.  Manpower Planning: Larger jurisdictions must learn from the private (FedEx, UPS, Amazon, etc.) how to use the seasonal help. For example, the seasonal help could be used to process exemptions, appeals applications, income/expense statements, etc. They can be used to perform field operations and inspections too. Alternatively, counties/taxing jurisdictions may maintain a "Central Pool" agency to cater to the different agencies based on their seasonal workforce requirements. Maintaining full-time personnel to handle seasonal activities is often the case – is an expensive and wasteful proposition, returning minimal value to the taxpayers. Even when an internal modeling group is maintained, it must be hired and kept outside of the civil service/union system, allowing navigational adjustments.

8. Practice Assessment: Instead of having an autonomous appeal or review department, it's better to have it under the Assessment umbrella. Having a separate government agency or department is inherently political and is not necessarily in the taxpayers' best interest. Often, the focus of such autonomous appeals/review agencies is to hire appraisers, rather than trained assessors, which does not promote equity of the roll as they generally lack the understanding of assessment equity. Moreover, such autonomous political agencies quickly learn that the workforce's growth solidifies the ground under their feet, thus gradually distancing themselves more and more from the Assessor's office and growing into a powerful political body. None of this helps improve the quality of the assessment roll. 

9. Commercial Assessment: To promote equity of the commercial segment of the tax roll, departments must practice assessment, not an appraisal. Therefore, it's prudent to collect a sizeable Income and Expense (I/E) sample to develop meaningful local metrics for the commercial properties. These I/Es could also be used to formulate use-based AVMs to process the homogeneous properties like 4-to-10 family rental properties, small office complexes, mixed-use properties, public storages, smaller warehouses, and industrial properties, etc., in turn, creating more equity across such denominations. This approach would free up assessors to devote more time to the complex commercial properties where modeling is futile or unnecessary. Commercial appraisers must also be trained and groomed to transition from an all-appraisal mindset to an assessment equity-oriented thinking.

Flood Zone Insurance: Last but not least, it's essential to enforce the flood zone insurance. Homeowners inside the designated flood zones must be required to carry flood zone insurances. Should the situation arise, insurance companies would be on the hooks, not the rest of the taxpayer population. Of course, an annual credit could be offered to offset a minimum policy deductible.

Again, having an all-encompassing residential AVM does not make the roll fair and equitable; it must be properly sourced and targeted. Likewise, training and promoting a group of unqualified employees as AVM/CAMA technicians do real injustice to the roll. Finally, use-based Income models off of actual I/E data could promote equity across commercial property groups' homogeneous stretch.

-Sid Som, MBA, MIM

President, Homequant, Inc.

homequant@gmail.com

Link to the Book

Property Tax Appeals Consulting could be a Good Part-time Business Opportunity

If you are a marketing-oriented personality and are looking for a part-time business opportunity, explore property tax appeals consulting. Evenings and weekends are perfect for hosting outreach programs to market such services to the homeowners (i.e., present your unique tax-saving program as compared to the competition's and signing them up on the spot or via personal follow-up meetings).

Every tax jurisdiction offers short windows to allow the filing of appeals, both on and offline, followed by the face-to-face presentation of (over-assessed) cases to the hearing officers or the assessing staff. Of course, the case presentation generally takes place on weekdays, so you have to make yourself available on weekdays (check with your county/town regarding the filing period and case presentation windows) during that period. Assessment cycles are essential determinants of appeals, so you must know how your jurisdiction's process operates. While 1 to 2-year assessment cycles tend to be more common, 3 to 4-year cycles are not too uncommon.

Current Consulting Environment  

1.  Inappropriate Market AVMs: Though many consultants started using 3rd party automated valuation model (AVM) values, they do not work well in identifying the over-assessed parcels on the tax roll as the underlying challenger AVMs tend to be Market AVMs as well. Since the 3rd party AVMs and Assessor AVMs would share very similar modeling sales samples (in terms of the period, sales significance, etc.), they produce very identical values, thus essentially validating the tax roll values while costing a lot of money for those values.

2.  Flawed Sub-market level AVMs: Many tax roll AVMs are built at the sub-market level, without jurisdiction-wide equalization or smoothing, thus distorting values along the sub-market lines. Let's say Hillside Avenue divides the two sub-markets, despite having very similar, if not identical, housing stocks on both sides. Since the two sub-market level modeling samples would be mutually exclusive, the housing stocks on either side would be valued differently. If the internal QC fails to identify and correct those inconsistencies, the roll would be palatable to the consultants having access to jurisdiction-wide AVMs.

3.  Some Consultants Take More Specialized (Non-AVM) Approach: Of course, some consultants are more specialized in marketing their services; for example, homes with GIS implications, close to major arteries, back into the service roads to highway, industrial areas, new constructions, etc. Often, analysts apply a basic cost approach rather than a market-adjusted cost approach to new homes and subdivisions, thereby inflating their values significantly above the real market; incomplete improvements: Instead of applying some token values to incomplete improvements (hence unusable as of the roll date), analysts often apply cost (the cost to cure, etc.), causing the age-old controversy, etc.

4.  National Tax Firms Suffer from Similar Shortcomings: While some national tax firms pursue this business via their franchisees or licensees, they suffer from very similar shortcomings. They work with national AVM vendors to ascertain the "meaty" cases. Unfortunately, those are generic market model values, being sold to a wide range of users – from banks/mortgages to private mortgage insurances to mortgage REITs to tax consultants and others. Considering they are developed off of different sale periods and methodologies, they can produce very different values, not necessarily ideal for determining the tax rolls' accuracy. Even when the method is similar, they are nonetheless market models, thus untenable as good challengers. 

  

5.  Law Firms Often Challenge the Assessment Ratio: While most law firms in every jurisdiction represent individual homeowners, some are focused on the overall assessment ratio (a.k.a., residential assessment ratio, or RAR). Since assessment rolls must be fair and equitable, those law firms resort to independent RAR tests, internally or by hiring outside experts. Disputes over RAR, especially after significant reassessment, often force tax rolls to the court. The retail consultants, therefore, have limited competition with the local law firms.

The point is, the methodologies and strategies used by the existing (mass) filers are often based off significant inadequacies (resulting from hit or miss analytics or inappropriate AVMs), paving the way for the old domain to be renewed and reinvented, with more scientific baseline work coupled with creative marketing strategies.

Again, this professional domain is ready to be reinvented as the old strategies tend to significantly underperform in addressing and catering to the renewed market demands. Let's recap why the old systems have been underperforming:

Flawed AVM – At the parcel level (bottom-up), a competing Market AVM does not surgically identify the over-valued parcels on the roll; it usually cross-validates the roll values (as the AVM generating the roll and the challenger tax AVM share the same or very similar sales complexes and attributes). 

Failure to Establish the Ratio – While a leading local law firm may take the initiative to challenge the ratio (top-down), consultants rarely spend that extra money to study, negotiate, and establish the ratio. An unadjusted Market AVM is, therefore, utterly ineffective. A prudent consultant must research and develop the ratio first, leading to a Ratio-adjusted Market AVM.

Though an adjusted Market AVM is better than an unadjusted one, it nevertheless succumbs to an inherently inappropriate methodology given the market being targeted. Market research based on an inaccurate AVM could be more counter-productive, meaning a vast majority of the real "meaty" cases could remain unidentified.

Forward-looking Solutions

1.  A Custom Tax Appeals AVM, not a Market AVM, is generally required to identify Over-valued Parcels on the Roll – A Tax Appeals AVM is a specialized AVM that surgically identifies and categorically defines the over-valued parcels, irrespective of the original construction of the roll, i.e., AVM or Comps-derived. For example, after having identified the over-valued parcels on the roll, a good Tax Appeals AVM should break them down into major categories, e.g., (a) MaxiMax (ex: 30% & above), (b) MaxiMid (ex: 20-29%) and (c) MaxiMin (ex: 10-19%). Of course, the categories could change based on client requests. Though MaxiMax represents the most over-valued category, it's generally the least liquid (lowest frequency), while MaxiMin tends to be the most liquid.  

2.  A Custom Tax Appeals AVM Offers Biggest Bang for the Buck – Although a custom Tax Appeals AVM is more expensive than a generic Market AVM (as it is sold to a wide variety of users, including the competing appeals consultants), it identifies the truly over-valued cases, thus offering much bigger bang per buck. Moreover, by walking down on the curve (MaxiMax to MaxiMid to MaxiMin), a consultant ensures targeting the most over-valued (hence most profitable) cases first. In other words, if a new consultant decides to target only the top two categories in the first year, s/he still achieves the biggest bang for the buck, potentially expanding into the third category in the following year. On the other hand, a Market AVM is a total hit or miss for appeals consultants, without any assurance that the most over-valued would be targeted.

  

3.  A Custom Tax Appeals AVM makes marketing a Scientific Exercise (Smart Marketing) – Since Appeals AVM precisely identifies and categorizes the over-valued parcels, the market penetration (targeting those homeowners) becomes a straightforward exercise. Depending on the liquidity of the tranches, homeowners could be invited to attend outreach seminars. Due to the uniqueness and accuracy of the Tax Appeals AVM, a vast majority of those homeowners will – for the first time – find out how they are over-assessed. This precise and scientific approach will help discover a market hitherto unknown and unexplored. While the clueless competition will continue to pour money into the far less effective Market AVMs chasing the meatless-to-less meaty market segments, this new generation of consultants will sign up the truly over-assessed clients at a rapid rate, proving that marketing is more a modern-day science than an age-old art. Since this is primarily a contingency business (meaning the tax savings are generally split), targeting the right market segments is the key to success. Custom Tax Appeals AVM provides that scientific base.

What is Needed to Get Started

A. Acquire the Tax Roll from the Jurisdiction – As soon as the tax roll is published, they need to acquire a soft copy with a year's worth of sales, should they decide to research the ratio. Obtaining the sales data is highly recommended as the ratio study would get them started with the top-down knowledge. While negotiating with the AVM consultants, they should bundle the ratio study for a better price (if not for free). The tax roll cost varies by the jurisdiction – from free to low cost to $ 100's of dollars. For instance, our county charges $10 for the entire roll, including sales. In order to keep the cost of the data manageable, data about specific zip codes could be thought of (of course, the ratio study will require sales from across the county).

B. Subscribe to a Comps Program – While the AVM will point to the over-valued cases, it does not replace the individual comps reports needed at the hearing. Brokerage sites are inadequate for two reasons: a) they show the active listings primarily, and b) even if they carry the recent sales, they do not provide the pre-formatted comps reports. The programs that allow dollar adjustments in terms of (sales) time and quantitative variables are preferred. While evaluating the various commercially available programs, the ease of use and flexibility of the adjustment matrix (where the coefficient values are stored) must be closely examined to avoid dealing with significant wasteful time and agony during the crunch time. Month-to-month subscription contracts are preferred to annual contracts as the need to process the comps reports would last only two to three months. Other basic business requirements (including LLC, etc.) and bulk mailing costs must also be factored in.  

C. Two Complementary Minds could do Miracles – Teaming up with a buddy with a complementary skill-set could be the way to go. Since the targeting window is relatively short-lived, the outreach seminars could be simultaneously offered in two different town/county parts. The complementary skill-set works better as the business grows to handle operations and technical aspects while the other could concentrate on the marketing side. The advantage of the two-partner team from the get-go is that it helps create a similar passion, knowledge, and forward-thinking. 

People who pursue part-time opportunities must also know the conflict of interest rules their full-time jobs generally impose (of course, there is no need to jeopardize the full-time job!).

-Sid Som, MBA, MIM

homequant@gmail.com

Link to the Book

Sales Ratio Study is largely Ineffective, if not Counter-productive

A Sales Ratio study examines the relationship of Market Values on the Assessment Roll to Time-adjusted Sale Prices (adjusted to the Valuation/Taxable Status Date). A Sales Ratio study, unlike an Automated Valuation Model (AVM), is not an econometric solution that could be used in any meaningful decision-making. Unfortunately, sales ratio studies are often developed and used to test the assessment rolls' metallurgy and progression.

Since sales ratios are developed using sales complexes only, two very similar homes in a given neighborhood - with very different effective ages, say 15 vs. 50 - will be evaluated alike. On the other hand, a properly-developed AVM will effectively assess the differences and return values that are different, yet statistically significant and econometric.

What does a sales complex comprise?

-Sale Price

-Sale Date (to time-adjust sales to valuation/status date)

-Sale Validation (to ensure only arms-length sales are used)

-Classification (to ensure the right class of properties is used)

-Market Value (from the Tax Assessment Roll)

-Assessed Value (when Residential Assessment Ratio or RAR is also required)

Additionally, some consultants retain a few other variables like Town (to evaluate sub-markets if it is a county-wide study) and Living Area (to consider normalized scenarios). Of course, sub-market and normalized ratios are statutorily rare.

So, why does a sales ratio study become an ineffective solution? Let's consider the following reasons...

1. Sales Ratio Studies are at best Heuristic analyses -- Most large and even medium-sized tax jurisdictions have moved to primarily AVM-based tax rolls. Therefore, when the raw sale prices (generally time-adjusted) are compared with the scientifically-derived AVM values (to compute the sale ratio), it is no longer an apples-to-apples comparison. Sale prices do indeed reflect property characteristics (in addition to location, etc.). They are nonetheless highly subjective, reflecting individual (un-equalized) economic behavior, including personal tastes and preferences (e.g., when one is bent on buying a pink house, one will overpay). Exterior walls and conditions are actual modeling variables, while exterior color is not. Therefore, the presence of data variables will force AVMs (hence the tax roll values) to ignore those emotional premiums. Simultaneously, the standalone sale prices in sales ratio studies will fail to differentiate and ignore them.

2. Sales Ratio Studies do not require "Representative" Tests -- The underlying assumption of a sales sample is that it statistically represents the population it is derived from. But that assumption is not necessarily valid. When the sample is large, it tends to be representative at the body of the curve (between the 25th and 75th percentiles), but not necessarily on short (<25th percentile) and long end (>75th percentile) of the curve. The reason is simple: Not all segments of the market move in tandem. When a market starts its upswing, it usually begins at the lower end of the curve, followed by the mid-range and further up. Thus, without a proper representative test, a sales ratio study is, at best, a hit or miss. The additional sub-market or normalized ratios remain equally unreliable.

3. Sales Ratio Studies do not require Price Segmentation Tests -- Sales ratio studies are perfect "one size fits all," meaning only a median-based ratio does the real trick. The absence of the price-segmented (<25th percentile; 25th to 50th percentiles; 50th to 75th percentiles; >75th percentile) ratios is, at best, a limited scope analysis, which is the primary reason why many tax rolls are regressive, i.e., why the middle-class neighborhoods heavily subsidize the wealthy districts. State Boards and Industry Technical bodies must additionally (in addition to the median ratio) require the full price-segmented ratios to minimize the incidence of compensating errors, leading to fair and equitable adjustments    

4. Sales Ratio Studies do not require Champ-Challenger Validations -- Before an AVM is finalized, it is optimized and then tested against a mutually exclusive hold-out sample (Challenger). If the hold-out test results are very similar, the model is considered final (Champ) and is ready to be applied to the population. Of course, when it comes to sales ratio studies, there are no such requirements. A forward sales sample would be an ideal challenger. For example, if the statutory ratio is developed off of the 2018 calendar year sales, it could be tested against a forward sales sample (comprising validated Q1/Q2-2019 sales). Seasoned listings could be added to bolster the forward sample. The forward sample test must produce comparable (to the statutory sample) results. Before rushing to make a biblical prophecy to confirm the roll results, so the dust settles, the concerned 3rd parties like the local newspaper reporters and independent review consultants should, at least, undertake this challenger test.

5. Sales Ratio Studies do not require Stratified Time Adjustments -- As explained before, not all segments of the market move in tandem; hence time adjustment factors in each segment are often different. Applying one median factor generally distorts both ends of the curve, forcing the outer segment values to move further away from the AVM (Roll) values. Again, the State Boards and Industry Technical bodies must require all ratio analyses - from sales sampling to time adjustments to error ratios - performed and broken down into statistically significant price segments. In fast-moving markets, time becomes a critical issue, so the time adjustment factors must be analyzed and applied by statistical segments. Alternatively, even when a median time factor is used in an AVM, it does not pose any threat as it interacts with other variables, including location, and gets corrected.

6. Sales Ratio Studies do not require any meaningful Spatial Tests -- While a system-wide median ratio could be acceptable for a small and mostly homogeneous jurisdiction, it is not very meaningful for large and complex jurisdictions with multiple towns, boroughs, etc. For example, a system-wide coefficient of dispersion (COD) of 15 for New York City is neither very instructive nor very helpful, as a low COD of 9 for Staten Island, a relatively homogeneous borough in the City, may compensate for Brooklyn's 20 due to its highly heterogeneous housing stock. In this example, while Staten Island passes with flying colors, Brooklyn fails even though its overall COD remains compliant. Therefore, the study of assessment equity requires meaningful analysis by major spatial parts and the aforesaid economic/market attributes and segments.

7. Sales Ratio Studies do not require the use of MLS data to Test Data Validity -- Granted, most tax jurisdictions tend to be more careful as to the quality of the sales data (easy picking by media, etc.) than the unsold properties. Yet, this data quality is nowhere as clean and up-to-date as the MLS data that are all professionally inspected and verified. Therefore, the State Boards and other Technical bodies should urge that the jurisdictions develop the ratio-eligible database after comparing the internal sales data with those of the MLS'. Only the (arms-length) sales data as matched and confirmed by the MLS data should qualify for the ratio study. There is a long-term benefit to this exercise as well: By studying the unmatched data, an AI logic could be developed and applied on to the unsold population to isolate (or at least narrow down) the cases requiring immediate attention. An AI-driven auto-regressive data update process is always preferable (inherently more surgical) to the traditional cyclical approach (e.g., update all data on a 5-year cycle), considering only a small percent of the entire population might need attention or update, although costing taxpayers an unnecessary ton.

8. Sales Ratio Studies do not require any Data Convergence schema -- Although the raw sale prices are being compared with the modeled values, no data convergence schema is necessary to make the sale prices closely align with the data. As indicated before, absent the data variables, it is difficult to explain why two very similar homes in very close proximity of each other are fetching somewhat different prices. While an AVM will correct and explain that difference, sales ratio studies will have no explanations. Therefore, sales verification must also include "Effective Age" and "GIS Implications." If the GIS implications are noted alongside the verified sales, the highly impacted sales could easily be avoided, a priori. Similarly, the Effective Age ranges could effectively serve as sub-stratification criteria to compare and analyze the genuinely similar properties. The point is that the sale price alone is inadequate to form any meaningful decision-making.    

9. Sales Ratio Studies have yet to factor in the Impact of Cap on SALT deductions -- The new $10,000 cap on SALT deductions (including property taxes) has started to impact the high-end residential markets, especially in high-tax coastal markets. A report from the New York Federal Reserve concluded that the caps on taxes and mortgage debts "have negatively impacted the housing market" by lowering the sales volume. Of course, the market will take a while to manifest any meaningful medium to the long-term effect. As the volume wanes on the long end of the value curve, sales must be adequately replaced by hand-worked appraisals. It would be imprudent and highly regressive to re-populate that curve's stretch by drawing from the 50th to 75th percentile ranges. That type of re-population or idea perhaps works in physical sciences, but not in economic sciences. State Boards and Technical bodies must recognize and act on this emerging trend.

Sales ratio studies urgently need a fresh and forward look. The median-based one-size-fits-all concept has to be replaced with meaningful market segmentation analyses, coupled with a handful of spatial and economic attribute tests. 

-Sid Som, MBA, MIM

homequant@gmail.com

Link to the Book

9 Issues that make an AVM Inefficient, often Ineffective

The difference between a smart modeler and an average modeler is that the former quickly figures out and intelligently avoids the (valuation) modeling that tends to make the process inefficient, if not ineffective. That is why establishing a quality control or a modeling review process is essential to avoid having to deal with significant damage control down the line. A handful of such poor practices may raise serious concerns for the quality control reviewers. Here are some of those poor practices.

1. Time Adjustment: Some practitioners use the number of months since sale (NMSS) as an independent variable to ascertain the rate of growth (+/-) in the targeted price level, paving the way for the time coefficient to adjust the modeling sale prices, which then serves as the dependent variable in the regression equation. While this is acceptable as a lead-up regression to generate the primary dependent variable, it is unacceptable as an independent variable going into the regression equation. The reason is simple: since NMSS would be missing in the unsold population (the model would eventually be applied to), resulting in the application to fail. 

2. Sales GIS: Testing the Sales GIS representativeness is not an easy proposition, forcing many practitioners to skip this sampling test. Sales GIS is often a function of the market dynamics, deviating from the Population GIS. Therefore, an untested Sales GIS paves the way for an inefficient AVM. That is why many practitioners tend to use "fixed neighborhoods" in the modeling process as they are more stable and well-accepted, meaning they do not succumb to the short-term market swings and are generally liquid enough to help test the representativeness of the modeling sample.

3. Chasing Trophies: An AVM is not meant for the entire population. Trying to achieve a maximum of two sigma solution (95%) is more meaningful than the whole population, leaving out the admixture of the utterly unattainable 5%, including the limited number of trophy properties and large mansions. The reason is apparent: The paucity of such sales. Therefore, the smart modelers remove them from the modeling spectrum altogether, i.e., they do not let any of those sales enter the modeling sample, nor do they apply the model onto that subset.

4. Chasing Tiny Bungalows: This is the flip-side of dealing with the trophy properties. There is a tiny waterfront (in fact, many of them are prime oceanfront, e.g., Point Lookout, Atlantic shore Long Island, NY) bungalows all around the country, but this subset represents the land values primarily. Once they are sold, they generally take a completely different form, often as multifamily properties with re-zoning. Therefore, as long as the existing improvement is sound, they must be hand-worked by the appraisers. Letting them into the modeling process would be a red flag for the quality control reviewers.

5. Combining 2, 3, and 4-Family with SFRs: Considering this sub-class of residential properties is mostly the income-producing, the smart modelers know it's imprudent to group and model them with the single-family residences (SFR). New modelers often make the mistake of combining them with SFRS as they are part of the same tax class in individual states or sharing the same mortgage category. Of course, the properties within this sub-group are mostly transacted using comparable sales to be market modeled, but as an independent and mutually exclusive (of SFRs) group. Mother-and-Daughter, a relatively common form of set up in big cities, is not a technical 2-family to be modeled with the SFRs.

6. Synthetic Variables: Many modelers who come from the non-quantitative background become obsessed with better modeling stats, allowing irrational or unexplainable synthetic variables like (X * Y) ^ Z into the equation. Grated, such variables may enhance the model's favorable stats but reduces its explainability and decomposability, and therefore, it's overall utility! On the other hand, the modelers with sound quantitative background realize from the get-go that the use of such variables is nothing but sowing the seeds for inefficient modeling, knowing very well they could never explain the underlying market economics.

7. Untested Models: It's always a prudent practice to test the draft model on to a mutually exclusive hold-out sample before being applied on to the population. The hold-out sample test must produce very similar results, both before and after the outliers, as in the draft model, ensuring that the real model (not an interim version) is being applied. Caution: Since the hold-out sample is part of the original sales sample and therefore comprises sale dates, it will NOT detect the NMISS issue (# 1 above). Again, this is a critical step, the absence of which is tantamount to adventuresome modeling and could be costly at the end.

8. Sales Complex: Any part of the sales complex, directly or indirectly (e.g., ASP/SF, etc.), must not be used as independent variables in a regression equation as it violates the basic assumption of multiple regression analysis. NMISS is a classic case of such violation of the standard modeling practice. To reiterate, when any part of the sales complex is introduced on the independent side of the regression equation, it could not be applied to the unsold population as it would lack the sales attributes. Of course, this is one of the first rule violations any qualified quality control reviewer would look for.

9. Lack of Value Optimization: Since the final quality of the vast majority of the CAMA models is primarily error-based (usually the last model's Coefficient of Dispersion/COD, Price Related Differentials/PRD, etc.), there is hardly any practice of optimizing the final values from the regression models. In other words, those models are not subjected to the real test of optimality of the solution; for example, the CAMA modeler can hardly answer if their model COD of 8 is better than the model COD of 10. In individual events, the COD of 8 could be a post-optimal solution despite being a better error stat, while the COD of 10 could have been the optimal solution. Therefore, the final regression values need to be optimized via Linear/Non-linear Programming.

To conclude, modelers must remember to get into the habit of producing industry-standard models, without getting carried away by the quality stats. Case in point: A model with a conforming COD of 12 could receive a much better quality control score than its counterpart with a COD of 9.  

-Sid Som, MBA, MIM

homequant@gmail.com

Link to the AVM Book