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Our Free Home, Condo & Auto Valuation Sites
Showing posts with label Mass Appraisal. Show all posts
Showing posts with label Mass Appraisal. Show all posts
Tuesday, October 20, 2020
Promotional AVM & CAMA Modeling Services
Our Free Home, Condo & Auto Valuation Sites
Thursday, September 10, 2020
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
Saturday, September 5, 2020
Why AVM Error Rates (COD's) across Tax Rolls are not Comparable
The analysis of Assessment Rolls of major Jurisdictions requires advanced technical training and quantitative knowledge. It's hilarious when a local staff reporter settles the score annually, with a lengthy and superficial article, and the politicians run with it, silencing the unhappy taxpayers. And the cycle continues, year in and year out.
A recent local newspaper article indicated that the percent error rates "of the five largest cities for which studies have been completed in the last two years... including New York at 17.6 percent, Chicago at 25.1 percent, and Philadelphia at 20.2 percent. Houston's error rate was 7 percent in its most recent study, and Phoenix's was 8.1 percent."
Though Automated Valuation Modeling ("AVM") was used to develop all of the above Assessment Rolls ("Roll"), the modeling error rates as indicated above (generally defined by the Coefficient of Dispersion or "COD" of the underlying AVM) are not comparable.
While there are general AVM guidelines, they are not like the SAT or GRE. AVMs' development is highly subjective, depending mainly on the in-house modeler(s) or the hired consultant's acumen. Since the actual models are not published, the re-validation of those model CODs, externally, is even more subjective and circular.
So, why are the above CODs are not comparable? Here are the fundamental reasons:
1. Sales Validation -- All market AVMs are developed off of recent, arms-length sales. Thus, all sales have to be validated, and then a random or stratified random sample of arms-length sales serves as the modeling sample. Of course, there is no hard science behind the sales validation process. Therefore, if Jurisdiction X considers all of its border-line cases as arms-length, while Jurisdiction Y aggressively removes them from its identical universe, the resulting AVM of the former, ceteris paribus, will produce a higher COD than the latter's. Unfortunately, when the local reporters compare the competing CODs, they will have no idea how the respective jurisdictions validated the sales.
2. Sales Sampling -- From the universe of the validated arms-length sales, a sample properly representing the overall population is then derived. The sales sample must statistically "represent" the population, failing which the resulting AVM will be invalid, paving the way for a flawed Assessment Roll (statutorily, an Assessment Roll must be fair and equitable). Again, there is no hard and fast rule as to the extraction of the sales sample. If Jurisdiction X restricts the representative test to the 1st-to-99th percentile range while Jurisdiction Y takes a more lax approach of 5th-to-95th percentile, the AVM of X, ceteris paribus, will have higher COD than Y's. Of course, the local reporters would not even know of this requirement, let alone performing the test.
3. Removal of Outliers -- As part of the model optimization, a set of outliers are systematically identified and removed. While there are various methods to identify and remove outliers, the (sales) ratio percentile range is typical. Of course, some would use a very conservative range or approach while others (those obsessed with better stats, i.e., lower CODs) would be more aggressive. Ceteris paribus, the modeler who conservatively defines and removes outliers below the 1st percentile and above the 99th percentile range, will have a much higher model COD than someone who aggressively removes all below the 5th and above the 95th percentile range. Case in point: Chicago's 25.1 vs. Houston's 7. Unfortunately, the local reporters would try to justify both – perhaps they already have, without even knowing the underlying modeling criteria as models are rarely published.
4. Sub-market Modeling -- Many modelers and consultants build their AVMs bottom-up, instead of the customary top-down. Here is an example of what bottom-up modeling means: Let's say that the Roll is for the County as a whole, though the County comprises five Towns. Now, if the modeling takes place at the Town level (bottom-up), instead of at the normal County level (top-down), the average Town-wise CODs would be lower than the customary top-down modeling, even though the objective remains unchanged: To produce a fair and equitable County-wide Roll. This type of bottom-up modeling problem is that there will be significant noise along the Town lines, generating a considerable amount of inconsistent values. Of course, the rush-to-approve local reporters would never know any of this, as those models are rarely made public. They even disregard the FOIL requests by citing 3rd party software copyright, etc.
5. Spatial Tests -- Irrespective of #4 above, publications of Town-wise results are not typical. Again, while the County-wide COD could be compliant, the Town-wise CODs could be far apart. If Town-1 is highly urban (requiring complex modeling, hence higher COD), whereas Town-5 is highly suburban (involves easier modeling, thus much lower COD), the CODs are expected to be quite different. Of course, the modeling criteria (sales sampling, outliers, etc.) must remain uniform across all Towns. Absent publications of the actual models, taxpayer advocacy groups must, at least, insist on the CODs by major sub-markets (e.g., Towns), in addition to the system-wide COD. They must also insist on knowing if the modeling criteria were uniform across all of the major sub-markets. Of course, the local reporters vouching for the Rolls would confidently do so without even knowing how the modeling had taken place.
6. Equity Analysis -- A system-wide COD is just the beginning. It does not confirm that the Roll is fair and equitable. Let's assume that the reported COD is 15, which is compliant, a priori. Now, let's also assume that the unreported Town-wise average sales ratios range between 85 and 115. Since the Rolls tend to be regressive, it's highly likely that the 85 ratios would pertain to the most affluent Town in the County while the 115 would represent one of the middle-class Towns. In essence, the poor and middle-class neighborhoods perennially subsidize their wealthy counterparts. While the rich would make a lot of splash about their Roll values, they would be reticent when they sell their homes at twice the same Roll values. The average ratio of 85 does not mean that all homes in that Town are assessed strictly at that level. The 1st-to-99th range could be 70 to 100 (generally wider), while the Town with an average ratio of 115 could have a 1st-to-99th range of 100 to 130. Now, let's compare 70 to 75 with 125 to 130. The local reporters who boldly confirm the Rolls would be clueless about this regressivity.
7. Data Maintenance -- Intra (i.e., within the Jurisdiction) comparison: Sales are dressed and staged so the sale data are inherently cleaner and more up-to-date than the unsold property data, thereby producing lower CODs for the modeling sample. Also, the sold parcels with data inconsistencies fall off by way of model outliers and resurface upon applying the model to the population. It's a classic 'hide and seek' unless those data errors are heeded before the model application. Of course, nobody knows what happens behind the curtain. Generally, the local MLS plays a significant role in (indirectly), forcing the Jurisdiction to keep the sale data up-to-date (obviously, sale data are easy picking by the media and other interested groups). Inter (i.e., across Jurisdictions) comparison: Two adjoining Jurisdictions may have vastly different outlooks in managing the population data. One may be very proactive while the other may be reactive, at best. Ceteris paribus, the lot fraction defective of the former Roll, would be significantly lower, generating far fewer tax appeals (an excellent metric to follow) than the latter's. Again, the local reporters confirming those Rolls would be clueless of these competing scenarios.
8. Model Testing -- The modelers and consultants who apply their draft models to the mutually exclusive hold-out samples, ceteris paribus, will have more sound and reliable Rolls than those who tend to skip this critical modeling step. This step helps identify the errors and inconsistencies - from sample selection to outliers to optimization to spatial ratios and CODs - in draft models, often to the extent that they get sent back and are reworked from square one. The hold-out sample must have the same attributes of the modeling sample (and, in turn, of the population), so this test is one of the most established ways to finalize a model, leading to its successful application. Again, the Jurisdiction that methodically performs this step produces a more sound and reliable Roll, with potentially far fewer tax appeals than its counterpart, that boldly skips it. Of course, the local reporters confirming these Rolls would not know any of these crucial details.
9. Forward Sales Ratio Study -- A forward sales ratio study would be an ideal way to begin the Roll investigation process. For example, if the Roll was developed off of the 2018 calendar year sales, it could be tested against a set of forward sales ratios (comprising validated Q1/Q2-2019 sales, etc.). To bolster the size of the forward sales sample, seasoned listings could also be added. Once time-adjusted back to the valuation date, the forward sales ratio test should produce results that closely parallel the published Roll. Therefore, before rushing to hire expensive consultants, the taxpayer advocacy groups should consider hiring local analysts to compile forward sales samples and run the ratio tests. The results must then be studied multi-dimensionally, meaning by major sub-markets, value ranges, non-waterfront vs. waterfront, Non-GIS vs. GIS, etc. If the results turn out very different, a challenger AVM is in order. At that point, instead of hiring some from the universe of so-called industry experts (who would not shoot themselves in the foot), an outside economic consulting firm would be preferable as that firm would provide real analysis along with a coordinated strategic action plan.
So, what is the solution? To minimize the damage done by the low-knowledge local reporters who rush to confirm the Roll (to please the ruling party), the taxpayer advocacy groups must present their critical viewpoints via op-eds in competing papers and magazines.
No denial that well-respected billionaire businessmen like Warren Buffett and Sam Zell have written the print media off.
-Sid Som, MBA, MIM
President, Homequant, Inc.
homequant@gmail.com
Tuesday, July 28, 2020
Automated Valuation Modeling (AVM) and Mass Appraisal Modeling (CAMA) Services
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Our Free Home, Condo & Auto Valuation Sites
Friday, February 14, 2020
AVM is a Market Solution, Comparable Sales Analysis isn't
Part 1 – AVM
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In developing the analysis, the same sales population – derived from a single Zip Code – has been used across all three graphs (of course, one may use other fixed locations like Census Tract, School District, etc.). Considering all sales originated in the same Zip, it helped minimize the impact of location (of course, one 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 straightforward: Each sale is directly related to a buyer's judgment, causing a high level of subjectivity; for instance, the 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 bent on buying a pink house!) would succumb to the higher end of the range. Therefore, the R-squared is extremely low (0.189), thus 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) can produce a decent market solution. The fit is significantly tighter, especially at 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 bi-modal issues between 1,000 and 2,200 SF as the regression values are forked. It's important to note that such stacked values must be investigated for the underlying reasons. One of the simple ways to identify the issue is to scatter the normalized regression values against the other independent variables and look for possible explanations.
The above investigation guides 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. A sizeable portion of those buyers was willing to pay over $130/SF for the younger homes, while very few offered such a premium for the older stock. More precisely, none paid over $160/SF for the older stock.
So the Bldg Age variable had to be transformed from continuous to binary (younger homes vs. the rest). The regression model's re-run 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.
Part 2 – 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 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 that 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 reasonably 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 equal values – but not so in the world of the comparable sales analysis (aka, comp sales) as it is very party-specific.
Once the sales pool that closely represents the subject is scored correctly and quantitatively adjusted, it becomes comps. Generally, the five best comps are then selected to value a subject. Valuers tend to use one of the three standard methods – distance, least adjustments, and sales recency – to narrow their choices down to the five contributing comps.
In this analysis, the subject home attributes are Bldg SF=3,250, Lot SF=17,400, and Bldg Age=26. An optimal pool of 10 comps was algorithmically produced from an extensive sales population to demonstrate how subjectivity plays a vital role in this valuation process, removing the lowest ($308,770) and the highest value ($422,175) comps in each approach.
The above table represents the distance method, meaning the five closest (to the subject) comps were considered to be the best comps, producing a value range of $344,820 to $414,940, with a probable subject value of $388,775. Since least adjustments and recency of sales were ignored here, obviously, several comps needing large adjustments or of older originations managed to creep in, making the process sub-optimal.
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 compensated in value by smaller building sizes, and lesser time adjustments are proxying for older homes, etc. For example, the second least adjusted comp (# 6) with a much smaller lot was corrected by the larger and older building. It also sacrificed one of the closest (# 8) comps. This method produced a lower subject value of $371,150.
The above table represents the sales recency method, meaning the most recent five comps (in terms of sale dates) are the best ones. This is where the lowest and the highest value comps showed up on the initial line-up, hence substituted with the ones waiting in line. Though this method produced the most compact value range (upper bound was compacted down), it produced the lowest subject value of $360,340.
Therefore, if this comp sales analysis were to be used to cater to the target as mentioned earlier audience, this is how the game would be played out:
1. Assessors and Listing Agents (traditional) will be given the "distance" value (highest value).
2. Bank Appraisers and Listing Agents (buyback) will be given the "least adjustment" value (middle-of-the-road value).
3. Appeals Consultants and Buyer's Agents will be given the "sales recency" value (lowest value).
How to Reduce Subjectivity in Comp Sales
1. Apply meaningful selection, scoring/ranking, and adjustments to the sales population;
2. Build an AVM and insist on two AVM values (4th and 5th) on comps line-up;
3. Verify all comps spatially, ensuring they all come from the same or, at least, compatible neighborhoods;
4. Apply time adjustments in line with the local market (using national figures or adjustments could distort results);
5. Pay attention to valuation dates as 01-01-19 and 08-16-19 are different, often requiring additional adjustments;
6. While using sales recency, contract dates are preferred to closing dates (despite the industry norm);
7. If one is not allowed to use AVM values, one must show the AVM values below the comps grid with detailed value analysis;
8. If the sales population is large, a representative sample might be extracted from the most recent arms-length sales; and
9. If the subject population is large, automate the process with batching technology (batch comps).
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