Post Pandemic, Major Assessment Jurisdictions should consider Hiring AI Engineers (rather than Traditional Modelers)

"AI engineers don't write code to build scalable data pipelines like a data engineer...instead, they understand how to extract data efficiently from a variety of sources, build and test their own machine learning models, and deploy those models using either embedded code or API calls to create AI-infused applications."

Conversely, the existing mass appraisal (CAMA) regression models are not intelligent enough as they are highly modeler dependent (subjective). Thus, given the same sales dataset, five modelers may come up with five different models with very different results. Of course, the biggest failure is the Sales GIS -- generally developed off the current market attributes -- so it's representativeness relative to the population (which is more or less static) is difficult to establish and justify.

[FYI -- That is why, in my AVM books, I propose the use of fixed neighborhoods as they are not sales-dependent and are population-derived, rather than the Sales GIS, which is totally sales-dependent and is not necessarily representative of the population the model is applied to.]

AI engineers do not use any data-variable modeling. Their data extraction process is ingenious, leading to brilliant machine learning models. In a mass appraisal environment, they will precisely identify and demonstrate where the sales datasets and populations are at variance. Whereas, the mass appraisal modelers will remove them from the model as outliers, creating unexplainable gaps and significant fault lines they won't even know.

Alternatively, in a traditional CAMA environment, it's all sample-based, so the results are, at best, bell-curved with the customary 68% efficiency. The error-based CAMA regression models fail to test the solution; for example, is a model Coefficient of Dispersion (COD) of 8 better than a COD of 10? The COD of 8 could represent a post-optimal solution, whereas the COD of 10 could correctly represent an optimal solution. But in a CAMA environment, the COD of 8 would be universally preferred (In fact, several years ago, I presented a paper along these lines at a national conference, raising some serious questions). 

The mass appraisal industry is too antiquated, using the 30-year old regression modeling and mostly Sales GIS. That is why it is high time that the major mass appraisal jurisdictions start hiring some AI engineers, proving that the industry needs to look ahead. 

Granted, given the paucity of AI engineers, it will not be easy to hire AI engineers, but the agencies should widen the search and try. Given how the union-heavy civil service system works, they should also remember what Steve Jobs said, "It does not make sense to hire smart people and then tell them what to do. We hire smart people to tell us what to do." In other words, they must be given the necessary flexibility and autonomy to develop forward-looking solutions, without being bogged down to backward-bending maintenance. 

Of course, to make the modeling environment more efficient and solution-oriented, these agencies should also hire STEM graduates instead of traditional business and humanities graduates who lack advanced quantitative training and knowledge and make very poor modelers. Since a sizable percentage of municipal hires are non-civil servants, these folks could easily qualify in that segment, citing an urgent need for high-level quantitative talent – just the way the major US companies hire skilled foreign nationals under the annual H1-B visa quotas. 

The combination of STEM and AI could be the nirvana for these major jurisdictions.  

Similarly, in the futuristic consumer environment (e.g., free home valuation apps and online sites), the AI-based solutions would, optionally, ask the first-time users to take a short tutorial. As the user interacts with the tutorial, the machine learning models will extract and store the data (by reading the user's responses) and fine-tune the model for each user. When the user returns to value a subject, the stored model will populate the comps as soon as the issue is defined so that the ten-minute exercise would be reduced to fifteen seconds – and customized.

-Sid Som
homequant@gmail.com

https://www.datarobot.com/wiki/ai-engineer/ 

States Need to Develop Artificial Intelligence Models to Identify High Risk Clusters

Now that the states have significant amounts of targeted data from the ongoing coronavirus outbreak, they should use them to develop Artificial Intelligence (AI) models to identify and establish true risk curves. Developing the risk tranches based merely on age and medical conditions is inadequate. 

In partnership with the private sector, states need to immediately assemble task forces comprising leading AI engineers and data scientists. Such AI models will not only save thousands of lives until vaccines are available for the masses, but they will also help define better data standards and architectures to prepare for future pandemics. 

We must learn from our current experience that there is no room for trial and error experiments after the outbreak. We must be ready to leverage our existing preventive modeling infrastructure. The absence of such advanced preventive modeling infrastructure today paved the way for the vast loss of lives.

Advantages of such AI modeling...

1. Instead of one-size-fits-all national models, Ai models will surgically identify the risk clusters in each state, taking into consideration the local population (risk) attributes. For instance, the model that works effectively in MA may not necessarily be that effective in FL. This type of AI-based bottom-up modeling would be far more effective than the "tweaked' top-down (national or even regional) models to fit the requirements of individual states. 

2. These state-level AI models will help states manage their resources in a more forward-looking and scientifically-aggressive manner, without having to be guided by some national level make-shift task force with a generic, perhaps inexact, knowledgebase. Thus, the state governors would be empowered upfront.

3. Such advanced pandemic modeling infrastructure will free up the state health departments to focus on deployment and implementation, which in turn will save lives. It will also require far less taxpayer funding, which is usually committed to such lower-level research and modeling.    

4. This type of modeling expertise will lead to better sharing of resources depending on the intensity and direction of the outbreak. Just like the weather forecast models, these AI models will predict the outbreak before they happen, allowing meaningful deployment of resources to reduce the severity, as well as the clean-up and prevention of renewed outbreaks.   

5. The AI model could easily help robotize or, at least, automate testing and immunization centers around the states so the emergency rooms and doctors' offices are not overwhelmed with these basic services.

It's time we allow AI to take the lead in fending off future pandemics.

Stay safe!

-Sid Som
homequant@gmail.com