Keith Wolf - There's Only One Way to Value Property: Data, Stats, and Multicollinearity

There's a quiet risk emerging in modern valuation practice.

We have more data than ever. More tools. More statistical horsepower. More AI-assisted modeling. And yet, the core question hasn't changed:

Do we actually understand what our models are doing?

In a recent conversation on Assessment Matters, Keith Wolf---a longtime appraiser with deep experience in both fee and institutional settings---offered a perspective that should resonate with assessors and appraisal leaders alike:

"If you've got great data, you're a great appraiser. If you've got bad data, you're terrible. That's the way it works."

It sounds simple. It's not.

What Keith is really pointing toward is something more fundamental than better models. It's about understanding how variables interact---and why that interaction determines whether your valuation process is defensible or fragile.

There Is Only One Way to Value Property

One of the most useful reframes in the discussion was this:

Mass appraisal and fee appraisal are not different species. They are different scopes of work.

In both cases, we are attempting to estimate market value using observed market behavior. In mass appraisal, we do it at scale to distribute the levy equitably. In fee work, we do it to support lending or other transactional decisions.

But underneath? The same mechanics apply.

• Sales data

• Property characteristics

• Adjustment logic

• Model interpretation

As Keith put it:

"There's really only one way to value property. And it all starts with the data."

For assessors, that matters. Because if we concede that the underlying logic is shared, then the statistical discipline required in mass appraisal isn't optional---it's foundational.

Correlation Is Not Understanding

Most modern modeling begins with some form of correlation matrix. We look at:

• GLA

• Bedrooms

• Bathrooms

• Age

• Condition

• Lot size

• Design

• Quality

We measure how each relates to price.

But here's the trap: correlation only tells us that variables move together. It does not tell us why.

This is where multicollinearity enters the picture. Or, in the language of causal modeling: confounding.

Bedrooms, baths, and GLA are not independent actors. They are deeply intertwined. If you remove one, the influence doesn't disappear---it redistributes somewhere else in the model.

That's the "missing variable" problem.

And if we don't understand how these variables interact, we risk making adjustments that are statistically tidy but conceptually wrong.

As Keith noted:

"Statistics is one thing. Interpreting them and applying them credibly to get a reliable result is what's most important."

For assessment offices, that distinction is everything. Because in a hearing or appeal, it's not enough to show a model output. You have to explain it.

The Problem With Smoothing

Every assessor has seen it: apply enough smoothing and your metrics improve.

Lower dispersion. Better COD. Cleaner PRD. Nicer RMSE.

But what are we actually fixing?

Keith offered a vivid analogy:

"To me, a smoothing effect is like using painter's caulk. I'm just caulking over the error so you don't see it."

That's a hard truth.

Smoothing can stabilize volatility---but it can also mask structural issues:

• Poor variable specification

• Hidden collinearity

• Omitted variable bias

• Inconsistent data formatting

From a public trust perspective, masking is not the same as correcting.

Assessment exists to distribute the tax burden fairly. That requires transparency---not cosmetic performance improvements.

Why AI and Modeling Tools Demand More Literacy, Not Less

One of the more subtle warnings in the conversation concerned modern modeling environments, including AI-assisted coding.

Different tools can run the same dataset and produce slightly different answers. Even the same tool can produce variations depending on how the statistical library is invoked.

That's not a flaw---it's a reminder.

The math may be technically correct. But:

• Is the data formatted properly?

• Are variables interacting in unintended ways?

• Is the regression constant absorbing unexplained influence?

• Are coefficients stable or distorted by multicollinearity?

If we treat modeling tools as black boxes, we lose the ability to defend outcomes.

And in the public sector, defensibility is non-negotiable.

The Red House Problem

Will Jarvis offered a plain-language example that captures the issue perfectly.

Imagine a neighborhood where every home was built in 1980---and every home is painted red.

If those homes sell at a premium, what's driving value?

• The color?

• The era of construction?

• The quality typical of 1980 builds?

• Some combination?

When variables move together consistently, disentangling them is difficult. That's multicollinearity in its simplest form.

In real markets, the interactions are far more complex:

• Design and year built

• Quality and renovation status

• Location and lot size

• Age and condition

Without understanding these relationships, we risk attributing influence to the wrong variable.

And once that error enters the model, it propagates across the entire roll.

Why This Matters for Equity

At first glance, multicollinearity and causal diagrams sound academic. But they aren't.

They sit at the heart of uniformity.

If we misunderstand variable interaction:

• Certain property types may be systematically advantaged or disadvantaged.

• Adjustments may be inflated or muted.

• Appeals may expose inconsistencies.

Public trust in the assessment function rests on the idea that the tax burden is distributed fairly and rationally.

That fairness depends on disciplined modeling---not just technical modeling.

As Keith emphasized, there are variable types that most appraisal textbooks don't even address:

• Confounders

• Colliders

• Mediators

• Moderators

• Blockers

These aren't theoretical curiosities. They describe real-world relationships inside our datasets.

Ignoring them doesn't make them disappear.

From "Trust the Model" to "Understand the Model"

There's a temptation in every profession to rely on authority.

"Trust the model."
"Trust the software."
"Trust the course."

Keith described his own frustration with being told, essentially, "Just trust us. This is how it works."

That mindset is incompatible with the responsibility assessors carry.

We are not technicians executing scripts. We are public officials---whether elected or appointed---tasked with distributing billions in tax base.

Understanding variable interaction isn't about becoming a statistician. It's about:

• Knowing when correlation is sufficient.

• Knowing when regression coefficients are unstable.

• Knowing when group data is more defensible.

• Knowing when smoothing hides more than it helps.

It's about judgment.

And judgment is what separates a functioning model from a defensible valuation system.

Key Takeaway

Modern valuation tools are powerful---but power without understanding is fragile.

For assessors and appraisal professionals, the responsibility is not just to produce values, but to understand how those values are derived. Variable interaction, multicollinearity, and causal reasoning are not academic luxuries. They are foundational to uniformity, defensibility, and public trust.

We don't need to become statisticians.

But we do need to move beyond the black box.