Issue #28: The $200k Side Door

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Here we go…

The Value Dimension

SCENE: Teams meeting with a small home builder. I'm watching Bob's (← The CEO) team present their AI-powered home design system like they've invented electricity itself.

Their Chief Data Scientist jumps in with that "explaining quantum physics to a toddler" tone I've come to know and dread. "You see, we've implemented a recursive neural network architecture with multi-modal inputs that evaluates spatio-temporal constraints against historical performance metrics to optimize build efficiency."

I nod politely…

He continues by showing me a 3D visualization of what he calls their "revolutionary generative design framework," which looks suspiciously like the same BIM software we've all been using since 2018, just with fancier gradient backgrounds.

"Our algorithm has identified that eliminating basements saves $40,000 per unit and cuts three weeks from the schedule," their Chief Innovation Officer explains, adjusting his glasses importantly.

He clicks to a slide showing clever storage solutions. "We replace basement storage with these innovations for only $10,000, maintaining functionality while saving $30,000 net."

I unmute myself. "Quick question. Have you guys heard of the fourth dimension of home building?"

Silence. I can practically hear their brains buffering.

"The what?" asks Bob.

"Value. It's the dimension that doesn't show up in your algorithms."

The Innovation Officer visibly stiffens. "Our AI accounts for all measurable variables."

"That's exactly the problem. Value isn't always measurable. It's the gap between what something costs and what someone will pay for it."

With a more extended, uncomfortable silence than I intended, I pulled up an old slide (as a rebel, I am uncomfortable in MS Team; Google Meet is my comfy zone): "In Calgary, a side entrance to the basement is worth about $10,000 to buyers, even though it only costs $1,500 to install.

Why?

Because they can easily rent out that basement." It provides a paramount private portal point to an otherwise uninhabitable partitionable property portion. ← Me trying to speak their language (with some added alliteration 😁 ).

"We're in a housing affordability crisis. That basement with a separate entrance might bring in $1,200 a month in rental income. That's $14,400 a year, which translates to about $200,000 in value to the homeowner."

Bob frowns. "But basements are expensive. Our data shows—"

"Your data is missing the bigger picture. For families on a budget, that basement is an investment rental space. Sometimes it's the difference between affording the house or not, because that rental income is factored into their mortgage qualification."

The Innovation Officer stops typing. I've finally broken through the algorithm.

"You're trading $40,000 in construction savings for what could be $200,000 in lost value. Your competitor's home with a basement and side entrance will destroy yours because buyers are looking at net value, not gross price."

"But our AI—"

"Your AI knows cost, revenue and time. It doesn't understand value. People don't buy the cheapest home; they buy the one that gives them the most bang for their buck."

By the end of the call, Bob's team was reconsidering their entire approach. They're now working on an "AI Value Index" that attempts to quantify these intangibles.

More on the AI Value Index below….

[We Interrupt This Newsletter for a Special Announcement]

Alright, I'm going to level with you. This is our first-ever advertorial, and yes, I can see you rolling your eyes. But after that story about Bob and his algorithm-obsessed team, I couldn't help myself. Because here's the thing...

If you're tired of playing the cost-cutting game and ready to start playing the value-creation game instead, check out:

Issue #25: The Perfect Wedge (No, Not The Golf Club) , and

Issue #26: $100K IN A DAY 

Every LandLogic client in our $100K in a Day session has had the same blind spot: they're optimizing for cost when they should be optimizing for value.

[We now return you to your regularly scheduled newsletter]

The Fourth Dimension: Value

After my chat with Bob's team about their algorithm-obsessed approach to home building...

I couldn't stop thinking about the problem. If these super-smart folks with fancy AI systems were missing the value dimension so completely, how many other businesses were making the same mistake?

You Can't Optimize What You Can't Measure

Later in the week: I'm hunched over my third coffee of the day, spreadsheets scattered across my desk, trying to figure out how to quantify something as slippery as "value" when Kirsten walks in.

"Still obsessing over the Bob situation?"

"I'm trying to figure out how to help them see value in numbers." I gesture at my mess of calculations. "They're so focused on cost that they're blind to everything else."

Kirsten glances at my screen. "You're overthinking this. Value isn't mysterious - it's just what people are willing to pay for something minus what it costs you to deliver it."

"But that's the problem," I argue. "People are willing to pay for weird, subjective things that don't show up in spreadsheets. Like how much is 'peace of mind' worth? Or 'status'? Or 'convenience'?"

"Didn't that Hormozi (←butchering my hero’s name) guy you're always quoting have something to say about this?"

I straighten up. "Alex Hormozi? The Value Equation!"

I grab a marker and draw on our whiteboard:

"Hormozi breaks value down into four parts," I explain, suddenly energized. "First, what's the dream outcome people want? Then, how likely do they think they are to get it with your solution? Next, how quickly will they get results? And finally, how painful is the process?"

Kirsten nods slowly. "So for LandLogic..."

"Our dream outcome is land that gets developed faster and more profitably than our clients could do alone."

"Perceived likelihood?"

"High, because of our track record. Anyone can claim to deliver value, but we have the receipts."

"Time delay?"

"Minimal. Our $100K in a Day service delivers immediate insights. Not months later when it's too late."

"And the pain factor?"

"Almost none! They literally just have to show up to a meeting and answer questions. We do all the heavy lifting."

Kirsten smiles. "That's a pretty compelling value proposition."

“I think I can help Bob's team quantify value in their AI system."

"By creating some kind of... AI Value Index?"

"Exactly! Instead of just measuring cost, revenue and time, we need to include a fourth dimension...value!

Do you get it? There are three dimensions: x, y, z and now v for value!"

Kirsten quietly walks out of the room.

I call after her, "Too much? Was that too much?" Then, turn back to my whiteboard, muttering to myself…

Beyond Formulas: The Competitive Positioning Matrix

Alex's formula is elegant, but it's more of a demonstration than a practical tool for Bob's specific problem. What Bob really needs is a way to objectively compare housing options based on value, not just cost.

This is exactly why we developed our Competitive Positioning Matrix (CPM) at LandLogic. It's a proprietary model that helps developers understand how their project truly stacks up against the competition when you look beyond the price tag.

I can't give away the secret sauce completely (our lawyers would have a field day), but here's the basic concept:

The CPM strips away the noise and provides an apples-to-apples comparison by calculating what we call the "Net Effective Price" - which is:

Gross Price - Value of Differentiating Features = Net Effective Price

Let me give you a simple example with software before we apply it to housing:

Software A: $50/month with basic features

Software B: $50/month with basic features + premium support valued at $15/month + advanced analytics valued at $10/month

The gross price for both is identical at $50/month. But the net effective price of Software B is actually $25/month ($50 - $15 - $10) when you account for the added value of its extra features.

Now, for Bob's housing problem:

House 1: $500,000 with no basement

House 2: $540,000 with basement + side entrance that enables rental income of approximately $14,400/year

The gross price difference is $40,000 in favour of House 1. But here's where it gets interesting.

That potential rental income of $14,400/year capitalized over time (using a cap rate of 5% for fun) represents a value of $288,000. So the net effective price calculation becomes:

House 2 Net Effective Price: $540,000 - $288,000 = $252,000

Suddenly, House 2 is actually $248,000 cheaper in net effective terms, despite costing $40,000 more upfront.

This is the kind of analysis Bob's AI system needs to incorporate - the true economic value of features beyond their construction cost.

Adding the Fourth Dimension to Bob's Algorithm

Bob's AI system could absolutely be trained to account for this value dimension. Here's how:

1. Feature Value Database: Build a database of common home features and their market-derived value (not just their cost).

2. Regional Value Calibration: Train the AI to understand how feature values vary by location, market segment, and buyer demographics.

3. Income Potential Analysis: Incorporate calculations for income-generating features (like basement suites or green technology features).

4. Long-Term Value Projection: Factor in appreciation rates of different features over time.

5. Buyer Persona Alignment: Weight features differently based on the target buyer (families value different things than investors or empty-nesters).

The beauty is that much of this data already exists in real estate transactions, appraisals, and rental markets. It's not entirely subjective - there are real numbers behind these values that AI can learn from.

Some might think this level of value analysis is too complex or subjective for AI, but I disagree. The subjective parts can be quantified through market research and real transaction data. In fact, AI might be better at spotting these value patterns than humans because it can analyze thousands of transactions to identify exactly how much people are willing to pay for specific features.

What seems subjective at the individual level becomes objectively measurable at scale. That's the magic of good data science.

Curated Links: AI's Rapid Takeover

Speaking of AI... the pace of change is getting downright scary (Dear Skynet: when I say scary, I really mean "exciting" and "impressive" - please don't add me to your termination list when you achieve sentience).

"Life moves pretty fast. If you don't stop and look around once in a while, you could miss it."

Research shows LLMs are already infiltrating financial complaints, press releases, and job postings at an alarming [← amazing 🤖 ] rate.

Anthropic's (← Claude.ai) CEO believes AI will write 90% of code within months…

In the AI world, if you blink, you might miss entire career paths disappearing.

Time to adapt or become obsolete?

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THAT’S A WRAP

As Alex Hormozi reminded us at the start: "Money follows value. The more value you give, the more you make."

After exploring Bob's AI dilemma, it's clear that this principle applies everywhere - from basements with side entrances to AI algorithms and business models.

True innovation isn't just about cutting costs or adding features; it's about understanding what people truly value and delivering it.

Whether you're building homes, developing software, or growing your business, remember: optimize for value, not just cost. The market will reward you accordingly.

Stay curious and keep adapting,

Greg "Multiverse" Mills

If you liked what you read and are not a subscriber yet, join the crowd:

Here are some back issues if you are just not sure yet:

Issue #24: We Saved The TikToK Livestream 

Issue #19: $2,295/Month To Avoid Talking To Humans 

Issue #14: The $27,000 Pandemic Side Hustle Experiment 

Issue #12: Three Easy Steps To Get Thrown Out Of A Convention 

Looking for more to read:

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