House Rich, Cash Poor

Welcome to Click and Pledge's Fundraising Command Center podcast.

Hey everyone. Good to be here.

So we're doing something a little different today. This isn't our usual deep dive into, you know, the buttons, the forms, the receipts.

Right.

This is part of our special, the why series, where we really try to peel back the layers of the ecosystem and understand the invisible forces driving donor behavior.

Yeah. And today's not just about a new feature. It's about, well, it's about challenging a pretty sacred cow in our industry.

It really is. Yeah. We are tackling a topic that, and I'm being honest here, it really shook up our own team when the data came back. We're calling this deep dive the capacity illusion. And to set the stage, I just want to throw a number out there.

Okay.

500,000,000.

That's a massive number.

It is. And that's the estimated amount the nonprofit industry spends every single year on wealth screening services.

Every year. And the whole idea behind that half billion dollar spend is something. Well, fundraiser listening knows it by heart.

Oh yeah. You buy the list, you filter by the fancy zip codes, and you tell yourself that's where the capacity is.

Exactly. It's the prevailing wisdom. If you live in a wealthy zip code, high home value, you are automatically a major gift prospect. And it's seductive, right? Because it's so simple.

It's intuitive. Big house equals big wallet. Yeah. But here's the hook for today. The team here, we didn't actually set out to debunk this, we set up to prove it.

We did. We were building our new prediction model, the Binet score, and we wanted to make it the smartest tool out there. So naturally we thought, let's feed it all this demographic data.

You wanted to prove that home values and income levels would make the predictions even sharper.

That was the goal. Like, this is the part I love because usually you see a report and it's all about look how smart we were.

Right. We knew it all along.

Yeah. But we're basically coming out and saying, look how wrong we were about the industry standard.

We set out to prove one thing and found the exact opposite.

The exact opposite. And I wanna be really clear about the scale of this because this wasn't just a small sample.

Right.

We analyzed 2,700,000 charitable transactions. Wow. Nearly 1,000,000 unique donors across seven eleven different organizations going all the way back to 2010.

So this is not a fluke, it's a massive data set.

No. And the method was pretty straightforward. We took standard five digit zip codes, matched them to US Census Bureau data, income, home values, education, all of

it. And you tested, what was it, 17 different variables?

17 variables, yeah. To see what actually predicted giving.

So let's get right to it, the big question. Does living in a rich neighborhood actually mean you give more? What did you find?

We found it was near zero.

Near zero. I mean define that for me because there has to be some connection. Wealthy people have money, you need money to donate, there's a line there.

You'd think so but statistically speaking it was a flat line. When we looked at home value specifically, the correlation coefficient was point zero zero four.

Point o four. Yeah. Data science, that's just noise, isn't it?

It's noise. It tells you absolutely nothing about a specific individual's propensity to donate.

That is really hard to wrap my head around. So you're telling me if I see a donor lives in a million dollar home, that single piece of data is statistically useless?

On its own, yes. And this brings us to what we started calling the comparison trap. Because if you only look at the raw averages, you can get fooled. I can already hear the objections.

Okay. Well, let's play that out then. Yeah. If I look at the average gift from a rich zip code versus a modest one, the rich one has to be higher. Right?

It is. But barely. Let's talk median gift. So in a modest zip code where the median home value is about $230,000 the median gift is $53

$53. Got it.

Now, you take the wealthy zip codes, the top quartile, median home value is over $1,000,000.

Okay.

Their median gift is $100.

Alright. I'm gonna be the devil's advocate here.

Go for it.

$100 is almost double 53. If I'm a fundraiser, don't I want the $100 donor? Isn't that It's worth

a fair point. And it's the exact logic the screening companies use to sell their data. But you have to look at the efficiency.

The ROI.

Exactly. ROI. To get that 1.9 times increase in giving, have to target people with 4.6 times the wealth.

So the ratio is completely out of whack. You're hunting elephants to find peanuts.

It's so inefficient. But the bigger problem is the variance. That $100 figure is just a median. Within every single one of those wealthy zip codes, you have people giving $10, and you have people giving $10,000.

Right.

Knowing the zip code tells you nothing about who's who. You're paying for expensive data that gives you a $47 edge on average while ignoring the huge spread of actual human behavior.

It's like trying to guess if someone's a good basketball player just by knowing they're tall.

That's a perfect analogy. And we didn't just at the data doesn't work. We wanted to know why. Why doesn't having a million dollar house mean you have more cash for charity?

And this is the part of the research that just stopped me in my tracks. It's the reality of the modern homeowner. We started calling it house rich cash poor.

House rich cash poor. Yep. Because we all tend to confuse asset value with actual wealth or, you know, cash flow. If my house doubles in value, I feel richer. Sure.

But for most people, home value appreciation hits the cost side of your budget, not the income side. You can't use your home equity to buy groceries.

Okay. Let's break that down with a real example because I think a lot of people are feeling this personally. Yep. Take me back to say 2010.

Perfect. So 2010. Someone buys a house for, let's call it, $300,000. Back then, interest rates were lower, around 4.7%. You add it all up, mortgage, taxes, insurance, and the total annual cost on that home was about $20,000.

$20 a year. Manageable. Now let's fast forward to 2025.

Right. That same house, they haven't touched it, it's the same structure, is now worth $600,000.

Double. So the wealth screener goes ding ding ding, high capacity.

That's what it sees. But let's look at the reality for the person buying that house today. Interest rates are much higher, maybe 6.8%. But the real killers are things like insurance.

Oh, yeah. Insurance costs have exploded.

Tripled in some places?

Yeah.

D: And property taxes are way up because the assessment is higher.

So what's the total damage now?

That same house now costs about $48,000 a year to own.

Woah, from $20,000 to $48,000

$28,000 less discretionary income per year. The wealth screener sees a $600,000 asset and assumes they're loaded. The reality is they have less cash to spend on anything including That's

a massive, massive disconnect and I have to ask is this just a weird quirk in our data?

No and that's what's so important. We cross referenced this with Federal Reserve data just to be and the macroeconomic picture backs this up 100%.

Okay, Talk to me about debt. Because if people are getting richer from their homes, you'd think they'd be paying down their credit cards.

Logically, yes. That would be the smart move, but it's not what's happening. US household debt is at an all time high over 18 and a half trillion dollars. Trillion. But the really scary one is credit card debt.

It's at an all time high of 1,230,000,000,000.00, and it's up 60% just since 2021.

60% in just a few years. That sounds more like a crisis than capacity.

It is. We're seeing record home values, A and D record debt at the same time. If home equity meant cash, that debt should be going down. It's going up.

And there was one specific thing you found about Aloxi's home equity lines of credit that I thought was the smoking gun.

Yeah. This was really telling. Historically, people use a Aloxi seed to invest back into the asset. You know, you remodel the kitchen, finish the basement.

Right.

In 2022, about 65% of Aloxi's were used for renovations.

Okay.

In 2024, that number dropped way down. The use case that's skyrocketing now is debt consolidation.

So they're pulling money out of the house just to pay off their credit cards?

It went from 25% to 39% in just two years. People are using their homes like an ATM just to stay afloat, not to upgrade their lifestyle.

So the picture you're painting is, the wealth screening tool sees a high value home, but the person inside is house rich, cash poor, and borrowing against their dining room to pay a MasterCard bill.

That's the situation and if you approach them for a major gift based on that home value you are just completely misreading the room.

Okay so that's the bad news. The old way is broken. It's an illusion. But we're not just here to, you know, bum everyone out. If wealth screening doesn't work, what does?

The answer is actually so simple and it's something everyone listening already has. The phrase we keep coming back to is this: the best way to move forward is to look backward.

The best way to move forward is to look backward. Unpack that for me.

So we compared the predictive power. Remember, all those demographic factors explained about point 0002% of why someone gives basically nothing.

A rounding error.

But when we looked at just one thing, prior year giving, did they give last year and how much? That single data point explained 56% of future giving.

56%. That's, I mean, that's a staggering difference.

It makes past giving behavior 28,000 times more predictive than demographic data.

28,000 times. Usually in business you're happy with a 5% edge. This is just on another planet.

It is. And here's the best part. The part that should make every fundraiser listening smile. You pay vendors thousands of dollars for that demographic data that doesn't work. The giving history.

That's free. It's sitting right there in your CRM.

It's like you're searching the world for a treasure map and the gold is buried in your own backyard.

Exactly. The grass isn't greener elsewhere. It's right in your own database.

So what does this mean for strategy then? It feels like we need to shift from hunting for wealth to cultivating behavior. Because a donation is more than just a transaction.

It is. And that's the key. Past giving predicts future giving not because of some math equation, but because of the relationship. A donation is a signal that the donor trusts you.

And relationships need participation. You can't just automate a friendship. But everything is moving toward automation.

Which brings us to another surprise in the data. We called it the relationship paradox. We looked at responsive donors versus auto pay donors.

Okay, my gut says auto pay is the holy grail. Monthly recurring revenue, Right? Predictable cash flow.

That's what we thought too. And it is great for budgeting, but the data on long term value was shocking. Responsive donors, people who have to actively click the button each time, have a 1.7 times higher lifetime value.

Wait for real, even if they give less often? I would have lost that bet.

Most of us would have. The median lifetime value for a responsive donor was $530. For an autopay donor, it was 320.

So why is that? Is it because the autopay people just kind of drift off and forget?

It's because engagement drives value. An automated payment becomes a bill, like Netflix. A responsive donor is reading your emails, seeing your impact, and making a conscious choice. That active engagement builds a much stronger bond.

That's such a huge insight. We're trading lifetime value for the convenience of a recurring revenue metric.

And speaking of building those relationships, we have to talk about the make or break moment. The thirty day window.

This was my favorite stat in the whole report. It's just so clear and actionable.

So we found that sixty seven percent of first time donors never come back. They're one and done.

Which sounds awful. Almost seventy percent attrition right out of the gate.

It's high. But, for a fun comparison, that's actually better than online dating.

Is it really?

Yeah. Davy shows that about eighty percent of first dates from dating apps don't lead to a second date.

So your donors like you more than the average dating app match. That's a win.

It's a win. But here's the strategy: if you can get a second gift within thirty days of the first one, the retention rate just skyrockets. Seventy three percent of those donors go on to become recurring supporters.

Seventy three percent? That's a game changer.

It is. If you wait three or six months to ask again, which a lot of standard welcome series do, the rate drops to thirty seven percent. Speed matters. You have to strike while that connection is still fresh.

This completely changes the game for a welcome series. Most orgs just send a thank you receipt and drop you in the newsletter list.

And that's how you lose them. Show them the impact of that first dollar and invite them to give the second one while they still feel great about it. The capacity isn't in their zip code, it's in their engagement with you.

So let's bring this all home. We've busted the myth of the wealthy zip code, we've exposed the reality of being house rich, cash poor, and we've found that the real gold is in the behavioral data we already have.

That's the summary. And if there's one thought to leave folks with, it's to do a strategic reset. Look at what you're paying for wealth screening. Look at the actual results. Then compare that to the free census data that's out there.

And most importantly, at your own data.

Exactly. The data you're looking for is already in your database. It's your donor's actual behavior. The grass isn't greener elsewhere. It might be time to rethink the strategy.

I love that. And look, we know this is a big claim. We're challenging a half billion dollar So we want you to check our work.

Absolutely. We believe in transparency. The full research report with all the census data files we used, it's all available at clickandpledge.com for FlagShore.

And we mean it. Test it yourself. We would love to see what you find. If you have questions, email us: researchclickandpledge dot com.

We want to start a conversation. This is how the whole field gets better.

It is. So visit clickandpledge.com if you want to see how we're building this into our tools. But I want to leave you with one final thought. We spend so much time looking for capacity so we can ask for money, but if engagement is the real driver of value, maybe we should be looking for partners with curiosity. Cause curiosity leads to engagement and that leads to a lifetime value a zip code can never predict.

Thanks for joining this deep dive.

Thank you.

We'll catch on the next one.