Beyond the 1% Rule: How Do You Think About Market Selection?

Question

It’s probably fair to say that most long-distance investors would prefer to invest locally — but the math often doesn’t pencil out. That’s certainly what pushed me to look elsewhere.

That said, I’ve always found “best markets” discussions a little unsatisfying.

Rules of thumb like the 1% rule are great, but they tend to focus on a single dimension. I found myself wanting a way to look beyond that and think about markets across multiple tradeoffs.

As I’ve evolved, I realized that the 1% rule really answers just one question — “what markets cash flow?” — but misses other important questions like “which markets appreciate?” or “which markets offer the best overall balance of risk and return?”

To make that more concrete, I built what I think of as a balanced lens — not optimized for max cash flow or pure appreciation, but something that tolerates tradeoffs and avoids extremes.

The core idea was to compare cities relative to one another, rather than arguing whether a single metric is “good” or “bad” in absolute terms.

The dimensions I ended up looking at included things like:

• Home prices relative to national norms
• Rent affordability (rent vs. income)
• Employment diversity
• Liquidity indicators (days on market, inventory)
• Structural friction (e.g., landlord-friendly vs. tenant-friendly states)

Home prices relative to national norms

Rent affordability (rent vs. income)

Employment diversity

Liquidity indicators (days on market, inventory)

Structural friction (e.g., landlord-friendly vs. tenant-friendly states)

Everything is scored relative to the set of cities being compared, then stack-ranked. No claim that this produces “the answer” — just a way to make tradeoffs explicit.

I eventually put this into a spreadsheet so I could sanity-check my own intuition. What surprised me wasn’t the rankings themselves, but how often the exercise surfaced where my gut feeling disagreed with the data — and forced me to explain why.

At least for me, that’s been more valuable than chasing a single “top market” list.

I’m curious how others here think about this:

• Do you start with a preferred strategy and narrow markets from there?
• Or do you pick a market first and adapt your strategy around it?

Do you start with a preferred strategy and narrow markets from there?

Or do you pick a market first and adapt your strategy around it?

Would be interested to hear how others approach market selection before getting into individual deals.

Stuart Udis · Answer

You are overcomplicating the business. Real estate is much simpler than most people make it. At its core, you are in the service business. You provide housing.

Start by understanding the customer. Look for the type of housing people want but cannot easily find. Then ask why it is missing. Is it cost? design? Something else that keeps others from offering it? Once you understand that, figure out how to provide that product in a way that makes sense financially.

Finally, understand barriers to entry. Those same barriers protect you once you are in. The goal is a product people want and barriers that limit competition. That is the winning formula.

Jules Aton · Answer

I have always shopped for desirable neighborhoods in good school districts within reasonable driving distance. Sure they cost more but rent is higher, less tenant hassles and are likely to appreciate. Traveling too far out of your area unless looking at resort for STR doesn't make sense to me especially not when unintentionally ending up in class C & D neighborhoods.

Adam L. · Answer

Noticed your in Scotts Valley,

I would look into Watsonville , Salinas or Marina … those won’t be as profitable at Scotts Valley , but the barrier to entry will be less .

Michael Eskenasy · Answer

One thing I’ve started thinking about with market selection is less about finding the “best” market and more about understanding how a market fails.

Different markets tend to break in different ways:

• Cash-flow markets often fail through economic shocks or tenant risk.

• Appreciation markets tend to fail through liquidity freezes or financing conditions.

• High-growth markets sometimes fail when supply catches up faster than expected.

So instead of asking “which market scores best,” I’ve found it useful to ask a slightly different question:

If my assumptions are wrong, what specifically breaks first in this market?

For example, your point about something like a new data center or manufacturing expansion is interesting because those are structural shocks that can move a market off its current equilibrium. But the real question is how much that changes the long-term demand curve versus just creating a temporary construction cycle.

In practice, I usually start with strategy first (cash flow, balanced, appreciation), then look for markets where the failure modes of that strategy are less catastrophic.

For example:

• Cash-flow investors may tolerate slower appreciation but need stable rent demand.

• Appreciation investors may tolerate weak current yields but need structural scarcity.

• Balanced investors try to avoid markets where one variable dominates the outcome.

So the framework you built is useful because it forces those tradeoffs into the open.

Curious how much weight you give to future catalysts (like the data center example) versus structural fundamentals like population growth and supply constraints.

Erik Perotti · Answer

Right, @Lindsay Davis - agree with all points.

So, I am concentrating now on what may shake up a market, move it off its current pricing. A new data center announcement or maybe an Amazon fulfillment center moving into a metro area are a few examples. Better yet, both. Feel free to DM if you are interested in discussing.

Using your Shreveport example:

Shreveport City Council has approved zoning for a large data center project, indicating serious interest from hyperscale or large enterprise users. One proposal is tied to a ~810-acre parcel. Another proposal indicates potential water usage of roughly 7.5 million gallons per day, which is consistent with large-scale data center operations.

From a real estate perspective, large data center investments don't necessarily equate to large shift in permanent employment and/or changing demographics for an investor.

That said, SLB (Schlumberger) is expanding a manufacturing operation at a former GM facility. The project represents about a $30 million investment and is expected to create roughly 600 direct jobs and approximately 1,300 total direct and indirect positions.

Meaning, I am trying to move from current market signals to future state. As you said, Shreveport in not NYC, so the market is going to behave differently than say, Northern Virginia, where there are already a lot of data centers.

Lindsay Davis · Answer

@Erik Perotti,

This is a great topic.

In my view, the 1% rule is less about market selection as much as it is about deal selection. Obviously, there are expensive coastal markets with virtually no properties that meet the 1% rule, but for the most part, you can find individual deals in most markets that could pencil out.

Market-level metrics, like you mention, are more things like employment diversity and landlord-friendly laws. I’d also add things like supply and demand imbalances (undersupplied markets with high and increasing demand are ideal), median incomes (and income trends) in a one-, three-, and five-mile radius, as well as school district quality and crime rates—relative to other markets on a national level and to other neighborhoods in the same market.

That said, I don’t know if stack ranking markets makes too much sense, since many of these qualitative factors are priced in. New York City is a very different (and much more expensive) market than, say, Shreveport, and property values (along with appreciation and cash flow potential) reflect those differences. In other words, it’s hard to develop a strict hierarchy of markets. It’s more about what risks you’re willing to accept and how much you’re willing to pay.

Arman Ahmed · Answer

@Erik Perotti

I like this framework a lot because it reflects how real decisions get made beyond a single rule. When I apply a similar lens, Midwest markets often stand out as balanced rather than extreme, home prices are still reasonable relative to incomes, rents are affordable, employment is diversified, and liquidity is steady without being overheated. I usually start with the strategy, then look for markets that support that risk profile, and only after that drill into individual deals. The relative scoring approach you described is valuable because it forces you to confront tradeoffs instead of chasing one “perfect” metric.

Erik Perotti · Answer

Appreciate the pushback — I agree that this doesn’t replace city-level analysis or deal underwriting. That’s not the intent.

What I was trying to explore is the step before that: how to decide which markets deserve deeper attention, especially when comparing cities that are often lumped together.

Rules of thumb like the 1% rule are useful, but they answer a narrow question well. I found it helpful to look at markets across multiple tradeoffs instead of treating any single metric as decisive.

As a simple illustration, consider two geographically close Midwest markets — Cincinnati and Columbus — not to declare a winner, but to show how different lenses highlight different strengths.

Common heuristics investors tend to reference:

Cincinnati: lower median home price, lower typical rent, often meets the 1% rule, moderate historical appreciation.

Columbus: higher median home price, higher typical rent, rarely meets the 1% rule, stronger historical appreciation.

These signals are useful for understanding entry price and basic cash-flow potential.

Signals that surface broader tradeoffs:

Cincinnati: higher rent-to-income pressure, more concentrated employment base, slower liquidity (days on market and inventory), lower structural friction.

Columbus: more resilient rent-to-income, more diversified employment base, faster liquidity, moderate structural friction.

This second view doesn’t predict outcomes or replace deal analysis — it helps explain why similar-looking markets behave differently under stress, growth, or different strategies.

All of this is relative, not absolute, and weighting depends entirely on goals (cash flow, appreciation, balance, risk tolerance). For me, the value wasn’t the rankings themselves, but being forced to articulate where my intuition disagreed with the data — and why.

All of this is data-backed using mainstream sources (Zillow/Redfin, Census, BLS, FHFA). Happy to share sources if anyone’s interested.

Genuinely curious how others here think about this step:

Do you start with a strategy and then narrow markets?

Or do you pick a market first and adapt strategy around it?

Erik Perotti · Answer

That’s fair — and I don’t think we’re actually disagreeing on the importance of deep, micro-level analysis.

Where I’ve found some of these signals useful (including things like employment diversity) isn’t as a predictor of individual property value, but as a coarse filter earlier in the process.

When I’m looking across dozens of cities, I’m not asking “will this deal work?” yet — I’m asking “which markets are structurally more resilient vs. more fragile, given my risk tolerance?”

Once you’re at the point of analyzing micro-markets and individual deals, I agree that many of these macro signals lose relevance — and better tools take over.

At that stage, I’ve found relative, city-level signals useful not as answers, but as a way to surface tradeoffs and decide where to zoom in next.

It sounds like you adapt strategy to market; others seem to do the reverse. I’m curious how common each approach is.

Beyond the 1% Rule: How Do You Think About Market Selection?

Beyond the 1% Rule: How Do You Think About Market Selection?

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