By Agam | Keys by Agam, eXp Realty | Real advice. Real data. Real estate.
Before I ever got a real estate license, I spent 15 years as a software engineer. And when it came time to buy my own property, I did what any engineer would do: I built a spreadsheet. Then I built a better spreadsheet. Then I pulled data on 50 New Jersey listings — pricing history, days on market, price-per-square-foot, tax records, photo counts, listing language — and looked for patterns the way I’d debug a system that wasn’t behaving the way the documentation said it should.
What I found changed how I buy real estate, and it’s the same framework I now use for every client at Keys by Agam. Here’s what 50 listings taught me that no open house ever could.
Lesson 1: The Listing Photos Tell You More Than the Listing Description
I started tracking photo count against days on market and found a pattern immediately: listings with fewer than 15 photos sat on the market roughly twice as long as listings with 25+. That’s not a coincidence — it’s a signal. A seller (or agent) unwilling to invest in thorough photography is often unwilling to invest in the things buyers actually care about: staging, minor repairs, decluttering. Low photo count wasn’t the cause of the slow sale. It was a symptom of something deeper: a listing that wasn’t built to sell.
The inverse mattered too. Listings with suspiciously few interior photos — a full exterior gallery but only two or three interior shots — correlated almost perfectly with a specific problem: something inside the buyer would have wanted to see and didn’t. I learned to treat a photo gap the way I’d treat a gap in a log file — not proof of a problem, but a reason to look closer before trusting the system.
Lesson 2: Price-Per-Square-Foot Is a Necessary Metric and a Dangerous One
Every buyer eventually discovers price-per-square-foot as a comparison tool. It’s useful — and it’s also one of the easiest ways to draw the wrong conclusion if you stop there. Across my 50 listings, price-per-square-foot varied by more than 40% within the same town, sometimes on the same street. Lot size, renovation age, school catchment, and even which side of a somewhat arbitrary boundary line a house sat on could swing the number dramatically.
The engineering lesson here is one I already knew from years of looking at dashboards: a single aggregate metric without its underlying dimensions is an invitation to be misled. Price-per-square-foot is a starting filter, not a conclusion. I now treat it the way I’d treat a single KPI on a dashboard — useful for flagging outliers, useless for making a final call without segmenting further.
Lesson 3: Days on Market Is a Lagging Indicator, Not a Leading One
Conventional wisdom says a home sitting on the market a long time means something’s wrong with it. Sometimes that’s true. But when I plotted days-on-market against price-reduction history across my dataset, a different story emerged: the listings that sat the longest were disproportionately the ones that were overpriced at launch and never meaningfully repriced — not the ones with actual defects.
In other words, days on market doesn’t tell you whether a house has a problem. It tells you whether a seller has adjusted to reality. I started reading days-on-market alongside price-history, the same way I’d never trust a single system metric without checking its trend line. A home that’s been listed for 90 days at its original price is a very different situation from a home that’s been listed for 90 days after two price cuts — even if the “days on market” number looks identical.
Lesson 4: The Listing Language Is Optimized Copy — Read It Like Marketing, Not Fact
Agents write listing descriptions to sell, which means certain phrases function almost like keywords rather than descriptions. Across my sample, I started tagging recurring phrases and cross-referencing them against inspection outcomes and price history where I could find that data:
- “Cozy” correlated strongly with genuinely small square footage relative to the listed bedroom count.
- “Full of potential” / “bring your vision” correlated with deferred maintenance — sometimes cosmetic, sometimes structural.
- “Motivated seller” correlated, unsurprisingly, with listings that had already seen at least one price reduction.
- “Original charm” / “original details” was a near-perfect proxy for original systems — meaning original electrical, plumbing, or HVAC, worth budgeting for accordingly.
None of this makes a listing bad. It makes the language a data source, not a description — and like any data source, it needs to be interpreted rather than taken literally.
Lesson 5: Tax History Reveals What the Listing Won’t
This was the single most valuable dataset I pulled, and the one most buyers skip entirely. Municipal tax records show assessed value history, prior sale prices, and permit filings — and cross-referencing that against the current asking price surfaced things the listing itself never mentioned: additions without corresponding permits, assessments that hadn’t been updated after a major renovation (a future tax increase waiting to happen), and in a few cases, a current asking price meaningfully disconnected from any defensible valuation trend.
If you only read one section of this post, read this one: pull the public tax record before you fall in love with a listing. It takes ten minutes and it’s the closest thing to a paper trail a house actually has.
Lesson 6: Comps Have a Half-Life
I initially built my comparison model using all sales from the trailing 12 months. That turned out to be a mistake in a market moving as fast as New Jersey’s has over the past few years. Comps from 8–12 months ago were measurably less predictive of current value than comps from the trailing 90 days — sometimes off by 5% or more in either direction depending on the direction rates and inventory were trending.
The fix was the same one I’d apply to any model with drifting inputs: shorten the window and weight recent data more heavily. Now, when I run a comparative market analysis for a client, the trailing 60–90 days carries far more weight than anything older, and I explicitly flag when a comp is stale rather than let it quietly skew the number.
Lesson 7: The Best Deals Weren’t the Cheapest Listings — They Were the Mispriced Ones
This is the insight that mattered most, and it’s the one that took the longest to see clearly. The lowest-priced homes in my dataset were almost always cheap for a defensible reason — smaller lot, busier road, deferred maintenance, less desirable school catchment. Real value wasn’t hiding in the bottom of the price range. It was hiding in listings priced close to fair value that had something working against them in the market’s perception but not in the property’s actual quality: bad photos, an awkward listing date (holiday weekend, dead of winter), an overly narrow price band that filtered out otherwise-qualified buyers, or a description that undersold a genuinely solid property.
Finding those took the full dataset, not a gut feeling on one showing. It’s the same reason engineers don’t ship based on a single test run — the signal only becomes trustworthy once you’ve seen enough data points to know what’s noise.
What This Means for You
You don’t need to build a 50-row spreadsheet to buy a house well — that’s what I’m here for. But you should walk into any purchase with the same instinct I brought from engineering: don’t trust a single number, and don’t trust a listing’s own description of itself. Pull the tax history. Weight your comps toward recent data. Read “motivated seller” as a data point, not a courtesy. And when a listing looks too good relative to the market, spend the extra hour figuring out why before you spend the extra ten minutes falling in love with it.
That’s the whole philosophy behind Keys by Agam: real advice, real data, real estate. I’m not going to tell you a house is “a great deal” because it feels that way in the moment. I’m going to show you the numbers that back it up — or the numbers that tell you to walk away.
Frequently Asked Questions
What’s the most overlooked data point when evaluating a NJ listing? Municipal tax and permit history. It’s public, it’s free, and it surfaces issues — unpermitted additions, stale assessments, valuation gaps — that the listing description will never mention.
How far back should I look at comps in a fast-moving market? Weight the trailing 60–90 days most heavily. Older comps can still provide context, but in a market where rates and inventory are shifting, a 10-month-old comp can be meaningfully out of date.
Are “motivated seller” and similar phrases red flags? Not automatically — but they’re informative. Treat listing language as marketing copy that correlates with underlying conditions (like a prior price cut), not as a neutral description of the property.
Is price-per-square-foot a reliable way to compare homes? It’s a useful first filter but a poor final metric on its own, since it doesn’t account for lot size, renovation age, or micro-location differences that can swing value significantly even within the same town.
Want the Spreadsheet Approach Applied to Your Search?
I bring this same data-first process to every buyer I work with — pulling tax history, weighting recent comps, and reading past the listing copy to what’s actually happening with a property. If you’re house hunting in Edison or Central Jersey and want a second set of analytical eyes on a listing before you write an offer, let’s talk.
Reach out for a free buyer consultation — real advice, backed by real data.
Keys by Agam | eXp Realty LLC | Serving Edison and Central New Jersey
This post reflects the author’s personal home-buying research and general market observations. Individual results vary by property, timing, and local market conditions. Always consult a licensed real estate professional and review official municipal records before making a purchase decision.