How ‘Cherry-Picking’ Data & Tech Is Powering the Next Generation of PropTech Software

The real estate industry is changing fast. Technology is reshaping how we buy, sell, and manage properties. But here’s the thing: the most powerful PropTech tools aren’t always built from scratch. They’re built by finding and combining the right pieces.

From Coin Collecting to Real Estate: The Art of the ‘Cherry-Pick’ in PropTech

Think of PropTech like rare coin collecting. The best finds aren’t always the flashiest. They’re the details others miss. As a founder, I’ve seen firsthand how cherry-picking—spotting valuable but overlooked data, tech, or market clues—can build smarter real estate software. Some of our biggest wins came from obscure APIs, fragmented datasets, or old IoT protocols that others ignored.

Why ‘Cherry-Picking’ Works in PropTech

In coin collecting, the most profitable finds come from coins others misidentify or overlook. The same is true in PropTech. The edge comes from spotting mispriced, fragmented, or underused assets—especially when they’re buried in old systems or ignored by big platforms.

• Data asymmetry: Giants like Zillow and Redfin have huge datasets. But they miss niche signals—like off-market listings, tenant sentiment, or micro-leasing trends.
• API gaps: Major real estate APIs are powerful but slow to update. They often lack granular filters or exclude certain property types. The opportunity? Patching these gaps.
• Legacy tech debt: Many property management systems (PMS) still use outdated tech. This creates chances to build better integrations and workflows.

“The best PropTech innovations don’t reinvent the wheel—they curate the wheel, the axle, and the tire pressure sensor into a smarter vehicle.”

Smart Cherry-Picking: 3 High-Value Data & Tech Targets

1. Zillow & Redfin APIs: The Hidden Goldmines

Zillow’s ZTP and Redfin’s API offer massive datasets on listings, price history, and agent activity. But the real value isn’t in the obvious data—it’s in the metadata and edge cases most developers ignore.

• Off-market listings: “Coming Soon” and “Off Market” statuses on Zillow often reveal high-potential properties. Scrape these lists and enrich them with local comps to spot undervalued properties before they hit MLS.
• Price history anomalies: Sudden price drops, relaunches, or long days-on-market? These can signal motivated sellers. Use Zillow’s /properties endpoint to track these and set alerts.
• Agent activity: Watch agent assignments, open house frequency, and listing updates. These can hint at seller urgency.

Actionable Takeaway: Use Zillow’s zpid to track changes. Here’s a simple Python snippet to detect price drops:


import requests

zpid = "123456789"
headers = {"accept": "application/json"}
url = f"https://api.zillow.com/properties/{zpid}"

response = requests.get(url, headers=headers)
data = response.json()

if data['price']['change'] < 0: send_alert(f"Price drop detected: {data['address']} - New price: ${data['price']['current']}")

2. Property Management Systems (PMS): The Forgotten Integrations

Many PMS (Yardi, Buildium, AppFolio) run on old databases with clunky or missing APIs. The cherry-pick here? Bridge the gap between these systems and modern tools.

• Automate work orders: Use a headless browser (like Playwright) to mimic user actions in a PMS, then sync data to a modern dashboard.
• Lease analysis: Extract scanned lease PDFs using OCR (like Tesseract) and pull out key terms (rent escalations, service fees, maintenance clauses).
• Occupancy prediction: Combine PMS move-in/move-out data with local event calendars (festivals, construction) to forecast demand.

Actionable Takeaway: Build a middleware layer to normalize PMS data. Example architecture:

• Step 1: Use Puppeteer to scrape Yardi's work order dashboard (if no API exists).
• Step 2: Normalize data into a JSON schema.
• Step 3: Push to a cloud function (like AWS Lambda) to trigger SMS alerts for urgent repairs.

3. Smart Home & IoT: From Niche to Core Infrastructure

IoT devices (smart locks, HVAC systems, occupancy sensors) generate real-time data. But most are siloed. The cherry-pick is to aggregate these signals into a unified platform that predicts maintenance, saves energy, and improves tenant experience.

• Predictive maintenance: Use HVAC run-time data from smart thermostats (like Ecobee) to schedule servicing before failures.
• Energy arbitrage: Sync smart thermostats with utility rate data to run AC during off-peak hours.
• Security & access: Integrate smart locks (like August) with property management workflows (automatic access for cleaners after tenant move-out).

Actionable Takeaway: Use a platform like Home Assistant or OpenHAB as a central hub. Example: Automatically unlock smart locks for maintenance staff during scheduled work hours.

Case Study: The "Under-the-Radar" PropTech Stack

At our startup, we built a cherry-picked PropTech stack that outperforms off-the-shelf tools:

• Data layer: Zillow API + scraped Craigslist + local MLS (via RETS) → unified property feed.
• Analytics: Trained a model on Zillow's price change history + local school ratings to predict appreciation.
• IoT: Integrated Nest thermostats with our PMS to auto-adjust temps when tenants are away.
• Automation: Used Puppeteer to auto-fill Yardi work orders, reducing ops time by 40%.

The result? A 22% faster leasing cycle and 15% lower maintenance costs—all from spotting what others missed.

Lessons from the Coin Collectors: What PropTech Can Learn

• Most don't care about the niche: Just as most coin dealers ignore varieties, most real estate platforms ignore hyperlocal data, off-market deals, or IoT integration. That's where the edge is.
• Grading matters, but context matters more: A coin's value isn't in its slab—it's in its rarity, condition, and story. For PropTech, your software's value isn't in its features, but in how it contextualizes data.
• Owners must know what they have: As one collector said, "It's up to the owner to study their coins." In PropTech, it's up to you to audit your data sources, APIs, and integrations—and attribute value to the overlooked.

The Cherry-Picked Future of PropTech

The next wave of PropTech won't come from chasing shiny new tech. It will come from curating, connecting, and elevating what's already there. Whether it's a Zillow API edge case, a forgotten PMS integration, or a smart thermostat's data stream, the most valuable innovations come from seeing what others miss.

My strategy as a founder? Scour the edges, challenge assumptions, and value the undervalued. Because in PropTech, just like in coin collecting, the best finds are the ones no one else is looking for.

Related Resources

You might also find these related articles helpful:

• How ‘Cherry-Picking’ Market Inefficiencies Can Give Quant Traders a Real Edge in HFT - In high-frequency trading, speed matters. But it’s not everything. I spent years building faster models, shaving microse...
• Why Your Startup’s ‘Cherry-Pick’ Technical Decisions Signal Future Valuation Multiples - As a VC, I’m always scanning for that one signal—something beyond the pitch deck, the TAM, or the growth charts—that tel...
• How to Turn Hidden Developer Analytics into Business Intelligence Gold with Tableau, Power BI & Modern Data Warehousing - Your dev team generates a mountain of data every day. But unless you’re actively collecting it, you’re missi...