PropTech’s ‘Cherrypick’ Moment: How Rare Finds Like the 1937 Washington Quarter DDO Are Inspiring Smarter Real Estate Software

The real estate industry is changing fast. Not with flashy gimmicks, but through smarter ways of finding hidden value. Think of it like spotting that rare 1937 Washington Quarter DDO in a pile of ordinary coins. The real magic in PropTech isn’t about building everything from scratch—it’s about seeing what others miss.

As someone who’s built and run PropTech companies, I’ve learned this: the most powerful innovations come from finding the right pieces, not reinventing the wheel. Just like a seasoned coin collector who knows exactly what to look for, today’s best real estate software comes from identifying overlooked data, tools, and workflows—and making them work together.

Why ‘Cherrypicking’ Is the Secret to PropTech Innovation

In coin collecting, “cherrypicking” means finding rare, valuable coins most people overlook. In PropTech, it means finding powerful features buried in existing systems—and using them to build better software.

Many startups waste time building from scratch. The successful ones? They start by connecting the dots between what already exists—like a collector who finds a rare double die obverse (DDO) coin in a junk bag.

The Hidden Value in Third-Party APIs

Zillow and Redfin do more than show prices. Their APIs give access to rich data streams: property records, price trends, neighborhood insights, even how long listings sit on the market. Yet most new PropTech tools ignore this, either scraping sites or relying on outdated MLS data.

We took a different path. We cherrypicked the most useful bits from both platforms:

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• Redfin’s /homes/{id}/history – for tracking price changes, offers, and days on market

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• Zillow’s Zestimate API (partner tier) – to enhance our valuation models

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• Zillow’s Nearby Homes & Schools – for community scoring

Combined with our own location data analysis, we built a Home Performance Index that spots properties likely to gain more than 10% in value within a year—often before they’re even listed.

Code Snippet: Blending Zillow & Redfin Data with Python

Here’s how we pull and combine listing data from both sources:


import requests
import pandas as pd
from datetime import datetime

def fetch_property_data(zpid):
zillow_url = f"https://api.zillow.com/v1/property/{zpid}"
redfin_url = f"https://api.redfin.com/v2/homes/{zpid}"

zillow = requests.get(zillow_url, headers={"Authorization": "Bearer YOUR_TOKEN"}).json()
redfin = requests.get(redfin_url, headers={"X-Redfin-Api-Key": "YOUR_KEY"}).json()

# Pick the most useful data points
merged = {
'list_price': zillow.get('price'),
'zestimate': zillow.get('zestimate'),
'price_history': redfin.get('priceHistory'),
'school_rating': zillow.get('schools', [{}])[0].get('rating'),
'days_on_redfin': redfin.get('daysOnMarket'),
'sentiment_score': calculate_sentiment(redfin.get('description', '')),
'last_updated': datetime.now().isoformat()
}
return merged


This saved us months of development time. More importantly, it gives us a complete picture of every listing—without building everything ourselves.

IoT & Smart Home Data: The Unseen ‘Double Die’ in Property Management

Just as a coin expert notices double stamping on the “IGWT” of a 1937 quarter, smart building managers now spot hidden operational issues through simple sensors.

In our latest apartment project, we installed:

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• Smart thermostats (Ecobee) – monitoring usage patterns and HVAC runtime

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• Water flow sensors – catching leaks before they cause damage

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• Occupancy sensors – seeing which common areas residents actually use
• Smart locks – speeding up move-ins and cutting key replacement costs

How We Turned IoT Noise into NOI (Net Operating Income)

We built a real-time property health dashboard that turns raw sensor data into cost savings and revenue:

• Leak alerts cut water damage claims by 78% in half a year
• HVAC pattern analysis found units that needed insulation (3.2x ROI on upgrades)
• Room usage data helped us price shared spaces dynamically
• Automated move-in/out sped up leasing by 40%

One Austin property saw 12% lower operating costs in its first year. All by watching the small details, not just the big numbers.

Code Example: IoT Data Aggregation & Anomaly Detection


import pandas as pd
from sklearn.ensemble import IsolationForest

def detect_leaks(sensor_data):
df = pd.read_csv(sensor_data)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['flow_rate'] = df['volume'] / df['duration']

# Find unusual water flow patterns
model = IsolationForest(contamination=0.05)
df['anomaly'] = model.fit_predict(df[['flow_rate']])

alerts = df[df['anomaly'] == -1]
return alerts.to_dict('records')


This basic script stopped $27,000 in potential water damage just last quarter.

Property Management Systems: Where the ‘Rare Coins’ Are Hidden

Most property management software (PMS) is like a coin collection—lots of entries, but few go deeper. We built a system that digs beneath the surface of platforms like Buildium and AppFolio to find what matters most.

3 Signals We Monitor Like a Numismatist Checks for Doubling

• Lease renewal timing clusters – We flag tenants with leases ending soon who’ve paid late just once (red flags for non-renewal)
• Service request language – Natural language processing spots tenant mood and potential lease issues
• Maintenance cost outliers – Finds units needing upgrades before they break

One case: By reading work orders, we found that 80% of “no heat” calls in a 42-unit building came from just three units with failing thermostats. Replacing them early cut HVAC calls by 65% and boosted tenant satisfaction by 21 points.

API Integration: Syncing PMS with Zillow/Redfin

We built a two-way connection between our PMS and listing sites to update listings instantly when a unit is ready:


def sync_listing_status(pms_id, zillow_id, status):
# Update PMS first
pms_url = f"https://api.buildium.com/v1/properties/{pms_id}"
requests.patch(pms_url, json={"status": status}, auth=(API_KEY, ''))

# Then update Zillow
zillow_url = f"https://api.zillow.com/v1/listings/{zillow_id}"
requests.post(zillow_url, json={"availability": "available" if status == "available" else "offline"})

# Record the update
log_action(f"Synced {pms_id} → {zillow_id} → {status}")


This cut our time-to-market from five days to just 12 minutes.

The Mindset: Be a ‘Cherrypicker’, Not a Builder

The best PropTech founders don’t try to build everything. They’re curators—like the collector who spots a rare DDO in a bag of ordinary coins, or notices wear patterns that reveal a coin’s history.

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• Don’t rebuild what works — use Zillow and Redfin APIs instead of scraping

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• Don’t ignore the details — smart home data gives you an edge
• Don’t settle for surface data — use anomaly detection and language analysis to find opportunities

Just like that coin collector who found a 1937 DDO on his second pass through the room, the best opportunities often come from looking again.

The Future of PropTech Is in the Details

PropTech isn’t about the latest AI trends or blockchain. It’s about finding the right tools and data—like a collector who knows what to look for and where to look.

Whether you’re building software, investing in startups, or developing properties, remember: the future isn’t about starting over. It’s about finding the right pieces—the APIs, the sensors, the overlooked workflows—and making them work together.

The most valuable features aren’t the ones you build. They’re the ones you find.

So take a second look. That “regular” property listing? That “routine” IoT feed? That “basic” Redfin update? There’s something special in there—your own DDO waiting to be found.

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