Case Studies | VivaBoard.ai

Customer Success Stories

See how real estate professionals are using AI-powered analytics to transform their MLS data into actionable insights, win more deals, and serve clients better.

Case Study 1

Summit Buyer Advocates

Austin, Texas • Exclusive Buyer Agency • 4 Agents • $62M+ Annual Acquisitions

The Challenge

Summit Buyer Advocates was struggling to differentiate themselves in Austin's hyper-competitive real estate market. Their research process was manual and time-consuming—each property assessment took 3-4 hours of comparing sales on Zillow, Redfin, and scattered spreadsheets. Worse, they had no systematic way to identify undervalued properties before competing buyers.

"We were losing deals because we couldn't move fast enough. By the time we finished our research, another buyer had already made an offer."
— Michelle Torres, Principal Buyer's Agent

The Solution

Summit uploaded their accumulated sales data (3,200+ transactions from their CRM and purchased county records) into VivaBoard. Within minutes, they had access to:

  • Automated Market Segmentation — Instantly categorized properties into Premium, Mid-Market, and Entry-Level segments with price benchmarks
  • Undervalued Property Alerts — AI identified 87 potentially undervalued listings with predicted upside of 15-50%+
  • Days on Market Predictor — Forecasted how quickly properties would sell, helping prioritize urgent opportunities
  • Overpriced Property Dashboard — Flagged 52 overpriced listings (avg 156 DOM) as negotiation opportunities

The Results

75%
Faster Research
18
Deals Closed (6 months)
$94K
Avg Savings Per Client
"VivaBoard's undervalued property alerts changed everything. We found a Round Rock property listed at $485K that our model flagged as worth $560K. We moved fast, negotiated to $472K, and our client's equity position was incredible from day one. That's the kind of insight that builds a reputation."
— Michelle Torres, Principal Buyer's Agent
Case Study 2

Cornerstone Mortgage Partners

Phoenix, Arizona • Mortgage Brokerage • 12 Loan Officers • $240M+ Annual Originations

The Challenge

Cornerstone Mortgage Partners was experiencing a frustrating problem: 16% of their loan applications were failing at the appraisal stage. Bank appraisers were coming in below purchase price, causing deals to collapse and clients to lose earnest money. Each failed deal cost the brokerage approximately $3,800 in lost commission and damaged client relationships.

"We had no way to know if a property would appraise before we submitted the application. We were essentially guessing—and our clients were paying the price when we got it wrong."
— David Ramirez, Senior Loan Officer

The Solution

Cornerstone uploaded 24 months of closing data combined with Maricopa County records into VivaBoard. The platform immediately provided:

  • Price Prediction Model — AI-generated valuations based on comparable sales, with confidence intervals
  • Feature Impact Analysis — Showed that bedrooms add $47K and bathrooms add $32K to Phoenix metro property values on average
  • Neighborhood Benchmarking — Instant median prices and recent sales by ZIP code for client consultations
  • Overpriced Alerts — Flagged properties priced 40%+ above comparable sales, allowing loan officers to counsel clients before offers

The Results

16% → 3%
Appraisal Failure Rate
$91K
Saved in Lost Deals (Annual)
4.9★
Google Review Rating
"Now before any client makes an offer, I run it through VivaBoard. Last month I stopped a client from overpaying by $67K on a Scottsdale property—the data clearly showed it was priced 19% above comparable sales. They found a better property the next week. That's the kind of advice that earns referrals."
— David Ramirez, Senior Loan Officer

Features Used

Feature How It Helped
Market Segmentation Automatically grouped properties into Premium, Mid-Market, and Entry segments with distinct price benchmarks and characteristics
Price Predictions AI-generated property valuations identifying undervalued opportunities and overpriced listings to avoid
Days on Market Predictor Forecasted selling speed by location and price range—properties over 90 days had 95% price drop probability
Overpriced Alerts Dashboard showing properties with high DOM + price premium = motivated sellers open to negotiation
Feature Impact Analysis Showed which property features (bedrooms, bathrooms, location) had the biggest impact on price and selling speed

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