A brokerage used MLS data to design pricing for 15 listings.
Challenge: messy data — inconsistent square footage, missing dates.
Approach:
Feed MLS into PricerPro with validation rules
Filter comps by age, lot size, sale date
Apply neighborhood-level price smoothing and outlier removal
Results:
Days on market dropped from 42 to 19. Accepted offers were within 2% of recommended prices.
Key takeaway: Clean data plus simple analytics beats intuition when scaling pricing.