Private Listings Alter MLS Data Used by Lenders

Compass's practice of marketing homes privately before listing them on the Multiple Listing Service (MLS) is altering the historical sales data that lenders and valuation models rely on. This strategy, framed by some as a corporate dispute with Zillow, has significant implications for the integrity of mortgage underwriting and home appraisal processes. The core issue lies in how home values are determined: appraisals are typically based on recent comparable sales, predominantly sourced from the MLS. Lenders then underwrite mortgages against these appraisals, and government-backed entities purchase and securitize these loans. Automated valuation models, including Zillow's "Zestimate," also train on this same transaction data.

The integrity of the entire collateral chain depends on the assumption that the MLS record of sales, including prices and dates, is complete and accurate. Compass's approach involves marketing homes to their own agents and clients first, and potentially only releasing them to the MLS later, or not at all. This allows them to test pricing and make adjustments away from public view. A home initially listed at $900,000, reduced twice, and eventually sold for $825,000 presents a different market signal than if it appeared on the MLS solely as a clean $825,000 sale. The former indicates softening demand, while the latter obscures this trend.

When subsequent homes in the same area are appraised based on this potentially manipulated or incomplete data, their valuations could be inflated. This ripple effect impacts not only individual appraisals but also the training data for automated valuation models and bank risk systems. The "days on market" metric, a key indicator of housing market health, is also affected, as private listing periods are not publicly recorded. A federal hearing concluded in Chicago on July 2, with post-hearing briefs due July 9, concerning disputes between Compass and Zillow, highlighting the ongoing legal and operational challenges surrounding listing data practices.