Summary of my working paper Cheap Talk or Priced Information? In 63,067 Atlanta MLS transactions, residual listing information is associated with 7.7 percent higher sale prices and 2.5 fewer days on market, adverse disclosure with 17.4 percent lower sale prices, and urgency language with 4.7 percent lower sale prices, showing that remarks carry priced information beyond structured MLS fields.
Summary of Information Value of Property Description: A Machine Learning Approach, with Stephen L. Ross. A machine-learning measure of the soft information in listing text raises sale prices by 15 percent per standard deviation in a hedonic model and 10 percent in a repeat-sales model, and price indices that ignore it overstate prices by 10 to 23 percent and mistime the post-recession stabilization.
Summary of The Odd One Out? The Impact of Property Uniqueness on Selling Time and Selling Price, with Thomas M. Springer. Machine-learning measures of uniqueness from listing text show that a one-standard-deviation increase in market uniqueness raises sale prices by 13 percent ($48,490) while delaying sale by 1.7 days; universal uniqueness raises prices 11 percent ($41,030) at a 3-day delay.
Summary of The More, the Merrier: Agent Connectedness in Real Estate Trading Networks, with Xiaojin Sun. A one-standard-deviation increase in an agent's network connectedness is associated with 2.61 percent higher sale prices, and connected agents sell unique properties faster and price them more accurately, consistent with networks improving information flow.
Soft information is what a listing says that structured fields cannot: condition, character, seller circumstances, local demand. This explainer defines the term, contrasts it with hard information, and summarizes what my research finds when listing text is measured with machine learning: it is priced, it changes marketing time, and it moves before price indices do.