An introduction to this site: who writes it, the three research strands it covers (text as priced information in housing markets, machine learning on financial contracts, and AI and the people who intermediate the market), and how to cite the posts and the papers behind them.
A practical method for using large language models as measurement tools in economics and finance. The model discovers the vocabulary for each economic category across the full corpus; a fixed, published vocabulary then scores every document, so results are reproducible, traceable phrase by phrase, and testable out of sample.
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.
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 Information in Financial Contracts: Evidence from Securitization Agreements, with Brent W. Ambrose, Yiqiang Han, and Sanket Korgaonkar. A machine-learning comparison of the pooling and servicing agreements behind commercial mortgage-backed securities shows substantial variation across deals, underwriters, and time, partly explained by collateral, and correlated with later loan and bond performance.
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 Cleanliness is Next to Income: The Impact of COVID-19 on Short-Term Rentals, with Sean Wilkoff. In Austin, Texas, Airbnb listings fell 25 percent at the onset of COVID-19 and surviving listings lost 22 percent of income, but units reviewers perceived as clean, measured with a machine-learning cleanliness dictionary, gained 17.5 percent in income and 16.5 percent in occupancy.
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.