  Machine learning algorithms are increasingly employed to price or value homes
for sale, properties for rent, rides for hire, and various other goods and
services. Machine learning-based prices are typically generated by complex
algorithms trained on historical sales data. However, displaying these prices
to consumers anchors the realized sales prices, which will in turn become
training samples for future iterations of the algorithms. The economic
implications of this machine learning "feedback loop" - an indirect
human-algorithm interaction - remain relatively unexplored. In this work, we
develop an analytical model of machine learning feedback loops in the context
of the housing market. We show that feedback loops lead machine learning
algorithms to become overconfident in their own accuracy (by underestimating
its error), and leads home sellers to over-rely on possibly erroneous
algorithmic prices. As a consequence at the feedback loop equilibrium, sale
prices can become entirely erratic (relative to true consumer preferences in
absence of ML price interference). We then identify conditions (choice of ML
models, seller characteristics and market characteristics) where the economic
payoffs for home sellers at the feedback loop equilibrium is worse off than no
machine learning. We also empirically validate primitive building blocks of our
analytical model using housing market data from Zillow. We conclude by
prescribing algorithmic corrective strategies to mitigate the effects of
machine learning feedback loops, discuss the incentives for platforms to adopt
these strategies, and discuss the role of policymakers in regulating the same.
