Adverse selection in iBuyer business models—don’t buy lemons!

Abstract

The rise of instant buyer (iBuyer) businesses in the past years has made automated valuation models (AVMs) an important part of the property market. Although iBuyer services are in demand, large actors within the segment have reported dissatisfying profits over time. The business model is subject to adverse selection as homeowners based on their superior knowledge of their home are more likely to accept overpriced bids than underpriced bids, making the iBuyer purchase more overpriced dwellings. In this paper, we use a dataset consisting of 84,905 apartment transactions from Oslo, the Norwegian capital. We use 80% of the dataset to train three different AVMs similar to those used by iBuyers. Next, we construct some simple purchasing rules from the predictive accuracies found in the training dataset. Finally, taking the remaining 20% of the data in a test dataset, we introduce an adverse selection indicator based on accepted probability distributions and calculate the average expected resale profits per apartment for a hypothetical iBuyer. We find that adverse selection has a large negative impact on average profits for the hypothetical iBuyer. Furthermore, the simple purchasing rules are able to improve the profit by 1 percentage point per apartment when adverse selection is present.