The Geography of Lender Behavior in Peer‐to‐Peer Loan Markets

Theoretical and empirical research on the traditional credit market finds strong evidence that investors and lenders are sensitive to their distance from the borrower, especially early on in a venture. This is due to the cost of information gathering and monitoring. However, new online platforms could overcome the geographical constraints on investing. Recent empirical work, across many types of crowdfunding, has found mixed results. In this paper, using transaction data from a peer-to-peer lending site, I find that local lenders tend to bid earlier, both chronologically and relatively to other bidders in the auction, and bid larger amounts than nonlocal lenders. Additionally, local lenders are more informed in the sense that they are better able to evaluate the underlying risk of borrowers. This is demonstrated by the fact that they bid significantly higher interest rates on loans that ex-post default and lower rates on loans that ex-post pay back in full. Lastly, I develop a simple model of social learning with heterogeneous agents that provides testable predictions. My results are consistent with this model; a listing with more early local bidding activity will attract more lenders, leading to a higher probability of funding and a lower final interest rate, if funded. This work suggests that the behavioral differences between local and nonlocal lenders are driven mostly by informational frictions and not merely preferences. Local lenders are better informed because they have easier and cheaper access to information, and this asymmetry contributes to explaining why geographic-based frictions are still present and relevant in online lending markets.