What Can We Learn About Mortgage Supply from Online Data?

Abstract

We exploit a novel dataset on mortgages offered by banks through Italy's main online mortgage broker, which works with banks representing over 80 per cent of mortgages granted, to gain an up-to-date assessment of loan supply conditions. Characteristics of mortgages are reported for about 85,000 borrower-contract profiles, constant over time, available at the beginning of each month starting from March 2018. We document that riskier applications, characterized by high loan-to-value ratios, low borrower's income and long maturity, are, on average, offered by a smaller number of banks that charge higher interest rates. Online banks tend to provide better price conditions than traditional intermediaries. We use the online rates offered to nowcast bank-level official (MIR) interest rate statistics, available only several weeks later. By relying on both regression analyses and machine learning algorithms, we show that the rates offered have significant predictive content for fixed-rate contracts, also after controlling for time-varying demand conditions, market reference rates, and unobserved time-invariant bank characteristics. Machine learning algorithms provide further improvements over regression models in out of sample predictions.

Can Internet Banking Affect Households' Participation in Financial Markets and Financial Awareness?

“…Moreover, our assumption is consistent with the findings by Carella et al. (2020), who evaluate the mortgage offers of all the main Italian banks thought the most important Italian online mortgage broker. They find that banks' policies are homogeneous over the Italian territory, once one controls for applicants' observable characteristics.…”

Information or Persuasion in the Mortgage Market: The Role of Brand Names

“…See alsoCarella et al (2020); and for the UKBenetton (2018) andRobles-Garcia (2019).11 This interpretation is in line with models of optimal consumer search byOzga (1960) andStigler (1961), in which branding reduces consumers' search costs by conveying information on product's existence, price and quality. 12 With the 2008 Bersani Law, households are allowed to modify their contract terms and, specifically, reduce…”

“…Contract terms offered by banks via the platform are binding, provided that the information submitted by the borrower in the online application is accurate. Rates offered online are representative of the real market and have a high predictive power for the actual price of fixed-rate mortgages at the individual bank level (Carella et al 2020). This alleviates potential concerns about the lender posting teaser rates through the broker, and reassure us on the reliability of our dataset.…”

“…Quite the opposite, price dispersion is material and overpayment concerns all borrower types (Carella et al 2020). 4 Search costs, time constraints, brand loyalty, negotiation ability, they all offer a reasonable explanation for this (as in Allen et al 2014a andin Allen et al 2019), and they do even more so in a market, like the Italian one, dominated by traditional banks, where contracts are mainly settled at the physical branch.…”

“…Additional works includeAgarwal et al (2017),Alexandrov & Koulayev (2017) andMcManus & Yi (2018) for the US,Allen et al (2014a) for Canada,Iscenko (2018) for the UK, andDamen & Buyst (2017) for Belgium.4 Price dispersion in Italy is mainly driven by contract characteristics (in particular, the loan-to-value, the rate type and the maturity) and figures are comparable to those recorded in the US(Bhutta et al 2018) and Canada(Allen et al 2014a). 5 In Italy, mortgages originated online were about 6 per cent of the total in 2015, though on the rise(Carella et al 2020), while fintech lenders accounted for about 16 per cent of the US mortgage market(Buchak et al 2018).…”