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Disentangling the effects of household financial constraints and risk profile on mortgage rates Carbo-Valverde, Santiago; Mayordomo, Sergio; Rodr?guez-Fern?ndez, Francisco The Journal of Real Estate Finance and Economics

DOI: 10.1007/s11146-016-9595-7 Published: 01/01/2018

Peer reviewed version

Cyswllt i'r cyhoeddiad / Link to publication

Dyfyniad o'r fersiwn a gyhoeddwyd / Citation for published version (APA): Carbo-Valverde, S., Mayordomo, S., & Rodr?guez-Fern?ndez, F. (2018). Disentangling the effects of household financial constraints and risk profile on mortgage rates. The Journal of Real Estate Finance and Economics, 56(1), 76-100.

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12. Oct. 2021

Disentangling the effects of household financial constraints and risk profile on mortgage rates

Santiago Carb?-Valverde Bangor Business School and Funcas

(s.carbo-valverde@bangor.ac.uk)

Sergio Mayordomo* Banco de Espa?a

(sergio.mayordomo@bde.es)

Francisco Rodr?guez-Fern?ndez University of Granada and Funcas

(franrod@ugr.es)

*Corresponding author: Sergio Mayordomo, DG Economics and Statistics, Banco de Espa?a, C/ Alcal?, 28014 Madrid, Spain; e-mail: sergio.mayordomo@bde.es; phone: +34 917088149. Carb? and Rodr?guez-Fern?ndez acknowledge financial support from the Funcas Foundation. The views expressed in this paper are those of the authors and do not necessarily coincide with those of the Banco de Espa?a and the Eurosystem.

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Disentangling the effects of household financial constraints and risk profile on mortgage rates

Abstract In this paper we disentangle the impact of household financial constraints on mortgage rate from a number of dimensions of credit risk. This analysis relies on a dataset that contains information on the economic and financial decisions of Spanish households in four different years: 2002, 2005, 2008, and 2011. Our results suggest that banks' profitable customers are able to bargain for lower mortgage rates. However, contrary to other studies, the risk profile does not have a significant effect on mortgage rates. Credit institutions tend to charge higher rates during the crisis to all customers, irrespective of their risk profiles. JEL classification: G21, R21 Keywords: Households, mortgages, financial constraints, credit risk

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1. Introduction

We analyze credit institutions' mortgage rate lending policy to households, disentangling the impact of financial constraints from that of the borrower's risk profile. In particular, we study how the mortgage rates charged to households depend on their credit risk, the constraints they face, and their characteristics as profitable bank customers. This analysis relies on a dataset that contains information on the economic and financial decisions of Spanish households in four different survey waves conducted in 2002, 2005, 2008 and 2011. Having information on such years facilitates a better understanding of the business cycle and the recent financial crisis in the aforementioned analyses.

The focus on the conditions of household credit relies on the high relevance of this type of credit for both banks and households. Thus, Beck et al. (2012) document that over the period 1994 - 2005 household credit amounted to 80% of bank credit in Canada, 76% in the United States (U.S.), 66% in Australia, 60% in France and 56% in the United Kingdom. Mortgages are also the most important financial liabilities of households, accounting for 75% of household debt in the U.S. (Bucks et al. 2009) and 83% in the Eurozone (European Central Bank 2013).

There is extensive literature analyzing the effect that the financial constraints households face when looking for external funding has on the homeownership rate. The general result is that both income and wealth constraints affect the homeownership rate (see Linneman and Wachter 1989; Duca and Rosenthal 1994a; Linneman et al. 1997; Haurin et al. 1996, 1997; Rosenthal 2002; Barakova et al. 2003; Calem et al. 2010; among others for evidence based on U.S. households, or Bourassa and Hoesli 2010 for evidence based on Swiss households). Wealth constraints have been found to dominate income constraints in the U.S. (Barakova et al. 2014), Europe (Ampudia and Mayordomo 2015),

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and Italy (Diaz-Serrano 2005). Thus, credit constraints should affect borrowing rates as they affect the decision to buy/rent a home. Contrary to previous papers, our interest does not rely on the effect of financial constraints on access to housing but on the cost of that funding.

The effect of financial constraints on mortgage rates has been indirectly studied through household risk or credit scoring. Based on U.S. data, Einav et al. (2013) show that one of the advantages of the better risk classification of credit scoring is the ability of lending institutions to target more generous loans at lower-risk borrowers. Edelberg (2006) shows that lenders used risk-based pricing of interest rates in consumer loan markets in the U.S. during the mid-1990s. Along the same line, Magri and Pico (2011) analyze the mortgage pricing of Italian households between 2000 and 2007 to show that lenders have increasingly priced mortgage interest rates on household credit risk. Chiang et al. (2002) employ data from Hong Kong to document that households with the worst credit scoring are charged a higher mortgage rate spread. Tsai et al.'s (2009) model reaches a similar conclusion as default risk increases the mortgage yield. On the contrary, Duca and Rosenthal (1994b) provide little support for the hypothesis that U.S. lenders vary mortgage rates across loan applicants on the basis of observable differences in credit risk. Thus, lenders may respond to the borrower risk by varying non-rate terms such as the down-payment or payment-to-income constraints.

There is no consensus on the relationship between credit scoring and mortgage rates. In addition, the effect of financial constraints on mortgage rates has not been studied directly, but indirectly through household risk or credit scoring. Our paper aims to provide additional evidence to this discussion, analyzing the effect that household credit scoring or credit risk and financial constraints have on mortgage rates Moreover, our paper also

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contributes to the literature by analyzing the evolution of lending standards in the context of the recent crisis.

Beyond the household risk profiles and the conditions for access to funding, not much is known about the effect of other dimensions that could be of interest to banks' business lines, such as the future profitability of customers. The analysis of the effect of financial characteristics of banks' customers is related to the financial intermediation literature, which documents that relationship banking mitigates information asymmetries between lenders and borrowers. In line with this `information view' of relationship banking, extensive empirical literature explores the prevalence and economic relevance of firm-bank relationships in corporate lending. Recent studies also show the role of relationship banking in consumer credit in the U.S. (Holmes et al. 2007, Agarwal et al. 2010) and Germany (Puri et al. 2011) and mortgages in U.S. (Keys et al. 2010) and Switzerland (Brown and Hoffmann 2013). Keys et al. (2010) were the first to show evidence on the role of information asymmetries between lenders and borrowers in mortgage lending during the recent financial crisis. Brown and Hoffmann (2013) contribute to this strand of literature by providing empirical evidence on the scope, geographic proximity and duration of mortgage relationships, and by examining whether the heterogeneity of mortgage relationships across households can be rationalized by information asymmetries between lenders and borrowers. Finally, Allen et al. (2014) use transaction-level data on Canadian mortgage contracts to document the existence of price discrimination by banks that is explained by consumer bargaining leverage.

We share some of the objectives pursued by previous papers as several of the household dimensions used to characterize them as profitable customers for the bank can also be used to define household-bank relationship. Nevertheless, our paper analyzes in detail the former theory and presents an exhaustive study that divides the customer

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