Socio-technical imaginaries of data shaping access to housing

TY - CONF

T1 - Socio-technical imaginaries of data shaping access to housing

AU - Ciocanel, Alexandra

AU - Wallace, Alison

AU - Beer, David Gareth

AU - Roger, Burrows

AU - Cussens, James

PY - 2022

Y1 - 2022

N2 - “Late homeownership” has been characterised as a financialised housing regime in which housing wealth reshapes social structures given various socio-economic, demographic, institutional and policy transformations. Ray Forrest and Yosuke Hirayama (2018) show how such transformations have led to a decline in house buying with a mortgage and an expansion of private landlordism and propose a macro view on how housing equity reconfigure inequalities in contemporary societies. Drawing on their insights, in this paper we take a much more micro view, and we examine how socio-economic inequalities might be enacted in the more mundane decisions of underwriting a mortgage or tenant screening through the usage of credit scoring as a calculative device. Developed initially as a statistical tool to make consumer lending decisions, credit scoring has soon become an important device for mortgage underwriting and, more recently, for tenant screening, making a specific conception of “creditworthiness” a determinant of life chances through markets’ “classification situations” (Fourcade and Healy 2013). In terms of housing, a good credit score and report enables lower mortgage interest rates, easier access to rental housing, better utilities deals while a bad one might mean paying more for housing and utilities or even being denied housing. Given its importance and also its shortcomings in measuring the creditworthiness of “thin files” or “credit invisibles”, various public debates have emerged around what data should feed the credit scoring algorithms, with some start-ups challenging the status quo by claiming that “all data is credit data” and others proposing more focus on rental, transactional or behavioural data. In this paper we scrutinise how such debates unfold in the UK by examining the “socio-technical imaginary” (Jasanoff 2015) around the data used and proposed to be used in credit scoring. Although usually framed as being more “financially inclusive” we point out how the current and alternative data have important effects in reinforcing inequalities related to housing tenure given the “enhanced performativity” (Rona-Tas 2017) of these tools, people with lower incomes still being more likely to have poorer credit scores. In this way, the class scheme developed by Ray Forrest and Yosuke Hirayama (2018) distinguishing between “real estate accumulators”, “housing wealth dissipators”, and “perpetual renting families” is magnified not only through the value of housing equity but also through the way in which credit scoring as a market device functions in financial capitalism.

AB - “Late homeownership” has been characterised as a financialised housing regime in which housing wealth reshapes social structures given various socio-economic, demographic, institutional and policy transformations. Ray Forrest and Yosuke Hirayama (2018) show how such transformations have led to a decline in house buying with a mortgage and an expansion of private landlordism and propose a macro view on how housing equity reconfigure inequalities in contemporary societies. Drawing on their insights, in this paper we take a much more micro view, and we examine how socio-economic inequalities might be enacted in the more mundane decisions of underwriting a mortgage or tenant screening through the usage of credit scoring as a calculative device. Developed initially as a statistical tool to make consumer lending decisions, credit scoring has soon become an important device for mortgage underwriting and, more recently, for tenant screening, making a specific conception of “creditworthiness” a determinant of life chances through markets’ “classification situations” (Fourcade and Healy 2013). In terms of housing, a good credit score and report enables lower mortgage interest rates, easier access to rental housing, better utilities deals while a bad one might mean paying more for housing and utilities or even being denied housing. Given its importance and also its shortcomings in measuring the creditworthiness of “thin files” or “credit invisibles”, various public debates have emerged around what data should feed the credit scoring algorithms, with some start-ups challenging the status quo by claiming that “all data is credit data” and others proposing more focus on rental, transactional or behavioural data. In this paper we scrutinise how such debates unfold in the UK by examining the “socio-technical imaginary” (Jasanoff 2015) around the data used and proposed to be used in credit scoring. Although usually framed as being more “financially inclusive” we point out how the current and alternative data have important effects in reinforcing inequalities related to housing tenure given the “enhanced performativity” (Rona-Tas 2017) of these tools, people with lower incomes still being more likely to have poorer credit scores. In this way, the class scheme developed by Ray Forrest and Yosuke Hirayama (2018) distinguishing between “real estate accumulators”, “housing wealth dissipators”, and “perpetual renting families” is magnified not only through the value of housing equity but also through the way in which credit scoring as a market device functions in financial capitalism.

M3 - Abstract

T2 - ENHR Workshop on ‘Late Homeownership’

Y2 - 15 June 2022 through 15 June 2022

ER -