Price Discrimination and Mortgage Choice∗

Jamie Coen†

; Anil Kashyap‡; and May Rostom§

November 2023

Abstract

We characterize the large number of mortgage offers for which people qualify in

Britain. The proliferation of offers comes from lenders’ attempts to serve heterogeneous

consumers while respecting constraints on how much price discrimination is legally

permitted. While few people pick the cheapest mortgage, failing to do so is not too

costly. Some, however, do make very costly choices. These expensive picks are more

common when the menu offered has more expensive options in it. These expensive

menus tend to be presented to highly leveraged borrowers, who are often younger or

first-time home-buyers.

Keywords: Price discrimination, consumer choice, mortgages.

JEL Codes: G21, G51, G53, D12.

∗We thank Jo ̃ao Cocco, Patrick Coen, Kris Gerardi, Robert Hall, John Krainer, Guido Menzio, David

Miles, Daniel Paravisini, Morten Ravn, Francesc Rodriguez-Tous, Maximilian Schleritzko, Sergio Vicente,

Erkki Vihri ̈al ̈a and Tomasz Wieladek for helpful conversations. We thank David Bradnum, Chiranjit

Chakraborty, Alastair Firrell, and Simon Pittaway for in-depth conversations and help regarding the data.

We thank Tom Cui for outstanding research assistance and seminar participants at the Bank of England,

Bank of Lithuania, Chicago Booth, the Federal Reserve Bank of Dallas, the FDIC, Imperial College London,

the London School of Economics, ASSA 2023, Emerging Scholars in Banking and Finance Conference 2023,

Fed Day Ahead Conference 2023, Lapland Household Finance Summit 2023, NBER SI Household Finance

2022 and New Zealand Finance Meeting 2023 for comments. Kashyap thanks the Center for Research on

Securities Prices and the Initiative on Global Markets at the University of Chicago Booth School of Business

as well as the National Science Foundation for a grant administered by the National Bureau of Economic

Research for research support. The views expressed here are those of the authors only and not necessarily

of the institutions with which they are affiliated. All errors are our own.

†Imperial College London and Bank of England, w.coen@imperial.ac.uk. ‡University of Chicago, Booth School of Business, National Bureau of Economic Research, Centre for

Economic Policy Research, anil.kashyap@chicagobooth.edu. §Bank of England and Centre for Macroeconomics, May.Rostom@bankofengland.co.uk.
1 Introduction

We study how households in the United Kingdom choose their mortgage. Housing is the

main asset most adults purchase in their lifetime. And almost everyone who buys a house

does so by borrowing. Mortgages are complex financial products where the total cost of

the mortgage depends on an initial interest rate, the fees charged at origination, and the

prevailing interest rate once the introductory period lapses. We show that most borrowers

face a bewildering set of choices for which they qualify, with considerable dispersion in the

costs of different mortgages. Using a unique dataset covering the mortgages households

select and the mortgages they could have chosen, we study how customers make mortgage

choices, and how lenders set the menus they offer to customers.

We show that customers face menus with price dispersion, both within and across banks.

Importantly, the choice sets show three recurrent patterns. First, all banks offer many

mortgages with slightly different options. Second, most of the mortgages wind up with

nearly equivalent total costs to the borrowers. Third, mixed into some menus are a group of

very expensive choices, which if selected will net the bank some windfall payments.

We explain why these patterns point towards the banks recognizing consumers are heterogeneous and attempting to price discriminate subject to binding legal and informational

constraints. Whilst banks can vary mortgage terms according to the types of mortgages for

which customers are shopping, they cannot legally tailor menus to individual characteristics,

and nor can they tell which potential customers are shopping at other lenders. So while the

lenders would like to customize the terms for each borrower in various ways (for example

by offering better options to someone who is comparison shopping), they cannot do that.

Instead, the lenders can only offer menus with a myriad of options that aim to cater to a

wide range of borrower preferences. If a lender has a sense of which customers are not able

to identify cheap mortgages, all it can do to exploit that hunch is to include some expensive

ones in the menu and hope they are selected. One factor limiting the percentage of expensive

options is the knowledge that most borrowers will avoid such options; filling a menu with

them will only drive those borrowers elsewhere. Thus, the necessity of offering the same

menu to a heterogeneous set of borrowers limits the lender’s ability to price discriminate.

We show that customers facing large price dispersion are typically those borrowing large

2
amounts relative to both their income and the value of their house. These tend to be

younger customers, and people who are buying a house for the first time. Lenders thus price

discriminate, offering menus with greater price dispersion to customer groups who may be

less able to identify and avoid expensive options, or have fewer options to go elsewhere.

There are several reasons why these patterns are not easily explained by appealing to

differences in the costs (or risks) of serving different customers. First, menus cannot be

tailored to individuals– mortgage options are posted and borrowers choose among the many

for which they qualify. Second, the house serves as collateral and loans are with recourse, so

the risk levels for all borrowers are pretty low and similar across individuals. Third, direct

measures of who falls behind on mortgage payments (going into arrears) do not match the

variation in the menus we document, so ex-post measures of risk do not track the menu

variation. Most importantly, the big differences that drive our findings come from variation

in the range of terms of the mortgages that different customers pick from, rather than

differences in the average quality of the mortgages in the menu across customers.

Instead, it appears banks want to offer at least some cheap mortgage options to entice

sophisticated customers who might be comparison shopping at multiple banks. To attract

these customers they need to have some attractively priced options available. To the extent

they do not know exactly which features are most important to these customers, having

many close substitutes in the menu makes sense. At the same time, the banks also want

to offer expensive mortgages in case someone is careless or unable to choose well. Given

they can’t tell these types apart and cannot tailor the mortgage menu based on personal

characteristics, they offer a menu with price dispersion. For most groups of customers, the

menu does not include many expensive choices. This would be expected if the lenders worry

that some customers will not fully sample all the options and might opt to borrow elsewhere

if they see expensive options. This kind of competitive pressure protects most customer

groups. In some cases, banks suspect that certain customer groups are less able to shop

around and thus present them with a menu with many expensive options. For customers in

this group that do decide to proceed, they are more likely to select an expensive mortgage.

The results highlight the importance in recognizing both the heterogeneity in people’s

ability to select the cheapest mortgage option and the limits banks face in taking advantage

3
of this heterogeneity. As we describe next, a growing literature proposes structural models

of mortgage shopping by borrowers and lending competition by banks. We will explain why

as currently constituted those models will not match our facts. Instead, current models have

focused on different types of heterogeneity that appear to be less relevant than the aspects

that we highlight.

1.1 Related literature

Our paper contributes to a literature studying how customers choose complex financial products, and how this affects firms’ supply of these products. One strand of this literature studies

how customers choose between mortgages depending on their various price characteristics.

Two of these are particularly relevant for our study. Liu (2019) documents the heterogeneity

in fees that we also confirm. She emphasizes that slight differences in fees allow lenders

to boost profits by offering customers who are less cost-sensitive products with higher fees.

While that is true, we emphasize two different aspects of the data. First, that fees are

a small cost of the overall payments borrowers make, so that expensive choices cannot be

purely driven by high-fee choices. Second, initial rates and fees are negatively related, such

that expensive choices are more likely to come with low fees. For modeling household mortgage choices (which is the focus of our analysis), these two considerations are quantitatively

more relevant than the variation emphasized by Liu (2019).

Guiso et al. (2022) provide evidence that lenders steer borrowers to particular mortgage

products that they would like to keep on their books. This leads some borrowers who ask the

lender for mortgage advice to wind up with more expensive mortgages. In the UK, lenders

(and other mortgage advisers) have a fiduciary duty to the borrowers so this is illegal. Hence,

these considerations do not apply in the UK market, and we identify a different mechanism

that can lead to expensive choices.

Iscenko (2020) observes that many UK customers pick mortgages that are higher in all

price dimensions compared to other mortgages with the same non-price characteristics. We

build on this analysis in our empirical work.1 In a related paper, Woodward and Hall (2012)

1A number of other papers study these issues in various product markets, including financial products

(Duca and Kumar, 2014; Fo`a et al., 2019; Ru and Schoar, 2016) and non-financial products (Scott Morton

et al., 2003).

4
find that customers would make large savings if they shopped around more, in particular whether they consulted more brokers. The possibility of comparison shopping features

importantly our narrative.

A second branch of the literature seeks to rationalize observed choices and mortgage

offerings by structurally estimating models of the mortgage market. Allen et al. (2019) and

Allen and Li (2020) study the Canadian mortgage market which they argue is characterized

by extensive bargaining between borrowers and local loan officers. This kind of bargaining

is absent in the UK (and many other markets), so the way that they model the market does

not apply in our setting. Benetton et al. (2021) build a model of mortgage choices made by

heterogeneous consumers. The key source of heterogeneity is assumed to be differences in

consumers’ preferences over the different components of the mortgage contract. We adopt

the much more conventional economic assumption that people care about the total cost of the

product– not its various parts. Also, certain patterns in the data (such as the kinds of choices

identified by Iscenko (2020) and confirmed and extended by us) cannot be rationalized by

differences in preferences over rates and fees.

Other papers investigate the role of brokers in the UK. My ́sliwski and Rostom (2022)

show that customers with high search costs usually shop with a broker, and that customers

with different demographic characteristics place different values on the information brokers

provide. Robles-Garcia (2019) studies the trade-off between brokers facilitating competition

in the mortgage market and facing incentives to distort households’ decisions to profit from

commissions. Because brokers have a fiduciary duty to their customers their presence can’t

explain the patterns we document. So while there are many interesting questions related to

brokers, they are separate from the ones that motivate us.

Overall, none of the facts that we present are inconsistent with prior findings, but many

are new. Our characterization of the data is important because the extant structural models

do not fully explain the patterns we document. In particular, current models do not study the

impact of menu heterogeneity on price dispersion. Put differently, they do not characterize

lenders as posting menus to serve customer groups of varying sophistication, subject to the

constraint that lenders must post the same set of prices to all customers within each group.

Below, we reference the work by Menzio and Trachter (2018) which outlines a theoretical

5
model in this spirit. Structural models should move in this direction to fully describe the

patterns we see in the data.

A purely empirical paper using US data that is similar to ours is Bhutta et al. (2021).

They study shopping patterns in the market for 30-year fixed rate mortgages in 20 US

cities from 2016 to 2019 using data from a lending platform. As in our paper, they find

considerable dispersion in the prices of mortgages that co-exist in the market; people with

low credit scores, high loan-to-value and debt-to-income ratios, or first-time buyers select

more expensive mortgages than others. They use novel survey data to show customers who

shopped intensively and knew relatively more about the mortgage market got better rates.

Our paper primarily focuses on a different question from theirs. Using their data it is

not possible to construct the menu available to a borrower at the lender from which they

borrowed. This means they cannot disentangle the separate role that the menu offered to

the customer plays vis-`a-vis the customer’s ability to pick from the menu - a key focus of our

paper. By controlling for borrower characteristics and offered price dispersion, we find that

the bulk of the differences in mortgage outcomes across borrower groups are driven by the

menu they were offered, rather than the choice they made. Further, our distinction between

expensive choices within and across banks means we can distinguish between the customer’s

ability to pick well from a menu from whether or not they borrowed at a bank with mostly

cheap options. We find that both matter, but avoiding borrowing from an expensive bank

is more important.2

We also contribute to the large literature documenting and studying price dispersion.

A vast literature, starting with Stigler (1961) rationalizes price dispersion through search

frictions. More recently, Kaplan and Menzio (2015) document widespread price dispersion

both within and across firms. Menzio and Trachter (2018) rationalize this kind of price

dispersion in a model where customers vary in their ability to shop at different times and

across multiple firms. Because some customers may walk away if they find a bad option, the

presence of these customers disciplines the sellers.

2Importantly, we scale our estimated cost differences by income and focus on cases where this scaled

cost difference is large. This allows us to focus on choices that can reasonably be described as expensive.

Put differently, for people with very different incomes, we do not suppose the same nominal monthly cost

difference of a mortgage, e.g. £50 per month, is equivalent.

6
As has been shown for many markets in different countries, we document price dispersion

both within and across mortgage lenders. We show that lenders vary the extent of this price

dispersion across product types, so customers with different characteristics wind up facing

different menus. As predicted by Menzio and Trachter (2018), these menus are set so lenders

can attract a range of customers to their firm while making it easy for unsophisticated

customers to choose expensive products. Importantly, competition appears to be a strong

enough force that for most customer types, the menus have relatively few expensive options.

In the following section we describe our data and the relevant institutional features of

the UK mortgage market. In Section 3, we explain how we characterize customers’ choice

sets and rank the choices by cost. We then study how well customers choose, and which

customers make particularly expensive choices. We show that this is driven by both the

quality of the customer’s choice and the quality of the menu they face. In Section 4, we study

what determines the menus customers face, and in Section 5 we study which customers pick

expensive mortgages. In Section 6, we discuss our results and interpret them in terms of

price discrimination.

2 Data and institutional setting

2.1 Data

Our sample period is 2009 to 2014. Our main data source is the Product Sales Database

(PSD), a loan-level administrative dataset capturing all newly issued mortgages in the UK.

The data contain information recorded by financial institutions at the time of mortgage

take-out. This includes information on the borrower characteristics, such as income and age;

information on the property, such as postcode and house price; and loan details, such as the

amount borrowed, initial rate, and the mortgage term.

The PSD, however, misses information on product fees and the standard variable rate

(SVR), the rate the mortgage resets to when the fixation period expires. To get this information, we merge the PSD with a secondary data source, Moneyfacts, which records the set

of mortgages on offer in the UK at any given time. Moneyfacts at this time had a strong

commercial interest in accurately identifying which mortgages were actually available, be7
cause their revenue came from selling this information to lenders and brokers who wanted

to compare offerings across the market. This enables us to construct the set of mortgages

on offer to customers when they shopped. We also use it to merge in the minimum and

maximum loan amount sizes for each product.3 We match the two datasets together using

a matching algorithm that uses the name of the bank, the product type, the initial rate, the

length of initial period and whether the purchase date fell in the time period the mortgage

product was on offer in the market.

In all of our analysis, we focus on loans granted by the six largest lenders for which

reliable information is available. Appendix A1 describes how the dataset is formed and

summarizes key variables. We restrict the analysis to loans of no more than £1 million, with

loan-to-value ratios (LTV) between 65 and 95%.4 Our final dataset comprises just under

900,000 mortgages between 2009 and 2014.

2.2 Institutional setting

Most mortgages in the UK amortize over a period of 25 years in our sample period. In their

loan offerings, lenders advertise initial promotional rates that would apply for loans up to

a given amount, and conditional on the amount borrowed relative to the value of a home

(LTV). For example, a bank might be willing to lend up to £1 million to any borrower who

makes a down-payment of at least 20 percent (so that the LTV would be no more than 80%)

provided the borrower pays a fee of £995. Borrowers typically have the option to pay this fee

up front, or add it to the mortgage. A different rate might apply for a different, maximum

loan size or LTV. The combinations of different fees, loan amounts, rates and LTV limits

means that most people qualify for many different mortgages, and these mortgages have

different required mortgage payments.

Table 1 shows an example of a typical menu a borrower might face at two banks. Several

3Several papers have used the PSD data for research. These include Benetton (2021), Benetton et al.

(2020), Cloyne et al. (2019), Robles-Garcia (2019), Iscenko (2020) and Bracke and Tenreyro (2021). More

recent vintages of the PSD have included many of these additional variables, but as our Moneyfacts data

runs until 2014, our analysis stops there.

4The £1 million limit rules out only a small minority of borrowers while making sure that most banks

have a mortgage on offer for each loan category in most time periods. In many cases, low LTV loans are

associated with only small amounts being borrowed. This doesn’t yield many expensive choices, but more

generally menu offerings and incentives to shop around may be quite different when the stakes are lower.

8
key features of the data are evident. In particular, lower fees and higher maximum loan

amounts tend to be associated with higher initial rates. Nonetheless, there are some low fee

mortgages that have low initial rates, and some high fee mortgages that have high initial

rates. The range of initial rates and fees differs noticeably between the two banks and at each

of them the borrower has at least 15 mortgages (at the indicated LTV) that are available.

After having selected a mortgage, customers pay the initial promotional rate for a set

period, after which the interest rate changes. Table A5 in Appendix A1 shows the distribution of the length of the initial period across our sample. 58% percent are fixed for two years

and another 17% are fixed for three. The remaining loans are either fixed for five years or

are a floating rate.5

Once the initial period expires for fixed rate mortgages (FRMs), the borrower moves

on to the firm’s Standard Variable Rate, or reset rate, which fluctuates depending on the

prevailing Bank Rate set by the Bank of England, macroeconomic conditions, and banks’

own idiosyncrasies, such as their funding costs.6 Banks have discretion to change the reset

rate at any time but, at the time the loan is granted, all that the borrower is told is the

current value of the SVR. In other words, she will not know what the SVR will be when the

initial period expires. The payment schedule the borrower would receive presumes that the

payments will revert to the currently posted SVR.

Some borrowers may plan to refinance the loan once the promotional period expires.

Whether that will be possible depends on a number of factors, including the overall interest

rate environment and whether the value of the home or the borrower’s income has changed.

So the ability to refinance and avoid facing the reset rate is uncertain.

This is just one example of a more general choice problem issue that can arise because

the borrower will know more about her own circumstances than we do. Below we explain

the various different analyses we undertake to show our finding cannot be explained by such

private information.

5In more recent years as interest rates have dropped, the share of five year fixed rate mortgages has grown

significantly.

6Typically the SVR is common across all a bank’s loans. However, as we discuss below in some cases our

banks have two subsidiaries with different SVRs.

9
In practice, many borrowers do wind up having their rate switch to the reset rate. Reset

rates vary across lenders and over time. Figure 1 shows the reset rate during our sample

period for the 6 lenders that we analyze. To facilitate a comparison, the rates are shown as

deviations from the average level (across the six banks) in each month. There is considerable

dispersion at each point in time across the lenders, but their relative positions are quite stable.

So although a borrower would not know the rate to which her mortgage will reset when she

enters the contract, if she had done some investigation she could know whether her lender’s

reset rate is likely to be relatively low or high.

Summary statistics for our data are shown in Table 2. Fees are typically set in terms of

a fixed number of pounds, rather than as a percentage of the amount borrowed. The level of

fees overall are low compared to the US. The median borrower pays about £760, and 17%

pay no fees at all. Less than 5% of the sample pays more than £1,100. Given the small size

of these fees, in the UK it is hard to make a really expensive choice just because of fees. The

median borrower in the sample is taking out a mortgage of £136,000 and making a down

payment of 20%. The median income is £37,000 after tax, meaning that for the median

person we will only label mortgages as expensive if they cost at least £925 (0.025×$37, 000)

per year more than the reference choice we describe below. 7 First-time buyers constitute

40% of the sample. The sample is mostly made up of younger borrowers, no doubt in part

because older ones would be re-financing mortgages that have lower LTVs than we consider.

Banks in the UK cannot tailor the choice set facing individual borrowers. Legal restrictions prohibit lenders from customizing loans to individual borrowers. If two borrowers

qualify for a mortgage at a given lender, with a mortgage of the same size and a home of

the same value, they would generally have the same set of mortgage options from which to

choose.

Some borrowers may use independent mortgage brokers to secure a mortgage. Brokers

have networks of lenders with whom they have a relationship (Robles-Garcia, 2019). Brokers

are bound by a fiduciary responsibility to their customers to present all suitable options

within their network of lenders for which they are eligible, and legally cannot steer borrowers

7We subtract income tax from gross income based on the UK’s tiered income tax system, described

at https://www.gov.uk/income-tax-rates. We follow the industry practice when calculating loan-to-income

ratios and base this variable on gross income.

10
towards a particular lender or product. We would expect that the largest lenders - on whom

we focus here - are present in most brokers’ networks. Brokers typically receive their payment

from lenders in the form of procuration fees, and from borrowers as broker fees. Procuration

fees are similar across all the big lenders, and do not vary by product type. Broker fees are

normally a fixed sum, regardless of the loan size, and are usually small.8 We cannot identify

who in our sample uses brokers, but during our sample period it would be conservative to

assume half the borrowers used a broker to find a mortgage. For all these reasons, brokers

are not going to lead their customers to expensive loans and we explain how their presence

might influence the results in Section 6.

Alternatively, borrowers may consult loan officers at the bank, but these employees are

also bound by conduct guidelines. The Financial Conduct Authority sets these guidelines

which are very similar to how they also regulate brokers. They cannot be paid based on

whether someone picks an expensive mortgage, nor do they get fees or commissions for the

loans they initiate. Rather, many are salaried employees, and if they do receive bonuses these

would be tied to other performance considerations such as effectiveness of work, quality of

key judgements, service levels, and meeting compliance objectives. They may be rewarded

in part for loan volume, however to preserve incentives for maintaining standards and not

taking excessive risk, this component of a bonus is likely to be small. So if the two borrowers

mentioned above wind up with different mortgages, this will be due to choices they made

rather than because of a decision directly controlled by the lender (or broker).

3 The choice problem

To characterize the choice problem we need to define the choice set customers face and

establish a metric by which to compare their options.

To define a customer’s choice set we first identify all mortgages that were on offer when

they were actively shopping.9 We then restrict this set to include only mortgages with the

8See https://www.fca.org.uk/publication/policy/ps20-01.pdf for expectations set out by the FCA regarding broker conduct. See https://www.legalandgeneral.com/adviser/mortgage-club/lenders/procurationfees/ for recent information on current procuration fees, although these are subject to regular updates.

9In the UK, it is rare to put in an offer on a house before a mortgage has been secured, and for the vast

majority, a mortgage is usually agreed 4 months before the closing date. In the data, however, we observe

11
same initial period as the mortgage they chose, available for the amount they borrowed and

with the lowest LTV band for which they qualify. For example if a customer took out a

FRM worth £160k with a 2-year initial period on a house worth £200k, we would define

their choice set as all 2-year FRMs on offer for loan sizes greater than or equal to £160k, at

a maximum allowable LTV of 80%, at the time they were shopping.

We consider two different potential choice sets: within banks and across banks. The

within-bank menu includes only the mortgages a customer qualified for at the bank that

granted the mortgage they selected. The across-bank menu considers the mortgages that

the customer qualified for at all banks. Each menu is informative about different questions.

The within-bank menu allows us to study individual banks’ price discrimination, because

that bank can control the offerings that its customers see, and to explore how well borrowers

pick from a set of choices that were definitely available.

Having characterized the choice set, we need a metric by which to compare mortgages. We

use three different metrics to rank choices given a customer’s LTV. In our ‘baseline’ approach,

we take the four key elements of the mortgage contract - the fees, initial promotional period,

initial interest rate, and the reset rate - and compute the present value of the payments for

the borrower over the first seven years of the mortgage. Seven years is about the half-life of

the stock of mortgages outstanding for people buying a house with a mortgage.10 Calculating

the payments only over the first seven years has two benefits. First, as a practical matter

most borrowers do refinance at some point, so our baseline approach takes that into account.

Second, if we did the calculation over the full length of the mortgage, the level of the reset

rate would dominate the size of the payments since it would be the operative rate for the

vast majority of the payment.

In the calculation, we assume the reset rate remains constant, which is the assumption

embedded in the initial monthly payment the borrower will be given upon signing the contract.11 We use the seven year LIBOR rate to discount the payments. Hence the formula

the reset rate on the closing day. To ensure all rates are from the time the customer made the mortgage

decision, we lag the reset rate by 4 months.

10Gianinazzi (2019) finds that 61% of borrowers in the UK were on their bank’s SVR. Given the average

initial period of the mortgages in our sample is 2.6 years (Table A5) and most mortgages amortise over 25

years, this is consistent with customers refinancing once every 8 to 9 years.

11Because of the option to refinance and the variability of the reset rate there is risk associated with the

12
we use to compute the net present value is:

X

TF X

84 IP RP NP V = fee + + (1) (1 + i)t (1 + i)t

t=1 t=TF +1

where fee is the initial fee, TF is the length in months of the initial promotional period

over which the initial payment is fixed, IP is the initial monthly payment during the initial

period, RP is the monthly payment after the initial period (implied by the reset rate), and

i is the seven year LIBOR rate, scaled to its monthly equivalent. For the 2 year ARMs, we

assume the initial payments are identical for the first two years because that is the way the

payment would be computed for a borrower who asks to see a payment schedule.12

We now describe two other ranking algorithms that are included to demonstrate that

the seven year cutoff is not responsible for our main findings. Our first alternative ranking

assumes people automatically refinance once the initial period is over. In this ‘immediate

refinancing’ approach, the reset rate will no longer matter for any of the calculations. This

extreme assumption means we will never assume someone has made an expensive choice

because they cannot refinance. Rather, we suppose they can always avoid that problem.

Even though this assumption is counter to what we know about the frequency of refinancing,

it is nontheless helpful in seeing whether our description of the choice sets and the choices

made are particularly sensitive to the refinancing assumption.

Our last approach to evaluating choices builds on Iscenko (2020) by comparing the chosen mortgage to an alternative that dominates it in at least one dimension. For instance,

if instead of the mortgage someone picked there existed a competing one with an identical

reset rate, fee, and promotion period, but a lower initial rate, we would conclude the selected

mortgage was dominated by at least one better option– in other words, it would be a domchoices households make. However, those rates are extremely persistent: the first three autocorrelations

of the average reset rates are all greater than 0.95. We calibrate a Markov process that matches these

correlations closely and then simulate the distribution of rates which a borrower would face at different

horizons. The differences between the simple expected value of the rates and the rates that are adjusted

for risk (to deliver a certainty equivalent mortgage payment) are very small. Hence, we abstract from that

uncertainty.

12As part of the mortgage contract, banks are required to tell the borrower the mortgage payments for

the duration of their mortgage term.

13
inated choice. We further refine this definition by saying the choice is strongly dominated

if the alternative mortgage would lead to savings of at least 2.5% of the borrower’s income

over the first seven years of the loan. Our refinement is intended to separate loans that

might have trivial cost consequences from those that would make a material difference in

the mortgage payments.

This ‘strongly dominated’ ranking removes the possibility a borrower had private information that could justify a choice. This definition has the disadvantage of overlooking

some potentially very expensive choices that are not strongly dominated. For instance, if

the initial interest rate is relatively high but that mortgage has no fees, then this mortgage

by construction cannot be dominated by any mortgages that include fees, even if they are

much cheaper. In contrast, our baseline approach and immediate refinancing alternative

suppose the borrower cares only about the total cost of the mortgage rather than the various

components that contribute to the overall cost. Regardless, our main findings are present

using each of the these ranking criteria.

In cushioning our analysis by (i) choosing a mortgage that isn’t the cheapest in a customer’s menu to compare their choice to; (ii) characterizing an expensive choice as costing

at least 2.5% of net income; (iii) having three different ranking algorithms to characterize

the choice set, we are confident that the patterns we observe in the data are not driven by

how we compute the menu nor how expensive choices are defined. Rather, they are robust

features of the data with important ramifications for future modelling efforts.

3.1 Customer choices

Table 3 summarizes the size of the menus and describes the cost of the choices people make.

The median customer faces a menu with over 15 options at a single bank, and more than 70

across banks. Customers do not pick particularly well - only 5% pick the cheapest mortgage

in their choice set at their bank and 51% pick worse than the median choice.

The quality of a customer’s choice depends on how much money their choice causes

them to save or lose - picking a poorly ranked mortgage isn’t important if it’s not much

more expensive than the cheapest mortgage. To understand the financial consequences of

customers’ decisions, in our baseline ranking we define a ‘reference mortgage’ against which

14
we evaluate customers’ choices. We set this reference to be the 15th percentile option in

their menu, where options are ordered from cheapest to most expensive. We then compute

the amount of money a customer saves or leaves on the table relative to this reference as a

percentage of the customer’s monthly income after tax.

This reference point reflects a couple of considerations. Given the fluctuating size of

the choice set across banks and over time, we would like a reference point that scales with

the choice set. The 25th and 75th percentile of the within-bank choice set size is 11 and 23

respectively, so this cut off means that we are using the 2nd or 3rd cheapest mortgage for

most people rather than the absolutely cheapest mortgage.

Figure 2 shows the distribution of customers’ savings relative to the reference choice

within banks (left panel) and across banks (right panel). Most customers’ mortgage choices

do not save or cost them a large amount of money. At their chosen bank, for example, 85%

of customers’ possible choices neither save them more than 0.5% of their monthly income

nor cost them more than 1% (shown in the shaded area in the figure). Looking across banks,

66% of the choices are within that same range.

It’s therefore clear that while customers don’t pick well in ordinal terms, for most borrowers, the cost consequences of their decisions are relatively minor. We attribute this to

the role of competition, which disciplines the banks and protects customers. The fact that

customers can shop elsewhere prevents banks from offering primarily expensive products: if

a customer picks an expensive option to use in comparisons with other lenders, the bank will

likely lose the customer. As a result, the menu customers are given is often full of products

with similar prices, meaning they won’t lose a large amount of money even if they fail to pick

particularly well. Offering a variety of choices increases the chance any particular borrower

can find a product that suits her well and can make borrowing at that bank appealing.

If we characterize the choice sets and choices made using the other two ranking criteria,

similar patterns are present. Figure A1 in the appendix replicates Figure 2 under the immediate refinancing assumption. The within-bank results barely change, whilst across banks

the share of customers that neither lose nor gain a large amount of money from their choice

is even larger. The same must also be true if we consider only mortgages that are strongly

dominated: if few customers leave a large amount of money on the table, then even fewer

15
must leave a large amount of money on the table and make choices that are dominated (by

the reference mortgage) on all price dimensions.

3.2 Expensive choices

Despite the fact that most mortgages yield similar mortgage payments, some customers do

make expensive choices (Figure 2). Within banks, 2.3% of borrowers make choices that cost

them more than 2.5% of their income, and across banks this figure is 6.7%.13 For the average

borrower, this amounts to around £88 per month - a meaningful amount of money given the

net income of the typical borrower.

In what follows, we study the subset of customers that makes these expensive choices. In

our baseline rankings, we define an expensive choice as a choice that costs a borrower more

than 2.5% of their monthly household take home pay relative to the reference mortgage.

Figure 3a plots the average probability of an expensive choice through time, both within and

across banks. There is significant time series variation, with expensive choices more prevalent

early and in the middle of the sample.14 Figure 3b shows how the monthly percentages of

strongly dominated choices within and across banks vary over time, where again there is

significant time variation, with the strongly dominated picks being least common at the end

of our sample.

A customer can make an expensive choice for two reasons: choosing badly from a given

menu, or when facing a menu with many poor choices making a more typical choice that

is expensive. To isolate the role of the menu in driving expensive choices we define a new

variable, bad tail, which is the percent of mortgages on the customer’s menu that - if chosen

- would represent an expensive choice for that customer. The variable bad tail is a function

of price dispersion. If prices are identical, bad tail is, by definition, 0. If there is a large

amount of price dispersion, then some of the choices are likely to be expensive, and bad tail

is large and positive.

13Under the immediate refinancing assumption, these figures are 1.9% and 3.5%. We provide more details

on the strongly dominated choices below.

14From Figure 1, we know that the dispersion of reset rates was larger at the start of the sample and one

way to make an expensive choice (across banks) is to borrow from a bank with a high reset rate when there

are other banks with noticeably lower reset rates.

16
Figure 4 plots the distribution of bad tail through time. Many borrowers face menus with

almost no bad choices. A small subset of customers face menus that are filled with many

bad choices. As with expensive choices, there is significant variation through time, with the

choice sets being worse at the start of our sample.15

Our most remarkable finding about the time series patterns, however, is the strong influence the menu quality plays in driving expensive choices. In Figure 5, we show a strong and

positive correlation between the monthly percentage of expensive choices and the average

size of the bad tail in that month, both within and across banks. As the menu quality deteriorates, the percentage of expensive choices rises. Simply put, customers make expensive

choices when banks make it easy for them to do so. Figure 6 shows the equivalent plot

for the strongly dominated criterion. The same basic patterns documented in these figures

are present if we rank choices with the immediate refinancing assumption. The analogous

pictures are shown in the appendix in Figures A2 to A4. In particular, expensive choices remain rare, but they are tightly tied to the quality of the menu that people face. Specifically,

the left panel shows the within-bank (monthly) scatter plot of the probability of strongly

dominated choice against the size of the strongly dominated tail, defined as the percentage

of choices in the menu that are dominated by the cheapest option, and cost 2.5% of income

more than it. As with our baseline ranking there is a strong positive correlation between the

two. The across-bank results, shown in the right panel, similarly mimic the patterns using

the baseline ranking.

Overall, we conclude that our use of the baseline ranking algorithm is not responsible for

our conclusions about the importance of menus. In particular, the percentage of strongly

dominated choices is highly variable over time and when the tail is larger, more strongly

dominated choices are selected.

These conclusion lead to two obvious questions. First, what leads banks to price discriminate and give some customers menus with more expensive options? In other words, what

determines the menu structure and who gets the ones with more expensive options? Second,

for any given menu, what explains why some customers pick an expensive mortgage?

15In the interest of brevity, we omit the time series pictures showing how the strongly dominated tail varies

over time. They exhibit the same basic patterns as the bad tail.

17
4 Setting the mortgage menu

In this section we assess what determines the choice set a customer faces, and what drives

the heterogeneity in choice sets across customers.

Legal constraints limit lenders’ abilities to fine-tune the menu they offer to different customers. Although it’s illegal to vary menu prices by certain individual characteristics (such

as gender or race), they can price discriminate by altering the characteristics of the mortgage

contract presented to all borrowers. We focus on two key dimensions of the mortgage: loanto-value and loan-to-income (LTI) ratios. The LTV is directly relevant to the loan contract,

and as we noted earlier, for a given a loan amount, the cost of borrowing rises with the LTV,

reflecting increased risk of mortgages with higher leverage. The LTI is indirectly relevant

because banks’ feed this into their internal risk models to judge a borrower’s credit risk and

ultimately their ability to make their mortgage payments.16

Figure 7 plots the average size of bad tail and the strongly dominated tail according to

customers’ LTVs and LTIs at the banks they shopped at. Here, we define high LTV to be

85% and above, while high LTI is a ratio of 4 or more.

Customers with both low LTV and LTI ratios make up half the sample, and receive good

menus with very few bad choices using either metric of menu quality (Figure 7). This is to be

expected. These customers have low credit risk and can probably qualify for a mortgage at

many lenders. Banks are unlikely to have any market power with respect to these customers.

Where either the loan-to-value or the loan-to-income ratio becomes high, the size of the bad

tail or strongly dominated tail doubles.17 For customers who are borrowing a lot relative to

both their income and their house value, over 6% of choices on the menu would represent an

expensive choice and about 13% of the menu would be strongly dominated.18 This also is

somewhat expected. These borrowers would not necessarily sail through a mortgage approval

16It is worth noting that in 2014, the Bank of England’s Financial Policy Committee instituted a set of

rules for mortgage lending in the UK that prevented banks from offering more than 15% of new mortgages

in a given quarter to borrowers with LTIs above 4.5. See Kashyap (2020) and Peydr ́o et al. (2020).

17See Figure A5 for the corresponding figures under the immediate refinancing ranking, where the patterns

are very similar to the baseline.

18Note the reference mortgage used for the two metrics differ. Whereas the strongly dominated set compares the menu to the cheapest mortgage, the bad tail compares the menu to the cost of the 15th percentile

of the menu.

18
process at any other bank.

This pattern of menu quality across customers is consistent with banks offering menus

to customers who are potentially constrained, and might not have many options elsewhere.

Customers with high LTV and LTI ratios cannot borrow more against their income, and

didn’t save enough to make a large deposit. They may not qualify for many mortgages

at other banks. Thus these customers’ outside options are probably inferior compared to

customers who are borrowing less against their home and/or their income.

We summarize customers’ outside options according to their LTV and LTI in Figure 8.

We define a new variable - outside tail - which captures the quality of a customer’s outside

options. We take all mortgages a customer could have chosen at all banks except the one from

which they borrowed, and compute the percentage of these mortgages that would represent

an expensive choice relative to the reference mortgage at their chosen bank. Figure 8 plots

the average of outside tail by LTV and LTI bucket, along with the average of bad tail at

their chosen bank. The same pattern of deteriorating choices at the customer’s own bank

is true for the choice sets at other banks. This is consistent with banks all making similar

conjectures about which types of customers would be able to qualify for loans at competing

banks and pricing accordingly.

For three reasons, this menu variation likely reflects price discrimination and not variation

in risk across customers. First, all mortgages in the UK are made with recourse to the

borrower, making default extremely rare. Even in 2009, when house prices fell by 20% and

unemployment rose to 8%, banks suffered few losses on mortgage loans, and the default rate

rose by only 1 percentage point, to 1.5%.19 Second, the probability of going into arrears

is very similar across higher and lower LTVs and LTIs.20 This suggests default cannot be

driving the kind of menu variation that we observe in Figure 7. Third, our measure of

menu quality is relative, and thus relates to price dispersion - not average prices. While

19Default is defined as mortgage payments that have fallen into arrears of more than 6 months. See

Aron and Muellbauer (2016) for the default statistics, and Bank of England (2010) for house price and

unemployment statistics.

20We directly calculate the percentage of mortgages going into arrears by LTI and LTV for a large sample

of borrowers in 2015. We find that across the four categories of borrowers in Figure 7 the percentages are

all between 1.9 and 3.9 percent and whilst the levels are higher for high-LTV loans they are no higher for

high-LTI loans. So the arrears pattern does not follow the monotonic pattern for the bad tail size shown in

Figure 7. We use PSD data from 2015 as this is the first available year for which arrears data is collected.

19
variation in risk may provide some rationale for lenders to increase average prices for highLTV customers, risk alone cannot explain why price dispersion is also higher for high-LTV

customers. In contrast, a price discrimination motive does predict that price dispersion

should vary in the way that it does.

Setting the menus according to loan characteristics results in the menus varying with

demographic characteristics. In Table 4, we show who takes out high loan-to-value and

loan-to-income mortgages. We run probit regressions of a customer taking out one of these

mortgages based on the three borrower characteristics that we observe in our data: whether

the customer is young or old, a first-time buyer or not, and rich or poor. Table 4 reports the

marginal effects that these variables have on mortgage choice.21

Young people and first-time buyers are significantly more likely to take out mortgages

where they’re borrowing a large amount relative to their income and house value. These

differences are economically large. A customer under 30 years old is around 5 percentage

points more likely to take out a high-LTV and high-LTI mortgage than a customer over

45 years old. This effect is large relative to the population average probability of around

5%. The effect is similarly large for first-time buyers. While low income customers are more

likely to borrow a large amount relative to their income, they’re less likely to borrow a large

amount relative to their house value.

Young people and first-time buyers thus opt for mortgages that come with bad menus.

Given the evidence in the literature that young people tend to have lower financial knowledge (Lusardi and Mitchell, 2011) and make more financial mistakes than the middle aged

(Agarwal et al., 2009), this would be consistent with banks offering customers who are less

able to pick effectively mortgage menus where the consequences of a bad choice are greater.22

Table 5 shows how banks vary the menu across different customers. It summarizes the

dispersion customers’ face in all three price components of a mortgage, within and across

banks. The main dimension of dispersion is the initial rate: for the median borrower the

difference between the 85th and 15th percentiles of the initial rate distribution is 1 percentage

point at the bank where they took out their mortgage, and 1.3 percentage points across banks.

21The table shows results for our within-bank sample. Results for the across-bank sample are equivalent.

22Lusardi and Mitchell (2011) and Agarwal et al. (2009) document an inverted U-shape in financial literacy

and decision making, with performance increasing in age up to age 50 and declining thereafter.

20
Across banks customers also face significant variation in the reset rates they face.

Table 6 relates the size of the bad and strongly dominated tails that borrowers face to

the dispersion in price. The key determinant of the size of the bad tail is the dispersion

in initial rates across products. Combining the information in Tables 5 and 6, a customer

whose initial rate dispersion is at the 75th percentile of the sample distribution has a bad tail

at the bank from which she borrowed that is 4.8 percentage points larger than a customer

at the 25th percentile. This difference is double the average size of bad tail in our sample.

Taking Tables 5 and 6 together it is clear that banks predominantly rely on the initial rate

to vary their menu offering.

In the case of the strongly dominated tail, at the selected bank an increase in fee dispersion

reduces the size of the strongly dominated tail. This is essentially mechanical because if

the menu includes any zero fee options, then the candidate pool of dominating mortgages

is reduced to only including other zero fee mortgages. Across banks, fee dispersion is still

associated with a larger strongly dominated tail of mortgages. More importantly, the rate and

reset rate dispersion are again correlated (both within and across banks) with a worse menu.

Quantitatively, the dispersion in initial rates is the most important factor in explaining the

size of the strongly dominated tail. Moving from the 25th percentile to the 75th percentile

of the (within bank) sample would raise the size of the strongly dominated tail by 6.6

percentage points, which is one and half times the size of the mean of that variable. So if

we rank mortgages according to whether they are strongly dominated we reach the same

conclusion about the importance of avoiding high initial rates.

5 Customer choices

Having established how banks tailor the menu they offer to different customers, we now

ask how customers pick from a given menu. What leads to an expensive choice? Who

makes expensive choices? Were these expensive choices driven by the menu the customer

was offered, or some aspect of the choice they made?

21
5.1 Expensive choice mechanisms

Table 7 reports the marginal effects from probit regressions of making an expensive choice

on the various loan features that a customer faces when choosing. For each customer, we

calculate the distribution of the fees, initial rates and reset rates on the menu she faces.

Picking a high price is defined as picking a product whose price is greater than the 85th

percentile on offer, and picking a low price is defined as picking better than the 15th percentile.

We control for the menu quality by including bad tail as a regressor.

As would be expected from the prior tables, the choice of initial interest rate is the

key driver of making a poor choice: choosing a high rate increases the likelihood of an

expensive choice by 5 percentage points relative to picking a low rate within a bank, and

15 percentage points across banks. These effects are more than double the probability of

making an expensive choice in the population. Controlling for the other price dimensions,

the choice of fee has close to zero impact on expensive choices, as fees are generally not

large enough to materially impact mortgage cost. Across banks there is a quantitatively

significant role for the reset rate - picking a high reset rate makes a customer 8 percentage

points more likely to make an expensive choice than picking a low reset rate.23

Given the key to making an expensive choice is choosing a high initial rate, what do

mortgages with high initial rates look like when they are selected? Table 8 answers this

question for within-bank expensive choices. The first column regresses the likelihood of

picking a product with a high initial rate on the other two price dimensions. Customers

that pick products with low fees are significantly more likely to pick a product with a high

initial rate. These effects are large: a customer who picks a low fee at their bank is over 20

percentage points more likely to pick a product with a high rate than one who picks a high

fee, an effect which is roughly equal to the proportion of customers that pick high rates in

our sample. The second column of Table 8 regresses the likelihood of an expensive choice on

the customer’s fee choice. Given low fee products tend to have high rates, picking a low fee

23An analogous set of results for strongly dominated mortgages is shown in the appendix in Table A9.

Given how dominated choices are defined, picking a low fee, low initial rate or low reset rate, necessarily

reduces the odds of making a dominated choice, yet the same patterns hold using that ranking criteria. Table

A6 in the appendix shows the same regressions using the immediate refinancing criteria, where the patterns

for fees and interest rates are very similar to the baseline results. The reset rate plays no role using the

immediate refinancing critieria, as all borrowers by assumption refinance at the end of the initial period.

22
increases the chances of making an expensive choice.24 Even though many of the expensive

choices arise because of mortgages that have low fees and high initial rates, the coefficient

on low fees controlling for bad tail is modest for two reasons. First, about 40% of all the

mortgages (at the selected bank) that are not in the bad tail have low fees. So picking one of

those mortgages does not cost the borrower too much. Second, there are also some expensive

choices that are strictly dominated, and some of these have higher fees and a high initial

rate. These two considerations bring down the coefficient, even though many of the within

bank expensive choices combine low fees and high initial rates. Across banks some people

also make an expensive choice by picking a mortgage with a very high reset rate.

Figure 9 sets out how the role of the fee choice in driving expensive choices depends on

the initial period of a product. For each length of fixation period in our sample, we compute

the percentage of customers that chose a product with a low fee conditional on whether the

customer made an expensive choice or not.25 Where the initial period is only 2 years, picking

a low fee is not associated with making an expensive choice. For products with longer initial

periods, however, picking a low fee leads to expensive choices. This is consistent with the

evidence in Table 8, which shows that low-fee products come with high initial rates. The

longer the customer must pay this initial rate, the more likely it is that picking a low-fee

product proves costly enough that it leads to an expensive choice.

At most banks there is only one reset rate available. In some cases, however, a banking

group may have several distributional channels which are branded differently. This typically

happens when there is a merger and the acquiring bank allows the target bank to operate

using its existing policies for some time. So in several cases, there is a transition period -

usually between three and eighteen months - where a lender offers its own reset rate and a

different reset rate for the legacy bank it has acquired.

Picking a low reset rate, where there are two or more available in that banking group,

is generally associated with picking a high initial rate, though the magnitude of this effect

is smaller than the effect of the fee choice. Cases of multiple reset rates are uncommon, so

24It makes no sense to replicate these tests for strongly dominated choices because picking low fees will

lower the odds a choice will be dominated. The results for the immediate refinancing ranking are very similar

to the baseline results and are omitted to save space.

25We group 2 year fixed and adjustable rate mortgages together.

23
cannot be the drivers of the main results in the paper. For instance, if we rerun the withinbank specification in Table 7 without controlling for multiple reset rates, the coefficients on

the high and low fee are effectively identically to those we report in Table 7.

We now assess the drivers of expensive choices across banks. Table 9 shows the incidence

of expensive choices within and across banks. Just about as many borrowers who pick poorly

at their own bank do not make an expensive choice when the choice is defined across banks.

This can only happen if some banks offer enough cheap options that picking badly at that

bank is costly, but the chosen mortgage judged against the universe of offerings elsewhere is

not that expensive. Likewise, there are some people (about 1 in 18) who do not pick badly

relative to the choice set at their bank, but wind up with an expensive mortgage judged

against what is available elsewhere. This pair of facts suggests that in any customer’s choice

set there are some relatively cheaper banks and others that are more expensive.

Tables 10 and 11 disaggregate the roles of picking the wrong bank and picking poorly at

a given bank in driving expensive choices. To capture how well a customer chose their bank,

we compute the cost of the average mortgage at the bank where they shopped minus the

cost of the average mortgage at the cheapest bank they could have shopped at, scaled by

their income. To capture who well they chose at the bank where they shopped, we compute

the difference between the cost of the mortgage they chose and the cost of the 15th percentile

mortgage at the bank where they shopped, scaled by income.

Table 10 summarizes these two variables. At most of the banks, for most customers

even picking at the 90th percentile would not lead to an expensive choice. In contrast, when

comparing different banks the dispersion in mortgage costs is greater.

Table 11 shows how expensive choices vary according to the quality of a customer’s

choice of bank and of their choice at their bank. Both play a quantitatively significant role

in driving expensive choices, though the choice of bank plays the larger role. Combining the

information in Table 10 and the 5th column of Table 11, a customer whose quality of choice

of bank is at the 75th percentile of the distribution is 4 percentage points more likely to make

an expensive choice than one at the 25th percentile. A customer whose within-bank choice

is at the 75th percentile is 1.4 percentage points more likely to make an expensive choice

than one at the 25th percentile. Both of these effects are significant relative to the average

24
probability of making an expensive choice, 6.7%.

These results thus establish the ways in which customers choose expensive mortgages.

Customers that fail to shop across banks, or even upon doing so pick an expensive bank, are

significantly more likely to make an expensive choice. Conditional on the bank a customer

chooses, the driver of expensive choices is the initial interest rate. Customers that focus on

paying a low fee, rather than a low initial rate, are thus liable to make expensive choices.

5.2 Expensive choices by customer type

Having established how customers make expensive choices, we now ask who makes expensive

choices? Table 12 reports the marginal effects from regressions of making an expensive and

strongly dominated choice on customers’ loan-to-value and loan-to income ratios, with and

without controlling for the quality of the menu, within and across banks. The first four

columns show the results for expensive choices. Customers borrowing large amounts relative

to their house and/or income are significantly more likely to make expensive choices, both

within and across banks. The effects are economically large: customers with high LTV

and LTI are 9 percentage points more likely to make an expensive choice within-bank than a

customer with low LTV and LTI, which is over four times the average probability of expensive

choices in our sample. Within banks, this effect is almost wholly due to the quality of the

menu - the differences disappear once we control for the menu quality.

Across banks, while most of the variation in expensive choices can be explained by the

menu, customers with high LTIs are more likely to make an expensive choice even after

controlling for the quality of the menu. The effects once we control for the menu remain

significant, though relatively small, with a customer with high LTV and LTI two percentage

points more likely to make an expensive choice than one with low LTV and LTI, relative to

a sample average of 7%. This suggests that, given the same choice set as other customers,

these customers are marginally worse at shopping across banks.

The final four columns of Table 12 shows the analogous regressions for strongly dominated

choices. The combination of high LTI and LTV increases the probability of a strongly

dominated choice by 20 percentage points at their chosen bank. Even controlling for menu

quality, there is almost a 5 percentage point increase in chances of a strongly dominated

25
choice; recall the mean rate of strongly dominated choices within-bank is 8 percentage points,

so this is still a meaningful effect. The across-bank results are similarly large.

Table 13 reports the results of probit regressions of expensive and strongly dominated

choices on customer demographics. The first four columns show the results for expensive

choices. Young people and first-time buyers are significantly more likely to make expensive

choices both within and across banks, though the economic magnitudes are modest. This

is driven almost wholly by the menus they are given, with little variation in the likelihood

of expensive choices across demographics once we control for the tail of the menu. The

final four columns repeat these regressions with a dependent variable that identifies strongly

dominated choices. Once again the menu quality is the dominant predictor of who makes

strongly dominated choices.

6 Discussion

We summarize our findings as follows. Despite most people facing a bewildering number of

choices, the cost implications of their decision are fairly small. There are, however, some

people who do face menus that includes some very expensive options. The details of how

we identify these expensive mortgages are not important, as all three of our measurement

strategies point to four robust facts about the mortgage market.

First, menus with lots of pricey options are more common for borrowers with higher LTV

and LTI ratios. Second, borrowers with higher LTVs and LTIs are typically younger, more

likely to be first time buyers, and have lower incomes. Third, the expensive choices primarily

arise from having a high interest rate during the initial promotional period. Moreover, those

who pick mortgages with higher rates tend to pick products with low fees. Fourth, there are

two ways by which people wind up with an expensive mortgage. A minority of the cases

come from people who select a mortgage that is expensive despite have a menu that did not

have an exceptional number of expensive choices. More often, these people simply faced a

menu with many expensive products to start of with.

The existing literature fails to fully explain these facts. One can appeal to cognitive

challenges in explaining why some people choose expensive options when cheaper ones are

26
present. Yet, that approach can neither explain why the menus combine so many similar

cost products with the expensive ones nor why the size of the bad tail varies in the way that

we establish.

Here, instead, is a more promising way to model the market. Suppose the lender assumes

there are two types of customers: sophisticated customers and randomizers. Randomizers

walk into a bank, and pick a random choice from the menu. They don’t shop at other banks,

perhaps because they find it too costly to shop around, are unaware of alternatives, or don’t

qualify for mortgages with other lenders. Since they prefer having a mortgage to not having

one, they take the mortgage that they randomly select. Sophisticated customers go to all

banks, consider all options and pick the cheapest available.

How should a lender design their menu in this environment? It must balance two considerations: providing cheap options in order to entice sophisticated customers to shop at the

bank, and to offer expensive options to profit from the randomizers. The menu on offer will

feature price dispersion, with good options for sophisticated customers and bad options to

profit from the randomizer. The higher the percentage of randomizers, the more they want

to fill the mortgage menu with bad options.

This characterization is inspired by Menzio and Trachter (2018), who present a model in

which customers vary in their ability to shop within and across firms. They find that the

equilibrium price distribution will have price dispersion both within and across firms.

Our evidence is consistent with this framing of the problem. We find that customers

who are borrowing a lot relative to their income and home value are given worse choice

sets. These customers are more likely to resemble the randomizers than those who are not

borrowing much relative to their home or income. They didn’t save for a large deposit, which

would have given them a cheaper mortgage. They can’t afford a bigger house, and may not

qualify for mortgages at many banks. They’re disproportionately likely to be taking out a

mortgage for the first time, and tend to be younger. As a result, these customers are the

type that lenders would like to exploit, in order to profit from them either because of their

lack of sophistication or because of their possible lack of choices. The market equilibrium

will mean that these customers are more likely to wind up with expensive mortgages.

27
6.1 Alternative explanations

There are a number of other potential explanations for our evidence. We briefly explain why

the most obvious ones are inconsistent with our findings.

The first concern is that the variation in menu quality across borrowers could reflect

variation in risk or costs across borrowers, and not price discrimination. In Section 4 we

explain in detail why such concerns cannot explain our findings.

A second concern is that the use of brokers might be driving the results. This could be

true for two reasons. One possibility is that the menu could depend on who shops with a

broker. Alternatively, making use of brokers could be correlated with some other factor that

changes the probability of making an expensive choice. We dispense with each in turn.

As explained already, borrowers can use brokers’ help in selecting a mortgage. But this

does not affect the set of mortgages for which a borrower qualifies. Brokers might not

have all 6 lenders in their network of lenders (Robles-Garcia, 2019), but all our key results

hold within lenders, where brokers have a fiduciary duty to help the borrower find a good

mortgage. As a consequence, our finding that high-LTV and high-LTI borrowers (and hence

young borrowers and first-time buyers) are offered menus with higher price dispersion cannot

be explained by the presence of brokers.

Brokers can, however, affect how well borrowers choose from an offered set. Brokers’

fiduciary duty to borrowers, and reputation in a market with frequent re-mortgaging, mean

that it is likely that brokers help them pick well, not poorly. This implies brokers reduce the

likelihood of making an expensive choice.

In fact, as mentioned earlier, it might be reasonable to assume that anyone who uses a

broker will never make a dominated or expensive choice. In that case, the number of people

picking expensive products should be compared only to the set of borrowers who did not use

a broker. So the percentage of these expensive choices would be roughly double the numbers

we have reported. Whether or not this rough adjustment is correct, it seems unlikely that

the expensive choices arise because some borrowers use brokers.

Third, one could worry that differences in loan approval standards across lenders drive our

results. Maybe a borrower did not pick a cheaper mortgage not because they chose poorly,

28
but instead because they could not get a mortgage at the other bank. We do not observe

lender approval standards, so cannot rule this out of our across-bank analysis. However,

the key patterns that we observe in price dispersion and borrower choices are all observed

within-lender. Conditional on size of the loan and the house value, loan approvals do not

typically vary for products within a lender. This, coupled with the fact that lenders do

not typically vary the menu they offer across different borrowers, gives us confidence that

variation in loan approval standards cannot explain our main results.

Another concern about our interpretation of the findings is that the menu variation that

we emphasize could be vestige of the way we construct the choice set. In particular, we

construct a borrower’s choice set as the set of menus with the lowest LTV cap possible

conditional on the loan they took out. In principle, a customer could choose a mortgage

product with a higher LTV cap, but because mortgage rates rise with leverage, they rarely do

this. For example, a borrower needing to borrow only 80% of the cost of the home who picks

a mortgage available to borrowers with LTV of 85% would almost always face a higher initial

interest rate with the higher LTV. So in our main analysis, we only consider the 80% LTVs.

In the robustness exercises shown in Appendix A3, we demonstrate that our main results

carry through when we consider the even larger set of available choices a qualified borrower

could theoretically pick from. In our example, this would expand the choice offerings to

include 85%, 90%, and 95% loan-to-value offerings.26

Finally, one might worry that the choice set is mismeasured in way that explains the

expensive choices. For instance, if borrowers are constrained or do not qualify for a mortgage, then that might seem like a plausible alternative interpretation. Borrowing constraints,

however, seem unlikely to explain the findings because we have shown that expensive choices

arise primarily when the menu quality, based on the number of very costly mortgages available, is poor. The failure to pick a very cheap mortgage is not driving the results. Loosely

speaking, even picking the median mortgage in a choice set would not result in an expensive

mortgage. So this explanation is unlikely to explain our findings.

All in all, the robustness of the results across and within bank, implies that a particular

2691% of borrowers pick a mortgage with the lowest possible LTV cap for which they qualify at their

chosen bank. See Tables A11 and A12 for the results with the larger choice set which can be compared to

Tables 12 and 13.

29
type of price discrimination best explains the patterns we observe in the data. Going forward,

this suggests modeling mortgage choices ought to evolve in two ways. First, introducing

heterogeneity in people’s borrowing experience and allowing for choices to differ according

to income levels seems appropriate. Second, it is important to model the constraints on how

menus can be tailored, that limit lenders’ ability to customize offerings. Absent either of

these two features it will not be possible to fit the facts we have presented.

7 Conclusion

Despite the importance of mortgage choices for personal financial well-being, relatively little

is known about how people choose their mortgages. A key reason for the dearth of evidence

on this question is the difficulty in observing the choice sets that borrowers face in selecting

among loans. Usually all that can be analyzed is the mortgage that is chosen and little is

known about the other available options. In this paper, we assemble a unique data set that

allows us to see the other mortgages that were on offer both at the bank where the loan was

taken out and other banks offering similar mortgages. We establish a number of facts about

mortgage selection.

The number of possible options that most people face is large, even at a single lender.

But lenders constantly vary their product mix and even for a given loan type the number of

choices fluctuates over time. Few people pick the absolutely cheapest loan that is available,

but the vast majority pick a loan that is not much more expensive than the best option.

About seven percent of people pick a mortgage that is much more expensive than others

that are available. The best predictor of when these expensive choices occur is whether the

borrower has a relatively large number of expensive options in the menu they are facing. The

variation in menus seems designed with price discrimination in mind. Banks try to make

it easy for customers who might be prone to select badly to do so, without scaring away

other borrowers that they expect have the ability to shop at other lenders. This competitive

pressure seems to explain why most borrowers can find a reasonable mortgage even if they

do not pick particularly well.

The borrowers that are presented with these unfavorable menus are seeking loans that

30
are large multiples of their incomes and involve high loan amounts relative to the value of

the house. They tend to be first-time buyers and to be younger. There is some evidence

that the expensive choices come from focusing more on fees associated with a loan instead

of the promotional interest rate and not paying sufficient attention to the interest rate that

prevails once an introductory, promotional interest rate expires.

It may be surprising to some people to discover that some borrowers select such expensive

(or even dominated) choices. Yet, we know from other domains this kind of behavior also

occurs. Perhaps the best example is the way slot machines are priced in casinos. The typical

casino not only has many different types of slots, but also has multiple versions of each of

those types. Within a given type of slot, the expected percentage that the casino retains

(the “hold percentage”) varies. Indeed, in some jurisdictions (e.g the state of Nevada), that

hold percentage has to be displayed on the machine. Yet, most people do not choose which

particular machine to play by comparing hold percentages and playing only the low ones.

If you walk through a large casino in Las Vegas you will find a mix of high and low hold

machines within each type. You will also find that hold percentages vary across types of

slots in a given casino and across casinos. So the type of results we find are not confined to

the particular market that we have studied. Instead the noteworthy aspect of our results is

that this kind of behavior persists even in a high stakes situation.

We know that there are many other circumstances where people must select between

multiple outlets that are offering fairly similar products: cars, wedding venues and charter

vacations are just a few examples. It would be interesting to assess the extent to which

these same patterns in menu offerings and consumer choice are present in those markets.

With further analysis of these markets, some lessons for financial literacy education could

be drawn.

References

Agarwal, S., Driscoll, J. C., Gabaix, X., and Laibson, D. (2009). The age of reason: Financial

decisions over the life cycle and implications for regulation. Brookings Papers on Economic

Activity, 2009(2):51–117.

Allen, J., Clark, R., and Houde, J.-F. (2019). Search frictions and market power in

negotiated-price markets. Journal of Political Economy, 127(4):1550–1598.

31
Allen, J. and Li, S. (2020). Dynamic competition in negotiated price markets. Bank of

canada staff working paper.

Andersen, S., Badarinza, C., Liu, L., Marx, J., and Ramadorai, T. (2022). Reference dependence in the housing market. American Economic Review, forthcoming.

Aron, J. and Muellbauer, J. (2016). Modelling and forecasting mortgage delinquency and

foreclosure in the UK. Journal of Urban Economics, 94:32–53.

Bank of England (2010). Inflation Report - November 2010.

Benetton, M. (2021). Leverage regulation and market structure: An empirical model of the

UK mortgage market. Journal of Finance, forthcoming.

Benetton, M., Eckley, P., Garbarino, N., Kirwin, L., and Latsi, G. (2020). Capital requirements and mortgage pricing: Evidence from Basel II. Journal of Financial Intermediation.

Benetton, M., Gavazza, A., and Surico, P. (2021). Mortgage pricing and monetary policy.

Bank of England Staff Working Paper No. 936.

Bhutta, N., Fuster, A., and Hizmo, A. (2021). Paying too much? Borrower sophistication

and overpayment in the US mortgage market. CEPR Discussion Paper No. 14924.

Bracke, P. and Tenreyro, S. (2021). History dependence in the housing market. American

Economic Journal: Macroeconomics, 13(2):420–443.

Cloyne, J., Huber, K., Ilzetzki, E., and Kleven, H. (2019). The effect of house prices on

household borrowing: a new approach. American Economic Review, 109(6):2104–36.

Duca, J. V. and Kumar, A. (2014). Financial literacy and mortgage equity withdrawals.

Journal of Urban Economics, 80:62–75.

Fo`a, G., Gambacorta, L., Guiso, L., and Mistrulli, P. E. (2019). The supply side of household

finance. The Review of Financial Studies, 32(10):3762–3798.

Gianinazzi, V. (2019). Framing bias in mortgage refinancing decisions and monetary policy

pass-through. Working paper, Nova School of Business and Economics.

Guiso, L., Pozzi, A., Tsoy, A., Gambacorta, L., and Mistrulli, P. E. (2022). The cost of

steering in financial markets: Evidence from the mortgage market. Journal of Financial

Economics, 143(3):1209–1226.

Iscenko, Z. (2020). Strictly dominated mortgage choices: Evidence from the UK. Financial

Conduct Authority Occasional Paper No. 33.

32
Kaplan, G. and Menzio, G. (2015). The morphology of price dispersion. International

Economic Review, 56(4):1165–1206.

Kashyap, A. K. (2020). My reflections on the FPC’s strategy. Journal of Money, Credit and

Banking, 52(S1):63–75.

Liu, L. (2019). Non-salient fees in the mortgage market. Bank of England Staff Working

Paper No. 819.

Lusardi, A. and Mitchell, O. S. (2011). Financial literacy and retirement planning in the

United States. NBER Working Paper No. 17078.

Menzio, G. and Trachter, N. (2018). Equilibrium price dispersion across and within stores.

Review of Economic Dynamics, 28:205–220.

My ́sliwski, M. and Rostom, M. (2022). Value of information, search, and competition in the

uk mortgage market. Bank of England Staff Working Paper No. 967.

Peydr ́o, J.-L., Rodriguez Tous, F., Tripathy, J., and Uluc, A. (2020). Macroprudential policy,

mortgage cycles and distributional effects: Evidence from the UK. CEPR Discussion Paper

No. 15275.

Robles-Garcia, C. (2019). Competition and incentives in mortgage markets: The role of

brokers. Working paper, Stanford Graduate School of Business.

Ru, H. and Schoar, A. (2016). Do credit card companies screen for behavioral biases? NBER

Working Paper No. 22360.

Scott Morton, F., Zettelmeyer, F., and Silva-Risso, J. (2003). Consumer information and

discrimination: Does the internet affect the pricing of new cars to women and minorities?

Quantitative marketing and Economics, 1(1):65–92.

Stigler, G. J. (1961). The economics of information. Journal of Political Economy, 69(3):213–

225.

Woodward, S. E. and Hall, R. E. (2012). Diagnosing consumer confusion and sub-optimal

shopping effort: Theory and mortgage-market evidence. American Economic Review,

102(7):3249–76.

33
Tables & Figures

Table 1: Example of products on offer

Initial rate (%) Fee (£) Reset rate (%) Max LTV (%) Max loan (£000)

Bank A 3.39 999 3.94 90 400

Bank A 3.59 1,499 3.94 90 400

Bank A 3.64 999 3.94 90 400

Bank A 4.15 0 3.94 90 400

Bank A 4.15 599 3.94 90 400

Bank A 4.19 599 3.94 90 400

Bank A 4.19 399 3.94 90 400

Bank A 4.19 299 3.94 90 400

Bank A 4.19 0 3.94 90 400

Bank A 4.59 0 3.94 90 400

Bank A 4.99 399 3.94 90 400

Bank A 3.64 999 3.94 90 3,000

Bank A 3.69 999 3.94 90 3,000

Bank A 4.19 399 3.94 90 3,000

Bank A 4.19 599 3.94 90 3,000

Bank A 4.19 299 3.94 90 3,000

Bank A 4.59 0 3.94 90 3,000

Bank B 3.29 0 3.99 90 500

Bank B 4.99 999 3.99 90 750

Bank B 4.99 1,264 3.99 90 750

Bank B 3.94 1,260 3.99 90 1,000

Bank B 3.99 1,260 3.99 90 1,000

Bank B 4.54 265 3.99 90 1,000

Bank B 4.59 0 3.99 90 1,000

Bank B 4.79 1,260 3.99 90 1,000

Bank B 4.89 0 3.99 90 1,000

Bank B 5.19 1,260 3.99 90 1,000

Bank B 5.19 0 3.99 90 1,000

Bank B 5.39 0 3.99 90 1,000

Bank B 5.39 1,260 3.99 90 1,000

Bank B 5.69 0 3.99 90 1,000

Bank B 5.69 1,260 3.99 90 1,000

Note: This table reports example choices for a given customer across banks. For simplicity only a subset of

the menu customers typically face is shown.

34
Table 2: Summary statistics

Mean Std. dev. 25th pctile Median 75th pctile

Demographics

Young (%) 36 48 0 0 100

Old (%) 11 31 0 0 0

First-time buyer (%) 40 49 0 0 100

Net income (£000s) 42 26 28 37 50

Loan characteristics

Loan value (£000s) 157 90 100 136 190

House price (£000s) 201 119 125 172 242

Loan-to-value (%) 79 8 74 80 85

Loan-to-income ratio 3.2 0.9 2.6 3.2 3.8

Prices

Fee (£000s) 0.66 0.57 0.10 0.76 1.00

Initial rate (%) 4.0 1.0 3.2 3.9 4.7

Reset rate (%) 4.1 0.4 4.0 4.0 4.2

Note: This table summarizes the key variables used in our analysis. Young customers are under 30. Old

customers are over 45. A customer’s net income is measured as reported (gross) income minus tax.

First-time buyers are buying a house for the first time. Loan value and house price are reported in the

mortgage contract, and loan-to-value (LTV) is the ratio of the loan value to the house price in percent.

The loan-to-income ratio follows the industry convention and is calculated by dividing the loan amount by

reported gross income. The fee, initial rate and reset rate are also taken from the mortgage contract.

Table 3: Characteristics of choice sets and choices made

Within Across

Choice set size Pctile chosen Choice set size Pctile chosen

25th pctile 11 33 46 27

Median 16 53 73 47

75th pctile 23 75 101 70

Note: This table summarizes customers’ choice sets and choices made. Within-bank figures restrict a

customer’s choice set to mortgages on offer at the bank that granted their mortgage. Across-bank figures

include mortgages on offer at all banks in a customer’s choice set. For each customer we rank the mortgages

in their comparison set from cheapest to most expensive. The variable Pctile chosen is equal to the rank of

the mortgage they choose as a percentage of the number of mortgages in their comparison set. The table

summarizes the distribution of this variable, along with the size of the choice set, across the sample.

35
Table 4: Probit regressions of high loan-to-value and high loan-to-income on borrower types

Young

High LTV

(1)

0.071∗∗∗

(0.001)

High LTI

(2)

0.023∗∗∗

(0.001)

High LTV & LTI

(3)

0.016∗∗∗

(0.001)

Old −0.095∗∗∗

(0.002)

−0.079∗∗∗

(0.001)

−0.035∗∗∗

(0.001)

First-time buyer 0.234∗∗∗

(0.001)

0.037∗∗∗

(0.001)

0.042∗∗∗

(0.001)

Poor −0.076∗∗∗

(0.001)

0.065∗∗∗

(0.001)

−0.003∗∗∗

(0.001)

Rich 0.032∗∗∗

(0.001)

−0.067∗∗∗

(0.001)

−0.014∗∗∗

(0.001)

Bank dummies

Product dummies

Pseudo R-squared

Mean dependent variable

Observations

Yes

Yes

0.12

0.32

894,901

Yes

Yes

0.05

0.2

894,901

Yes

Yes

0.05

0.05

894,901

Note: This table reports average marginal effects from probit regressions with the dependent variables

shown at the top of each column. High LTI customers have loan-to-income above 4. High LTV customers

have loan-to-value above 85%. Young customers are under 30. Old customers are over 45. Poor (rich)

customers have income in the lower (upper) tertile of the income distribution. Product dummies are

indicator variables for 2, 3 and 5 year fixed rate mortgages and 2 year adjustable rate mortgages. Bank

dummies are indicator variables for each of the 6 banks. *, ** and *** indicate that the coefficient is

different from zero at the 10, 5 and 1 percent level of significance respectively.

Table 5: Dispersion in the price components of mortgage contracts

Within Across

25th pctile Median 75th pctile 25th pctile Median 75th pctile

Initial fee (£000s) 0.80 0.99 1.00 0.99 1.14 1.25

Initial rate (pp) 0.70 1.00 1.35 1.07 1.30 1.65

Reset rate (pp) 0.00 0.00 0.00 0.75 0.85 1.05

Note: This table summarizes the dispersion customers face in fees, initial rates and reset rates. We take

each unique value of these three elements of a mortgage in a customer’s choice set and compute the

difference between the 85th and the 15th percentiles of the distribution of each of these variables. The table

summarizes the distributions of these dispersions across customers. Within-bank figures restrict a

customer’s choice set to mortgages on offer at the bank that granted their mortgage. Across-bank figures

include mortgages on offer at all banks in a customer’s choice set.

36
Table 6: Determinants of the size of the tail for each borrower

Bad tail

Within Across

Strongly dominated tail

Within Across

Fee dispersion (£000s)

(1)

0.103∗∗∗

(0.011)

(2)

1.960∗∗∗

(0.035)

(3)

−0.564∗∗∗

(0.018)

(4)

0.902∗∗∗

(0.056)

Rate dispersion (pp) 7.440∗∗∗

(0.011)

13.900∗∗∗

(0.014)

10.200∗∗∗

(0.018)

7.930∗∗∗

(0.022)

Reset rate dispersion (pp) 0.657∗∗∗

(0.023)

3.250∗∗∗

(0.022)

3.200∗∗∗

(0.038)

0.599∗∗∗

(0.035)

Bank dummies

Product dummies

Mean dependent variable

R-squared

Observations

Yes

Yes

2.2

0.42

894901

No

Yes

7.83

0.6

883459

Yes

Yes

4.53

0.32

894901

No

Yes

13.06

0.16

883459

Note: This table reports coefficients from OLS regressions with the size of borrowers’ bad and strongly

dominated tails as the dependent variables. We take each unique value of the three elements of a mortgage

- the initial rate, the fee and the reset rate - in a customer’s choice set and order them from low to high.

The dispersion is the difference between the 85th and the 15th percentiles of the distribution of each of

these variables. The bad tail measures the percentage of a customer’s choice set that would represent an

expensive choice whilst the strongly dominated tail computes the fraction of mortgages in a customer’s

choice set that are strongly dominated by the cheapest mortgage on offer. Expensive and strongly

dominated choices are defined in Section 3. The first and third columns take the choice set to consist only

of mortgages on offer at the bank that granted the customer their mortgage, whilst the second and fourth

include all banks. Dummy variables are defined as in Table 4. *, ** and *** indicate that the coefficient is

different from zero at the 10, 5 and 1 percent level of significance respectively.

37
Table 7: Probit regressions of expensive choice on choices of price components

Low Fee

Expensive choice within

(1)

−0.003∗∗∗

(0.0002)

Expensive choice across

(2)

−0.009∗∗∗

(0.0003)

High Fee 0.0003∗

(0.0002)

0.027∗∗∗

(0.001)

Low Inital Rate −0.017∗∗∗

(0.0003)

−0.037∗∗∗

(0.001)

High Initial Rate 0.032∗∗∗

(0.0004)

0.122∗∗∗

(0.001)

Low Reset Rate −0.006∗∗∗

(0.0003)

−0.023∗∗∗

(0.0004)

High Reset Rate 0.006∗∗∗

(0.0002)

0.057∗∗∗

(0.0004)

Bad tail 0.124∗∗∗

(0.001)

0.314∗∗∗

(0.001)

Bank dummies

Product dummies

Pseudo R-squared

Mean dependent variable

Observations

Yes

Yes

0.82

0.023

894,901

No

Yes

0.69

0.067

883,459

Note: This table reports average marginal effects from probit regressions with the dependent variables

shown at the top of each column. We take each unique value of the three elements of a mortgage - the

initial rate, the fee and the reset rate - in a customer’s choice set and order them from low to high. Choices

above the 85th percentile are high, whilst choices below the 15th percentile are low. The bad tail measures

the percentage of a customer’s choice set that would represent an expensive choice, where expensive choices

are defined in Section 3. The first column takes the choice set to consist only of mortgages on offer at the

bank that granted the customer their mortgage, whilst the second includes all banks. Dummy variables are

defined as in Table 4. *, ** and *** indicate that the coefficient is different from zero at the 10, 5 and 1

percent level of significance respectively.

38
Table 8: Determinants of within-bank expensive choices

Low Fee

High rate within

(1)

0.206∗∗∗

(0.001)

Expensive choice within

(2)

0.002∗∗∗

(0.0002)

High Fee −0.032∗∗∗

(0.001)

0.001∗∗∗

(0.0002)

Low Reset Rate 0.008∗∗∗

(0.001)

High Reset Rate −0.061∗∗∗

(0.001)

Bad tail 0.116∗∗∗

(0.001)

Bank dummies

Product dummies

Pseudo R-squared

Mean dependent variable

Observations

Yes

Yes

0.09

0.216

894,901

Yes

Yes

0.69

0.023

894,901

Note: This table reports average marginal effects from probit regressions with the dependent variables

shown at the top of each column. We take each unique value of the initial rate, the fee and the reset rate in

a customer’s choice set and order them from low to high. Choices above the 85th percentile are high, whilst

choices below the 15th percentile are low. The bad tail measures the percentage of a customer’s choice set

that would represent an expensive choice, where expensive choices are defined in Section 3. The dependent

variable in column (1) is a dummy variable equal to 1 if the customer picks a product with a high initial

rate, and in column (2) is a dummy variable equal to 1 if the customer makes an expensive choice. The

choice set consists only of mortgages on offer at the bank that granted the customer their mortgage.

Dummy variables are defined as in Table 4. *, ** and *** indicate that the coefficient is different from zero

at the 10, 5 and 1 percent level of significance respectively.

Table 9: Expensive choices within and across banks

Expensive choice across Not

Expensive choice within 1.2 1.1

Not 5.5 92.2

Note: This table shows the distribution of expensive choices within and across banks. A customer’s

within-bank choice set consists only of mortgages on offer at the bank that granted the customer their

mortgage, whilst their choice set across banks includes all banks. An expensive choice costs a customer at

least 2.5% of their income more than the 15th percentile in their choice set. The figures show the

percentages of the sample in each of the four combinations of expensive and inexpensive choices.

39
Table 10: Choice quality within and across banks

25th pctile Median 75th pctile 90th pctile

Cost difference within bank 0.09 0.32 0.69 1.20

Cost difference vs best bank 0.06 0.63 1.45 2.23

Note: This table summarizes the quality of customers’ choices within and across banks. The cost difference

vs best bank measures the difference in cost between the mean product at the bank where the customer

took out their mortgage and the cheapest bank they could have shopped at – where banks are ranked by

the mean cost of the products they offer that customer – as a percentage of the customer’s income. The

cost difference within bank measures the difference in cost between the mortgage the customer chose and

the 15th percentile mortgage on offer at their bank. The entries in the table show the values of these

variables at different points in the customer distribution.

Table 11: Determinants of across-bank expensive choices

Cost difference within bank

(1)

Expensive choice across

(2) (3) (4)

0.016∗∗∗

(0.0002)

(5)

0.023∗∗∗

(0.0002)

Cost difference vs. best bank 0.023∗∗∗

(0.0002)

0.029∗∗∗

(0.0002)

Bad tail 0.304∗∗∗

(0.001)

0.279∗∗∗

(0.001)

0.224∗∗∗

(0.001)

0.150∗∗∗

(0.001)

Bank dummies

Product dummies

Pseudo R-squared

Mean dependent variable

Observations

No

Yes

0.09

0.067

883,459

No

Yes

0.56

0.067

883,459

No

Yes

0.63

0.067

883,459

No

Yes

0.59

0.067

883,459

No

Yes

0.71

0.067

883,459

Note: This table reports average partial effects from probit regressions with the dependent variable being

an expensive choice across banks. The bad tail measures the percentage of a customer’s choice set that

would represent an expensive choice, where expensive choices are defined in Section 3. The cost difference

vs best bank measures the difference in cost between the mean product at the bank where the customer

took out their mortgage and the cheapest bank they could have shopped at, as a percentage of the

customer’s income. The cost difference within bank measures the difference in cost between the mortgage

the customer chose and the 15th percentile mortgage on offer at their bank. Dummy variables are defined

as in Table 4. Bank dummies are indicator variables for each of the 6 banks. *, ** and *** indicate that

the coefficient is different from zero at the 10, 5 and 1 percent level of significance respectively.

40
Table 12: Expensive & strongly dominated choices and loan characteristics

Expensive choice Strongly dominated choi

Within Within Across Across Within Within Across 

(1) (2) (3) (4) (5) (6) (7) 

High LTV & High LTI 0.093∗∗∗ −0.002∗∗∗ 0.228∗∗∗ 0.023∗∗∗ 0.208∗∗∗ 0.049∗∗∗ 0.361∗∗∗ 

(0.002) (0.0003) (0.002) (0.001) (0.002) (0.001) (0.002) 

High LTV & Low LTI 0.044∗∗∗ 0.0001 0.076∗∗∗ 0.001 0.091∗∗∗ 0.017∗∗∗ 0.125∗∗∗ 

(0.001) (0.0002) (0.001) (0.0005) (0.001) (0.001) (0.001) 

Low LTV & High LTI 0.025∗∗∗ 0.002∗∗∗ 0.087∗∗∗ 0.008∗∗∗ 0.068∗∗∗ 0.031∗∗∗ 0.249∗∗∗ 

(0.001) (0.0003) (0.001) (0.0005) (0.001) (0.001) (0.001) 

Bad tail 0.118∗∗∗ 0.299∗∗∗

(0.001) (0.001)

Strongly dominated tail 0.363∗∗∗ 

(0.001) 

Bank dummies Yes Yes No No Yes Yes No 

Product dummies Yes Yes Yes Yes Yes Yes Yes 

Pseudo R-squared 0.35 0.69 0.15 0.57 0.2 0.38 0.08 Mean dependent variable 0.023 0.023 0.067 0.067 0.081 0.081 0.277 

Observations 894,901 894,901 883,459 883,459 894,901 894,901 883,459 

Note: This table reports average partial effects from probit regressions with the dependent variables shown at the top of eacLTI customers have loan-to-income above (below) 4. High (low) LTV customers have loan-to-value above (below) 85%. The percentage of a customer’s choice set that would represent an expensive choice whilst the strongly dominated tail computes tin a customer’s choice set that are strongly dominated by the cheapest mortgage on offer. Expensive and strongly dominateSection 3. Columns headed ‘within’ take the choice set to consist only of mortgages on offer at the bank that granted the cuwhilst columns headed ‘across’ include all banks. Dummy variables are defined as in Table 4. *, ** and *** indicate that th

from zero at the 10, 5 and 1 percent level of significance respectively.
Table 13: Expensive & strongly dominated choices and borrower characteristics

Expensive choice Strongly dominated c

Within Within Across Across Within Within Acro

(1) (2) (3) (4) (5) (6) (7) 

Young 0.005∗∗∗ 0.001∗∗∗ 0.018∗∗∗ 0.005∗∗∗ 0.022∗∗∗ 0.010∗∗∗ 0.043

(0.0004) (0.0002) (0.001) (0.0004) (0.001) (0.001) (0.00

Old −0.008∗∗∗ −0.0003 −0.031∗∗∗ −0.006∗∗∗ −0.022∗∗∗ −0.006∗∗∗ −0.075

(0.0004) (0.0003) (0.001) (0.001) (0.001) (0.001) (0.00

First-time buyer 0.006∗∗∗ −0.0003 0.005∗∗∗ −0.005∗∗∗ 0.010∗∗∗ −0.002∗∗∗ −0.003

(0.0004) (0.0002) (0.001) (0.0004) (0.001) (0.001) (0.00

Poor 0.0005 0.001∗∗∗ 0.003∗∗∗ 0.001∗∗ 0.007∗∗∗ 0.008∗∗∗ 0.034

(0.0004) (0.0002) (0.001) (0.0004) (0.001) (0.001) (0.00

Rich −0.0001 −0.001∗∗∗ −0.006∗∗∗ −0.006∗∗∗ −0.010∗∗∗ −0.010∗∗∗ −0.043

(0.0003) (0.0002) (0.001) (0.0004) (0.001) (0.001) (0.00

Bad tail 0.117∗∗∗ 0.303∗∗∗

(0.001) (0.001)

Strongly dominated tail 0.382∗∗∗ 

(0.001) 

Bank dummies Yes Yes No No Yes Yes No Product dummies Yes Yes Yes Yes Yes Yes Yes Pseudo R-squared 0.3 0.69 0.09 0.56 0.16 0.37 0.04 

Mean dependent variable 0.023 0.023 0.067 0.067 0.081 0.081 0.27Observations 894,901 894,901 883,459 883,459 894,901 894,901 883,4

Note: This table reports average partial effects from probit regressions with the dependent variables shown at the top of eac

customers are under 30. Old customers are over 45. Poor (rich) customers have net income in the lower (upper) tertile. The 

percentage of a customer’s choice set that would represent an expensive choice whilst the strongly dominated tail computes tin a customer’s choice set that are strongly dominated by the cheapest mortgage on offer. Expensive and strongly dominate

Section 3. Columns headed ‘within’ take the choice set to consist only of mortgages on offer at the bank that granted the cuwhilst columns headed ‘across’ include all banks. Dummy variables are defined as in Table 4. *, ** and *** indicate that th

from zero at the 10, 5 and 1 percent level of significance respectively.
−2

−1

0

1

2009 2010 2011 2012 2013 2014 2015

Demeaned reset rate (%)

Bank 1

Bank 2

Bank 3

Bank 4

Bank 5

Bank 6

Figure 1: Across-bank variation in reset rates

Note: This figure shows the deviation of bank-specific reset rates relative to the average reset rate for each

month. For each bank we compute the average reset rate across all products they offer in a given month.

We then demean these by the simple average of the reset rate across banks, and plot these series.

85%

0.00

0.25

0.50

0.75

1.00

−4 −3 −2 −1 0 1

Cost saving vs reference (% income)

Cumulative distribution

66%

0.00

0.25

0.50

0.75

1.00

−4 −3 −2 −1 0 1

Cost saving vs reference (% income)

Cumulative distribution

(a) Within each bank’s choice set (b) Across all banks’ choice sets

Figure 2: Cost savings of chosen mortgage vs reference (% of net income)

Note: These figures plot the distribution of the amount a customer saves relative to a reference mortgage

as a percentage of their income, at the bank where the customer shopped and across banks. We first

compute the present value of the mortgage that a customer chooses using equation (1), subtract it from the

cost of the 15th percentile mortgage in a customer’s choice set (where mortgages are ordered from cheapest

to most expensive), and divide by the customer’s net income. The figures plot the cumulative distribution

of this figure across all customers, where the choice set consists only of mortgages on offer at the bank that

gave them their mortgage (left) and of mortgages on offer across banks (right). The shaded areas show the

fraction of each sample that fall between savings of 0.5% of net income and a cost of 1% of net income. The

dotted lines show where the choice costs more than 2.5% of net income relative to the reference mortgage.

43
0

10

20

30

2009 2010 2011 2012 2013 2014 2015

Probability expensive choice (%)

Across Within

0

20

40

60

2009 2010 2011 2012 2013 2014 2015

Probability strongly dominated choice (%)

Across Within

(a) Expensive choices (b) Strongly dominated choices

Figure 3: Expensive & strongly dominated choices through time

Note: These figures plot the percentage of customers that make expensive choices (left) and strongly

dominated choices (right) each month, at the bank where they shopped and across banks. Expensive and

strongly dominated choices are defined in Section 3. The blue line takes the choice set to consist only of

mortgages on offer at the bank that gave the customer their mortgage, and the red line includes mortgages

on offer across all banks. The horizontal lines plot the means over the sample period for each comparison.

0

5

10

15

20

2009 2010 2011 2012 2013 2014 2015

Bad tail (%)

75th pctile 90th pctile Median

0

20

40

60

80

2009 2010 2011 2012 2013 2014 2015

Bad tail (%)

75th pctile 90th pctile Median

(a) Within each bank’s choice set (b) Across all banks’ choice sets

Figure 4: Bad tail through time

Note: These figures summarize the distribution of the fraction of a customer’s choice set that would

represent an expensive choice if chosen, both within and across banks, through time. The bad tail

computes the fraction of the mortgages in a customer’s choice set that would represent an expensive choice,

where an expensive choice is defined in Section 3. The figures plot percentiles of the distribution of bad tail

through time for the median (green) 75th percentile (red) and 90th percentile (blue), where the choice set

consists only of mortgages on offer at the bank that gave the customer their mortgage (left panel) and of

mortgages on offer across banks (right panel). The horizontal lines plot the means over the sample period.

44
8 

6 

4 

2 

0 

2 4 6

Bad tail (%)

P(expensive choice)

30

20

10

0 

10 20

Bad tail (%)

●●●●●●

●● ●●●

P(expensive choice)

(a) Within each bank’s choice set (b) Across all banks’ choice sets

Figure 5: Expensive choices and bad tails

Note: These figures summarize the relationship between the frequency of expensive choices and the average 

quality of customers’ choice sets in a given month. The bad tail computes the fraction of the mortgages in 

a customer’s choice set that would represent an expensive choice, where an expensive choice is defined in 

Section 3. The figures plot the percentage of customers that make expensive choices in a month against the 

average size of bad tail in that month, where the choice set consists only of mortgages on offer at the bank 

that gave the customer their mortgage (left panel) and of mortgages on offer across banks (right panel).

The blue line shows a linear regression of the probability of an expensive choice on the size of bad tail, with 

equation displayed in each panel. The shaded area represents the 95% confidence interval.

20

15

10

5 

0 

4 8 12

Strongly dominated tail (%)

P(strongly dominated choice)

60

40

20

5 10 15 20 25

Strongly dominated tail (%)

P(strongly dominated choice)

(a) Within each bank’s choice set (b) Across all banks’ choice sets

Figure 6: Strongly dominated choices and tails

Note: These figures summarize the relationship between the frequency of strongly dominated choices and 

the average quality of customers’ choice sets in a given month. The strongly dominated tail computes the 

percentage of a customer’s choice set that is strongly dominated by the cheapest option in their choice set, 

where strong domination is defined in Section 3. The figures plot the percentage of customers that make 

strongly dominated choices in a month against the average size of the strongly dominated tail in that 

month, where the choice set consists only of mortgages on offer at the bank that gave the customer their 

mortgage (left panel) and of mortgages on offer across banks (right panel). The blue line shows a linear 

regression of the probability of a strongly dominated choice choice on the size of the strongly dominated 

tail, with equation displayed in each panel. The shaded area represents the 95% confidence interval.

45
Low LTI Low LTI High LTI High LTI

Low LTV High LTV Low LTV High LTV

(53%) (26%) (15%) (5%)

0

5

10

15

20

Tail (%)

Bad tail Strongly dominated tail

Figure 7: Bad and strongly dominated tail by loan-to-value and loan-to-income ratio

Note: This figure summarizes the average quality of customers’ choice sets by combinations of their LTV

and LTI ratios. The bad tail computes the fraction of the mortgages in a customer’s choice set that would

represent an expensive choice, whilst the strongly dominated tail computes the percentage of a customer’s

choice set that is strongly dominated by the cheapest option in their choice set. Expensive and strongly

dominated choices are defined in Section 3. This figure plots the average of bad tail and strongly dominated

tail according to a customer’s LTV and LTI. High LTV is defined as LTV> 85%, and low LTV as

LTV< 85%. High LTI is defined as LTI> 4, and low LTI as LTI< 4. The numbers in parentheses are the

percentages of the sample in each bin.

46
Low LTI Low LTI High LTI High LTI

Low LTV High LTV Low LTV High LTV

(53%) (26%) (15%) (5%)

0

5

10

15

20

Bad tail (%)

Outside Within

Figure 8: Tails within and outside banks by loan-to-value and loan-to-income ratio

Note: This figure summarizes the average quality of customers’ choice sets by their LTV and LTI, both at

the bank that granted them their mortgage and at the banks where they did not borrow. The bad tail

computes the fraction of the mortgages in a customer’s choice set that would represent an expensive choice,

where an expensive choice is defined in Section 3. The within-bank bad tail (blue) is the average of bad tail

at the bank where the customer borrowed. The variable outside tail (red) computes the fraction of the

mortgages the customer could have chosen at the other five banks that would represent an expensive

choice, relative to the same reference of the 15th percentile at the customer’s chosen bank. This figure plots

the average of the within-bank bad tail and outside tail according to a customer’s LTV and LTI. High LTV

is defined as LTV> 85%, and low LTV as LTV< 85%. High LTI is defined as LTI> 4, and low LTI as

LTI< 4. The numbers in parentheses are the percentages of the sample in each bin.

0

20

40

60

2 3 5

Initial fixation period

Percentage choosing low−fee

mortgage

Expensive choice

Not

Figure 9: Expensive choices by choice of fee

Note: This figure shows the percentage of borrowers that choose low-fee mortgages for mortgages with

different initial fixation periods. A mortgage is low-fee if its fee is below the 15th percentile of the

distribution of unique fees in the customer’s choice set. A mortgage is high-fee if its fee is above the 85th

percentile. Expensive choices are defined in Section 3.

47
Internet Appendix

A1 Sample formation

Our main data source is the Product Sales Database (PSD), a loan-level administrative

dataset capturing all newly issued mortgages in the UK. A typical set of choices an individual

would be presented with is shown in Table 1.

The PSD contains information recorded by financial institutions at the time of mortgage take-out. In principle, this includes borrower characteristics, such as income and age;

information on the property, such as its postcode and value, and loan details such as the

initial interest rate, length of fixation period, and the issuing institution. Table A1 shows

the raw data between 2009 and 2014, after discarding observations for which no initial rate

information is available. Crucially, PSD contains no information on the reset or standard

variable rate.

We merge the PSD with a secondary data source, Moneyfacts, which records the reset

rates on all products at a given point in time. We also merge in product fees, and the

minimum and maximum loan size available for each product. Most loans have no minimum

loan requirement, and a maximum loan requirement of £1 million. We match the two

data sources together using a matching algorithm that uses the institutional name, product

type, initial rate, fixation period, and whether the purchase date fell during the period the

mortgage product was on the market. Not all this information is populated in the data,

but we are able to successfully match 73% or 2.6 million observations out of the 3.5 million.

Table A2 shows the resulting dataset broken down by year and product type.

The most popular products are the 2, 3 and 5 year fixed-rate mortgages (FRMs) and the

2 year adjustable-rate mortgage (ARM). So we focus on only these 4 types of mortgages,

and drop the roughly 475,000 other bespoke mortgages that would be difficult to compare

and may not be available at many lenders.27 We further drop observations that have missing

data or are outliers. These screens exclude cases with very small loan values, very high

loan-to-income values or if income or loan values are missing. The sample characteristics

27The ‘OTHER’ category contains less popular products such as ARMs with different initial period durations and ARMs with an upper cap on the initial rate.

A1
after these filters are applied are shown in Table A3.

Although there are many small mortgage lenders, most of the mortgage market is dominated by a handful of top players. Given our interest in contrasting choices within the set of

loans made by an individual lender, we want enough loans every month to make meaningful

comparisons. We eliminate peripheral lenders, and focus on six large mortgage providers

that provide the most loans in our sample. Table A4 shows the loan characteristics of those

six lenders. Focusing on these lenders only shifts the sample from just over 2 million loans

to just under 1.6 million loans.

We further restrict our sample to households with loan amounts less than £1 million,

who have a loan-to-value ratio between 65% - 95% LTV, and who have at least 5 products

to choose from at their bank. This reduces the sample to one shown in Table A5. Around

a third of our sample are taking out a mortgage for the first time. The 2yr FRM is by far

the most popular product in our sample period, representing over half the sample.28 For

the across-bank analysis we restrict our sample to customers with at least 25 products to

choose from if they choose a 2yr FRM, and 15 if they chose the other products, reflecting

the larger menus for the 2yr FRM. Our final samples are 894,901 observations for analysis

within banks, and 883,459 observations for analysis across banks.

A2 Calculations

To compare mortgage options, we first specify the set of all possible mortgages a household

could have chosen from. We do this by creating a dataset with unique product-level observations, arranged by month, six LTV buckets (65%, 70%, 75%, 80%, 85%, 90% and 95%),

the minimum and maximum loan sizes allowable, and the product type. Our final sample of

mortgage choices is then compared against with eligible product.

The observed date in the PSD is the completion date on the house purchase. For the

majority of homes, a mortgage is usually agreed 3-5 months in advance of the sale. The

initial rates and fees associated with these completion dates will therefore be from offers

available 3 to 5 months ago. But the observed reset rate will be at the time of completion,

28After the sample ended, as mortgage rates fell further and the yield curve became extremely flat, many

borrowers shifted to 5 year fixed rate mortgages. So currently that mortgage is the most common.

A2
not at the time of the mortgage offer. We therefore lag the reset rate by 4 months to ensure

it coincides with the quoted SVR at the time of the mortgage offer, instead of the time of

closing.

Second, we compare each chosen mortgage against mortgage products of the same product

type and available in the same month. We also only compare products where the actual loan

size does not exceed the maximum loan amount, nor fall short of the minimum; and the

actual LTV is in the same bucket as the maximum allowable LTV on that product. For

example, a customer with a LTV of 82% will have a choice set consisting of mortgages where

the max LTV is 85%.29

Finally, we use each product’s initial rate, reset rate, fee and initial promotional period,

together with the borrower’s loan size, to compute the net present value (NPV) of each

mortgage in a borrower’s choice set. To compute the NPV we first compute the borrower’s

monthly payments during the initial promotional period:

(1 + i)Q IP = PT 1 + (1 + ri )−j

j=1 12

where Q is the loan amount, T is the mortgage term and ri is the initial interest rate.

The borrower’s monthly payments after the initial period are given by:

Qe RP = PTe 1 + (1 + rr )−j

j=1 12

where Te = T −TF is the mortgage term T minus the initial period TF , Qe is the loan balance

remaining at the end of the initial period, rr is the reset rate and

29We use the lowest LTV that is actually available for which the borrower qualifies. So in the example,

if the customer’s bank does not offer a product with an LTV of 85% but does have one at 90%, we will

use the 90% loan terms in forming the menu. For the across-bank analysis, if in this case other banks do

offer products with an LTV of 85% then the menu will be based on the set of loans with an LTV of 85%,

supplemented with the 90% LTV loan the customer chose.

A3
Qe = Q − (IP − ri

Q)TF 12

We then compute the net present value as the discounted sum of repayments over the

first 7 years of the contract:

X

TF X

84 IP RP NP V = fee + + (1 + i)t (1 + i)t

t=1 t=TF +1

where i is the seven year LIBOR rate.

A3 Robustness

A3.1 Refinancing decision

In this section we demonstrate that our key results are robust to alternative assumptions

regarding when customers choose to refinance their mortgage. In our main analysis we

assume customers refinance their mortgage after 7 years. In this section we instead assume

that customers refinance immediately after the initial period of their mortgage ends, after 2, 3

or 5 years depending on their choice of mortgage product. Under this alternative assumption,

customers can only make expensive choices due to their choice of initial rate or fee, as the

reset rate no longer affects the computed cost of a mortgage. This robustness exercise checks

that our findings are not the result of assuming customers switch onto expensive reset rates

when in reality they might refinance.

Figures A1 to A5 and Tables A6 to A8 replicate the key charts and tables of the paper

under this alternative assumption. The paper’s key results are unchanged. In particular,

customers’ expensive choices are driven by the price dispersion in the menu they are offered.

Customers borrowing large amounts relative to the value of their house and/or their income

are more likely to make expensive choices, and this is driven by the menus they are given by

banks.

Across banks, restricting the cost calculation to the initial period reduces both the prevalence of expensive choices and the price dispersion in mortgage menus as captured by the size

A4
of the bad tail (Figures A2 and A3). Expensive choices and price dispersion within banks

are largely unchanged, as there is typically only one reset rate available at a given bank.

The correlation between the quality of the choice set and the frequency of expensive choices

in a given month (Figure A4) remains strong.

Figure A5 shows the pattern of tail size by LTV and LTI for the alternative assumption

about refinancing. The relationship is the same as in the main analysis: customers borrowing

large amounts relative to the value of their home and/or their income receive menus with

significantly more bad choices. Table A6 shows that expensive choices continue to be driven

primarily by the choice of initial rate under this alternative assumption about refinancing.

Table A7 shows that these customers continue to be more prone to make expensive choices,

and that this is largely driven by the quality of the menu that they were offered. As in

the main analysis, this means young people and first-time buyers are more likely to make

expensive choices, and this is largely driven by the quality of the choice set they receive

(Table A8).

A3.2 Strongly dominated mortgages

Throughout the main text we have emphasized that our results are robust to an alternative

ranking of mortgages according to the criteria of strict dominance. Table A9 sets out how

strongly dominated choices depend on a borrower’s choice of initial rate, fee and reset rate.

The choice of initial rate is the key driver of strongly dominated choices within banks, whilst

across banks all price dimensions play a role.

A3.3 Choice set construction

We demonstrate that our key results are robust to the way we construct customers’ choice

sets. To define a customer’s choice set we first identify all mortgages that were on offer

when they were shopping around, with the same initial period as the mortgage they chose,

and that were available for the amount they borrowed. In our main results we then further

restrict the choice set to those mortgages with the lowest LTV band for which the customer

qualifies. We do so on the basis that this is the relevant menu for most customers, on the

basis that over 90% of customers choose a mortgage with the lowest LTV band for which

they qualify. In this section we replicate our results without this further restriction on choice

A5
sets, so that the menu of a customer with an 85% LTV includes mortgages with a maximum

LTV of 85%, but also mortgages with maximum LTVs greater than 85%.30

Figures A6 to A9 and Tables A10 to A12 replicate the key charts and tables of the paper

with these alternative choice sets. The size of the choice sets and the tails increases, and

the fact that low-LTV mortgages cost less mechanically introduces a negative correlation

between LTV and the size of the tail. However, the key results - the positive correlation

between expensive choices and price dispersion and the fact that high-LTV and high-LTI

customers are more likely to make expensive choices - are unchanged. This is consistent with

the main messages of our paper: customers borrowing large amounts relative to their house

value and/or their income are more likely to make expensive choices, and this is driven by the

menus that they are given by banks. The relevant menu to consider is the set of mortgages

with the lowest LTV for which a customer qualifies, as this is what drives expensive choices.

The alternative choice set significantly increases the number of options in a customer’s

menu (Table A10). The percentile chosen decreases, which is as one would expect given most

customers choose the lowest LTV option available, and the high LTV options we’ve added to

the choice set will generally be more expensive. The likelihoods of expensive choices within

and across banks are 3% and 5% respectively, versus 2% and 7% in the main results. The

pattern of expensive choices through time is similar to that in the main results (Figure A6).

Expanding the choice set significantly increases the size of the bad tail (Figure A7). The

average bad tail is now 8% within bank and 13% across banks, versus 2% and 8% in the

main results. This is exactly as one would expect - the reason customers tend to pick low

LTV products is that they come at a lower price, so expanding the choice set to include

products with higher LTVs will increase price dispersion. Nonetheless, there remains a

positive correlation between the quality of the choice set in a given month and frequency of

expensive choices in a given month (Figure A8). The relationship within banks is somewhat

weaker than in the main results. This is to be expected, as expanding the choice set to

include a set of choices people rarely pick is likely to reduce the predictive power of price

dispersion for expensive choices.

30Note that as we’re changing the size of the choice set, the baseline mortgage (the 15th percentile) relative

to which we measure mortgage cost will also change.

A6
Figure A9 shows the pattern of tail size by LTV and LTI for the alternative choice sets.

The relationship between LTI and the size of the tail is the same as in the main results -

higher-LTI customers receive menus with greater price dispersion. High-LTV customers now

receive menus with lower price dispersion. This is to be expected and is largely mechanical

- with the alternative choice sets low-LTV customers qualify for all mortgages with high

LTVs. Given these are typically highly priced, low-LTV customers’ menus have greater

price dispersion - though most of them would have ignored these more expensive options.

Table A11 shows how results vary by LTV and LTI. As in the main results, customers

borrowing a large amount relative to their house value and/or income are significantly more

likely to make expensive choices, both within and across banks (first and third columns of

Table 12). This means that the fact low-LTV borrowers have a large tail of expensive highLTV products does not translate into an increased likelihood of making expensive choices.

This would suggest that our decision to focus only on the menu of contracts at the lowest

possible LTV band was a good one, as the shape of the choice set for higher LTVs is not a big

driver of expensive choices. When we control for the size of the tail in columns two and four,

the marginal effects of LTV and LTI are diminished, but not as dramatically as in our main

results in Table 12. This again suggests that the menu variation that drives expensive choices

is generally variation for mortgages with the lowest LTV for which a customer qualifies.

Table A12 shows the likelihood of expensive choices by demographic. The results are

similar to the main results in Table 13 - young people and first-time buyers are slightly more

likely to make expensive choices, and this is largely driven by the quality of the choice set

they receive.

A7
Table A1: Raw data

Year Number of observations Percentage of sample

2009 580,431 16

2010 531,273 15

2011 592,492 17

2012 587,846 17

2013 602,417 17

2014 639,672 18

Total 3,534,131 100

Note: This table summarizes the raw Product Sales Data.

Table A2: After merging with Moneyfacts

Year Observations % Product Observations %

2009 345,746 13 2yr FRM 1,054,041 41

2010 335,026 13 3yr FRM 348,091 13

2011 417,292 16 5yr FRM 420,955 16

2012 455,004 18 2yr ARM 218,991 8

2013 493,116 19 SVR 76,360 3

2014 548,003 21 OTHER 475,749 18

Total 2,594,187 100 Total 2,594,187 100

Note: This table summarizes the dataset after merging the Product Sales Data with the Moneyfacts data.

Table A3: Top 4 products after dropping outliers and missing data

FTB % NFTB % Total %

By Year

2009 64,862 11 177,858 12 242,720 12

2010 67,919 12 170,258 12 238,177 12

2011 85,259 15 219,120 15 304,379 15

2012 99,669 17 244,459 17 344,128 17

2013 121,579 21 294,940 20 416,519 21

2014 145,884 25 339,921 23 485,805 24

By Product

2yr ARM 45,622 8 171,901 12 217,523 11

2yr FRM 305,803 52 742,986 51 1,048,789 52

3yr FRM 125,472 21 221,110 15 346,582 17

5yr FRM 108,275 19 310,559 21 418,834 21

Total 585,172 100 1,446,556 100 2,031,728 100

Note: This table summarizes the dataset after removing outliers and missing data, and retaining only 2

year, 3 year and 5 year fixed rate as well as 2 year adjustable rate mortgages from the merged

PSD-Moneyfacts dataset.

A8
Table A4: After restricting to 6 lenders

FTB % NFTB % Total %

By Year

2009 54,622 12 136,351 12 190,973 12

2010 54,021 12 136,572 12 190,593 12

2011 68,686 15 181,616 16 250,302 16

2012 79,541 17 194,573 17 274,114 17

2013 99,229 21 224,330 20 323,559 20

2014 110,424 24 245,080 22 355,504 22

By Product

2yr ARM 38,356 8 131,993 12 170,349 11

2yr FRM 257,836 55 598,572 54 856,408 54

3yr FRM 88,986 19 161,636 14 250,622 16

5yr FRM 81,345 17 226,321 20 307,666 19

Total 466,523 100 1,118,522 100 1,585,045 100

Note: This table summarizes the dataset after removing outliers and missing data, and retaining only 2

year, 3 year and 5 year fixed rate as well as 2 year adjustable rate mortgages from the merged

PSD-Moneyfacts dataset, but restricting to the six lenders in our final sample.

Table A5: Final dataset

FTB % NFTB % Total %

By Year

2009 39,150 11 52,901 10 92,051 10

2010 38,320 11 56,431 11 94,751 11

2011 54,158 15 88,335 17 142,493 16

2012 62,790 17 95,770 18 158,560 18

2013 79,946 22 115,750 22 195,696 22

2014 86,576 24 124,774 23 211,350 24

By Product

2yr ARM 26,259 7 60,729 11 86,988 10

2yr FRM 209,171 58 310,905 58 520,076 58

3yr FRM 70,032 19 80,995 15 151,027 17

5yr FRM 55,478 15 81,332 15 136,810 15

Total (within) 360,940 100 533,961 100 894,901 100

Total (across) 357,044 100 526,415 100 883,459 100

Note: This table summarizes our final dataset after restricting the sample to mortgages with LTV between

65% and 95% of value less than £1mn, and removing any borrowers who had fewer than 5 options to

choose from at their bank. For across-bank analysis we further remove any customers who had fewer than

15 options across banks or who selected a 2yr FRM and had fewer than 25 options.

A9
Table A6: Probit regressions of expensive choice on choices of price components: robustness

to refinancing

Low Fee

Expensive choice within

(1)

−0.002∗∗∗

(0.0001)

Expensive choice across

(2)

−0.006∗∗∗

(0.0002)

High Fee −0.001∗∗∗

(0.0001)

0.002∗∗∗

(0.0002)

Low Inital Rate −0.018∗∗∗

(0.0004)

−0.036∗∗∗

(0.0001)

High Initial Rate 0.020∗∗∗

(0.0002)

0.046∗∗∗

(0.001)

Bad tail 0.094∗∗∗

(0.001)

0.088∗∗∗

(0.001)

Bank dummies

Product dummies

Pseudo R-squared

Mean dependent variable

Observations

Yes

Yes

0.85

0.019

894,901

No

Yes

0.86

0.036

883,459

Note: This table reports average marginal effects from probit regressions with the dependent variables

shown at the top of each column. We take each unique value of the three elements of a mortgage - the

initial rate, the fee and the reset rate - in a customer’s choice set and order them from low to high. Choices

above the 85th percentile are high, whilst choices below the 15th percentile are low. An expensive choice is

defined as a choice that costs a customer at least 2.5% of their income more than the 15th percentile in

their choice set. The variable bad tail measures the percentage of a customer’s choice set that would

represent an expensive choice. The first column takes the choice set to consist only of mortgages on offer at

the bank that granted the customer their mortgage, whilst the second includes all banks. Dummy variables

are defined as in Table 4. *, ** and *** indicate that the coefficient is different from zero at the 10, 5 and 1

percent level of significance respectively. In contrast to the main analysis, the cost of a mortgage is

computed assuming all customers refinance their mortgage after its initial period ends.

A10
Table A7: Expensive choices and loan characteristics: robustness to refinancing

Expensive choice within Expensive choice across

High LTV & High LTI

(1)

0.094∗∗∗

(0.002)

(2)

0.001∗∗

(0.0003)

(3)

0.112∗∗∗

(0.002)

(4)

0.001∗∗∗

(0.0003)

High LTV & Low LTI 0.043∗∗∗

(0.001)

0.002∗∗∗

(0.0002)

0.060∗∗∗

(0.001)

−0.0001

(0.0002)

Low LTV & High LTI 0.020∗∗∗

(0.001)

0.003∗∗∗

(0.0003)

0.024∗∗∗

(0.001)

0.004∗∗∗

(0.0003)

Bad tail 0.086∗∗∗

(0.001)

0.096∗∗∗

(0.001)

Bank dummies

Product dummies

Pseudo R-squared

Mean dependent variable

Observations

Yes

Yes

0.43

0.019

894,901

Yes

Yes

0.74

0.019

894,901

No

Yes

0.3

0.036

883,459

No

Yes

0.78

0.036

883,459

Note: This table reports average partial effects from probit regressions with the dependent variables shown

at the top of each column. High (low) LTI customers have loan-to-income above (below) 4. High (low)

LTV customers have loan-to-value above (below) 85%. The bad tail measures the percentage of a

customer’s choice set that would represent an expensive choice, where expensive choices are defined in

Section 3. The first 2 columns take the choice set to consist only of mortgages on offer at the bank that

granted the customer their mortgage, whilst the final 2 columns include all banks. Dummy variables are

defined as in Table 4. *, ** and *** indicate that the coefficient is different from zero at the 10, 5 and 1

percent level of significance respectively. In contrast to the main analysis, the cost of a mortgage is

computed assuming all customers refinance their mortgage after its initial period ends.

A11
Table A8: Expensive choices and borrower characteristics: robustness to refinancing

Young

Expensive choice within

(1) (2)

0.006∗∗∗ 0.001∗∗∗

(0.0003) (0.0002)

Expensive choice across

(3) (4)

0.012∗∗∗ 0.003∗∗∗

(0.0005) (0.0002)

Old −0.008∗∗∗

(0.0004)

−0.0005∗

(0.0003)

−0.015∗∗∗

(0.001)

0.0002

(0.0004)

First-time buyer 0.006∗∗∗

(0.0003)

0.001∗∗∗

(0.0002)

0.007∗∗∗

(0.0004)

−0.001∗∗∗

(0.0002)

Poor 0.001

(0.0003)

0.001∗∗∗

(0.0002)

0.001∗∗

(0.0005)

0.002∗∗∗

(0.0002)

Rich 0.0002

(0.0003)

−0.001∗∗∗

(0.0002)

−0.002∗∗∗

(0.0004)

−0.002∗∗∗

(0.0002)

Bad tail 0.087∗∗∗

(0.001)

0.095∗∗∗

(0.001)

Bank dummies

Product dummies

Pseudo R-squared

Mean dependent variable

Observations

Yes

Yes

0.36

0.019

894,901

Yes

Yes

0.74

0.019

894,901

No

Yes

0.24

0.036

883,459

No

Yes

0.78

0.036

883,459

Note: This table reports average partial effects from probit regressions with the dependent variables shown

at the top of each column. Young customers are under 30. Old customers are over 45. Poor customers have

net income in the lower tertile whilst rich customers have net income in the upper tertile. The bad tail

measures the percentage of a customer’s choice set that would represent an expensive choice, where

expensive choices are defined in Section 3. The first 2 columns take the choice set to consist only of

mortgages on offer at the bank that granted the customer their mortgage, whilst the final 2 columns include

all banks. Dummy variables are defined as in Table 4. *, ** and *** indicate that the coefficient is different

from zero at the 10, 5 and 1 percent level of significance respectively. In contrast to the main analysis, the

cost of a mortgage is computed assuming all customers refinance their mortgage after its initial period ends.

A12
Table A9: Probit regressions of strongly dominated choice on choices of price components

Low Fee

Strongly dominated choice within

(1)

−0.028∗∗∗

(0.001)

Strongly dominated choice across

(2)

−0.140∗∗∗

(0.001)

High Fee 0.017∗∗∗

(0.001)

0.155∗∗∗

(0.002)

Low Inital Rate −0.030∗∗∗

(0.001)

−0.101∗∗∗

(0.001)

High Initial Rate 0.064∗∗∗

(0.001)

0.130∗∗∗

(0.002)

Low Reset Rate −0.069∗∗∗

(0.001)

−0.091∗∗∗

(0.001)

High Reset Rate −0.004∗∗∗

(0.001)

0.185∗∗∗

(0.001)

Strongly dominated tail 0.384∗∗∗

(0.001)

0.868∗∗∗

(0.002)

Bank dummies

Product dummies

Pseudo R-squared

Mean dependent variable

Observations

Yes

Yes

0.42

0.081

894,901

No

Yes

0.25

0.277

883,459

Note: This table reports average marginal effects from probit regressions with the dependent variables

shown at the top of each column. We take each unique value of the three elements of a mortgage - the

initial rate, the fee and the reset rate - in a customer’s choice set and order them from low to high. Choices

above the 85th percentile are high, whilst choices below the 15th percentile are low. The strongly dominated

tail computes the percentage of a customer’s choice set that is strongly dominated by the cheapest option

in their choice set, where strong domination is defined in Section 3. The first column takes the choice set to

consist only of mortgages on offer at the bank that granted the customer their mortgage, whilst the second

includes all banks. Dummy variables are defined as in Table 4. *, ** and *** indicate that the coefficient is

different from zero at the 10, 5 and 1 percent level of significance respectively.

A13
Table A10: Characteristics of choice sets and choices made: alternative choice set

Within Across

Choice set size Pctile chosen Choice set size Pctile chosen

25th pctile 22 16 107 15

Median 37 30 176 30

75th pctile 56 50 272 53

Note: This table summarizes customers’ choice sets and choices made. Within-bank figures restrict a

customer’s choice set to mortgages on offer at the bank that granted their mortgage. Across-bank figures

include mortgages on offer at all banks in a customer’s choice set. For each customer we rank the

mortgages in their comparison set from cheapest to most expensive. The Pctile chosen equals the rank of

their choice as a percentage of the number of mortgages in their comparison set. The table summarizes the

distribution of this variable and the size of the choice set across the sample. In contrast to the main

analysis, a customer’s choice set is taken to include mortgages with maximum LTV greater than or equal to

the customer’s LTV.

Table A11: Expensive choices by loan characteristic: alternative choice set

High LTV & High LTI

Expensive choice within

(1) (2)

0.065∗∗∗ 0.014∗∗∗

(0.001) (0.001)

Expensive choice across

(3) (4)

0.207∗∗∗ 0.040∗∗∗

(0.002) (0.001)

High LTV & Low LTI 0.031∗∗∗

(0.001)

0.020∗∗∗

(0.0005)

0.063∗∗∗

(0.001)

0.031∗∗∗

(0.001)

Low LTV & High LTI 0.020∗∗∗

(0.001)

−0.004∗∗∗

(0.0002)

0.053∗∗∗

(0.001)

−0.016∗∗∗

(0.0004)

Bad tail 0.140∗∗∗

(0.001)

0.295∗∗∗

(0.001)

Bank dummies

Product dummies

Pseudo R-squared

Mean dependent variable

Observations

Yes

Yes

0.43

0.032

930,849

Yes

Yes

0.74

0.032

930,849

No

Yes

0.14

0.046

927,860

No

Yes

0.48

0.046

927,860

Note: This table reports average partial effects from probit regressions with the dependent variables shown

at the top of each column. High (low) LTI customers have loan-to-income above (below) 4. High (low)

LTV customers have loan-to-value above (below) 85%. The bad tail measures the percentage of a

customer’s choice set that would represent an expensive choice, where expensive choices are defined in

Section 3. The first 2 columns take the choice set to consist only of mortgages on offer at the bank that

granted the customer their mortgage, whilst the final 2 columns include all banks. Dummy variables are

defined as in Table 4. *, ** and *** indicate that the coefficient is different from zero at the 10, 5 and 1

percent level of significance respectively. In contrast to the main analysis, a customer’s choice set is taken

to include mortgages with maximum LTV greater than or equal to the customer’s LTV.

A14
Table A12: Expensive choices by demographic: alternative choice set

Young

Expensive choice within

(1) (2)

0.006∗∗∗ 0.003∗∗∗

(0.0004) (0.0003)

Expensive choice across

(3) (4)

0.016∗∗∗ 0.010∗∗∗

(0.001) (0.0004)

Old −0.005∗∗∗

(0.0005)

0.00002

(0.0004)

−0.023∗∗∗

(0.001)

−0.005∗∗∗

(0.001)

First-time buyer 0.003∗∗∗

(0.0004)

0.004∗∗∗

(0.0003)

0.005∗∗∗

(0.0005)

0.002∗∗∗

(0.0004)

Poor 0.004∗∗∗

(0.0004)

−0.001∗∗∗

(0.0003)

0.001∗

(0.001)

−0.004∗∗∗

(0.0004)

Rich −0.002∗∗∗

(0.0004)

0.0003

(0.0003)

−0.004∗∗∗

(0.0005)

−0.003∗∗∗

(0.0004)

Bad tail 0.150∗∗∗

(0.001)

0.313∗∗∗

(0.001)

Bank dummies

Product dummies

Pseudo R-squared

Mean dependent variable

Observations

Yes

Yes

0.41

0.032

930,849

Yes

Yes

0.72

0.032

930,849

No

Yes

0.09

0.046

927,860

No

Yes

0.45

0.046

927,860

Note: This table reports average partial effects from probit regressions with the dependent variables shown

at the top of each column. Young customers are under 30. Old customers are over 45. Poor customers have

net income in the lower tertile whilst rich customers have net income in the upper tertile. The bad tail

measures the percentage of a customer’s choice set that would represent an expensive choice, where

expensive choices are defined in Section 3. The first 2 columns take the choice set to consist only of

mortgages on offer at the bank that granted the customer their mortgage, whilst the final 2 columns

include all banks. Dummy variables are defined as in Table 4. *, ** and *** indicate that the coefficient is

different from zero at the 10, 5 and 1 percent level of significance respectively. In contrast to the main

analysis, a customer’s choice set is taken to include mortgages with maximum LTV greater than or equal to

the customer’s LTV.

A15
89%

0.00

0.25

0.50

0.75

1.00

−4 −3 −2 −1 0 1

Cost saving vs reference (% income)

Cumulative distribution

85%

0.00

0.25

0.50

0.75

1.00

−4 −3 −2 −1 0 1

Cost saving vs reference (% income)

Cumulative distribution

(a) Within each bank’s choice set (b) Across all banks’ choice sets

Figure A1: Cost savings of chosen mortgage vs reference (% of net income): robustness to

refinancing

Note: These figures plot the distribution of the amount a customer saves relative to a reference mortgage

as a percentage of their income, at the bank where the customer shopped and across banks. We first

compute the present value of the mortgage that a customer chooses using equation (1), subtract it from the

cost of the 15th percentile mortgage in a customer’s choice set (where mortgages are ordered from cheapest

to most expensive), and divide by the customer’s net income. The figures plot the cumulative distribution

of this figure across all customers, where the choice set consists only of mortgages on offer at the bank that

gave them their mortgage (left) and of mortgages on offer across banks (right). The shaded areas show the

fraction of each sample that fall between savings of 0.5% of net income and a cost of 1% of net income.

The dotted lines show where the choice costs more than 2.5% of net income relative to the reference

mortgage. In contrast to the main analysis, the cost of a mortgage is computed assuming all customers

refinance their mortgage after its initial period ends.

A16
0

5

10

15

20

2009 2010 2011 2012 2013 2014 2015

Probability expensive choice (%)

Across Within

Figure A2: Expensive choices through time: robustness to refinancing

Note: This figure plots the percentage of customers that make expensive choices each month, at the bank

where they shopped and across banks. Expensive choices are defined in Section 3. The blue line takes the

choice set to consist only of mortgages on offer at the bank that gave the customer their mortgage, and the

red line includes mortgages on offer across all banks. The horizontal lines plot the means over the sample

period for each comparison. In contrast to the main analysis, the cost of a mortgage is computed assuming

all customers refinance their mortgage after its initial period ends.

0.0

2.5

5.0

7.5

2009 2010 2011 2012 2013 2014 2015

Bad tail (%)

75th pctile 90th pctile Median

0

20

40

60

80

2009 2010 2011 2012 2013 2014 2015

Bad tail (%)

75th pctile 90th pctile Median

(a) Within each bank’s choice set (b) Across all banks’ choice sets

Figure A3: Bad tail through time: robustness to refinancing

Note: These figures summarize the distribution of the fraction of a customer’s choice set that would

represent an expensive choice if chosen, both within and across banks, through time. The bad tail

computes the fraction of the mortgages in a customer’s choice set that would represent an expensive choice,

where an expensive choice is defined in Section 3. The figures plot percentiles of the distribution of bad tail

through time for the median (green) 75th percentile (red) and 90th percentile (blue), where the choice set

consists only of mortgages on offer at the bank that gave the customer their mortgage (left panel) and of

mortgages on offer across banks (right panel). The horizontal lines plot the means over the sample period.

In contrast to the main analysis, the cost of a mortgage is computed assuming all customers refinance their

mortgage after its initial period ends.

A17
A18

6 

4

2 

0 

1 2 3 4

P(expensive choice)P(expensive chioce) 25

20

15

10

5 

0 

0 5 10 15 20

Bad tail (%)

●●●●●●●● ●●●●●●

Bad tail (%)

(a) Within each bank’s choice set (b) Across all banks’ choice sets

Figure A4: Expensive choices and bad tails: robustness to refinancing

Note: These figures summarize the relationship between the frequency of expensive choices and the average 

quality of customers’ choice sets in a given month. The bad tail computes the fraction of the mortgages in 

a customer’s choice set that would represent an expensive choice, where an expensive choice is defined in 

Section 3. The figures plot the percentage of customers that make expensive choices in a month against the 

average size of bad tail in that month, where the choice set consists only of mortgages on offer at the bank 

that gave the customer their mortgage (left panel) and of mortgages on offer across banks (right panel).

The blue line shows a linear regression of the probability of an expensive choice on the size of bad tail, with 

equation displayed in each panel. The shaded area represents the 95% confidence interval. In contrast to 

the main analysis, the cost of a mortgage is computed assuming all customers refinance their mortgage 

after its initial period ends.
Low LTI Low LTI High LTI High LTI

Low LTV High LTV Low LTV High LTV

(53%) (26%) (15%) (5%)

0

2

4

6

Bad tail (%)

Figure A5: Bad tail by loan-to-value and loan-to-income ratio: robustness to refinancing

Note: This figure summarizes the average quality of customers’ choice sets by combinations of their LTV

and LTI ratios. The bad tail computes the fraction of the mortgages in a customer’s choice set that would

represent an expensive choice, where expensive choices are defined in Section 3. This figure plots the

average of bad tail according to a customer’s LTV and LTI. High LTV is defined as LTV> 85%, and low

LTV as LTV< 85%. High LTI is defined as LTI> 4, and low LTI as LTI< 4. The numbers in parentheses

are the percentages of the sample in each bin. In contrast to the main analysis, the cost of a mortgage is

computed assuming all customers refinance their mortgage after its initial period ends.

0

10

20

30

2009 2010 2011 2012 2013 2014 2015

Probability expensive choice (%)

Across Within

Figure A6: Expensive choices through time: alternative choice set

Note: This figure plots the percentage of customers that make expensive choices each month, at the bank

where they shopped and across banks. Expensive choices are defined in Section 3. The blue line takes the

choice set to consist only of mortgages on offer at the bank that gave the customer their mortgage, and the

red line includes mortgages on offer across all banks. The horizontal lines plot the means over the sample

period for each comparison. In contrast to the main analysis, a customer’s choice set is taken to include

mortgages with maximum loan-to-value greater than or equal to the customer’s loan-to-value.

A19
0

10

20

30

40

2009 2010 2011 2012 2013 2014 2015

Bad tail (%)

75th pctile 90th pctile Median

0

20

40

60

80

2009 2010 2011 2012 2013 2014 2015

Bad tail (%)

75th pctile 90th pctile Median

(a) Within each bank’s choice set (b) Across all banks’ choice sets

Figure A7: Bad tail through time: alternative choice set

Note: These figures summarize the distribution of the fraction of a customer’s choice set that would

represent an expensive choice if chosen, both within and across banks, through time. The bad tail

computes the fraction of the mortgages in a customer’s choice set that would represent an expensive choice,

where an expensive choice is defined in Section 3. The figures plot percentiles of the distribution of bad tail

through time for the median (green) 75th percentile (red) and 90th percentile (blue), where the choice set

consists only of mortgages on offer at the bank that gave the customer their mortgage (left panel) and of

mortgages on offer across banks (right panel). The horizontal lines plot the means over the sample period

In contrast to the main analysis, a customer’s choice set is taken to include mortgages with maximum LTV

greater than or equal to the customer’s LTV.

A20
A21

7.5

5.0

2.5

0.0

6 8 10

Bad tail (%)

30

20

10

0 

10 15 20 25 30

P(expensive choice)

(a) Within each bank’s choice set

Bad tail (%)

(b) Across all banks’ choice sets

Figure A8: Expensive choices and bad tails: alternative choice set

Note: These figures summarize the relationship between the frequency of expensive choices and the average 

quality of customers’ choice sets in a given month. The bad tail computes the fraction of the mortgages in 

a customer’s choice set that would represent an expensive choice, where an expensive choice is defined in 

Section 3. The figures plot the percentage of customers that make expensive choices in a month against the 

average size of bad tail in that month, where the choice set consists only of mortgages on offer at the bank 

that gave the customer their mortgage (left panel) and of mortgages on offer across banks (right panel).

The blue line shows a linear regression of the probability of an expensive choice on the size of bad tail, with 

equation displayed in each panel. The shaded area represents the 95% confidence interval. In contrast to 

the main analysis, a customer’s choice set is taken to include mortgages with maximum LTV greater than 

or equal to the customer’s LTV. P(expensive choice)
Low LTI Low LTI High LTI High LTI

Low LTV High LTV Low LTV High LTV

(54%) (26%) (15%) (5%)

0

5

10

15

20

Bad tail (%)

Figure A9: Bad tail by loan-to-value and loan-to-income ratio: alternative choice set

Note: This figure summarizes the average quality of customers’ choice sets by combinations of their LTV

and LTI ratios. The bad tail computes the fraction of the mortgages in a customer’s choice set that would

represent an expensive choice, where expensive choices are defined in Section 3. This figure plots the

average of bad tail according to a customer’s LTV and LTI. High LTV is defined as LTV> 85%, and low

LTV as LTV< 85%. High LTI is defined as LTI> 4, and low LTI as LTI< 4. The numbers in parentheses

are the percentages of the sample in each bin. In contrast to the main analysis, a customer’s choice set is

taken to include mortgages with maximum LTV greater than or equal to the customer’s LTV.

A22