Reverse mortgages and financial literacy

ARTICLE

Ismael Choinière-Crèvecoeur

and Pierre-Carl Michaud

ESG UQAM, Montreal, Canada and

HEC, CIRANO and NBER, Montreal, Canada

Corresponding author. Pierre-Carl Michaud; Email: [email protected]

(Received 15 November 2022; revised24 February2023;accepted 25February2023; first published online24May 2023)

Abstract

Few retirees use reverse mortgages. In this paper, we investigate how financial literacy and prior

knowledge of the product influence take-up by conducting a stated-preference experiment. We exog-

enously manipulate characteristics of reverse mortgages to tease out how consumers value them and

investigate differences by financial literacy and prior knowledge of reverse mortgages. We find that

those with higher financial knowledge are more likely to know about reverse mortgages, not more

likely to purchase them at any cost but are more sensitive to the interest rate and the insurance

value of these products in terms of the non-negative equity guarantee.

Keywords: reverse mortgages; savings; retirement planning; insurance

JEL Codes: G53; G21; R21

1 Introduction

Much attention in the financial literacy literature has been directed to the accumulation

phase of the life cycle (Lusardi and Mitchell, 2014). Financial knowledge is associated with

retirement planning and better outcomes in terms of savings. Much less attention has been

devoted to the role of financial literacy for decumulation decisions and outcomes.

Decumulation is a hard problem as it involves making decisions as to how to spend down

savings, insure against risks, and manage illiquid assets such as housing.

In fact, housing is a major component of household wealth in retirement. The primary

residence accounts for approximately 33% of the median wealth accumulated by Canadian

households

. In retirement, owning a house may provide an important service flow. In

addition, home equity may act as an insurance policy against financial risks due to disabil-

ity risk, since the house is typically sold when individuals enter a nursing home (Davidoff,

2009). Given that housing is to some extent illiquid (at least at the intensive margin), many

households are house-rich and cash-poor, which limits their capacity of extracting home

equity to smooth consumption in retirement.

Borrowing against home equity is feasible using two different products. For those who

qualify, home equity lines of credit (HELOCs) allow borrowing against equity. HELOCs are

quite popular among near-retirees in Canada. Bedard and Michaud (2021) report that

17.9% of Canadians aged 62–66 years have a positive HELOCs balance compared with

4% in the United States. Americans are much more likely to have a mortgage at these ages.

However, borrowing using a HELOC exposes owners to the risk that the loan accumulated

Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, dis-

tribution and reproduction, provided the original article is properly cited.

https://www150.statcan.gc.ca/n1/daily-quotidien/171207/dq171207b-eng.htm

Journal of Financial Literacy and Wellbeing (2023), 1,79–102

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will end up being greater than the value of the house. Minimum payments need to be made

in retirement. Furthermore, qualification for these loans is restricted among the elderly

because of more stringent income testing.

Reverse mortgages (RMR) have emerged as an alternative solution. A reverse mortgage

is a financial product that allows a homeowner to convert a portion of the current equity

of their principal residence into cash. Unlike many other mortgage products, the borrower

is not obligated to make payments before moving out, selling, or dying. In addition, the

borrower is insured against the risk that the loan is worth more than the house when it is

sold. This is called the non-negative equity guarantee (NNEG) of the reverse mortgage. This

feature means that the borrower ’s longevity risk, as well as the risk of a decline in house

prices, is transferred to the lender. Given that the guarantee is costly, a reverse mortgage

will typically command a higher interest rate. In 2017, the rate on a reverse mortgage was

roughly 2 percentage point higher than that of a HELOC.

Yet, the market for reverse mortgage purchases is small in many countries. In 2014,

only 2.11% of Canadian households reported planning to obtain a reverse mortgage as

a source of income upon retirement (Statistics Canada, 2014). Nakajima and Telyukova

(2017) report similar low figures for the United States. Financial literacy may play an

important role. The valuation of these products for consumers is complicated. While

the consumption smoothing value will be intuitive to many, the distinct feature of reverse

mortgages, the insurance value of the NNEG, is likely more difficult to grasp and compute.

It involves projecting house prices in the future, survival risk, and other considerations

such as when one expects to sell the house. Consumers with limited financial literacy

may have a harder time making sense of the price and value of the products offered.

For example, if consumers value predominantly the NNEG, those who expect negative

price growth for their house should favor reverse mortgages over HELOCs because the

NNEG is larg er in those cases. Davidoff and Wetzel (2014) show that consumers appear

to fail to take advantage of this feature of the product when house prices are declining.

This paper aims to understand the interplay between financial literacy and the valuation

of reverse mortgage products.

In situations where the take-up of a financial product is low and data scarce at the micro

level, an experimental approach is well suited to learn about preferences and how they

interact with knowledge.

In the case of reverse mortgages, Davidoff et al. (2017) condu cts

a survey to learn about what consumers know about reverse mortgages. They find rela-

tively high basic awareness of reverse mortgages but poor understanding of actual pro-

visions of reverse mortgages. They also find that while product knowledge is positively

associated with demand, general financial literacy is associated with lower demand, a find-

ing similar to Fornero et al. (2016) in the Italian context. To understand how consumers

value reverse mortgages, we conduct a stated-choice experiment in which respondents

were asked to evaluate various reverse mortgage products. We investigate how financial

literacy as well as prior knowledge of reverse mortgages shape the evaluation of reverse

mortgage products, in particular the actuarial value of the NNEG and the interest rate

charged on the product.

We find that more than half of eligible Canadians (55.48%) lack the basic fundamental

knowledge of reverse mortgages prior to participating in our stated-preference experi-

ment. Knowledge of reverse mortgages is positively associated with higher financial

Several recent papers demonstrate the usefulness of such an approach. Ameriks et al. (2020) use an experi-

mental approach to learn about preferences regarding end-of-life savings and long-term care. Brown et al. (2017)

use an experiment to learn about the valuation of annuities. Boyer et al. (2020) use an stated-preference approach

to learn about demand for long-term care insurance and highlight the importance of product knowledge. Boyer

et al. (2020) use a similar approach to learn how consumers value life annuities in Canada whereas Boyer et al.

(2022) investigate how financial education helps consumers make better use of tax-sheltered savings accounts.

80 Ismael Choinière-Crèvecoeur and Pierre-Carl Michaud

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literacy as well as having high income and assets. Second, we estimate that consumers are

price-sensitive to the interest rate charged on reverse mortgages. Importantly, price sen-

sitivity is larger for those with higher financial literacy, while the elasticity is lower and

statistically insignificant among those with low financial literacy. Third, only financially

literate consumers are sensitive to the actuarial value of the NNEG in these scenarios.

Consistent with a poor understanding of the NNEG and a stronger weight put on the con-

sumption smoothing possibilities of reverse mortgages, consumers who expect the lowest

house price growth are less willing to purchase a reverse mortgage than those who expect

prices to increase.

The paper is organized as follows. In section 2, we present the main type of reverse

mortgage product offered to Canadians and discuss the theoretical foundations for the

valuation of reverse mortgages. In section 3, we present survey evidence on knowledge

of reverse mortgages. In section 4, we present the experiment and how we compute

the actuarial value of the NNEG for each respondent and scenario. In section 5, we analyze

how respondents value reverse mortgages in the experiment, while section 6 concludes.

2 A primer on reverse mortgages

2.1 The Canadian home income plan

Canadians have access to reverse mortgage products through the Canadian Home Income

Plan (CHIP) offered by HomeEquity Bank. This program was first offered in the Vancouver

area in 1986, and then in Ontario and Alberta starting in 2001. In the following years, the

program was gradually offered across the country. In order to be eligib le to the program,

the borrower must be a Canadian citizen and at least 55 years old. In addition, he or she

must be the owner of their own residence. Only primary residences are eligible. The initial

loan must be at least $25,000.

The program allows the borrower to remain the owner of the residence, as long as cer-

tain conditions are met. These conditions are that the residence must be kept in good con-

dition, property taxes paid, and the property must be insured. Eligibility is dependent on a

good record in terms of mortgage re-payment. If there is an existing mortgage on the

property at the time of initiation, the mortgage must be paid off first with the proceeds

from the reverse mortgage.

The CHIP program provides a NNEG, which means that it guarantees that the amount to

be repaid will never exceed the fair market value of the property at the time of sale. Once a

loan-to-value limit for the reverse mortgage has been set, the homeowner has several

options to choose from in order to receive the funds. They can receiv e 100% of the funds

allowed in one lump sum. They can also initially receive a fraction of the funds granted, in

the form of an initial lump sum of $25,000, with subsequent advances. This line of credit

option is similar to the Home Equity Conversion Mortgage (HECM) offered in the United

States.

There are administrative fees charged to the borrower. First, CHIP charges a closing and

administrative fee of $1,495, which includes security lookup, title insurance, and registra-

tion. Fees ranging from $175 to $400 are added for an assessment of the pro perty. Finally, a

fee between $300 and $500 is charged for independent legal advice.

In 2017, year when we ran our survey and experiment, the CHIP program allowed the

borrower to borrow between 10% and 55% of the estimated equity of the residence. Most

conditions have not changed since then. The loan-to-value depends on the borrower’s age,

sex, and marital status. It also depends on the type of residence and its geographical loca-

tion. Table 1 provides an example of loan-to-value limits for a single-family dwelling by a

single woman between 55 and 75 years old, in the cities of Montreal, Toronto, and

Vancouver, in 2017.

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In order to reduce the losses related to the NNEG, the loan-to-value is lower for younger

borrowers. It is also lower for women, since they have a higher life expectancy than men.

When compared with single individuals, couples can borrow less since the joint probability

of survival is taken into consideration. Finally, according to the type of dwelling and its

location, a higher loan is allowed for those for which a higher price growth and a lower

price volatility are expected. These reverse mortgages were offered at an interest rate of

5.59% (in 2017) which is above the rate charged on HELOC (4% at the time). Contrary to the

United States, these reverse mortgages are not federally insured.

2.2 The value of reverse mortgages

How should households evaluate reverse mortgages? Their value derives from two distinct

sources. The first one is the possibility of shifting consumption earlier when an illiquid

asset cannot be sold earlier. This first component is not unique to reverse mortgages.

One can also shift consumption earlier by extracting home equity using HELOCs. The sec-

ond source, which is unique to reverse mortgages, is the insurance against the downside

risk that the value of the house falls below the value of the loan at the time the house is

sold. Hence, a household may be willing to pay a premium on the interest rate charged for

a reverse mortgage due to the NNEG.

The impact of illiquid housing wealth on the desire to borrow from home equity was

first studied by Artle and Varaiya (1978 ). Consumers may value borrowing from home

equity in retirement because they are liquidity-constrained. The presence of this illiquid

asset endogenously creates these constraints. Consider a household deriving a utility flow

from living in their home. While healthy, the household wants to stay in their home. The

house value could be expected to appreciate in retirement. Assume the house will only be

sold near death, potentially when sickness occurs. These are states of the world where the

marginal utility of consumption could be low, in particular in Canada where out-of-pocket

medical expenditures in the case of sickness (nursing homes) are not as large as in other

countries, such as the United States (Boyer et al. 2020). If that is true, the consumption

smoothing motive could be strong and push households to extract home equity earlier

in retirement, while the marginal utility of consumption is higher. This is the consumption

smoothing motive for borrowing against home equity. This should be relevant for those

with low levels of liquid assets (relative to income) and with substantial home equity or

home equity which is expected to growth fast in retirement.

The second source of value from reverse mortgages is the NNEG. Borrowing a substan-

tial portion of home equity in a HELOC woul d be risky for the household and their heirs.

Since house prices fluctuate substantially, in particular at longer horizons, the loan value

at the time the house is disposed off could be larger than the value of the house. Either the

Table 1. CHIP maximum loan-to-value: This table presents the maximum loan-to-value ratios of the home equity

that can be borrowed by a single woman living in a single-family dwelling. These limits are reported by age and city.

Source: HomeEquity Bank, 2017

Montreal Toronto Vancouver

Age

55 0.260 0.253 0.245

65 0.354 0.364 0.347

75 0.420 0.434 0.413

In the United States, the Federal Housing Administration (FHA) provides that insurance.

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consumer or his/her heirs would be liable to repay the financial institution who granted a

HELOC. A reverse mortgages allows to transfer this risk to the financial institution offering

the loan. Hence, from the point of view of the financial institution, a reverse mortgage is

more risky than a regular HELOC, since it cannot recuperate some portion of the loan if

house prices fall substantially. Therefore, a mortgage insurance premium must be charged

to cover the losses associated with this risk. The actuarial value of the NNEG is a function of

various risks including longevity risk and house prices. The higher the likelihood that

someone lives longer in their house, the higher the likelihood that the value of their loan

exceeds the value of their house. Similarly, drops in house prices increase the likelihood

that loans exceed the value of house. Hence, someone who expects house prices to decline,

or appreciate more slowly, should perceive a higher value from a reverse mortgage over a

HELOC. With growing degree of sophistication in the modeling of risks, there is a substan-

tial literature which evaluate the NNEG for various reverse mortgage products (Li et al.

2010; Cho et al. 2015; Alai et al. 2014; Shao et al. 2015). Davidoff (2015) estimates that

the value of the NNEG embodied in products offered in the United States can be large,

in particular when idiosyncratic house price risk is taken into account. Hence, the value

of the NNEG could be nontrivial.

Both motives for borrowing out of home equity suggest a different relationship

between the value of reverse mortgages and expected house price growth. They also sug-

gest that the value of reverse mortgages should vary according to a number of other char-

acteristics of borrowers. Putting the two motives together, there has few been attempts to

evaluate the value of reverse mortgages to households using a well-defined life cycle

framework. For example, Nakajima and Telyukova (2017) estimates using a life cycle model

that the value of a reverse mortgage to households in the United States is relatively mod-

est, at 252$ per homeowners and 1770$ per reverse mortgage borrower. They attribute this

low demand to bequest motive, uncertainty about health, and high cost. Cocco and Lopes

(2019) also estimate relatively low value of existing products which they attribute to the

requirement of having to maintain the house when contracting a reverse mortgage and to

other design features of the product. There is substantial uncertainty around the value of

reverse mortgages to retirees.

3 Survey evidence: Knowledge of reverse mortgages

3.1 The survey

In 2017, we conducted a survey experiment with Asking Canadians, an online panel provider

in Canada. Respondents were aged 55 to 75 years and lived in the provinces of Quebec,

Ontario, or British Columbia. In each province, 50% of respondents came from major cen-

sus metropolitan areas (CMA), while the rest came from outside the CMA. We focus on

those aged 55 to 75 years because this is an age group where reverse mortgages are likely

to be most relevant. Because the value of reverse mortgages is tightly linked to house pri-

ces, we focus on provinces in which house price growth has been steady over the last dec-

ades. This increases the likelihood that respondents have substantial home equity.

The questionnaire consists of five parts. First, we collect socioeco nomic, demographic,

and health information from respondents. The second section is on preferences, risk per-

ception, and expectations for the future. The third section measures respondents’ level of

financial literacy and knowledge of probabilities. A fourth section asks respondents about

their general knowledge about reverse mortgages. Finally, the last section consists of a

stated-choice experiment, where respondents were offered different reverse-mortgage

products and had to evaluate them by giving their probability of buying each of these

financial products within the next year. A copy of the questionnaire can be found in

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the Online Appendix.

Because the resulting sample is slightly more educated than the

general population, we created a set of weights based on the Canadian Community

Health Survey (CCHS) for the year 2010. We construct weights based on age group (5 years),

gender, province, and education (three levels).

Of the 3,000 Canadians surveyed, 2,399 reported owning a home. A total of 2,306

respondents had enough home equity to borrow from a reverse mortgage. Of these

respondents, 2,163 were single or had a spouse aged 55 years or older, making them eligi-

ble for the CHIP program. Finally, 2,140 respondents did not have any missing information

and therefore were included in the analysis. Descriptive statistics on those respondents is

reported in Table 2.

Respondents are 63.4 years old on average, and half of them are male. Around 20% of

them are from British Columbia, 30% from Quebec and 50% from Ontario. Nearly three-

quarters (75.5%) of them are married or in a common-law relationship and 76.5% reported

having at least one living child. Close to two-thirds (66%) of respondents consider them-

selves retired. More than half (56.1%) of the sample have an employer pension plan or

Table 2. Descriptive Statistics: This table presents descriptive statistics on the respondents from the survey.

N = 2140. Statistics weighted according to 2010 Canadian Community Health Survey (CCHS)

Mean SD Min Max

Age 63.388 5.305 55.000 75.000

Men 0.488 0.500 0.000 1.000

Ontario 0.502 0.500 0.000 1.000

British Columbia 0.193 0.395 0.000 1.000

Quebec 0.305 0.460 0.000 1.000

Married 0.755 0.430 0.000 1.000

Has kids 0.765 0.424 0.000 1.000

Less than high school 0.180 0.384 0.000 1.000

High school 0.384 0.486 0.000 1.000

College 0.436 0.496 0.000 1.000

Retired 0.663 0.473 0.000 1.000

Total income ($1,000) 88.544 66.092 0.001 500.000

Total non-housing saving ($1,000) 265.681 424.297 0.000 5000.000

Home value 570.049 468.803 25.322 3000.000

Has a mortgage 0.341 0.481 0.000 1.000

Equity ($1,000) 519.638 456.115 25.322 3000.000

House-rich and cash-poor 0.092 0.287 0.000 1.000

Employer pension plan 0.561 0.496 0.000 1.000

Bequest motive 0.178 0.383 0.000 1.000

House must be sold only if financial hardship 0.580 0.494 0.000 1.000

Financial literacy (three correct answers) 0.541 0.498 0.000 1.000

Understands surv. probabilities 0.846 0.358 0.000 000

The survey also included a stated-choice experiment for annuities. This experiment was analyzed in Boyer

et al. (2020).

84 Ismael Choinière-Crèvecoeur and Pierre-Carl Michaud

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receive income from one. On average, their annual household income is $88,544, and they

have average total non-housing savings of $265,681.

The average current market value of

their home is $570,049. Slightly more than a third of respondents (34.1%) still have a mort-

gage on their primary residence. The median equity value of their residence is around

$520,000. To define a group who is house-rich and cash-poor, we borrow from the defini-

tion of wealthy hand-to-mouth households proposed by Kaplan et al. (2014). They define

wealthy hand-to-mouth consumers as those with positive home equity but liquid assets

less than half of total inco me. To capture the house-rich aspect of the definition we are

after, we tighten the criterion on home equity and use a threshold of three times total

income instead of zero home equity. Respondents who have home equity larger than three

times their income but liquid assets (savings) less than half their total income are defined

as house-ri ch and cash-poor. In the sample, 9.2% of respondents qualify as house-rich and

cash-poor.

In terms of preferences, we keep two variables for our analysis: one which is a proxy for

the presence of a bequest motive and the other to proxy attachment to the house. On a

5-point Likert scale, respondents were asked if they agreed with the following statement:

Parents should set aside money to leave to their children or heirs once they die, even when it means

somewhat sacrificing their own comfort in retirement. We recoded those who agree or strongly

agree have shown preferences consistent with a bequest motive (or bequest norm). In the

sample, 17.8% of respondents were classified using this statement as having a stronger

bequest motive. We also use the response to the statement: A house is an asset that should

only be sold in the case of financial hardship to characterize a respondent’s preference or norm

for staying in the home. In the sample, 44% agreed or strongly agreed with the statement.

We asked respondents a series of three questions to assess their level of financial liter-

acy following Lusardi and Mitchelli (2007). The first question is on interest rates, the sec-

ond on purchasing power, and the third on risk diversification. We create a binary

indicator taking value 1 if the respondent correctly answers all three questions and 0

if not. Overall, 54.1% of respondents correctly answer all three questions. Another question

asks the respondent about survival probabilities. Respondents are told the probability of

surviving to 85 is 60% and asked whether the probability of surviving to 60 is larger, or

smaller than 60%. We create a binary indicator if the respondent correctly answers this

question. 84.6% of respondents correctly answered this question.

3.2 Prior knowledge of RMR

Respondents were asked a sequence of questions with the objective of measuring their

level of prior knowledge of reverse mortgages.

Without naming the financial product, we first presented a sentence containing the

definition of a reverse mortgage to the respondents

. Then, respondents were asked if they

had ever heard of this financial product. As shown in Table 3, 77.3% of eligible Canadian

homeowners claimed to have heard of that kind of financial product. Fewer homeowners

from Quebec answered having heard this definition, a difference of nearly 20 percentage

points with the two other provinces.

Then, we asked those who claimed to have heard of this financial product if they could

name it. 59.5% of these homeowners claimed to be able to name the product in question.

To prevent the effect of outliers, we imposed a maximum annual household income of $500,000 and maximum

total savings of $5,000,000

The definition was presented as follows: “Imagine a financial product that lets you turn part of your current home

equity into cash. You’re not obligated to make any payments until you move, you sell your home, or you die. You have the

certainty that once your residence is sold, the required amount to repay the loan will not exceed the selling price of the

residence.”

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Once again, there was a noticeable difference between provinces. Fewer homeowners from

Quebec who had heard of this financial product claimed to be able to name it, a difference

of 15 percentage points with the two other provinces.

Finally, those who claimed to be able to name the product were asked to identify it from

a list of financial product names. 96.8% of them answered correctly. Once again, fewer

homeowners from Quebec answered this question correctly. Overall, 44.52% of all home-

owners had heard of the existence and correctly identified the reverse mortgage as the

name of that financial product. Moreover, the level of knowledge was twice as big among

homeowners in Ontario and British Columbia than it was in homeowners in Quebec. One

plausible explanation for this phenomenon is that the CHIP program has been offered lon-

ger in Ontario and British Columbia than in the province of Quebec.

To understand how prior knowledge of the product is distributed, we estimate a logit

regression with as a dependent variable an indicator for whether or not respondents were

able to identify the product (answer all three questions and named the product correctly

as a reverse mortgage). We include as controls an indicator for financial literacy, for

understanding survival probabilities, sociodemographic characteristics, and controls for

economic resources. In Table 4, we report estimates of marginal effects along with stan-

dard errors.

We find that those with higher levels of financial literacy have a substantially higher

probability of knowing what a reverse mortgage is. Even after controlling for a host of

factors, there is a 12.2 percentage point difference in knowledge of reverse mortgages

between those with and those without financial literacy. There is also a substantial differ-

ence between those who understand of survival probabilities and those who don ’t. In

Table 3. Prior knowledge of reverse mortgages:N= 2140. Statistics weighted according to the 2010 Canadian

Community Health Survey (CCHS)

Canada B.C. Ont. Que.

1: Ever heard of the existence of this fin product: based on

definition of reverse mortgages (N=2,140)

No 22.7% 15.3% 17.1% 36.5%

Yes 77.3% 84.7% 82.9% 63.5%

2: Can you name the financial product: based on definition of

reverse mortgages (if heard) (N=1,705)

No 40.5% 35.2% 36.5% 53.3%

Yes 59.5% 64.8% 63.5% 46.7%

3: Name that financial product: based on definition of reverse

mortgages (if can name) (N=1,065)

Annuity 0.4% 0.1% 0.0% 1.7%

Reverse mortgage 96.8% 96.6% 98.9% 90.9%

Life insurance 0.1% 0.0% 0.0% 0.3%

Line of credit 1.2% 2.9% 0.4% 1.7%

None of the above 1.6% 0.4% 0.7% 5.4%

Correctly answered all three questions (N=2,140)

No 55.48% 46.94% 47.87% 73.09%

Yes 44.52% 53.06% 52.13% 26.91%

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Table 4. Who can identify reverse mortgages?: Marginal effects from a logit regression of whether or not

respondents understand reverse mortgages (could identify by name based on description) on a series of

controls. Total wealth, home value, and savings are included as quartile dummies and the 4th quartile is excluded

(1) (2) (3)

Financial literacy 0.174*** 0.130*** 0.122***

(0.0206) (0.0211) (0.0213)

Understands survival probabilities 0.150*** 0.128*** 0.128***

(0.0317) (0.0305) (0.0303)

Age of respondent 0.00575*** 0.00701***

(0.00196) (0.00200)

Male 0.0709*** 0.0649***

(0.0211) (0.0209)

Ontario 0.230*** 0.220***

(0.0236) (0.0261)

Bc 0.263*** 0.264***

(0.0230) (0.0263)

High school −0.0695 −0.0732

(0.0701) (0.0695)

College 0.0232 0.00608

(0.0687) (0.0687)

Married 0.0199 0.00271

(0.0249) (0.0258)

Has kids −0.0545** −0.0531**

(0.0249) (0.0249)

Total income (4th q. excluded)

1st quartile −0.0772**

(0.0325)

2nd quartile −0.0292

(0.0310)

3rd quartile −0.0273

(0.0292)

Home value (4th q. excluded)

1st quartile 0.0376

(0.0349)

2nd quartile 0.0852***

(0.0303)

3rd quartile 0.0680**

(0.0289)

(Continued)

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terms of other correlates, males are more likely to know reverse mortgages . Those with

kids are less likely to know about reverse mortgages. In terms of income, those with the

lowest level of income (bottom quartile) are less likely to know about reverse mortgages.

Those in the second and thi rd quartile of home equity are more likely to know about

reverse mortgages (these effects are relative to the fourth quartile). Quite the opposite,

those in lowest quarti les of liquid savings are less likely to know about reverse mortgages.

Respondents with existing mortgages are more likely to know about reverse mortgages.

Interestingly, we do not find that those who are house-rich and cash-poor are more likely

to know about the existence of reverse mortgages. In terms of economic resources, those

who appear to know about reverse mortgages do not exactly fit the profile of house-rich

and cash-poor households. However, they are more financially literate.

4 Stated-preference experiment

For each of the respondents in our sample, we present five different reverse mortgage

scenarios. We focus on lump-sum reverse mortgage loans to simplify the description of

reverse mortgages and avoid having to specify the path of interest rates, etc. These are

simpler products than those offered in the market. In the scenarios, we vary interest rates

offered and loan-to-value that can be borrowed. We reproduce below the introductory text

presented to the respondents.

We will refer to a reverse mortgage as a financial product that lets you turn part of your

current home equity into cash. Unlike many mortgage-based financial products, you’re not

obligated to make any payments until you move, you sell your home, or you die.

Table 4. (Continued )

(1) (2) (3)

Savings (4th q. excluded)

1st quartile −0.0841**

(0.0359)

2nd quartile −0.137***

(0.0313)

3rd quartile −0.112***

(0.0295)

Has mortgage 0.0387*

(0.0225)

House-rich and cash-poor −0.0295

(0.0440)

Has DB pension −0.0165

(0.0213)

Observations 2,140 2,140 2,140

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

A French version was presented to the respondents who chose to answer the questionnaire in French.

88 Ismael Choinière-Crèvecoeur and Pierre-Carl Michaud

https://doi.org/10.1017/flw.2023.4 Published online by Cambridge University Press

Importantly, you have the certainty that once your residence will be sold, the amount required

to repay the loan will not exceed the selling price of the residence. When we use the expression

“current home equity,” we are referring to the current market value of your primary residence

after subtracting outstanding mortgage balances. For the rest of this section, try to have your

current home equity in mind. We are going to show you some simple reverse mortgage prod-

ucts and ask you to rate them. Each reverse mortgage has three attributes:

1. The percentage of your current home equity that you can borrow. The amount borrowed

must be a minimum of $25,000.

2. A fixed annual interest rate on the balance of the loan, generating interests that you do not

need to pay before you move, sell, or die .

3. A fixed fee of $2,245 that you only have to pay once. The money you obtain from the reverse

mortgage will be used to pay this fee .

We then presented the scenarios as follows:

1. You can borrow a minimum of $25,000 and up to β% of your current home equity.

2. You will be charged a fixed annual interest rate of r% on the balance of the loan for as long

as you hold the loan.

Reminder: You’re not obligated to make any payments until you move, you sell your home,

or you die; and you have the certainty that once your residence will be sold, the amount

required to repay the loan will not exceed the selling price of the residence.

3. There is a fixed fee of $2,245 that you only have to pay once. The money you obtain from the

reverse mortgage will be used to pay this fee.

For each individual i and scenario j, we exogenously propose an interest rate, r

i;j

, which

can take the values in the range:

i;j

3:8%; 4:1%; 4:4%; 4:7%; 5%; 5:3%; 5:59%; 6%; 6: 5% ; 7%;

each with probability 1/10.

Therefore, the randomization is done aro und the interest rate of 5.59% proposed by

CHIP for a 5-year term at the moment when the survey was conducted. For each individual

i and scenario j, a loan-to-value β

i;j

that can be borrowed is shown. We denote the maxi-

mum loan-to-value that can be borrowed by the individual i from CHIP as β

CHIP

. We have

information on the CHIP’s average maximum loan-to-value, by 5-year age group

, gender,

marital status (single or couple), and residence location (inside or outside the metropolitan

area)

. These values come from the CHIP calculator that can be found on their website

and are presented at the end of the Online Appendix. To randomize the loan-to-value

around β

CHIP

, we draw a value, τ

i;j

0:5; 0:75; 1; 1 :25; 1:5; each with probability of 1=5:

The loan-to-value proposed in the scenario j of the respondent i will therefore

be β

i;j

 τ

i;j

CHIP

For couples, we used the average age of the couple,

age

age

, where is the age of the respondent and is the age

of the spouse as reported in the survey. We rounded the result to the nearest integer and set the age at 79 when

age

age

> 79.

To identify the residence location, we asked respondents to give us the first three digit of their postal code.

This information allowed us to identify the respondents who were or were not part of the main census metro-

politain area of their respective province (Montreal, Toronto and Vancouver).

https://www.chipadvisor.ca/calculator/

Journal of Financial Literacy and Wellbeing 89

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Following Manski (1999), we ask the respondent to report the probability, from 0% to

100%, that they would buy this reverse mortgage if a trusted financial institution offered it

within the next year. This provides a continuous measure of preference intensity in the

form of a probability and accounts for incompleteness of the hypothetical choice situations

presented.

4.1 Computing the actuarial value of the NNEG

To compute the actuarial value of each of the contracts offered to respondents, we con-

sider a simple pricing framework. Reverse mortgage pricing models can be extremely

sophisticated and account for a number of elements, including stochastic discount factors,

yield curve modeling, and endogenous termination probabilities (see e.g., Shao et al. 2015).

Since we are interested in the cross-sectional and cross-scenario variation in the actuarial

value of the contracts we presented, we will pay more attention to the variation induced

across respondents and scenarios than the absolute level of the actuarial value of the NNEG

in terms of a mortgage insurance premium. Another important distinction with other pric-

ing models is that we aim to measure the perceived value of the NNEG rather than the

actual NNEG. Hence, we will use subjective mortality risk instead of life table risk.

We compute fair mortgage insurance premiums to cover losses related to the NNEG. Let

i;j

be the loan-to-value ratio of the equity of the house with (net equity) value H

i;a

, bor-

rowed by an individual i of age a in scenario j. The initial value of the loan, L

a;i;j

, is then

given by L

a;i;j

 γ

i;j

i;a

. The value of the loan at a  t is given by:

at;i;j

 L

a;i;j

1  r

 π

i;j



; (1)

where r

represents the (fixed) interest rate for a HELOC, π

i;j

represents a fair mortgage

insurance premium to cover losses related to the NNEG in scenario j, and let



i;j

 r

 π

i;j

represents the (fair) interest rate for the reverse mortgage. Let H

i;at

be the resale value of the house if the borrower leaves or dies after t years (at age

a  t). The NNEG ensures that the amount recovered by the lender at the time of the sale

of the house is

minfL

at;i;j

; 1  cH

i;at

g; (2)

where c is a transaction cost calibrated at 5% of the selling price

of the selling price. The

potential loss by the lender at the time of selling the house is then defined as:

maxfL

at;i;j

1  cH

i;at

; 0g: (3)

The expected present value of future losses related to the NNEG is given by:

NNEGπ

i;j

E

t1

i;a;at

maxL

at;i;j

1cH

i;at

;0

1i



; (4)

where i is a discount rate-based calibrated to 4% and q

i;a;at

is the conditional probability

of dying at age a  t for someone of age a at t  0. These probabilities are respondent-

specific. Finally, E

is the expectation operator for the distribution of future house prices

which depends on the region of the country and the type of dwelling. For the lender, the

expected present value of the accumulated mortgage insurance premiums paid is given by:

MIPπ

i;j

π

i;j

t1

i;a;at

at;i;j

1i



; (5)

According to Sun Life Financial, the transaction costs in Canada are between 3% and 7%.

90 Ismael Choinière-Crèvecoeur and Pierre-Carl Michaud

https://doi.org/10.1017/flw.2023.4 Published online by Cambridge University Press

where s

i;a;at

is the conditional probability to survive at age a  t for someone aged a at

t  0. Finally, the actuarial fair mortgage insurance premium π

i;j

is such as:

NNEGπ

i;j

MIPπ

i;j

: (6)

The mortgage insurance premium π

i;j

is the actuarial value of the NNEG guarantee for

the risk profile of the respondent. The fair rate on the reverse mortgage is r



i;j

 r

 π

i;j

To compute the value of the NNEG for each respondent and scenario, we need estimates of

house price dynamics as well as survival probabilities.

We first set the interest rate of a HEL OC r

at 4%, which is the average rate that was

offered on the Canadian market in 2017

. We set the maximum loan-to-values offered by

CHIP as reported in Table 1 but use the loan-to-value offered in each scenario using the

randomization. We also use a constant discount rate of 4% (i) in the computations.

4.1.1 House price dynamics

We calibrate house price dynamics using the MLS Home Price Index from the Canadian

Real Estate Association (CREA), which provides information on housing prices in the major

CMAs in Canada. This data set provides information regarding the average price of all

types of dwellings, as well as the average price per type of dwelling, namely single-family

dwellings, townhouses, and condos. We used monthly data from January 2005 to August

2016 for the cities of Vancouver, Toronto, and Montreal. Figure 1 presents the evolution of

the composite price index between 2005 and 2018 for all of Canada, as well as for the cities

of Vancouver, Toronto, and Montreal. We see that Vancouver and Toronto are the cities

that have had the most substantial growth, with an average annual growth of 6% and 6.8%,

respectively, while the city of Montreal experienced an average annual growth of 3.7%.

The cities of Vancouver and Toronto also demonstrate having higher variability in prices

when compared to the city of Montreal. We drop the last 2 years (2017 and 2018) since the

survey was conducted in 2017.

Figure 1. MLS Home Price Index for the cities of Vancouver, Toronto, and Montreal, from 2005 to 2018

https://www.ratehub.ca

Journal of Financial Literacy and Wellbeing 91

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We estimate parameters of the house price dynamic using an AR(1) with a deterministic

trend:

log H

h;p;m

 δ

h;p

m  ɛ

h;p;m

(7)

h;p;m

 ρ

h;p

h;p;m1

 η

h;p;m

; (8)

where H

h;p;m

is the average house price of type h, in the city p in the month m, δ

h;p

is the

deterministic trend, and η

h;p;m

is an idiosyncratic error term which is assumed normally

distributed with an average of zero and a variance of σ

h;p

. Table 5 reports estimates by type

of dwelling for the cities of Vancouver, Toronto, and Montreal. In each specification, the

coefficient of the deterministic trend and the autocorrelation coefficient are significant at

a level of 1%. House prices exhibit behavior similar to a random walk with some degree of

mean-reversion. These estimates were used to calibrate the house price risk in the prov-

inces of Quebec (Montreal), Ontario (Toronto), and British Columbia (Vancouver). While

the dynamics in house prices evolve at the monthly level, they are aggregated in simu-

lations at the annual level (when survival and other outcomes are computed).

Using an aggregate house price index to estimate the dispersion of shocks, as we did,

underestimates the volatility in selling prices, since an important component of the vol-

atility in house prices is house-specific and likely idiosyncratic (Davidoff 2015). Since we do

not have Canadian information on the dispersion of house prices within CMA, we instead

resort to scaling up the standard deviation of shocks. Nakajima and Telyukova (2017) esti-

mate using zip-code-level data a process which is similar to ours (AR(1)). They find an

annual standard deviation of (log) house price shocks of 0.125. Our estimates, which vary

by CMA and dwelling, are of the order of 0.06 at the annual level. Hence, we scale up the

standard deviation of the shocks by a factor of 2 for our computations.

Respondents likely form their own exp ectations about house price growth. What do

respondents expect about house price growth in years following the experiment? We

asked respondents to categorize their expectation of their house’s price growth over

the next 5 years: more than 20%, between 5% and 20%, between −5% and 5%, between

−20% and −5%, and less than −20%. Table 6 reports the distribution of subjective expect-

ations for house price growth over the next 5 years by province. Homeowners from the

province of British Columbia are those who expected a higher growth, with almost 80% of

Table 5. House price dynamic estimates: This table reports estimated parameters of the house price dynamics by

city and type of dwelling. SFD refers to a single-family dwelling. δ

h;p

is the monthly deterministic trend, ρ

is the AR(1)

coefficient, and σ

h;p

is the standard deviation of shocks for a dwelling of type h and in city p. SFD refers to single-family

dwelling. * p < 0:1,**p < 0:05, *** p < 0:01



h;p



h;p



h;p

Prov Type

Vancouver SFD 0.006*** 0.964*** 0.023

Townhouse 0.004*** 0.988*** 0.018

Condo 0.004*** 0.993*** 0.018

Toronto SFD 0.006*** 0.949*** 0.022

Townhouse 0.006*** 0.956*** 0.021

Condo 0.005*** 0.966*** 0.020

Montreal SFD 0.003*** 0.965*** 0.011

Townhouse 0.004*** 0.912*** 0.016

Condo 0.003*** 0.968*** 0.011

92 Ismael Choinière-Crèvecoeur and Pierre-Carl Michaud

https://doi.org/10.1017/flw.2023.4 Published online by Cambridge University Press

them expecting a growth higher than 5%. Homeowners from Ontario and Quebec followed,

with 75% and 66% expected growth higher than 5%. If we use the expected growth rates in

Table 5, we obtain a 5-year expected growth rate for 43% in British Columbia for single-

family dwellings and 27% for other types of dwellings. Expected 5-year growth rates are in

excess of 35% in Ontario, while they are around 20% for Quebec. This suggests that a sig-

nificant fraction of respondents were more pessimistic than historical house price growth

at the time of the survey, in particular for British Columbia and Ontario. While there is no

direct way to incorporate these expectations in the calculation of the NNEG, we will assess

the role of these expectations in shaping demand for reverse mortgages.

4.1.2 Survival rates

Since respondents are likely to evaluate the NNEG using their own beliefs about survival,

we exploit a question on subjective survival beliefs which has been shown to be predictive

of actual mortality (Hurd and McGarry 2002). We asked respondents to provide us with the

probability they will live up to the age of 85 years. To transform this into a life table, we

use the structure of official life table survival risk by age and adjust those using the infor-

mation in the subjective beliefs. We follow the approach used by Salm (2010) to model

deviations from life table survival. Assume the subjective mortality hazard of respondent

i at age a be given by:

x

ψ

x

; (9)

where λ

x

 is the individual’ s objective mortality hazard based on life tables (x

corre-

spondents to province, sex and cohort). In the survey, each respondent was asked to give

his subjective probability of surviving until the age of 85 years, s

a;85

x

. We use this infor-

mation to estimate ψ

. Appendix A provides details on how we estimate this parameter. To

avoid indeterminate values at the bounds, we set s

a;85

x

0:01 as a minimum and

a;85

x

0:99 as a maximum for subjective risk responses. Based on the objective life

table of individual i, it is then possible to use ψ

and reconstruct their subjective life table

using equation 9.

Table 7 reports the distribution of remaining years of life among respondents by age

groups in the sample. It also reports the average remaining years of life according to the

official life tables. On average, the expected number of remaining years of life is 23.4 years

using the prospective life tables from Statistics Canada and 29 years using the subjective

probabilities. Hence, our respondents overestimate survival to the age of 85 years.

There

is also considerable dispersion in subjective remaining life expectancy with the 25th per-

centile at the age of 65–69 years expecting to live fewer than 14.8 years on average while

the same number is 29.8, nearly double, at the 75th percentile.

Table 6. Subjective expectation of house price growth over the next 5 years: This table presents the

distribution of subjective expectation of house price growth over the next 5 years by province (N=2140).

Statistics weighted according to the 2010 Canadian Community Health Survey (CCHS)

More than 20% 5 to 20% −5to5% −5to−20% Less than −20%

British Columbia 0.190 0.603 0.163 0.035 0.009

Ontario 0.165 0.583 0.217 0.020 0.014

Quebec 0.061 0.598 0.322 0.006 0.013

The finding that respondents overestimate at age 85 is common in many countries. While respondents tend

to underestimate when the target age is set to 75, they tend to over-estimate at older ages (Hurd and McGarry

2002).

Journal of Financial Literacy and Wellbeing 93

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4.2 Estimates of the actuarial value of the NNEG

For each respondent, we first use subjective survival probabilities to generate 1000 draws

of death ages which are termination probabilities for the sake of this exercise. For couples,

we use the death of the last spouse alive. For spouses, we do not have subjective survival

curves. Assuming life table probabilities would lead to an assumption that there is no cor-

relation in life expectancy across spouses. Instead, we use the subjective survival curve of

the respondent. Using the age of death as termination age for the reverse mortgage over-

estimates the duration of reverse mortgage contracts as many respondents are likely to

sell their property prior to death. This should lead to an overestimation of the value of the

NNEG. We then use the house price process by province and dwelling type of the respon-

dent to generate a distribution of house prices at the time of dispo sition. Since mortality

and house prices are independent, we assume there the decision to sell the house is inde-

pendent of house prices.

Using the distribution of selling prices and the distribution of durations (time to mor-

tality or termination), we compute both the expected present discounted value of mort-

gage insurance premiums and NNEG losses for the insurer. We then solve over a grid for

the mortgage insurance premium which solves the zero-profit condition. We do this for

each of the 2140 respondents and for each 5 scenarios. We express the estimates of π

i;j

basis points (100 basis point is one percentage point).

Figure 2 shows the distribution of actuarial values of the NNEG across respondents and

scenarios for two sets of assumption: the baseline scenario using house price growth esti-

mated over the period 2005–2016 and one where annual growth in house prices (δ) is 50%

lower. On average, the actuarial value of the NNEG, represented as a premium on the

HELOC rate, is 31 basis points, or 0.31 percentage point. It varies substantially across

the sample and scenarios with a standard deviation of 39.7, a first quartile of 13 basis

points, and a 90th percentile of 86 basis points. Under the alternative scenario with lower

growth, the premium is on average higher, with a mean of 77 basis points and a standard

deviation of 57, a 90th percentile 155 basis points. Hence, these estimates are well below

the observed premium in the market of over 200 basis points. One needs however to be

careful with concluding that the observed premium is too high. We focus on house price

trends in three major cities with substantial growth. If growth is much lower in rural areas,

for example, this could justify higher premiums, as the sensitivity of our premium esti-

mates to house price growth shows.

To shed light on the variation in the values we computed, we run a regression of the

premium in basis point on demographics, subjective remaining life expectancy, and the

amount borrowed in terms of Loan-to-Value (LTV) in the scenario. As one would expect

due to the effect of mortality on the value of the NNEG, the premium decreases with age

(duration lower) and is lower for males and higher for couples. The premium is higher in

Table 7. Expected remaining years of life: This table reports statistics for subjective and life table remaining life

expectancy (N=2140). Statistics weighted according to the 2010 Canadian Community Health Survey (CCHS)

Subjective Life table

Age 25

Median 75th Mean Mean

55–59 28.7 33.5 38.2 34.6 29.3

60–64 22.7 27.3 33.8 29.9 24.5

65–69 18.5 23.0 29.8 25.6 20.1

70–74 14.8 19.1 27.0 22.1 15.6

Total 21.8 27.3 34.8 29.0 23.4

94 Ismael Choinière-Crèvecoeur and Pierre-Carl Michaud

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British Columbia and Ontario. The premium increases with the LTV that is granted as a

reverse mortgage in each scenario.

5 Demand for reverse mortgages

5.1 House price growth expectations

Given the central importance of house price expectations, we first look at the correlation

between subjective house price expectations over the next 5 years and the probability of

purchasing a reverse mortgage in the scenarios presented. We use the average take-up

probability over the five scenarios. If the value of the NNEG is what drives demand for

reverse mortgages, we should expect a negative correlation between purchase probabili-

ties and house price growth expectations. On the other hand, if the consumption smooth-

ing motive is more important, we should see a positive correlation between the two

outcomes.

Table 8 reports aver age take-up probabilities by province and house price growth

expectations. For both Quebec and Ontario, there is a strong positive gradient between

subjective house price growth and take-up probabilities. Those who expect the highest

growth are more likely to purchase a reverse mortgage in the scenarios we presented.

Hence, this would suggest these respondents are motivated by the possibility of taking

advantage now of some of the home equity increase they expect to obtain in the future.

It could also mean they poorly understand the value of the NNEG. For British Columbia, we

do not observe such a gradient which may be due to the fact that most respondents

expected substantial house price growth.

Figure 2. Actuarial Value of NNEG: Density estimate of the distribution of NNEG mortgage insurance premiums

computed across respondents and scenarios (rate in excess of HELOC). The premium is reported in basis points

(100 = 1 percentage point). The distribution is reported in blue for the reference scenario (with historical growth in

house prices 2005–2016) and with an alternative scenario (in orange) where historical growth is half of what has been

observed by dwelling type and province.

Journal of Financial Literacy and Wellbeing 95

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5.2 Regression analysis

To understand the determinants of take-up probabilities and in particular how respond-

ents respond to the interest rate and the fair value of the NNEG, we specify the following

semi-log regression model:

i;j

 α

log r

i;j

 α

log r



i;j

 X

β  ε

i;j

: (10)

where p

i;j

is the reported take-up probability (in percentage points from 0 to 100), r

i;j

is the

interest rate on the reverse mortgage in scenario j, and r



i;j

 r

 π

i;j

is the interest rate

obtained by adding to the HELOC rate of 4% the fair mortgage insurance premium com-

puted for each respondent and scenario, π

i;j

. The vector X

contains a number of

respondent-level characteristics, while ε

i;j

is an error term. With this specification, the

interest rate elasticity of demand is given by

where p is a fixed level of the take-up prob-

abilities where the elasticity is evaluated (such as the mean).

The specification nests the case where α

α

in which case we can express the

choice probability as a function of the log of the ratio of the interest rate to the fair rate,

a measure of the unfairness of the interest rate charged in the reverse mortgage. Davidoff

and Wetzel (2014) show that consumers may have a hard time correctly evaluating the

value of the NNEG and therefore the fair rate in the reverse mortgage. This would lead

to the prediction of an imprecise estimate of α

, potentially different from α

. We can test

this assumption given estimation of equation (10).

We control for a rich set of covariates from the survey, including quartiles of income,

home value, and savings as well as controls for preferences as well as house price expect-

ations. We estimate parameters by ordinary least square (OLS) using clustered standard

errors at the respondent level.

Table 9 reports OLS coefficients for the full sample as well specifications where we esti-

mate parameters separately by financial literacy as well as a third group representing

those who know reverse mortgages and have a high level of financial literacy.

For the full sample, the estimated interest rate elasticity is − 0.823 a nd statisti cally

significant. This sugge st that consu mers are quite s ensitive to the p rice of reverse

mortgages. In the full sample, they are however insensitive to the fair rate tha t rep-

resents the actuarial value of the mortgage insurance premium that covers the NNEG.

The estimate is positive but statist ically insignificant. Although we do not reject the

equality α

α

(p-value = 0.843), this largely reflects the imprecision of α

.Hence,

we find respondents have trouble using the NNEG as in Davidoff and Wetzel (2014).

When we split the sample between those with high financial literacy (who could

answer correctly all three questions) and those w ith low financial literacy, we observe

Table 8. Probability of buying a reverse mortgage within the next year: This table presents the average

probability of buying a reverse mortgage within the next year by province and category of subjective expectation

on the house price growth over the next 5 years (N=2140). Statistics weighted according to the 2010 Canadian

Community Health Survey (CCHS)

Expected change in house price over next 5 years

More than 20% 5 to 20% −5to5% −5to−20% Less than −20%

British Columbia 0.059 0.052 0.050 0.064 0.000

Ontario 0.107 0.067 0.038 0.032 0.000

Quebec 0.188 0.052 0.070 0.031 0.052

We obtain very similar estimates by tobit regression to account for censoring at values of 0 and 100.

96 Ismael Choinière-Crèvecoeur and Pierre-Carl Michaud

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Table 9. Regression estimates: The table reports coefficients estimates from OLS along with (clustered) standard

errors in parenthesis. The dependent variable is the take-up probability in percentage points (from 0 to 100). The first

column reports estimates on the whole sample. The second and third column report estimates by level of financial

literacy. The last column reports results for the subset of those who have high financial literacy and also have prior

knowledge of reverse mortgages prior to the experiment. We report below the R-squared the interest rate elasticity

computed at the mean in the total sample as well as the standard error. We also report the p-value on the test for the

equality of the coefficients on the log interest rate and the log of the fair rate. Statistical significance is denoted using *

p < 0:1,**p < 0:05, *** p < 0:01

Total

High financial

literacy (FL)

Low financial

literacy (FL)

High FL and

knowledge RMR

Log interest rate −5.235*** −7.312*** −2.021 −9.412***

(0.890) (1.042) (1.545) (1.465)

Log fair rate 4.408 10.67** −3.727 10.37

(4.126) (4.852) (7.187) (6.658)

Age (65=0) −0.203*** −0.250*** −0.112 −0.216**

(0.0587) (0.0685) (0.104) (0.0892)

Male 2.622*** 2.438*** 2.916** 3.220***

(0.618) (0.687) (1.171) (0.958)

High school −0.855 −2.198 0.151 −7.428

(2.471) (4.512) (2.796) (7.634)

College −0.386 −1.412 −0.0545 −5.968

(2.448) (4.491) (2.768) (7.559)

Married −0.971 −1.057 −1.008 −1.018

(0.759) (0.953) (1.227) (1.236)

Has kids 0.0627 0.614 −0.459 1.714*

(0.704) (0.819) (1.355) (1.009)

Non-CMA region −0.00639 −1.540** 1.778 −0.991

(0.667) (0.766) (1.172) (1.025)

Total income (1st q) 1.872* 2.316** 1.585 1.837

(0.966) (1.166) (1.648) (1.443)

Total income (2nd q) −0.204 −0.241 0.231 0.236

(0.840) (0.968) (1.565) (1.385)

Total income (3rd q) 0.938 0.342 2.303 0.0632

(0.811) (0.925) (1.558) (1.160)

Home value (1st q) −0.427 −0.811 −0.366 −2.571

(1.037) (1.222) (1.813) (1.908)

Home value (2nd q) −0.793 0.0508 −2.060 0.256

(0.916) (1.057) (1.651) (1.434)

Home value (3rd q) −1.375* −0.529 −2.778* −1.143

(0.795) (0.926) (1.494) (1.155)

Savings (1st q) 2.885** 3.284** 1.290 4.546**

(Continued)

Journal of Financial Literacy and Wellbeing 97

https://doi.org/10.1017/flw.2023.4 Published online by Cambridge University Press

Table 9. (Continued )

Total

High financial

literacy (FL)

Low financial

literacy (FL)

High FL and

knowledge RMR

(1.204) (1.530) (2.128) (2.146)

Savings (2nd q) 1.479 2.448** −0.778 2.130

(0.924) (0.975) (1.958) (1.380)

Savings (3rd q) −0.694 0.0502 −2.709 −0.485

(0.717) (0.756) (1.685) (0.858)

Has mortgage 2.942*** 3.284*** 2.750** 2.845**

(0.688) (0.869) (1.112) (1.108)

House-rich cash-poor −1.253 −1.083 −1.161 −2.543

(1.281) (1.807) (1.766) (2.480)

Has DB pension 0.111 −0.365 1.008 −0.778

(0.651) (0.757) (1.163) (0.987)

House price: greater than 20 % 1.867* 1.321 2.819* 2.262

(1.039) (1.299) (1.679) (1.580)

House price: between 5% and 20% 1.480** 0.896 2.316** 1.776*

(0.659) (0.773) (1.164) (1.023)

House price: between −5% and −20% −0.939 −1.094 −2.471 −1.773

(1.282) (1.530) (2.191) (1.685)

House price: less than −20% −7.151*** −7.807*** −6.314*** −6.677***

(1.128) (1.595) (1.518) (2.078)

House only sold financial hardship 1.417** 0.493 2.685*** 0.605

(0.589) (0.718) (0.994) (0.925)

Leaving bequest important 1.942** 0.716 3.121* 1.400

(0.919) (0.970) (1.741) (1.455)

Financial literacy −0.631

(0.711)

RMR knowledge 0.388

(0.632)

Observations 10,700 6,440 4,260 3,605

R-squared 0.046 0.056 0.055 0.077

Interest rate elasticity −0.823 −1.149 −0.318 −1.479

(se) 0.140 0.164 0.243 0.230

Equality interest rate 0.843 0.495 0.426 0.890

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

98 Ismael Choinière-Crèvecoeur and Pierre-Carl Michaud

https://doi.org/10.1017/flw.2023.4 Published online by Cambridge University Press

that the interest sen sitivity is entirely driven by t hose with hig h financial lite racy.

Among those, the price elasticity is −1.14 9 (se=0.164), suggesting a relatively high

level of price sensitivi ty while it is −0.318 and statistically insignificant among those

with low financial literacy. The difference between the two estimates is statistically

significant (t = − 2.89). Moreover, those with high financial literacy are more likely

to pu rchase reverse m ortgage products with a hi gher value of the NN EG. A 10%

increase in th e value of the NNEG incre ases demand by 1.67 perce ntage points.

Among th ose with low financial literacy, the coefficient on the (log) value of the

NNEG is imprecisely estimated to be negati ve. Hence, financial lit eracy appears to help

respondents with the evaluation of the NNEG. In the last column, we refine even fur-

ther the specification to exclude those who have high f inancial literacy but did not

knowtheproductpriortotheexperiment.Althoughweobtainanevenlargerinterest

rate elasticity (−1.479), we d o not detect a higher sensit ivity to the value of the NNEG

among this group (the estimate is of simila r size but more imprecise). O verall, we find

that despite the lack of effect of financial liter acy on average take-up probabilities,

financial literacy appears to help consumers in evaluating the rever se m ortgage pr od-

ucts presented.

In terms of demographics, some differences in demand are observed. Demand appears

to decrease with age and is higher for males in the full sample. The effect of age is con-

centrated among those with higher financial literacy and knowledge of the product, while

gender differences are widespread across groups. In terms of economic resources, there is

some indication that those with low savings and low income, in particular among those

with higher financial literacy, have higher demand for reverse mortgages (the 4th quartile

is omitted for these variables). However, ther e is no relationship between demand and the

house-rich and cash-poor variable indicator. Therefore, there is some evidence that

demand is higher among those with lower resources (both income and savings) but not

necessarily the specific group of house-rich and cash-poor respondents. We find that those

with an existing mortgage are more likely to purchase a reverse mortgage. In this case,

they must pay first the existing mortgage with the funds from the reverse mortgage.

Purchasing a reverse mortgage for these households may allow to effectively postpone

mortgage payments until they sell the house (by clearing the existing mortgage and avoid-

ing payments while living in the house). For house price expectations, we find some evi-

dence that those who expect higher price growth are more likely to purchase a reverse

mortgage. Expecting negative growth is negatively correlated with demand for reverse

mortgages. However, this relationship is not always statistically significant and

monotonic.

Finally, there is some evidence that households who want to stay in their house unless

they experience financial hardship are more likely to demand a reverse mortgage. This is

consistent with the consumption smoothing motive as those households are less willing to

sell their house to finance consumption at older ages. We find that those who think that

leaving money to their heirs is important are more likely to purchase a reverse mortgage.

While a bequest motive should decrease demand for reverse mortgages, it is possible that

these households are inclined to make inter vivos transfers with the proceeds from the

reverse mortgage. Overall, several findings are puzzling and one interpretation is that

reverse mortgages are poorly understood by respondents, in particular those with lower

financial literacy.

6 Conclusion

In many countries, the take-up of reverse mortgage is low. While many factors can explain

this low take-up, few studies have looked at the relationship between financial literacy and

Journal of Financial Literacy and Wellbeing 99

https://doi.org/10.1017/flw.2023.4 Published online by Cambridge University Press

reverse mortgage take-up and in particular how financial literacy may change the evalua-

tion that consumers make of reverse mortgages. Reverse mortgage products are complex,

and their value arise from both a desire to smooth consumption for liquidity constrained

households as well as insuring against downside risk in house prices due to the NNEG. This

paper presents survey experimental evidence on the valuation of reverse mortgages by

near-retirees and retirees in Canada.

We find that financial literacy is associated with better knowledge of the existence of

reverse mortgages. However, we do not find a direct relationship between demand for

reverse mortgages and financial literacy. Instead, we find that the interest rate elasticity

of demand for reverse mortgages is negative and statistically significant only for respond-

ents who have higher financial literacy. Furthermore, we find that these respondents are

also more likely to take-up reverse mortgages when the value of the NNEG is larger. We

also uncover an interesting relationship between expected house price growth and

demand for reverse mortgages. Respondents who expect higher price growth appear to

be more likely to demand reverse mortgages and vice versa for those who expect declines

in house prices. This could suggest that the consumption smoothing motive is the value

component of reverse mortgages that these respondents value the most.

These results suggest that the effect of financial literacy on some decisions goes beyond

simply increasing or decreasing the likelihood of purchasing a product. In some instances,

such as reverse mortgages, financial literacy may enable respondents to judge better

financial products and assess their value. These results for reverse mortgages are in line

with results found in other domains, where financial literacy helps consumers minimize

borrowing costs (Huston 2012) or in the savings domain, obtain higher rates of return on

savings (Clark et al. 2017).

The findings in this paper highlight that insurance is a hard concept for households to

understand because it involves complex risk calculations. Those who are less financially

literate may have a harder time with understanding insurance products. More research

should be devoted to understanding how financial education may help households with

insurance decisions.

Supplementary material. To view supplementary material for this article, please visit https://doi.org/10.1017/

flw.2023.4

Acknowledgements. The authors thank David Boisclair, Martin Boyer, Philippe d’Astous, Amine Ouazad, Tom

Davidoff and Raquel Fonseca for their input. They also thank participants of the 2018 conference of the

Retirement and Savings Institute on Managing Risks in Old Age.

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A Appendix: Subjective Mortality Curve Estimation

In continuous time, let the subjective probability of surviving from age a to age 85 be given by:

a;85

x

exp ψ

x





; (11)

where ψ

is an individual level shifter. The objective probability of surviving based on the model for the same

ages is

a;85

x

exp 

x

ds



: (12)

Journal of Financial Literacy and Wellbeing 101

https://doi.org/10.1017/flw.2023.4 Published online by Cambridge University Press

Let Λ

a;85

x



x

ds. Then,

logs

a;85

x

  Λ

a;85

x

 (13)

and

logs

a;85

x

  ψ

a;85

x

 (14)

Dividing equation (14) by equation (13), we have



logs

a;85

x



logs

a;85

x



: (15)

Cite this article: Choinière-Crèvecoeur, Ismael, & Pierre-Carl Michaud (2023). Reverse mortgages and financial

literacy. Journal of Financial Literacy and Wellbeing 1,79–102. https://doi.org/10.1017/flw.2023.4

102 Ismael Choinière-Crèvecoeur and Pierre-Carl Michaud

https://doi.org/10.1017/flw.2023.4 Published online by Cambridge University Press