The Effect of LTV-Based Risk Weights on House Prices:

Evidence From an Israeli Macroprudential Policy

Nitzan Tzur-Ilan, Northwestern University and Bank of Israel

Steven Laufer, Federal Reserve Board

MAY 29, 2020

2020 AREUEA National Meeting

DISCLAIMER: The views expressed here are those of the authors and do not necessarily reflect the views of the Bank of Israel or the Board of

Govenrnors.

Motivation

• House prices and residential mortgages play central roles in the credit cycle that

sparked the Global Financial Crisis.

• As a result, many of the macroprudential policies imposed in the wake of the crisis

have specifically focused on banks’ provision of mortgage credit.

• Those policies serve two main purposes (Krznar and Morsink (2014); Lim et al.

(2013)):

1. discourage banks from originating riskier mortgages which reduce bank losses

during economic downturns.

2. Limiting the build up of financial imbalances by moderating the growth in

house prices.

Introduction 1

Motivation - Cont.

• A large literature has found that that an easing of mortgage credit leads to

stronger house price growth (e.g. Mian and Sufi (2009); Favara and Imbs

(2015); Di Maggio and Kermani (2017)).

• Therefore, one can expect that MPPs that limit mortgage credit would

affect the growth rate of house prices.

• However - open question in the literature regarding the effect of

macroprudential policy on house prices.

Introduction 2

LTV Limit - Affecting Housing Prices?

• This question received considerable attention in the literature,

but with mixed conclusions.

• Some studies do find that LTV limits reduce house price growth

(e.g. Igan and Kang (2011); Galati and Moessner (2013);

Akinci and Olmstead-Rumsey (2018)).

• Others fail to find any such effects.(e.g. Wong et al. (2011);

Kuttner and Shim (2016); Cerutti et al. (2017)).

Introduction 3

Literature - Identification Challenges

• Implementation of MPPs is highly endogenous to housing prices.

• These policies are typically used in combination with other

policies - challenge to attribute outcomes to specific tools.

• Challenges in controlling for country characteristics, quality of

MPP supervision, use and intensity of MPP and the phases of

the financial cycle.

• Availability of data.

Introduction 4

Goals of this paper

1. A cleaner identification of the effect of LTV caps on house

prices by studying policy that only affects part of the market.

2. The heterogeneity effect: which type of areas may be more

affected by these policies.

3. Generates an estimate of the semi-elasticity of housing prices

with respect to mortgage rates.

Introduction 5

MPP Measures Implemented

MPPs Date Type of MPP

MPP1 Oct-10 Increase capital provision for high-LTV-ratio loans

MPP2 May-11 Limit share of adjustable interest rate loans

MPP3 Nov-12 Limit LTV to 75% for FTHB, 50% for investors

MPP4 Feb-13 Raise risk weights for capital adequacy requirements

MPP5 Aug-13 PTI limited to 50% of HH income

Limit variable interest share of the loan to two-thirds

Limit loan period to 30 years

MPP6 Sep-14 Additional Tier One capital requirement

Introduction 6

The Housing Market and MPPs in Israel

The Rate of Change in Housing Prices in Israel, 01/2007-12/2015:

Introduction 7

Data and Identification

Data

• Property-level data from the Israel Tax Authority on the

universe of household purchases of residential properties.

• Detailed information on each property: date, location, price,

size and building year.

• Our analysis focuses on the period between Jan 2010 to May

2011 (90K obs.).

Data and Identification 8

LTV-based risk weights limit

• October 2010: risk-weight factor was raised from 35 to 100

percent for mortgages with:

1. An LTV of at least 60 percent.

2. A mortgage value higher than NIS 800,000 (USD 220,000).

Data and Identification 9

LTV-based risk weights limit

• The LTV limit required banks to set aside more capital against

risky loans.

• Regulation increased interest rates on high-LTV loans by

0.31-0.36 PP (Tzur-Ilan, 2017).

• LTV increases the yearly interest rate payments, on average, by

2,700-3,250 NIS (4% of average household gross yearly income).

• Thus, although the policy is statutorily imposed on lenders, it

appears as if a large portion of the economic burden ends up

being born by borrowers in the form of higher interest rate

(DeFusco et al., 2020).Data and Identification 10

Change in LTV Distribution

Incentivize risky borrowers (LTV>60%) to reduce leverage:

Less credit for the purchase of a housing unit.

Data and Identification 11

Identification Strategy

• Because this MPP only applied to mortgages over a certain

size, we can measure its effect on house prices by comparing

price growth in different segments of the Israeli housing market.

• Only for housing units above a certain price would a mortgage

with a given LTV ratio be larger than the 800K threshold.

• Assume that buyers always use an 75% LTV. Then only for

units with transaction prices above NIS 1.06M would the

mortgage be larger than the 800K threshold.

Data and Identification 12

Identification Strategy - cont.

• Use a Diff-in-Diffs approach to compare units with prices above

and below this NIS 1.06M threshold, before and after the policy.

• Similar to Adelino et al. (2012), that study the effect on house

prices in the US caused by the ability of the GSE to purchase

mortgages below a certain size.

• In that context, the authors argue that one can safely assume

that the marginal buyer will use an 80 percent LTV loan.

Data and Identification 13

Identification Strategy - cont.

• This paper’s setting is more complicated, as the Israeli housing

market is not dominated by a single LTV ratio.

• Construct a more general treatment measure: uses the observed

distribution of LTV, capture the likelihood that a particular unit

would be purchased using a mortgage affected by the policy,

given the transaction price.

• Then perform the Diff-in-Diffs estimation.

Data and Identification 14

Identification Strategy: Different Effects at DifferentPrice Ranges

Distribution of LTV Ratios Before and After LTV Limit, by Sale

Price:

• As we consider transactions at higher prices, a wider range of LTV ratios would

place the purchase mortgage above the NIS 800,000 threshold.

Data and Identification 15

Construction of the Treatment Effect

• Treatment: probability that the unit would be purchased with a

mortgage above NIS 800K and an LTV 60%.

• For a transaction at price p:

Treat(p) =1

∑LTV=0.6

I (p ∗ LTV > NIS800, 000) ∗ f (LTV ), (1)

• p*LTV - Mortgage Size

• f(LTV) - fraction of units purchased in the previous year using a mortgage with

that LTV ratio.

Data and Identification 16

Construction of the Treatment Effect

• Using the observed LTV distribution before the policy:

Data and Identification 17

Graphical Illustration of the Treatment Measure

Data and Identification 18

Empirical Methodology

• Diff in Diff: Compare purchases before and after the policy,

between more and less treated apartments.

• We estimate the following hedonic equation.

• For a transaction at price p:

ln(PPSMit) = α+ β̂Xi +Areai +Γ ∗ θt + δ ∗Treat(p)+σ ∗Treat(p) ∗ θt + εit(2)

where ln(PPSMit) - log price per square meter for unit i sold at time t. X includes

number of rooms and log age of the building. θt - time dummy equal to zero before the

policy was implemented and one afterwards. εit - well-behaved error term clustered at the

locality statistical area level. Our primary interest is in the coefficient σ.

Data and Identification 19

Results

The Estimated Effect of LTV limit on Housing PricesPPSM PPSM PRICE

(1) (2) (3)

3.roomsgroup -0.183*** -0.101*** 0.233***

(0.00598) (0.00603) (0.00660)

4.rooms group -0.345*** -0.179*** 0.441***

(0.00775) (0.00815) (0.00930)

5.rooms group -0.490*** -0.241*** 0.570***

(0.00851) (0.00927) (0.0107)

lnage 0.00371*** -0.00346*** -0.0126***

(0.000810) (0.000887) (0.00111)

Treatment 0.156*** 0.744*** 1.010***

(0.00623) (0.0175) (0.0176)

After 0.0812*** 0.0998*** 0.0940***

(0.00360) (0.00362) (0.00380)

TreatmentAfter -0.0404*** -0.0309*** -0.0235***

(0.0108) (0.0119) (0.0092)

Geographic FE NO YES YES

Constant 2.113*** 2.319*** 5.517***

(0.00977) (0.0619) (0.183)

Observations 90,332 90,332 90,332

R-squared 0.891 0.902 0.919

Results 20

Alternative treatment variable

• Potential concern: price is both the outcome variable and the input used to compute the

treatment effect.

• An alternative model: compute a predicted price (p̂) for each unit based on its hedonic

characteristics:

ln(p̂i ) = α + β̂Xi +monthi + εi (3)

• Then, used this predicted price to compute a treatment effect:

treatment(p̂) =1

∑LTV=0.6

I (p̂ ∗ LTV > NIS800, 000) ∗ f (LTV ) (4)

• Then, use the Diff-in-Diff approach but instead of Treat(p) use Treat(p̂).

• When we generate our estimates we take the statistical variation embedded in our

approach into account through a bootstrap procedure.

Results 21

The Estimated Effect of LTV limit on Predicted Pricespredicted price

PPSM PRICE

(4) (5)

3.roomsgroup -0.0591*** 0.170***

(0.00699) (0.00950)

4.rooms group -0.0789*** 0.525***

(0.00913) (0.00967)

5.rooms group -0.0811*** 0.804***

(0.0157) (0.0120)

lnage -0.00741*** -0.0254***

(0.00469) (0.00672)

Treatment 0.744*** 1.012***

(0.0231) (0.0193)

After 0.0959*** 0.0846***

(0.00674) (0.00699)

TreatmentAfter -0.0212*** -0.0287***

(0.0231) (0.0090)

Geographic FE YES YES

Constant 2.324*** 6.288***

(0.0965) (0.0152)

Observations 90,332 90,332

R-squared 0.902 0.893Results 22

Pre-Trends Test: The Estimated Effect of LTV limit a Year Before the Policy

predicted price

PPSM PRICE PPSM PRICE

(1) (2) (3) (4)

3.roomsgroup -0.232*** 0.237*** -0.096*** 0.235***

(0.00538) (0.00729) (0.00654) (0.00767)

4.rooms group -0.414*** 0.448*** -0.171*** 0.449***

(0.00544) (0.01011) (0.00838) (0.01061)

5.rooms group -0.600*** 0.574*** -0.235*** 0.575***

(0.00613) (0.01177) (0.01010) (0.01257)

lnage -0.001*** -0.012*** -0.003*** -0.0118***

(0.00038) (0.00112) (0.00092) (0.00160)

Treatment 0.019 0.018 0.010 0.022

(0.01359) (0.02177) (0.02028) (0.03285)

After 0.021 0.011 0.028 0.012

(0.04068) (0.01157) (0.03506) (0.05201)

TreatmentAfter 0.015 0.006 -0.009 0.013

(0.02528) (0.02164) (0.01089) (0.01186)

Geographic FE YES YES YES YES

Geographic FEAfter YES YES YES YES

Constant 2.354*** 5.489*** 1.777*** 5.259***

(0.00554) (0.02453) (0.19597) (0.03679)

Observations 82,242 82,242 82,162 81,734

R-squared 0.811 0.897 0.855 0.898

Results 23

Placebo Tests: Treatments using other mortgage sizes

Results 24

Direct Observation of High-LTV Mortgages

• Instead of using the transaction price to estimate the probability that a buyer

would use a mortgage affected by the policy, use information about the

mortgage itself to conduct what might be considered the non-instrumented

OLS version of this exercise.

• Merge the housing transaction dataset to loan-level data from the Bank of

Israel.

• Directly identify buyers affected by the policy, i.e. those who purchase with

loan above NIS 800 thousand and with LTV above 60 percent.

Results 25

Direct Observation of High-LTV Mortgages

(1) (2) (3)

3.rooms group -0.677*** -0.840*** -0.605***

(0.178) (0.167) (0.00703)

4.rooms group -0.686*** -0.999*** -0.708***

(0.180) (0.170) (0.00279)

5.rooms group -0.652*** -1.144*** -0.519***

(0.182) (0.174) (0.00234)

ln age 0.00495*** 0.00515*** 0.00489***

(0.000484) (0.000474) (0.000490)

price 0.00128*** 0.00120***

(3.81e-05) (2.63e-05)

price sq’ -1.64e-07***

(7.22e-09)

Treatment 0.0764*** 0.00747 0.00831

(0.0149) (0.0157) (0.0106)

After 0.134*** 0.0533*** 0.0446***

(0.00822) (0.00930) (0.00310)

Treatment#After -0.0693*** -0.0749*** -0.0544***

(0.0259) (0.0238) (0.0208)

Constant 3.016*** 2.848*** 1.809***

(0.186) (0.172) (0.0173)

Observations 33,311 33,311 33,311

R-squared 0.514 0.550 0.586Results 26

Semi-Elasticity with respect to Interest Rates

• The mechanism underlying these results is that banks charge higher interest

rates on these high-LTV loans.

• Earlier research (Tzur-Ilan, 2017) has found that interest rates on mortgages

affected by this policy were higher by 0.31-0.36 percentage points.

• Combining with the baseline estimate of the effect on house prices, this

paper produces an estimate for the semi-elasticity in the range of 6-10,

consistent with the upper range of the results reported in the literature (e.g.

Adelino et al. (2012), Pinto et al. (2018), Kuttner (2014), Anenberg and

Kung (2017)).

Results 27

LTV limit - Welfare Implications

• To the extent that LTV limits are effective in reducing housing price growth,

they will also make housing more affordable.

• However, the reduction in price will come about via a reduction in demand,

as LTV limits make (some) mortgages more expensive.

• The effects may be especially strong on lower income households, which are

more likely to be liquidity constrained and to rely on riskier mortgages (e.g.,

high LTV mortgages).

Results 28

LTV limit - Welfare Implications

• The Israeli Central Bureau of Statistics publishes a socioeconomic index of

neighborhoods quality for each neighborhood in Israel.

• This index combines 16 different variables, including education, employment,

income, family size and standard of living into a single index.

• Neighborhoods are then classified into one of twenty clusters, 1 being the

lowest socioeconomic status and 20 being the highest.

• For our analysis, we divide neighborhoods into two groups: low-quality areas,

those neighborhoods that are graded from 1 to 10, and high-quality areas,

neighborhoods that are graded from 11 to 20.

• We repeat our main estimation separately on these two groups of

neighborhoods.Results 29

Differential Effects by Neighborhood Quality

Low-Graded Areas High-Graded Areas

(1) (2)

3.roomsgroup -0.220*** -0.270***

(0.00768) (0.00524)

4.roomsgroup -0.611*** -0.536***

(0.00345) (0.00517)

5.roomsgroup -0.549*** -0.691***

(0.0108) (0.00563)

lnage -0.0109*** -0.0126***

(0.000713) (0.00111)

Treatment -0.129*** 1.820***

(0.00397) (0.00865)

After 0.744*** 0.0334***

(0.0175) (0.00389)

TreatmentAfter -0.0571*** -0.0274***

(0.00380) (0.00362)

Geographic FE YES YES

Geographic FEAfter YES YES

Constant 2.254*** 2.566***

(0.00502) (0.00510)

Observations 38,585 51,747

R-squared 0.897 0.875Results 30

Larger Effects in Poorer Neighborhoods of More Expen-sive Areas

Low-Graded Areas High-Graded Areas

Jerusalem -0.0255** -0.02077***

(0.0113) (0.00679)

North -0.0102 0.00974

(0.00999) (0.00715)

Haifa -0.0137*** -0.00970

(0.00348) (0.0113)

Center -0.0761*** -0.0454***

(0.00742) (0.00703)

Tel-Aviv -0.0667*** -0.0324***

(0.00670) (0.0131)

South -0.00463 0.00239

(0.00487) (0.00348)

Results 31

Summary and Conclusions

• Provides new quantitative evidence for the impact of credit markets on house

prices and supports the interpretation that MPPs affect house prices through their

effects on mortgage interest rates.

• Finds a semi-elasticity of house prices with respect to interest rates in the range of

6-10, consistent with the upper range of estimates reported in the literature.

• Larger effects in poorer neighborhoods of more expensive areas. Suggests that

policies may disproportionately affect households that are already struggling to

afford their housing needs.

Summary 32

Thank you!

Summary 32