the effect of ltv-based risk weights on house prices ... · ltv-based risk weights limit the ltv...
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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.
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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
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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
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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
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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
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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
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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
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The Housing Market and MPPs in Israel
The Rate of Change in Housing Prices in Israel, 01/2007-12/2015:
Introduction 7
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Data and Identification
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Construction of the Treatment Effect
• Using the observed LTV distribution before the policy:
Data and Identification 17
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Graphical Illustration of the Treatment Measure
Data and Identification 18
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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
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Results
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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
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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
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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
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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
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Placebo Tests: Treatments using other mortgage sizes
Results 24
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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
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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
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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
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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
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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
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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
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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
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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
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Thank you!
Summary 32