The Effect of LTV-Based Risk Weights on House Prices: Evidence From - PowerPoint PPT Presentation

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: 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 Different Price 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: 1 ∑ Treat ( p ) = I ( p ∗ LTV > NIS 800, 000 ) ∗ f ( LTV ) , (1) LTV = 0.6 • 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 ( PPSM it ) = α + ˆ β X i + Area i + Γ ∗ θ t + δ ∗ Treat ( p ) + σ ∗ Treat ( p ) ∗ θ t + ǫ it (2) where ln ( PPSM it ) - log price per square meter for unit i sold at time t . X includes number of rooms and log age of the building. θ t - time dummy equal to zero before the policy was implemented and one afterwards. ǫ it - 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 Prices PPSM 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 ( ˆ p ) for each unit based on its hedonic characteristics: p i ) = α + ˆ ln ( ˆ β X i + month i + ǫ i (3) • Then, used this predicted price to compute a treatment effect: 1 ∑ treatment ( ˆ p ) = I ( ˆ p ∗ LTV > NIS 800, 000 ) ∗ f ( LTV ) (4) LTV = 0.6 • Then, use the Diff-in-Diff approach but instead of Treat ( p ) use Treat ( ˆ p ) . • When we generate our estimates we take the statistical variation embedded in our approach into account through a bootstrap procedure. Results 21