Capital Flows, Real Estate, and Local Cycles: Evidence ...€¦ · Adam et al. (2012), Aizenman and Jinjarak (2009), Bian and Gete (2015) and Gete (2017), and Sa and Wieladek (2015)

Capital Flows, Real Estate, and Local Cycles:Evidence from German Cities, Firms, and Banks

Peter Bednarek Daniel Marcel te Kaat Chang Ma Alessandro Rebucci

Deutsche Bundesbank Osnabruck University Fudan University Johns Hopkins University

ESSIM 2019, Tarragona, Spain

May 7, 2019

∗The views expressed in this paper do not reflect the views or the position of the Deutsche Bundesbank.

Introduction Data Empirical Strategy Empirical Results Conclusions The German Boom

Motivation

Real estate has a large weight in economies’ income and wealth.

I In the case of Germany:

F Buildings, structures, and land are about 65 percent of households net worth,despite a 50 percent home ownership

F Construction investment is about 5 percent of GDP, or 20-25 percent of totalinvestment, roughly split between commercial and residential, in line with theOECD average

Capital flows are pro-cyclical and co-move with property prices—e.g., Jorda,Schularick, Taylor, and Ward (2018); IMF GFSR (2018).

Main question: what is the role of real estate markets in the transmission ofcapital flow shocks?

I Are there causal effects?I What are the mechanisms?

Bednarek, Kaat, Ma & Rebucci German Real Estate Boom May 7, 2019 2 / 48


Paper’s Contribution

Sets up a new matched data set based on bank-firm level data fromAmadeus, Bista and the German credit register, and on city-level commercialand residential real estate data from Bulwiengesa AG (a proprietary provider)

Proposes a new instrument for property prices: the city distribution ofrefugees

Distinguishes between commercial and residential real estate markets



Preview of Results

1 Capital flow shocks have stronger impact on output growth in cities more“exposed” to real estate markets

I (But only commercial real estate prices amplify the shock consistent withcollateral channel)

2 Collateral affects the allocation of scarce creditI Firms and sectors with higher share of tangible assets get a disproportionately

larger share of scarce creditI Capital allocation seemingly not distorted, consistent with Gopinath et al.

(2017)

3 This credit allocation has real effects: firms with higher tangible assets investmore and hire more workers; no significant TFP growth differences acrosssectors.

4 Credit contributes to commercial, but not residential property price growth,consistent with low and declining leverage of German households



Mechanism uncovered consistent with collateral channel onthe firm side (Liu et al., 2013; 2018)

Financial instability in Southern Europe ⇒ German banks rebalance theirportfolios toward domestic assets, arguably flying safer collateralized domesticlending

Firms with more tangible assets obtain relatively more credit ⇒

I Hire more people and invest more, contributing to local GDP growth

I Commercial real estate prices (CREP) ↑, amplifying initial impact of creditsupply shock on firms



Related literatureMacro evidence is mixed: we take a more granular approach to the analysis of impact andtransmission

Adam et al. (2012), Aizenman and Jinjarak (2009), Bian and Gete (2015) and Gete(2017), and Sa and Wieladek (2015) find a positive association;

However, Favilukis et al. (2013, 2017) find that capital flows cannot explain the UShousing boom.

Yet others document a strong association between capital inflows and house prices, butassume that households and firms are equally leveraged– Cesa-Bianchi, Cespedes, andRebucci (2015); Cesa-Bianchi, Ferrero, and Rebucci (2018)

Literature on the role of foreign investors: we show that refugees influxes can have similareffects

Sa (2016) finds that foreign investors have a positive impact on house price growth

Favilukis and Van Nieuwerburgh (2017) find that an increase in out-of-town home buyersdrives up local property prices

Literature on collateral channel: we consider both residential and commercial real estate in aproperty price boom without credit boom

Households and firms: Iacoviello (2005), Liu et al. (2013), Chaney et al. (2012), Schmalzet al. (2017), Cvijanovic (2014).

Distortive nature of house price booms (e.g. Chakraborty et al., 2018, Doerr, 2018)



Data

Matched bank-firm-level data from Amadeus, Bista, and the Germancredit register

Quarterly frequency, from 2009Q1–2014Q4

Coverage: 2/3 of all bank loans and 44% of German firms in the creditregister

Matching described in detail in the paper

Matched city-level residential and commercial property price data fromBulwiengesa

Annual frequency, from 2009 to 2014

Coverage: 79 urban areas/cities matching the national account definition

Residential indexes: average of detached homes, condominiums, and townhouses prices, based on transaction and valuation data

Commercial indexes: average of retail and office properties, based ontransaction and valuation data

Matching straightforward as datasets share a common city identifier



Empirical Strategy: Road-Map

Capital Flows GDP

PropertyPrices

CreditSupply

Country-level Data

City-level Data

City-level Data

Bank-firm-level Data



Empirical Strategy Step-by-Step

Step 1 Identification:

I We use the PIGS spread to instrument bank inflowsI We build a measure of real estate market tightness (or exposure) for

identification by geographic variation

Step 2 (Effects) Bank inflows, real estate exposure and city business cycle

I Reduced formI First StageI IV ResultsI Amplification evidence

Step 3 (Mechanism)I Bank inflows, collateral and creditI Firm-level real effects (employment, capital expenditure, TFP and capital

misallocationI Credit supply and property price growth



Step 1. PIGS spread and bank portfolio rebalancing: whenthe spread widens, German banks retrench

Country-Level Country-Level Country-Level Country-Level Country-Level Bank-Level

(1) (2) (3) (4) (5) (6)Net Bank Inflows Net Bank Inflows Gross Bank Inflows Gross Bank Outflows Lending-Deposit Spread Foreign AssetsOutside Eurozone Inside Eurozone Inside Eurozone Inside Eurozone Domestic of Banks

Spreadt 0.790 0.991∗∗∗ -0.160 -1.151∗∗∗ -0.115∗∗∗ -0.289∗∗∗

(0.855) (0.223) (0.209) (0.261) (0.026) (0.030)Bank FE - - - - - YesObs 60 60 60 60 48 122,121R2 0.033 0.216 0.009 0.238 0.247 0.815

Note. The regressions are based on quarterly data over the period 2000:Q1-2014:Q4.

Econometric Specification:CFt = γ ∗ SPREADt + εt

where CFt represents alternative measures of capital flows and the lending-deposit spread.



Step 1. PIGS Spread, Bank Flows, and the European Crisis

2007Q1 2009Q1 2010Q1 2011Q1 2012Q3 2013Q3 2014Q4

-8

-6

-4

-2

0

2

4

1

2

3

4

5

6

7

8

9

10Net Bank Inflows (left)Spreads (right)

Note. The five vertical lines mark the following events: (1) the beginning of the German recovery in 2009:Q1;(2) Greek bonds downgrade to junk status and the Troika’s launch of the 2010 110-billion euro bail-out; (3)2011 downgrade and euro area leaders disagreement on the rescue package for Greece; (4) “Whatever ItTakes” speech by ECB Governor Draghi; and (5) announcement interest rate cuts by the ECB.



Step 1. Exposure to real estate market tightness

Real estate market exposure at the city level defined as:

“Share of gross non-developable area” × “Share of refugees”

Is the product of an traditional indicator of supply-side tightness (Saiz, 2010)and a novel demand-side one

Supply elasticity does not control for quality and amenities, which can becorrelated with characteristics such as education and wealth and, as aconsequence, with demand-side variables, and particularly with productivityand output—Davidoff (2016), Gyourko et al (2013), Chaney et al (2012).



Real Estate Exposure Data

Non-Developable Area

This is the gross share of total area that cannot be developed according tozoning restrictions and geographic constraints

Share of Refugees

Share of refugee inflow allocated by governments to a given city in totalGerman refugee inflow

I Government allocates refugees across States following the KoenigsteinerSchluessel, established in 1949 and used also to allocate resources (e.g.,determine each state’s share of research funding to universities and researchinstitutions)

I Quotas set based on lagged population (1/3) and tax revenue (2/3) criteriaI Each state uses its own distribution system: cities bid down their refugee

intakes, and there is political negotiation, but refugees cannot settle freely(more details on this is work in progress)

I Deviations from the norm are minor (Brookings, 2016)I Unlikely impact on labor supply due to language barriers



Exposure and Property Price Growth

Panel A: Commercial Real Estate Panel B: Residential Real Estate

-50

510

15C

omm

erci

al H

P G

row

th

0 100 200 300 400 500Real Estate Exposure

-10

-50

510

15R

esid

entia

l HP

Gro

wth


Note. The correlation coefficient in Panel A (B) is equal to 28% (26%) with a p-value of 0 (0).

Refugee can have strong impact on housing markets—see, for instance, Tumen (AER,2016) on Turkey.

German policy makers complain that allocation rules put strain on local housing marketsbecause they don’t take population density or developable area into account.



Exposure uncorrelated with city output (and employment)Growth

-10

010

2030

GD

P G

row

th


The correlation between both variables is equal to -4% with a p-value of 35%



Step 2. Bank inflows, real estate exposure and citybusiness cycles: reduced form

∆GDP ∆GDP ∆GDP ∆GDPSpreadt−1 0.077* 0.024 0.022 -

(0.043) (0.053) (0.030)Spreadt−1 × Exposurec,t−1 0.001** 0.001** 0.001**

(0.000) (0.000) (0.000)Exposurec,t−1 -0.006** 0.042 0.037**

(0.024) (0.030) (0.017)Time FE No No No YesCity FE No No Yes YesObs 466 466 466 466R2 0.002 0.006 0.114 0.509

Spread has a positive, linear impact

Effect is in cities with higher exposure



Step 2. First Stage result: the instruments are relevant forboth sectors (residential and commercial sectors are tightlycorrelated)

(1) (2)∆RREP ∆CREP

Spreadt−1 0.610∗∗∗ 0.031(0.049) (0.068)

Exposurec,t−1 0.002 -0.001(0.002) (0.005)

Spreadt−1 × Exposurec,t−1 0.001∗∗ 0.002∗∗

(0.000) (0.001)City FE Yes YesObs 466 466R2 0.306 0.139F-Statistics 108.0 15.2



Step 2. The causal effects of bank flows on output growthvia property price changes: 2SLS results (property pricesinstrumented with PIGS spread, Exposure, and theirinteraction)

IV IV

∆GDP ∆GDP∆RREPc,t−1 0.131∗∗

(0.062)∆CREPc,t−1 0.437∗∗

(0.212)City FE Yes YesObs 466 466R2 0.276 0.234



Step 2. Amplification Role of Real Estate Prices: 2SLS

Net Bank Inflows Inside Eurozone Gross Bank Outflows Inside Eurozone Lending-Deposit Spread

(1) (2) (3) (4) (5) (6)∆GDP ∆GDP ∆GDP ∆GDP ∆GDP ∆GDP

∆RREPc,t−1 -0.421 -0.691 0.852(0.911) (1.166) (1.198)

∆CREPc,t−1 0.084 -0.259 1.950∗∗

(0.168) (0.257) (0.786)CFt−1 × ∆RREPc,t−1 0.187 -0.258 -0.395

(0.161) (0.229) (0.854)CFt−1 × ∆CREPc,t−1 0.144∗∗ -0.217∗∗ -0.926∗∗

(0.060) (0.094) (0.415)Time FE Yes Yes Yes Yes Yes YesCity FE Yes Yes Yes Yes Yes YesObs 466 466 466 466 466 466R2 0.267 0.304 0.310 0.309 0.468 0.371

Note. The regressions are based on annual city-level data over the period 2009-2014. The dependent variableis real GDP growth. The main regressors are alternative proxy variables for capital flows (CF) and theinteraction with commercial and residential property price changes. The interaction term is instrumented withthe interaction the PIGS spread and our exposure measure. Property price changes are instrumented with theexposure measure. As a proxy for CF, we consider net bank inflows from inside the euro area, gross bankoutflows inside the euro area, and the domestic lending-deposit spread. All regressions include time and cityfixed effects. The heteroskedasticity-robust standard errors clustered at the city-level are shown in parentheses.∗, ∗∗ and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% levels, respectively.



Step 2. Amplification Effects (Semi-Reduced Form)

Econometric specification same for commercial and residential sectors:

∆GDPct = αc + αt + µ · (SPREADt−1 × ∆CREPc,t−1)

+υ · ∆CREPc,t−1 + εct

∆GDPct = αc + αt + µ · (SPREADt−1 × ∆RREPc,t−1)

+υ · ∆RREPc,t−1 + εct



Step 2. Commercial real estate price growth amplifiesimpact of capital flow shock, while residential sector doesnot

OLS OLS IV IV

(1) (2) (3) (4)∆GDP ∆GDP ∆GDP ∆GDP

∆RREPc,t−1 -0.105 -1.596(0.144) (1.910)

∆CREPc,t−1 -0.172 -0.620∗∗

(0.100) (0.254)Spreadt−1 × ∆RREPc,t−1 0.019 0.233

(0.021) (0.180)Spreadt−1 × ∆CREPc,t−1 0.038∗∗ 0.121∗∗∗

(0.017) (0.039)Time FE Yes Yes Yes YesCity FE Yes Yes Yes YesObs 466 466 466 466R2 0.506 0.509 0.374 0.487

Table: Amplification Role of Real Estate Prices



Step 2. Marginal Effect of Property Price Change on GDPGrowth

Panel A: Residential Real Estate Panel B: Commercial Real Estate

Note. The figure plots the marginal effect of commercial and residential real estate price changeson GDP growth, conditional on the PIGS spread. They are—based on the previous specification.



Step 3. Bank Inflows, Collateral and Credit: Firm LevelEvidence

(1) (2) (3)∆L ∆L ∆L

Spreadqy−1 × TSqy−4 0.013∗∗∗

(0.003)Spreadqy−1 × TS(Industry)qy−4 0.014∗∗∗

(0.003)Spreadqy−1 × TSqy−4 × Interbankqy−1 0.467∗∗

(0.231)TSqy−4 × Interbankqy−1 -3.832∗∗

(1.813)Firm-Year FE Yes Yes NoFirm-Year-Quarter FE No No YesBank-Time FE Yes Yes YesObs 573,119 707,742 387,734R2 0.142 0.145 0.456

Econometric specification:

∆Li,j,qy = αi,qy + αj,y + β · (SPREADqy−1 × TSj,qy−4) + εi,j,qy,

where {i, j, q, y} stand for bank i, firm j, quarter q and year y.



Step 3. Bank Inflows, Collateral and Credit: Industry LevelEvidence

Agriculture

Mining

Energy/Utility

Construction

Manufact.

Wholesale Trade

Retail Trade

Transport/Warehousing

Information

Finance

Real Estate

Technical Services

Education Services

Health Care

Recreation

Accomodation

Other Services

-4-3

-2-1

Ave

rage

Cre

dit G

row

th

20 30 40 50 60 70Average TS Share



Step 3. Bank Inflows, Collateral and Credit: Industry Level

Dependent variable: ∆LSpreadqy−1 × IAgriculture 2.980∗∗∗

(0.97)Spreadqy−1 × IEnergy/Utility 1.053∗∗∗

(0.27)Spreadqy−1 × ITransport/Warehousing 0.628∗∗

(0.24)Spreadqy−1 × IInformation -1.635∗∗

(0.80)Spreadqy−1 × IReal Estate 0.960∗∗∗

(0.33)Bank-Time FE YesFirm-Year FE YesObs 708,714R2 0.133


∆Li,j,qy = αi,qy + αj,y + β · (SPREADqy−1 × I(SECTOR)j) + εi,j,qy,



Step 3. Firm-level and Industry-level Real Outcomes

Firm-Level Firm-Level Firm-Level Industry-Level Industry-Level

(1) (2) (3) (4) (5)∆EMPL ∆K ∆TFP ∆TFP SD(TFP)

Spreadt−1 × TSt−1 0.009∗∗∗ 0.144∗ -0.002 - -(0.00) (0.08) (0.01)

Spreadt−1 × TS(Industry)t−1 - - - 0.016 0.005(0.02) (0.03)

TSt−1 -0.042 1.225∗∗∗ 0.386∗∗∗ - -(0.07) (0.34) (0.08)

TS(Industry)t−1 - - - 1.308 0.335(0.86) (1.14)

LDVt−1 -0.484∗∗∗ 0.031 -0.425∗∗∗ -0.358∗∗ -0.024(0.03) (0.03) (0.03) (0.12) (0.21)

Firm FE Yes Yes Yes - -Time FE Yes Yes Yes Yes YesIndustry FE - - - Yes YesObs 41827 50774 15143 64 64R2 0.364 0.031 0.389 0.340 0.735

Note.



Step 3: Credit Supply and Property Price Growth


∆RREPct = αc + αt + γ · ∆LCREc,t−1 + δ · Xc,t−1 + εct

∆CREPct = αc + αt + ψ · ∆LCREc,t−1 + ζ · Xc,t−1 + εct

∆RREP (∆CREP) is residential (commercial) property price inflation,

∆LCRE is the city-level-aggregate of the simple average of credit growth inagriculture, energy and utilities, transport and warehousing, and real estateitself.



Step 3. Credit Supply Affects the Commercial Sector, butnot Residential One

OLS OLS IV IV IV IV

(1) (2) (3) (4) (5) (6)∆RREP ∆CREP ∆RREP ∆CREP ∆RREP ∆CREP

∆LCREc,t−1 0.009 0.022∗ 0.046 0.101∗∗ 0.051 0.099∗∗

(0.014) (0.013) (0.057) (0.049) (0.059) (0.048)GDP per capitac,t−1 6.787∗∗ 0.278

(2.603) (2.502)∆BuildingPermitsc,t−1 -0.000 -0.001

(0.003) (0.002)∆Migrantsc,t−1 4.423 -0.561

(2.684) (3.955)BUND Yieldt−1 -1.387∗∗∗ -1.056∗∗∗

(0.359) (0.347)∆HouseholdCreditt−1 -0.465∗∗∗ -0.064

(0.111) (0.093)∆HouseholdAssetst−1 0.167∗∗∗ 0.017

(0.057) (0.064)Time FE Yes Yes Yes Yes No NoCity FE Yes Yes Yes Yes Yes YesObs 460 460 460 460 451 451R-squared 0.654 0.658 0.650 0.632 0.624 0.621



Robustness Checks

Step 1: Restricted analysis to crisis period (2007-2014); added macro andbank controls as in column (6)

Step 2: Dropped 4 largest cities more exposed to real estate; added macroand bank controls in column (6)

Step 2: Dropped 2009-2010 observations, a period during which Germangovernment provided credit guarantees to firms in distress; calculated TS atindustry-level, horse-raced Spread*TS with interactions with other firmcontrols

Step 3: Run a regression of the following form:

∆REPct = αc + αt + µ · (SPREADt−1 × TS-Dummyc,t−1) + δ · Xc,t−1 + εct

Here, we see that cities with higher average share of firm-level tangible assetshave higher commercial, but not residential real estate prices



Conclusions

Cities more exposed to real estate market tightness respond more to capitalflow shocks

I Commercial real estate prices amplify transmission of capital flow shocks tooutput growth while residential prices do not in the case of Germany

Tangible collateral affects the credit allocationI Firms with larger share of tangible assets receive a disproportionately larger

share of total creditI Firms (and sectors) with higher share of tangible assets hire and invest moreI But TFP grows as fast as in other sectors

Credit allocation affects commercial real estate price growth, but notresidential property price inflation (as households are not levered in Germany)



THANK YOU


Documents

Capital Flows, Real Estate, and Local Cycles: Evidence ...€¦ · Adam et al. (2012), Aizenman and Jinjarak (2009), Bian and Gete (2015) and Gete (2017), and Sa and Wieladek (2015)