A Housing Portfolio Channel of QE Transmission
What this paper finds — and why it matters
Layer 1 — Overview
Research Question
This paper identifies and quantifies a housing portfolio channel of quantitative easing (QE) transmission that operates through household portfolio rebalancing toward second homes (as opposed to the well-studied bank credit channel). The central question is whether, and how much, the ECB’s formal adoption of QE in January 2015 induced households with larger pre-existing bond holdings to shift wealth into residential real estate—specifically second homes held for investment—and what the downstream effects on regional housing market outcomes were.
Setting and Motivation
Germany is used as the empirical laboratory because it experienced a sustained housing boom from 2009 onward that was not accompanied by a household credit boom—a “housing boom without a credit boom.” The national house price-to-rent ratio rose markedly from 2009, especially accelerating after QE adoption in 2015, while the stock of mortgage credit to households as a share of GDP was flat or declining. This decoupling makes Germany well-suited for isolating a non-credit portfolio rebalancing mechanism.
Data
Household-level data come from the Deutsche Bundesbank’s Panel on Household Finances (PHF), a triennial survey fielded in 2011, 2014, and 2017, from which the authors construct a panel of 1,651 households. The key exposure variable is each household’s pre-QE (2014) share of total wealth invested in bonds, both directly and indirectly via mutual funds and insurance. Regional housing outcomes (prices, rents, rental yields) are from Bulwiengesa AG for all 401 German administrative regions (Kreise) at annual frequency, and listing data come from Immoscout 24, Germany’s largest online real estate platform.
Methodology
The household-level analysis uses a difference-in-differences (DiD) specification comparing changes in housing portfolio shares between the pre-QE wave (2014) and the post-QE wave (2017), against the pre-period change (2011 to 2014), with the degree of exposure measured by the 2014 bond share. The specification includes household and time fixed effects. A parallel-trends check using all three survey waves (Figure 2) shows that more- and less-exposed households tracked identically before QE adoption, diverging sharply thereafter. Two indirect placebo tests—using households’ share in non-financial, non-housing assets as a spurious treatment, and using the change in non-financial assets as a spurious outcome—both return null results, supporting the identification assumption. For regional housing outcomes, the authors use a panel regression interacting lagged ECB debt-securities-to-GDP (the QE intensity measure) with a regional exposure variable—the 2008 pre-QE share of refugees housed in independent accommodations—across 401 regions from 2010 to 2017.
Main Findings with Quantitative Magnitudes
Benchmark portfolio rebalancing: A household with an ex-ante bond share that is 10 percentage points higher (roughly the interquartile range of the bond share distribution) increases its portfolio share of second homes by 1.72 to 1.87 percentage points more than a less-exposed household after QE adoption, conditional on household and time fixed effects. This result is statistically significant at the 1% level across multiple specifications and is robust to alternative bond share definitions, alternative portfolio denominators, and controlling for negative interest rate policy exposure (via initial deposit shares).
Equity rebalancing: Controlling for risk aversion does not attenuate the second-home result. Strikingly, households with larger ex-ante bond shares reduce, rather than increase, their equity shares after QE (coefficient: −0.042, significant at 5%), ruling out the interpretation that the housing result merely picks up broad rebalancing toward all risky assets. This implies that cash purchases of second homes are funded by liquidating bonds, drawing down deposits, and also selling equities.
Heterogeneity—household characteristics: Rebalancing is stronger for (a) bank-advised households (triple-interaction significant at 5%), (b) financially more literate households (significant at 1%), and (c) households aged 40–60 (significant at 5%), consistent with a lifetime-income-peak, tax-optimization motive rather than a bequest motive. The result for age 61+ is positive but statistically insignificant.
Tax-motive heterogeneity: In Germany, rented-out second homes (or those declared for future letting) benefit from substantial tax deductions not available for owner-occupied primary residences, with the advantage rising in marginal tax rates. Rebalancing is stronger for higher-income households (triple interaction with income per capita positive and significant, especially after controlling for deposit shares) and for church-affiliated households, who face an additional 8–9% church tax surcharge on their regular tax bill, amplifying the tax gain from rental property deductions. For church members, the income-interaction triple coefficient is statistically significant; for non-church members it is not, directly linking the rebalancing gradient to the church tax burden.
Buy-to-let motive: The benchmark result is driven entirely by households that already owned a second home in the pre-QE period and were generating rental income from it (coefficient 0.821, significant at 1%); households without a pre-owned second home show a near-zero, statistically insignificant coefficient (0.000). This establishes that the rebalancing is driven by experienced buy-to-let investors, not vacation-home buyers or commuters.
Credit channel control: The portfolio rebalancing result is not driven by credit access or credit growth. The triple interactions of the bond-share × Post term with both (a) pre-QE leverage (mortgage credit to housing wealth) and (b) post-QE mortgage credit growth are statistically insignificant. Restricting the sample to households with no mortgage credit growth leaves the main coefficient essentially unchanged (0.175, significant at 1%). Nonetheless, an independent credit-channel effect is also present: mortgage credit growth has its own positive and significant effect on second-home share increases, confirming the two channels operate in parallel but independently.
Regional housing market outcomes—prices and yields: In regions more exposed to rental market tightness (higher refugee-in-independent-accommodation share), QE is associated with larger declines in rental yields. A one-standard-deviation increase in QE (approximately 4.3 pp higher ratio of ECB debt securities to GDP) reduces the rental yield in the 75th-percentile-exposure region relative to the 25th-percentile region by 2 to 12 basis points per year (depending on whether the refugee share or the renter share is used as the exposure measure). As ECB holdings rose from 7% of GDP in 2014 to 24% in 2017, the cumulative implied rental yield decline at the regional interquartile range is 8 to 48 basis points, sizable relative to the average regional rental yield decline of 140 basis points (from 7.4% to 6.0%) over the same period. House prices increase more than rents in more exposed regions.
Regional housing market outcomes—listings: Using Immoscout 24 data, both sale and rental listings decline in more exposed regions as QE expands, but the ratio of sale to rental listings falls significantly: sale listings decrease significantly more than rental listings in more exposed regions. This relative shift in supply toward the rental market is interpreted as evidence consistent with the buy-to-let motive documented at the household level and as potentially having benign implications for housing affordability through increased rental supply.
Scope Conditions
All household-level findings are conditional on the German institutional setting: Germany’s combination of a low-homeownership norm, substantial tax incentives favoring rental properties, triennial household survey data spanning one pre- and one post-QE wave, and a housing boom that was decoupled from household credit prior to 2015. The regional results apply to 401 German administrative regions (Kreise) over 2010–2017, using exposure instruments that are argued to capture rental-market tightness or depth rather than direct household bond holdings.
Layer 2 — Q&A
Q1: What is the housing portfolio channel of QE transmission, and how does it differ mechanically from the credit channel?
A: In the housing portfolio channel, the ECB’s bond purchases reduce the net supply of bonds available to private investors, raising bond prices and reducing expected bond returns. Under the assumption that bonds and houses are substitutes in household portfolios, households with larger initial bond positions rebalance toward housing to restore their target allocation, bidding up house prices. This mechanism operates through changes in risk premia rather than through future short-term rates or bank reserves and loan supply. The credit channel, by contrast, operates through increased bank reserves enabling expanded mortgage lending. The authors show empirically that the two channels operate in parallel and independently, but that greater prior credit access and post-QE mortgage credit growth do not amplify the portfolio rebalancing effect.
Q2: What is the key exposure variable and why is it a valid identification strategy?
A: The exposure variable is each household’s 2014 (pre-QE) share of total wealth invested in bonds, including both direct holdings and indirect holdings via mutual funds and insurance companies. The logic, drawn from the bank-portfolio-rebalancing literature (Rodnyansky and Darmouni, 2017; Luck and Zimmermann, 2020) and from the authors’ own portfolio model, is that the larger a household’s bond share, the stronger its incentive to rebalance when the central bank reduces bond supply. Identification rests on the parallel-trends assumption: Figure 2 shows that before 2015, more- and less-exposed households (defined by a median split on the 2014 bond share) followed identical trends in second-home shares; the trends diverge sharply post-QE. Two indirect placebo tests corroborate this: using a spurious treatment variable (non-financial, non-housing asset share) and using a spurious outcome (change in non-financial, non-housing asset share) both yield null results.
Q3: What is the benchmark magnitude of the portfolio rebalancing effect and how robust is it?
A: A 10-percentage-point higher 2014 bond share (the approximate interquartile range) is associated with a 1.72–1.87 percentage point larger increase in the second-home portfolio share post-QE relative to the pre-QE period (Table 3, columns 1–2, significant at 1%). This result is robust to: scaling second-home shares by a model-consistent denominator (bonds + housing + deposits, column 3); using total housing wealth instead of second-home wealth alone (column 4); using the count of second homes rather than their value share to rule out valuation-effect confounds (column 5); using direct bond holdings without imputation, or indirect holdings only, as alternative exposure measures (columns 7–8, where the coefficients are if anything larger at 0.403 and 0.420); controlling for a broad set of time-varying household characteristics including net worth, age, household size, financial literacy, and risk aversion (Table 4, range 0.19–0.23); and explicitly controlling for the deposit-share post-interaction to rule out the negative interest rate policy as a driver (column 6, main bond coefficient unchanged at 0.122).
Q4: Do households with higher bond exposure also rebalance toward equities after QE?
A: No. Column (7) of Table 4 shows that households with larger ex-ante bond shares reduce their equity shares after QE adoption (coefficient: −0.042, significant at 5%). This rules out the interpretation that the second-home finding merely captures broad rebalancing toward all risky assets due to general risk-appetite changes. Combined with the evidence that deposit shares also decline (though not precisely estimated), the result implies that households fund second-home purchases by selling bonds, drawing down deposits, and reducing equity positions.
Q5: Which household characteristics amplify the rebalancing, and what do they reveal about the mechanism?
A: Five characteristics are shown to amplify rebalancing (Table 5 and Table 7): (1) being actively advised by a bank on asset allocation (triple interaction significant at 5%), consistent with banks that own real estate agencies steering clients toward property; (2) higher financial literacy (significant at 1%), consistent with more informed investors acting more quickly on QE-induced return differentials; (3) middle age (40–60), significant at 5%, but not older age (61+), ruling out bequest motives and pointing to households near their lifetime income peak optimizing their tax burden; (4) higher income per capita (positive and significant, especially among church members), reflecting the progressive German tax schedule that makes property-related deductions more valuable; and (5) church affiliation (the income-triple interaction is significant only for church members, who face an 8–9% church tax surcharge, amplifying the tax advantage of rental property ownership). Tenure status (renter vs. owner of main residence) shows that both groups rebalance, but the triple interaction is significant only at 10%, suggesting the effect is not confined to existing homeowners.
Q6: How is the buy-to-let motive established directly in the data, as opposed to vacation-home or commuter motives?
A: The authors use variation in whether households owned a second home and generated rental income from it before QE adoption (Table 8). Households that owned a second home and reported rental income in the pre-QE wave rebalance very strongly (coefficient 0.821 on Bonds × Post, significant at 1%). Households that owned a second home but did not generate rental income show a positive but imprecisely estimated coefficient (0.641, significant at 10% in a very small sub-sample of 138 households). Critically, households that did not own any second home prior to QE show a coefficient of essentially zero (0.000). This pattern establishes that rebalancing is driven by experienced buy-to-let investors rather than by households acquiring second homes for personal use, and is consistent with the income-seeking motive documented in the Australian context by Gargano and Giacoletti (2022).
Q7: How does the paper demonstrate that the effect is independent of the credit channel, while also acknowledging the credit channel operates?
A: The paper employs three complementary tests (Table 6). First, triple interactions of the Bonds × Post coefficient with pre-QE leverage (mortgage-to-housing-wealth ratio) and with post-QE mortgage credit growth are both statistically insignificant (columns 5–6 of Table 5), meaning that greater credit access does not amplify the bond-share rebalancing effect. Second, restricting the sample to households with zero mortgage credit growth between 2014 and 2017 leaves the main coefficient unchanged at 0.175 (column 1 of Table 6). Third, including the two credit variables as additional controls only marginally reduces the bond-share coefficient without affecting its significance (columns 2–3 of Table 6). At the same time, column 3 of Table 6 shows that mortgage credit growth does have its own statistically significant positive effect on second-home shares (coefficient 0.009, significant at 1%), confirming a separate, independently operating credit channel.
Q8: How is regional exposure to the channel proxied, given that household survey data cannot be aggregated to the regional level?
A: Because the 1,651-household panel provides only 3–4 observations per region on average across 401 German Kreise, the authors cannot construct representative regional averages of household bond shares. Instead, they use the pre-QE (2008) share of refugees housed in independent accommodation in each region as developed by Bednarek et al. (2021), arguing that a larger refugee share creates tighter rental housing market conditions and therefore makes buy-to-let investment more attractive. For robustness, they also use the 2011 census share of renters in each region as an alternative measure of rental market depth. Both regional exposure variables take higher values in urban areas (refugee share: 21% urban vs. 10% rural; renter share: 70% urban vs. 46% rural), consistent with household-level rebalancing being stronger in urban regions.
Q9: What are the quantitative effects on regional rental yields, house prices, and rents?
A: Table 9 shows that a one-standard-deviation increase in QE (approximately 4.3 percentage points higher ECB debt securities-to-GDP ratio) reduces the rental yield in a region at the 75th percentile of the refugee-share exposure distribution relative to the 25th percentile by 2 basis points per year (using the refugee share) to 12 basis points per year (using the renter share). Comparing the 5th vs. 95th percentile of exposure, the yield differential is 5–24 basis points per year. Over the full 2014–2017 QE expansion (from 7% to 24% of GDP), the cumulative implied rental yield decline at the interquartile range of exposure is 8 to 48 basis points—sizable relative to the average regional decline of 140 basis points. House prices increase more than rents in more exposed regions. Using the Campbell-Shiller decomposition, about 70% of return variation is attributable to future price-to-rent increases, 36% to lower future rent growth (consistent with more rental supply), and only 5% to discount rate differentials.
Q10: What do the listing data reveal about the supply implications of the channel?
A: Table 10 shows that QE reduces both sale and rental listings in more exposed regions (both significant at 1%), consistent with the aggregate national decline visible from 2015 onward. Critically, the ratio of sale listings to rental listings declines significantly in more exposed regions: sale listings fall more than rental listings (columns 3 and 6, significant at 1% with both exposure measures). This relative shift implies that the share of properties available for rent increases relative to properties available for sale in regions more exposed to the portfolio rebalancing channel, providing evidence of an expanded rental supply. This finding is interpreted as a potentially beneficial side effect of QE-induced buy-to-let investment for housing affordability, to the extent that a larger rental supply mitigates rent increases even as house prices rise.
Q11: What is the theoretical model underlying the empirical analysis?
A: The model (Appendix C) features a representative local household with mean-variance preferences managing a portfolio of bonds, housing, and cash (equities are omitted for tractability). Preferred habitat investors segment both the national bond market and the local housing market. QE reduces the fixed net supply of bonds, raising bond prices and reducing expected bond returns. Under the substitutability of bonds and houses, households rebalance toward housing to restore optimal allocation, bidding up house prices; the larger the initial bond share, the larger the required rebalancing. Housing supply constraints determine how much rebalancing depresses expected housing returns (rental yields). The model does not unambiguously predict the response of the cash (deposit) share, motivating the empirical investigation reported in column (6) of Table 3.
Q12: What are the aggregate household balance sheet patterns consistent with the individual-level results?
A: Table 1 shows that Germany’s aggregate household real estate share rose from 55% of total assets in 2014 to 56–57% in 2017–2018, while the bond share declined by roughly 0.5 percentage points. The homeownership rate declined by about 2 percentage points over the sample period (from 52.5% in 2014 to 51.4–51.5% in 2017–2018), consistent with an increasing share of landlords and renters—which is compatible with the buy-to-let mechanism since more than 60% of German renters lease from other households. Household leverage also declined (loans-to-assets from 13% in 2014 to 12% in 2017), consistent with portfolio rebalancing rather than credit-driven housing acquisition. The deposit share remained constant over the period, weighing against the negative-interest-rate policy as a driver of portfolio rebalancing.
Key Concepts
Housing portfolio channel of QE transmission: The paper’s central concept—a mechanism by which central bank bond purchases (QE) induce households holding bonds to rebalance their portfolios toward second homes held for investment (buy-to-let), operating through changes in risk premia (bond prices and expected returns) rather than through bank lending channels or future short-term interest rates.
Ex-ante bond share (QE exposure measure): Each household’s share of total wealth invested in bonds (direct holdings plus indirect holdings via mutual funds and insurance) measured in the 2014 pre-QE survey wave. Used as a continuous household-level treatment intensity: the larger this share, the stronger the portfolio pressure to rebalance when the ECB reduces bond supply to the private sector. Corresponds roughly to 10 percentage points per interquartile range.
Buy-to-let motive: In the paper’s usage, the investment purpose of purchasing second homes specifically to rent them out—or to declare them for future letting—in order to exploit Germany’s substantial tax advantages for rented properties (depreciation allowances, deductibility of mortgage interest, management costs, and property taxes against rental income), which are unavailable for owner-occupied primary residences. Distinguished from vacation-home or commuter motives by the presence of pre-QE rental income.
Segmented housing markets / preferred habitat investors: Assumptions embedded in the paper’s theoretical model (following Flavin and Yamashita, 2002; Gete and Reher, 2018; Greenwald and Guren, 2021) that local real estate markets are insulated from national or international housing markets, and that some investors have a binding preference to hold bonds or local housing, so that QE-induced price changes in the bond market are not fully arbitraged away by shifting into liquid alternatives.
Parallel trends (DiD validity): The identifying assumption that, absent QE, households with larger and smaller initial bond shares would have followed the same trajectory in their second-home portfolio shares. The paper documents this graphically using all three survey waves (Figure 2) and supports it with two indirect placebo tests involving unrelated treatment and outcome variables.
Regional rental yield: The rent-to-price ratio at the regional (Kreise) level, derived from Bulwiengesa data. Used as the primary regional outcome variable because it jointly captures discount rate, rent-growth, and price-to-rent dynamics. A Campbell-Shiller decomposition decomposes its predictive content into three components: discount rates (5%), future rent growth (36%), and future price-to-rent ratio changes (70%) in the German regional panel.
Sale-to-rental listing ratio: The ratio of sale listings to rental listings for apartments on Immoscout 24, used as a quantity-side outcome variable. A decline in this ratio in more-exposed regions is interpreted as evidence of a relative increase in rental supply, consistent with the buy-to-let motive and with potentially beneficial implications for housing affordability.
Church tax (Kirchensteuer): A German institutional feature—formally affiliated church members pay an additional 8–9% surcharge on their regular income tax bill (varying by state). Because the tax advantage of owning rental property is proportional to the marginal tax rate, church members face a higher effective marginal tax rate and thus derive larger tax benefits from buy-to-let investment, producing stronger QE-induced portfolio rebalancing for this sub-group.