G11 | Macro Paper Warehouse

A Housing Portfolio Channel of QE Transmission

Mon, 01 Jan 0001 00:00:00 +0000

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.

In depth

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.

Rent Guarantee Insurance

Mon, 01 Jan 0001 00:00:00 +0000

Abramson and Van Nieuwerburgh study Rent Guarantee Insurance (RGI), a product in which an insurer pays the landlord on behalf of a tenant who defaults on rent due to a negative income or health expenditure shock, in exchange for a monthly premium proportional to rent. The central question is whether RGI can be designed to be both welfare-improving and financially viable, given the frictions of moral hazard and adverse selection.

The authors develop a dynamic overlapping-generations equilibrium model of the rental market that features endogenous rent default, security deposits, evictions, and homelessness. Households face idiosyncratic persistent and transitory income risk, idiosyncratic medical expenditure risk, and aggregate (cyclical) income risk. Rental contracts are non-contingent, households face borrowing constraints, and housing is indivisible with a minimum quality floor. Landlords set deposits to break even in expectation given observed tenant characteristics. An insurance agency can offer RGI and must also break even in the long run. The model is calibrated to the United States at monthly frequency. Income dynamics are estimated from CPS data (1994–2023) and incorporate transitions among employment, unemployment, out-of-labor-force, and retirement states along with transfer income (unemployment insurance, disability, food stamps) and a progressive tax system. Key moments targeted by Simulated Method of Moments include a delinquency rate of 12.15% (model: 12.69%), average security deposit of $984 (model: $992, from approximately 500,000 Craigslist listings across the 100 largest MSAs), homelessness rate of 1.43% (model: 1.42%), and home-ownership rate of 63.6% (model: 63.2%).

The model’s pre-RGI analysis establishes that persistent income shocks — not transitory shocks or medical shocks — are the primary driver of rent defaults. Default risk remains elevated for 3–6 months following a persistent shock, implying that short-duration RGI coverage is insufficient to prevent eviction; coverage must span multiple months.

The paper’s main policy experiments introduce RGI under different access rules and provider types. Unrestricted RGI (available to all renters) generates large welfare gains through improved risk-sharing and lower security deposits — because insured tenants pose less default risk, landlords lower deposit requirements — but is not financially viable for either a public or private insurer due to moral hazard and adverse selection. Even a public insurer that internalizes the fiscal savings from reduced homelessness cannot break even under unrestricted access.

Restricting access changes the viability calculus sharply. A publicly provided RGI targeted to households at the bottom of the wealth distribution can achieve financial viability: these households are precisely those most prone to homelessness, so the reduction in homelessness expenses — which the public insurer internalizes — offsets the insurance deficit. This restricted public RGI generates substantial welfare gains for the most vulnerable households.

A privately provided RGI must instead target higher-wealth renters to break even, because these households have low default risk (limiting claim payouts) while remaining sufficiently risk averse to pay the premium. The intersection of financial viability and take-up is small, yielding a limited target audience. The private program has minimal impact on housing insecurity, and the most vulnerable households derive little benefit. This pattern matches observed private RGI markets, where providers restrict access to renters in good financial condition.

An RGI mandate — requiring all renters to purchase coverage — mitigates adverse selection by improving the pool of insured tenants, dramatically increasing financial viability and allowing the insurer to reduce the premium substantially while still breaking even. Mandated RGI is highly effective at preventing housing insecurity and generates welfare gains concentrated among the most financially vulnerable households.

Scope conditions: results are calibrated to U.S. income, medical, and housing market parameters as of 2019. The insurer’s borrowing cost matters: the public insurer faces lower, counter-cyclical municipal bond spreads, whereas private insurers face higher, pro-cyclical corporate spreads, which constrains the generosity of private contracts in recessions.

Q: What is Rent Guarantee Insurance and how does it work mechanically in the model? A: RGI is a contract under which a tenant pays a flat monthly premium equal to a fraction kappa of rent. When the insured tenant defaults, the insurer pays the landlord directly and deducts one period from the tenant’s stock of “insurance credit.” The tenant remains housed. Once insurance credit is exhausted, the insurer no longer covers defaults. The insurer sets the premium and the maximum coverage duration to break even in the long run.

Q: Why do most rent defaults arise from persistent rather than transitory shocks? A: The model shows that the renter population is disproportionately exposed to persistent unemployment and labor-force-exit spells, and that negative persistent income shocks are harder to smooth through savings than transitory ones. Default risk remains elevated for 3–6 months after a persistent shock but dissipates quickly after a transitory shock. This implies that RGI coverage periods of only a few months would fail to prevent eviction for the majority of defaulting tenants.

Q: How does RGI affect security deposits in equilibrium? A: Because landlords observe the tenant’s insurance status at lease signing and deposits are set to make landlords break even in expectation, insured tenants pose lower default risk and thus face lower upfront deposit requirements. This deposit reduction is a key welfare channel of RGI, as large deposits tie up a disproportionate share of poor households’ wealth and price the most vulnerable out of housing entirely.

Q: Why is unrestricted RGI financially non-viable even for the public insurer? A: Unrestricted access induces both adverse selection — riskier households self-select into coverage — and moral hazard — insured households alter their default and savings behavior. These effects cause the insurer to run a persistent deficit. Even a public insurer that internalizes the fiscal cost savings from reduced homelessness cannot recoup enough to break even, implying that an unrestricted program would require an ongoing subsidy.

Q: How does publicly provided restricted RGI achieve financial viability? A: By targeting households at the bottom of the wealth distribution — precisely those most prone to homelessness — the public RGI program produces large reductions in homelessness. Because the public insurer internalizes the fiscal expenses associated with shelters, health services, and policing that accompany homelessness, these savings are passed through to the insurer and are sufficient to offset the insurance deficit. No such mechanism is available to a private insurer.

Q: Why must private RGI target higher-wealth renters, and what are the consequences? A: Private insurers must break even using only premium revenue, without access to homelessness cost savings. Higher-wealth renters have lower default probabilities, which limits claim payouts, while remaining sufficiently risk averse to demand coverage and pay the premium. The viable target audience is small given these competing requirements. As a result, private RGI covers few households, has minimal effect on housing insecurity, and provides essentially no benefit to the most vulnerable renters. This pattern is consistent with observed private RGI markets.

Q: What are the two differences between public and private insurers in the model? A: First, the public insurer internalizes the fiscal costs of homelessness (shelters, health services, policing), raising its net benefit from offering coverage. Second, the public insurer borrows at municipal bond spreads — which are lower than corporate spreads and counter-cyclical — whereas the private insurer faces higher, pro-cyclical corporate spreads. Counter-cyclical borrowing costs allow the public insurer to extend more generous coverage precisely when aggregate conditions deteriorate and claims rise.

Q: How does an RGI mandate improve financial viability? A: Mandatory enrollment forces all renters, including low-risk ones, into the insurance pool, which counteracts adverse selection. The expanded and higher-quality pool dramatically reduces per-insured expected claim costs, allowing the insurer to lower the premium substantially while still breaking even. The low-premium mandated policy is then both affordable and effective at preventing housing insecurity, with welfare gains concentrated among the most financially vulnerable renters.

Q: What novel data does the paper use for calibration of security deposits? A: The authors construct a dataset of approximately 500,000 Craigslist rental listings scraped across the 100 largest U.S. metropolitan statistical areas between November 2022 and March 2024 to measure the cross-sectional distribution of security deposits. The average deposit in this dataset is $984, which the model matches closely at $992. The data also reveal that the deposit-to-rent ratio is decreasing in house quality, reflecting the higher default risk of low-income renters in lower-quality units.

Q: What is the paper’s definition of homelessness and what rate does the model match? A: Homelessness is defined broadly to include sheltered homeless, unsheltered homeless (0.6% of households), and doubled-up families (0.83% of households), for a total of 1.43% of U.S. households. The model matches this rate closely at 1.42%.

Q: What is the paper’s key implication for the design of housing policy? A: The central implication is that financial viability and impact on housing insecurity are in tension for private insurers, and cannot both be achieved simultaneously. Only a publicly provided program that internalizes homelessness fiscal costs and faces counter-cyclical borrowing spreads can target the most vulnerable renters, break even, and materially reduce housing insecurity. Private RGI, while viable for a narrow segment, cannot substitute for public provision as a tool against homelessness.

Q: How does RGI relate conceptually to rental assistance programs? A: The paper distinguishes RGI from rental assistance on a structural basis: insurance contracts require tenants to pay premiums, making them potentially self-financing for private providers, whereas rental assistance is a net transfer that can never be self-financing. This conceptual distinction motivates studying whether RGI can be designed to eliminate the need for ongoing fiscal transfers, though the analysis ultimately shows that a public subsidy or mandate is required to serve the most vulnerable renters.

Rent Guarantee Insurance (RGI): A contract under which an insured tenant pays a monthly premium equal to a flat percentage of rent; when the tenant defaults, the insurer pays the landlord directly, preserving tenancy, for a limited number of periods governed by the tenant’s stock of insurance credit.

Insurance Credit: An endowment of periods of RGI coverage that households receive upon entry into the model; each time the insurer pays on behalf of a defaulting tenant, one unit of credit is consumed, and no further coverage is available once credit is exhausted.

Housing Insecurity: In the paper’s framework, the set of outcomes — rent delinquency, eviction, and homelessness — arising from the combination of non-contingent rental contracts, borrowing constraints, and idiosyncratic or aggregate income and medical shocks.

Security Deposit: An upfront payment from tenant to landlord, set by the competitive landlord to break even in expectation given the tenant’s characteristics and insurance status; a key channel through which RGI affects welfare by reducing the upfront cost barrier to obtaining housing.

Moral Hazard (in RGI context): The change in a tenant’s default, savings, and housing choices induced by the presence of insurance coverage, which increases expected claim costs for the insurer relative to a world where behavior is held fixed.

Adverse Selection (in RGI context): The tendency of renters with higher default risk to self-select into RGI when access is unrestricted, worsening the insurer’s risk pool and driving up expected payouts relative to premiums.

Homelessness Externality: The fiscal costs borne by government — for shelters, health services, and policing — that accompany homelessness; the public insurer internalizes these costs, creating a net benefit from RGI that private insurers cannot capture.

Counter-cyclical Borrowing Spread: The feature of public (municipal bond) financing whereby borrowing costs fall during recessions, allowing the public insurer to expand coverage when claims are highest; contrasted with private insurers’ pro-cyclical corporate bond spreads that tighten precisely when aggregate conditions worsen.

The Price of Housing in the United States, 1890–2006

Mon, 01 Jan 0001 00:00:00 +0000

Lyons, Shertzer, Gray, and Agorastos construct the first consistent, annual, quality-adjusted market rent and home sales price series for American cities spanning 1890–2006. The paper addresses a fundamental data gap: no annual city-level series existed for market rents at any point in the 20th century, and no annual city-level sales price series existed prior to 1975. Existing national series—the BLS Rent of Primary Residence (RoPR) for rents and the Shiller index for sales—carry well-documented methodological limitations that the authors argue have produced materially misleading stylized facts about long-run U.S. housing markets.

The Historical Housing Prices (HHP) dataset draws on just under 2.7 million newspaper real estate listings from 30 U.S. cities across 1890–2006. Listings must contain a price, a size measure (rooms or bedrooms), property type (house or apartment), and a location indicator. The authors construct hedonic price indices using a rolling-windows methodology—baseline three-year rolling windows with annual step size—that controls for size, type, and standardized within-city location, allowing coefficients to vary over time rather than imposing a fixed vector across the full century. City-level indices are aggregated to national indices using population weights from census data interpolated between census years. Listed prices serve as proxies for transaction prices; the authors validate these against census distributions and against post-1975 FHFA and Case-Shiller series.

The paper’s findings revise several established stylized facts. First, real market rents did not fall over the 20th century as implied by the RoPR series. Instead, real rental price levels were approximately 20% higher in 2006 than in 1890, fluctuating within a relatively narrow band. The RoPR series, by contrast, implies a near-halving of real rents between 1914 and 2006. Second, the paper documents a substantial interwar housing boom-bust absent from the Shiller index: real sales prices rose approximately 47% between 1920 and 1928, then fell 27% by 1935, with the 1928 peak not recovered in real terms until 1968. Third, contrary to the Shiller index’s depiction of minimal housing price growth from 1950 to 1995, the HHP series shows real sales prices rising 21% between 1953 and 1974—a period for which Shiller relies on a truncated sample of government-backed mortgages that excluded higher-valued homes.

On the return to homeownership, the paper finds average nominal housing returns across 1890–2006 of approximately 11% per year, composed of 3.8% capital gain and 7.2% rental return. Gross market rental yields exceeded 8% annually for much of 1900–1945, fell to 7% by 1960, and to 3% by 2006. Capital gains were largely unimportant before the 1940s and became the dominant return component only from 1970 onward; the post-1980 period with sustained capital gains is characterized as historically anomalous. Returns varied substantially across cities, with some cities outperforming the S&P 500 in the prewar era while most underperformed equities from 1981–2006.

The paper also examines implications for the CPI. The HHP series implies nominal rents grew at approximately 3.5% per year from 1914 to 2006, versus 2.6% per year for the RoPR component. A back-of-the-envelope alternative CPI using HHP rental data yields overall price growth of 3.3% per year rather than the official 3.1%, suggesting the measured increase in U.S. living standards since World War I may be modestly overstated. Finally, cross-city analysis shows that land constraints and, increasingly, regulatory constraints explain divergence in price growth across cities, with the role of zoning becoming more pronounced after 1980.

Q: What is the core data source and how are the indices constructed? A: The HHP dataset comprises just under 2.7 million newspaper real estate listings from 30 U.S. cities, 1890–2006, sampled from real estate sections (typically the last Sunday of each month). Valid listings require price, size, property type, and within-city location. Hedonic indices are estimated using rolling three-year windows with annual steps, controlling for size, type, and standardized location, allowing hedonic coefficients to evolve over time rather than imposing a fixed vector. City indices are aggregated to national indices using population-weighted census data interpolated between census years.

Q: Why are the HHP series based on listing prices rather than transaction prices, and how is this limitation addressed? A: Transaction-price records require local archival effort infeasible across 30 cities over 116 years, and rental transaction data are essentially unavailable historically. The authors argue that hedonic mix-adjustment makes listed prices strong predictors of selling prices during normal market conditions, and that a substantial share of houses transact at their exact listing price. Validation against census distributions and against post-1975 FHFA and Case-Shiller series supports the approach; the authors acknowledge listing prices may diverge from transaction prices at cyclical peaks and troughs.

Q: What does the paper find about the long-run trajectory of real market rents, and how does this revise existing understanding? A: The HHP series shows real rental price levels in 2006 were approximately 20% higher than in 1890 or 1914, fluctuating within a relatively narrow band over the century. The BLS RoPR series implies real rents fell by nearly half between 1914 and 2006. The HHP findings align with the most influential proposed corrections to the RoPR by Gordon & van Goethem (2007) for 1915–1939 and broadly with Crone et al. (2010) in terms of overall growth levels for 1940–1995, though the HHP series shows a sharper rental spike after World War II rent controls were lifted that the BLS methodology captures only with deliberate lag.

Q: What does the paper find about the interwar housing cycle, and why does the Shiller index miss it? A: The HHP series documents that real sales prices rose approximately 47% between 1920 and 1928, then fell 27% by 1935, with the 1928 nominal peak not regained until 1946 and the real peak not until 1968. The Shiller index for 1890–1934 is based on a 1934 survey of owner recollections of past transaction prices and assessed values, which the authors argue reflects homeowners’ lack of awareness of the changing value of their homes over prior decades. The HHP finding is consistent with census data, Nicholas & Scherbina’s study of New York City, and Fishback & Kollmann’s analysis of New Deal reports.

Q: What does the paper find about the 1953–1974 period, and what explains the divergence from the Shiller index? A: The HHP series shows housing sales prices increased 21% in real terms between 1953 and 1974, while the Shiller index (based on the Home Purchase Component of the CPI) implies a moderate decline of around 10%. The Shiller index for this period uses a truncated sample of government-backed mortgages subject to FHA loan limits; when the authors truncate their own data using the same statutory FHA limits ($30,000 in 1973, $45,000 in 1974, $60,000 in 1977), approximately 50% of their 1971–1979 listings are excluded and their truncated series matches the Shiller index more closely. This supports the Greenlees (1982) critique of downward bias in the Home Purchase CPI component.

Q: What are the long-run return components to homeownership at the national level? A: Average nominal housing returns across 1890–2006 were approximately 11% per year: 3.8% capital gain and 7.2% rental return. Before World War II (1890–1945), average nominal rental returns ranged from 7.9% to 8.3% per sub-period while capital gains averaged near zero or negative in real terms. Only in 1981–2006 did capital gains (averaging 5.8%) exceed the rental return (averaging 5.3%). The return to housing has thus been dominated by rental income over the long run, with the post-1980 era of sustained capital gains constituting a historical anomaly.

Q: How do rental yields evolve over the sample period? A: Gross market rental yields exceeded 8% annually for much of 1900–1945, with spikes after both World Wars and a dramatic fall from nearly 11% to below 7% during the early 1920s boom, consistent with a bubble dynamic before the Great Depression. Yields fell to approximately 7% by 1960 and to 3% by 2006. City-level heterogeneity was substantial: rental returns exceeded 15% in some cities in the two decades before the Great Depression, and most cities saw returns above 10% nominally during 1930–1945, while even by 1981–2006 cities like Phoenix and St. Louis averaged above 12%.

Q: What does the paper find about housing and the business cycle? A: Real growth rates in GDP and housing prices moved in the same direction in 72 of 116 years for sales prices and 65 of 116 years for rental prices. The paper identifies three major downturns where falling rents led falling prices which led falling GDP: the Great Depression (rents fell from 1924, prices from 1929, GDP from 1930), the early 1990s recession (rents from 1988, prices from 1990, GDP from 1991), and the end-of-sample period (rents from 2002). Only after World War I (1920–21) and World War II (1945–46) did clear economic contractions occur without equivalent housing price downturns.

Q: What does the paper find about cross-city variation in housing returns, and what does this imply for the volatility puzzle? A: Capital gains and rental returns vary substantially across cities and time periods; some cities saw returns exceeding the S&P 500 before World War II (including New York and Chicago), while most underperformed equities from 1981–2006. The authors argue that the apparently low volatility of housing returns at the national level documented by Jordà et al. (2019) is partly an aggregation artifact: local housing markets with very different trajectories are combined into a national index, dampening measured variance. The mild positive correlation between city-level capital gains and rental returns has an R² of 0.24.

Q: What are the implications for CPI measurement? A: The HHP series implies nominal rents grew at approximately 3.5% per year from 1914 to 2006, compared with 2.6% per year for the BLS RoPR component, with higher growth concentrated in the years after both World Wars and in the 1965–1985 period. A back-of-the-envelope alternative CPI substituting HHP rental data yields overall price growth of 3.3% per year rather than the official 3.1%. If rental price growth before 1985 is understated in the BLS data, then there has been less improvement in the U.S. standard of living since World War I than was previously understood.

Q: What does the paper find about the role of supply constraints in explaining cross-city price divergence? A: Natural land constraints are positively linked to price growth throughout the 20th century, with the relationship sharpest during 1930–1945 (before the postwar suburban expansion) and again after 1980. Regulatory constraints—measured at the turn of the millennium—have become an increasingly important driver of cross-city price differences, consistent with zoning functioning as a tax (Gyourko & Krimmel 2021). The paper also finds evidence suggesting land-use regulations are partly driven by expectations of future price growth, consistent with the homeowner-voter hypothesis (Fischel 2015; Trounstine 2018).

Q: How does the paper validate its series against existing sources? A: The HHP rental series aligns closely with the Rees and Jacobs (1961) series for 1890–1914. For sales, the HHP series matches the Case-Shiller-Weiss and FHFA repeat-sales indices at both national and city level after 1990 despite methodological differences. The paper finds approximately 25% more price growth than the CSW series over 1975–2006 (117% versus 90% in the 30 HHP cities), attributing some of the divergence to OFHEO appraisal-based valuations before 1992 and the HHP coverage of the broader owned housing market beyond single-family homes.

Historical Housing Prices (HHP) Project: A dataset of just under 2.7 million newspaper real estate listings from 30 U.S. cities, 1890–2006, used to construct annual, quality-adjusted hedonic price indices for both rented and owned housing segments at the city and national level.
Rolling-windows hedonic methodology: An index construction approach that runs sequential hedonic regressions over two-, three-, or five-year overlapping windows with annual step size, allowing the coefficients on size, type, and location to evolve over time rather than imposing a fixed vector across the full sample period, reducing bias from unobserved quality changes.
Market rent vs. contract rent: Market rent (the listing price for a rental unit actively advertised) is conceptually distinct from contract rent (the rent paid by tenants currently in situ), which is what the BLS RoPR series measures. Market rents adjust to vacancy and lease resets faster than contract rents, producing substantially more short-run volatility and a materially different long-run trend.
Gross rental yield (rent-to-price ratio): Annual rental income from a property divided by its market sales price, computed as RI_{c,t} / HPI_{c,t}. Gross yields exceeded 8% annually for much of 1900–1945 and fell to 3% by 2006 nationally, making rental income the dominant component of total housing returns for most of the century.
Total return to housing: The sum of the capital gain (percentage change in sales price) and the rental return (rental income divided by sales price), computed at annual, city, and national frequency for 1890–2006. The average nominal total return was approximately 11% per year, with 3.8% from capital gains and 7.2% from rental income.
Rent of Primary Residence (RoPR): The BLS survey-based series measuring changes in contract rents for a rotating panel of rental units, used as the shelter component of the CPI. The HHP series implies this series understates rental price growth by approximately 0.9 percentage points per year (3.5% vs. 2.6% nominal growth), concentrated in post-World War periods and 1965–1985, due to tenant non-response bias and delayed incorporation of new construction.
Supply constraints and cross-city divergence: Natural land constraints (geographic barriers to development) and regulatory constraints (zoning and land-use regulation) that limit housing supply, both positively associated with price growth, with regulatory constraints becoming increasingly important after 1980 and consistent with the hypothesis that land-use regulations are partly driven by homeowner expectations of future price appreciation.