G5 | Macro Paper Warehouse

Racial Disparities in Housing Returns

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

Layer 1 — Overview

Research Question

This paper estimates the racial/ethnic gap in realized housing returns using administrative data on individual housing transactions, and investigates the mechanisms that generate those gaps. The central question is: why do Black and Hispanic homeowners accumulate less housing wealth than White homeowners, even as minority homeownership rates have risen substantially over the last century?

Data and Methodology

The authors merge three primary data sources. First, a nationwide panel of residential property records from ATTOM covering 146.8 million arm’s-length home purchases from 1990 to 2020, which records transaction prices, mortgage characteristics, and property-level identifiers. Second, Home Mortgage Disclosure Act (HMDA) records, which contain self-reported race and ethnicity for mortgage applicants. Third, supplementary administrative sources including McDash mortgage servicing records, Equifax credit bureau data, Fannie Mae/Freddie Mac/ABSNet modification records, and the Survey of Income and Program Participation (SIPP). After applying sample restrictions — including requiring an observed purchase price, a linked HMDA record, an arm’s-length repeat sale, a combined loan-to-value ratio of at most 102.5%, and an ownership spell of at least 12 months — the baseline analysis sample comprises 13.6 million ownership spells for Black, Hispanic, and White homeowners who purchased homes with a mortgage between 1990 and 2016 in 40 states. Ownership spells unsold by March 2020 have their value imputed using the FHFA county-level house price index, a procedure that is conservative in that it understates racial gaps.

The authors construct two complementary return measures. The unlevered return compares the annualized ratio of sale price to purchase price. The levered return (internal rate of return) sets the net present value of all homeowner cash flows — down payment, monthly mortgage payments, implicit rent, maintenance, taxes, insurance, transaction costs, and limited liability in foreclosure — equal to zero.

Main Findings

Among mortgaged home purchases, mean annual unlevered returns are 0.5% for Black homeowners, 0.6% for Hispanic homeowners, and 2.8% for White homeowners, implying Black-White and Hispanic-White gaps of approximately 2.3 percentage points per year. Mean annual levered returns are 1.6%, −3.0%, and 6.6% for Black, Hispanic, and White homeowners respectively, yielding gaps of 5.0 and 9.6 percentage points. After adjusting for the approximately one-fourth of purchases made in cash (for which no racial gap is found), preferred estimates of the unlevered gap are 1.9 (Black-White) and 1.4 (Hispanic-White) percentage points.

Distressed sales — foreclosures and short sales — statistically account for the entire gap in returns. Within non-distressed sales, the Black-White gap in annual unlevered returns falls to less than 40 basis points, and the Hispanic-White gap reverses sign. Two distinct factors drive the role of distressed sales: (1) Black and Hispanic homeowners are approximately twice as likely as White homeowners to experience a distressed sale, and (2) minority homeowners live in neighborhoods where distressed sale price discounts are larger — estimated at 39%–40% for Black and Hispanic homeowners versus 28% for White homeowners. A Blinder-Oaxaca decomposition indicates that equalizing distressed sale rates (holding the distressed sale penalty fixed) would eliminate 84.6% of the Black-White unlevered returns gap and 133.6% of the Hispanic-White gap, confirming that the frequency margin dominates the severity margin.

A counterfactual wealth-accumulation exercise using PSID data shows that equalizing housing returns reduces the Black-White gap in housing wealth at retirement by 37%. Equalizing first-time purchase rates reduces the gap by only 1%, illustrating that promoting homeownership without addressing the returns gap is largely ineffective. Equalizing both returns and purchase rates reduces the gap by 49%.

Mechanisms

Approximately one-third of the gap in unlevered returns can be explained by purchase year and county fixed effects, with much of this timing effect attributable to the Great Recession. Controlling additionally for income, family structure, gender, and leverage reduces the gap by a further ~0.3 percentage points, leaving a substantial residual. About half of the racial gap in mortgage default can be attributed to observable credit risk (family structure, income, leverage, credit score). The remainder is associated with unobservable liquidity shortfalls and income instability: median liquid wealth among Black and Hispanic homeowners is $2,400 and $5,400 respectively, and minority homeowners are 2–4 percentage points more likely to transition to unemployment conditional on pre-unemployment income. Using quasi-experimental variation from adjustable-rate mortgage resets, the paper shows that in response to a 10% increase in monthly payments, White homeowners increase 90-day mortgage default by 3.0 percentage points after 12 months, while Black and Hispanic homeowners show increases of 4.5 and 7.1 percentage points respectively — excess sensitivity that is not captured by credit scores. The early-2000s credit supply expansion through private securitization and portfolio lending channels (as distinct from GSE/FHA) contributed to 61.5% of the 6.2-percentage-point increase in the Black-White distressed-sale gap between the 2002 and 2006 purchase cohorts, and 52.0% of the 12.2-percentage-point increase in the Hispanic-White gap. Evidence from the National Survey of Mortgage Originations suggests that Black homeowners hold overoptimistic expectations about future house price growth and income growth relative to their realized outcomes, which may explain why high-risk minority households do not self-select out of homeownership.

Scope Conditions

Results pertain to mortgaged home purchases (approximately three-fourths of all purchases) by Black, Hispanic, and White homeowners in 40 states (non-disclosure states excluded), with primary coverage from 2000 to 2016. No racial gap in returns is found for cash purchases. The racial gap in non-distressed returns is small and not economically meaningful, so the findings specifically pertain to the realized-return distribution that includes the distressed-sale tail.

In depth

Q1. How large is the racial gap in housing returns, and how does it compare to previously documented racial disparities in housing costs?

A: Among mortgaged purchases, Black and Hispanic homeowners each realize annual unlevered returns approximately 2.3 percentage points lower than White homeowners; levered return gaps are 5.0 percentage points (Black-White) and 9.6 percentage points (Hispanic-White). In dollar terms, this translates to a difference of roughly $5,920 per year for the average Black homeowner and $6,762 per year for the average Hispanic homeowner on a ten-year holding horizon. These gaps are an order of magnitude larger than previously documented racial disparities in housing costs, such as post-origination interest rate disparities of about 40 basis points (~$500 annually for a $200,000 home) or inflated property tax assessments amounting to $300–$390 per year.

Q2. What is the role of distressed sales in explaining racial gaps in returns, and how do frequency versus severity contribute?

A: Distressed sales statistically account for nearly the entire racial gap in realized housing returns. Within non-distressed sales, the Black-White unlevered gap falls to less than 40 basis points and the Hispanic-White gap inverts. Two channels operate: (1) Black and Hispanic homeowners are approximately twice as likely as White homeowners to experience a distressed sale; and (2) within distressed sales, minority homeowners realize lower returns because they tend to live in neighborhoods with larger distressed-sale price discounts (estimated at 39–40% below imputed market value for Black and Hispanic homeowners, vs. 28% for White homeowners). A Blinder-Oaxaca decomposition indicates that equalizing distressed sale frequency (holding severity fixed) would close 84.6% of the Black-White gap and 133.6% of the Hispanic-White gap, so the frequency margin is quantitatively dominant.

Q3. Are racial differences in house price appreciation responsible for the gap in non-distressed returns?

A: No. Among non-distressed sales, realized returns closely track county-level FHFA house price index growth for Black, Hispanic, and White homeowners alike, essentially one-for-one regardless of race. There is no economically meaningful racial gap in house price appreciation conditional on avoiding a distressed sale. This finding implies that the gap in average realized returns is not generated by differential neighborhood-level appreciation but rather by the incidence of distressed sales and the price penalties they entail.

Q4. How much of the racial gap in housing returns can be explained by observable homeowner characteristics such as income, family structure, and leverage?

A: Controlling for county and purchase year fixed effects reduces the raw Black-White and Hispanic-White unlevered returns gaps from 2.3 to 1.5 and 1.6 percentage points, respectively. Additionally controlling for income, family structure (gender and co-applicant status), and leverage reduces the gap by a further ~0.3 percentage points. Even among the ostensibly safest group — high-income couples with low leverage — the Black-White (Hispanic-White) gap in unlevered returns is 0.7 (0.5) percentage points. Among high-leverage, low-income, single-male homeowners the gap is 1.8 (1.7) percentage points. Gaps exist within every demographic subgroup, and neighborhoods (Census tract fixed effects) explain roughly half of the remaining gap for Black homeowners and one-third for Hispanic homeowners, but substantial residual gaps persist even within neighborhood.

Q5. What observable credit risk characteristics explain racial differences in mortgage default?

A: Raw racial gaps in 90-day mortgage delinquency are 2.6 percentage points (Black-White) and 1.8 percentage points (Hispanic-White). Controlling for purchase year and county reduces these to 2.2 and 1.6 percentage points respectively. Controlling for family structure, income, leverage, and credit score reduces the gaps to 0.98 and 0.94 percentage points — implying that observable characteristics explain approximately 55% and 41% of the Black-White and Hispanic-White default gaps respectively. Credit scores contribute the most explanatory power among these controls, while mortgage contract characteristics (a test of differential lender treatment) contribute negligibly.

Q6. What is the evidence that liquidity and income instability — factors not observable to lenders — explain the residual racial gap in default?

A: Survey data from SIPP reveal that median liquid wealth (bank accounts, stocks, bonds) for Black and Hispanic homeowners is only $2,400 and $5,400 respectively, while minority homeowners are 2–4 percentage points more likely to transition to unemployment conditional on pre-unemployment income. In SIPP mortgage delinquency regressions, controlling for liquidity, job loss in the prior year, and income reduces the Black-White coefficient by about 30% and the Hispanic-White coefficient by about 41% (and 29% and 70% respectively when also controlling for income level, current loan-to-value, and family composition). In administrative data using ARM payment resets as liquidity shocks, a 10% increase in monthly payments raises 90-day default by 3.0 percentage points for White homeowners, 4.5 percentage points for Black homeowners, and 7.1 percentage points for Hispanic homeowners after 12 months. This excess sensitivity is not substantially reduced by controlling for credit scores, income, or leverage — indicating that the liquidity risk of minority homeowners is largely unobservable to lenders at origination.

Q7. Is there evidence that strategic default explains higher minority distress rates?

A: No meaningful evidence supports strategic default as a driver of excess minority distress. Using quasi-experimental variation in ex-post leverage from diverging option ARM indices (following Gupta and Hansman 2022), the paper finds large causal impacts of leverage on default but no evidence that these impacts are larger for minority homeowners. Separate survey evidence from the NSMO shows a statistically insignificant Black-White difference of 0.05 percentage points (s.e. 0.65) in agreement that “it is okay to default if it is in the borrower’s financial interest” (relative to a White mean of 6.1%). The absence of larger leverage-driven default responses combined with the presence of larger payment-shock-driven responses points specifically to liquidity — not strategic behavior — as the relevant mechanism.

Q8. What is the evidence for information frictions contributing to excess minority homeownership risk?

A: Black homeowners in the NSMO report future house price expectations that are 0.07 standard deviations more optimistic than White homeowners, conditional on past price experiences, yet realized house price growth in the subsequent two years is actually 1.1 percentage points lower for Black homeowners. Although Black homeowners are 2.8 percentage points more likely to report past personal financial crises, their stated expectations about future financial crises are similar to those of White homeowners — despite 90-day default rates that are 2.5 percentage points higher in the first two years post-origination. Black homeowners also report income growth expectations 0.3 standard deviations higher than White homeowners, while SIPP and CPS data show minorities are more likely to experience income losses. These patterns of overoptimistic expectations relative to realized outcomes are consistent with information frictions causing high-risk minority households to suboptimally select into homeownership.

Q9. How much of the racial gap in distress can be attributed to the early-2000s credit supply expansion?

A: The paper identifies the expansion as concentrated in portfolio loans and privately securitized mortgages, which are distinct from GSE/FHA mortgages that did not exhibit a comparable supply increase. Between the 2002 and 2006 purchase cohorts, the Black-White gap in distressed sales rose by 6.2 percentage points overall but only 2.4 percentage points among GSE/FHA loans. A decomposition using this contrast attributes 61.5% of the overall 6.2-percentage-point increase to the credit supply expansion. Analogously, 52.0% of the 12.2-percentage-point increase in the Hispanic-White gap between 2002 and 2006 is attributed to credit supply. Within-race decompositions find that credit supply accounts for 42%, 30%, and 35% of the increase in distress relative to 2002 for Black, Hispanic, and White homeowners respectively, for mortgages originated 2004–2006.

Q10. What is the implied contribution of the returns gap to the racial wealth gap?

A: Using a simple wealth accumulation model calibrated to PSID data on first-time homebuyer rates and home values (average first home for Black households: $142,587; for White households: $208,621), the paper finds an estimated Black-White gap in housing wealth at retirement of $169,389 versus an observed PSID gap of $182,771. Equalizing housing returns would reduce this gap by 37%. In contrast, equalizing first-time purchase rates alone reduces the gap by only about 1%, because low returns nullify the benefit of purchasing earlier. Equalizing both returns and purchase rates reduces the gap by 49%. Housing wealth in the primary home constitutes 43% of total net wealth for the average retirement-age Black household in PSID, implying the returns gap explains a quantitatively large share of the overall racial wealth gap.

Q11. What do the COVID-19 pandemic forbearance experience and mortgage modification evidence imply for policy?

A: Quasi-experimental estimates using servicer-level variation in modification propensity show that mortgage modifications cause economically large increases in housing returns for Black, Hispanic, and White homeowners alike, suggesting that since minority homeowners are more likely to become distressed, expanded modifications would disproportionately benefit them. The pandemic experience provides macroeconomic confirmation: after the onset of COVID-19 forbearance and foreclosure moratoria in March 2020, the Black-White gap in unlevered returns and distressed sales fell by approximately half, while the Hispanic-White gap (whose pre-pandemic distress convergence was already underway) remained comparatively stable. Administratively, Black homeowners who default are already 3–7 percentage points more likely than observationally similar White homeowners to receive a modification, even controlling for neighborhood and servicer, suggesting servicers partially internalize the larger distressed-sale discounts in minority neighborhoods.

Q12. Are neighborhood-level factors — specifically distressed-sale price discounts from illiquid real estate markets — important for explaining racial heterogeneity in returns conditional on distress?

A: Yes. Using MLS data on median days-on-market as a measure of real estate market thickness, the paper shows that distressed sale discounts are substantially larger in less-liquid markets, with discounts experienced by Black homeowners approximately 13 percentage points lower in the least-thick markets relative to the thickest. Black and Hispanic homeowners are disproportionately likely to realize distressed sales in thin markets. Regular sale returns are not affected by market thickness. This establishes that neighborhood market illiquidity is a second-order channel through which neighborhood-level factors contribute to the racial gap — primarily by amplifying the severity of distressed sale penalties rather than by affecting ordinary house price appreciation.

Key Concepts

Distressed sale: In this paper’s usage, an ownership spell that ends in either a foreclosure (where a lender seizes and sells the property after payment default) or a short sale (where the lender allows the homeowner to sell for less than the outstanding mortgage balance without holding the homeowner liable for the deficiency). Distressed sales are the central mediating factor between race and housing returns.

Unlevered return: The annualized ratio of sale price to purchase price, capturing property-level capital gains without reference to the financing structure. Computed as (P_sale / P_purchase)^(1/T) − 1. Does not capture leverage amplification or limited homeowner liability in foreclosure.

Levered return (internal rate of return): The discount rate that sets the net present value of all homeowner cash flows to zero, including down payment at purchase; monthly payments (principal, interest, taxes, insurance, maintenance); implicit rent; and the net proceeds at sale (property sale price minus outstanding principal balance, subject to a floor of $0.01 capturing limited liability). This measure accounts for both the amplifying effect of leverage on gains and the homeowner’s limited liability in underwater foreclosures.

Distressed sale frequency versus severity: The two distinct components through which distressed sales generate racial gaps. Frequency refers to the higher probability that a minority homeowner’s ownership spell terminates in a distressed sale. Severity refers to the larger price discount at distressed sale that minority homeowners experience, concentrated in neighborhoods with illiquid real estate markets. The paper’s decomposition finds frequency is the dominant margin.

Unobservable liquidity risk: Default risk arising from insufficient liquid wealth (cash, bank deposits, liquid securities) and income instability that is not captured by credit scores or other characteristics observable to lenders at mortgage origination. The paper’s ARM-reset event study shows this risk generates excess minority default responses even conditional on credit score and income.

Information friction (overoptimism): The tendency of minority homeowners, particularly Black homeowners, to hold expectations about future house prices, personal financial crises, and income growth that are more optimistic than their realized outcomes and than observationally similar White homeowners’ expectations. The paper uses this to explain why high-risk minority households do not self-select out of homeownership despite the high cost of distressed sales.

Credit supply channel: The mechanism by which the early-2000s expansion of private securitization and portfolio lending — channels that exhibited substantially greater growth among Black and Hispanic borrowers than among White borrowers — contributed to increased rates of minority distress during the Great Recession. Distinguished from GSE/FHA channels that did not exhibit comparable credit expansion and serve as the counterfactual.

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.

Why Is Intermediating Houses So Difficult? Evidence from iBuyers

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

This paper examines frictions in dealer intermediation in durable consumer goods markets, using iBuyers — technology-driven real estate companies such as Opendoor and Offerpad — as a lens. The central research question is why dealer intermediation, which provides immediate liquidity by purchasing assets onto a balance sheet and reselling, is so limited in the U.S. housing market (valued at $50 trillion and representing roughly 70% of the median household’s net worth) relative to other durable goods markets such as automobiles.

The authors use CoreLogic deed transaction data and MLS listing data from five markets with substantial iBuyer presence (Phoenix, Las Vegas, Dallas, Orlando, and Gwinnett County, Georgia) over 2013–2018, covering arm’s-length, non-foreclosure single-family home and condominium transactions. They supplement this with Redfin ZIP-level data on listing speed and American Community Survey demographics. iBuyers are identified as Opendoor, Offerpad, Knock, Zillow, and Redfin.

The empirical analysis documents that iBuyers grew from roughly 1% market share in Phoenix in 2015 to about 6% by 2018, acting as balance-sheet intermediaries who hold properties for a median of 105 days. iBuyers purchase homes at a 3.1 percentage point (pp) discount relative to comparable homes sold in the same ZIP-quarter, and sell at a 2.2 pp premium relative to other institutional sellers, for a combined gross spread of approximately 5.3 pp (reported in the abstract and body as ~5%). Sellers to iBuyers show a 6.8 pp higher rate of market exit post-sale and a 4.0 pp higher probability of purchasing before selling, consistent with demand for immediacy from impatient, relocating households.

Two key frictions constrain intermediation. First, adverse selection: iBuyers rely on algorithmic valuation models (AVMs) that explain over 80% of price variation in iBuyer transactions versus only 68% in non-iBuyer transactions, leaving a residual of soft information (odor, neighbor quality) that sellers know but algorithms cannot capture. iBuyer presence is over three times greater in the lowest pricing-uncertainty tercile versus the highest, and a one standard deviation increase in pricing uncertainty reduces iBuyer presence by 1.23 pp within a ZIP and reduces gross spread per transaction by 1.5 pp. Second, underlying illiquidity: iBuyers are almost entirely absent in market segments where the probability of sale within three months (PSALE) falls below 50%, despite strong seller demand.

To quantify these frictions, the authors build and calibrate a continuous-time directed search equilibrium model with a dealer intermediary subject to adverse selection. Six parameters are calibrated to match empirical moments: iBuyer market share (5%), purchase discount (3.1 pp), sale premium (2.2 pp), iBuyer concentration in the most versus least liquid PSALE quartiles, impatient seller fraction, and median iBuyer holding time. The calibrated adverse selection parameter (α = 0.35) means the intermediary correctly identifies 35% of low-quality homes as such; the impatient seller share (μ = 0.18) means 18% of unmatched sellers are highly impatient; and the vacancy depreciation rate (d = 0.02) means 2% per period for unoccupied homes. External validation via a difference-in-differences comparison of Phoenix against other markets yields model-consistent predictions of a 0.5 pp reduction in time on market and a 0.8 pp increase in house prices.

Counterfactual experiments reveal that introducing a 30-day acquisition delay (rather than near-instantaneous) reduces iBuyer market share from 5% to below 2%; eliminating the signal entirely (α = 0) drops market share to just above 1%; and enabling iBuyers to rent vacant properties during the holding period could raise market share above 7.5 pp. A 50% reduction in PSALE reduces iBuyer market share roughly proportionally.

The calibrated model is then applied to other durable goods markets by varying informational asymmetry, liquidity, and depreciation parameters. Cars — more homogeneous (year/make/model/mileage fully characterizes value), mobile (transportable across markets), and depreciating primarily through use — are predicted to support dealer intermediary market shares of 40–55%, consistent with observed U.S. car dealer market share of ~50%. Reducing the depreciation rate from the housing level (d = 0.02) to a car-like level (d = 0.005) alone increases intermediary market share by about 5 pp. Houses — heterogeneous, immobile, and depreciating through time rather than use — are predicted to support near-zero intermediation under pre-iBuyer technology. The authors also explain COVID-19 iBuyer suspensions (reduced market liquidity made resale untenable) and Zillow’s November 2021 exit (very liquid markets eroded the iBuyer speed premium, worsening adverse selection while rapid price appreciation degraded AVM accuracy).

Q: What discount do iBuyers pay when purchasing homes, and what premium do they earn when selling? A: iBuyers purchase homes at a 3.1 pp discount relative to comparable homes sold in the same ZIP code and quarter, with a t-statistic of 8.55. They sell at a 2.2 pp premium relative to other institutional sellers. The combined gross spread is approximately 5.3 pp (referred to throughout the paper as roughly 5%).

Q: How large is the iBuyer market share, and in which markets did they operate? A: iBuyer market share grew from approximately 1% in Phoenix in 2015 to roughly 6% by 2018. In Gwinnett County, Las Vegas, and Dallas/Orlando, shares reached approximately 4%, 4%, and 2% respectively by 2018. The analysis covers five markets: Phoenix, Las Vegas, Dallas, Orlando, and Gwinnett County (suburban Atlanta).

Q: What is the evidence that iBuyer sellers are impatient rather than simply lower-quality-house owners? A: Sellers to iBuyers exhibit a 6.8 pp higher rate of market exit (defined as purchasing a home outside the county or making no subsequent real estate purchase within 12 months), consistent with relocation-driven impatience. They also have a 4.0 pp higher probability of purchasing a new home before completing the sale of their current home, which is enabled by the iBuyer transaction’s speed facilitating mortgage approval conditional on the existing property’s sale.

Q: How do the authors measure adverse selection risk and what is its relationship to iBuyer presence? A: Adverse selection is proxied by the squared residual from a hedonic pricing regression — the variation in transaction prices unexplained by observable characteristics — computed at the ZIP-year level for non-iBuyer transactions. iBuyer presence is over three times greater in the lowest pricing-uncertainty tercile than in the highest. A one standard deviation increase in pricing uncertainty reduces iBuyer presence by 1.23 pp within a ZIP (controlling for ZIP fixed effects, local prices, house age, and square footage), and reduces gross spread per transaction by 1.5 pp.

Q: What role does underlying asset liquidity play in constraining iBuyer intermediation? A: iBuyers concentrate almost entirely in market segments where the ex ante probability of selling within three months (PSALE) exceeds 50%, and are essentially absent where PSALE falls below 50%. This holds even though sellers in low-PSALE segments have strong demand for immediacy, implying that illiquidity raises intermediation costs above the demand-side willingness to pay a discount.

Q: What does the model’s calibration reveal about the share of impatient sellers and the accuracy of iBuyer signals? A: The calibrated adverse selection parameter α = 0.35 means the intermediary correctly identifies 35% of low-quality homes as low quality (the signal is moderately but imperfectly informative). The calibrated impatient seller share μ = 0.18 means approximately 18% of unmatched sellers are highly impatient and willing to accept a significant price discount for immediacy. The vacancy depreciation rate d = 0.02 implies a 2% per period cost for unoccupied properties.

Q: How important is transaction speed to the iBuyer model? A: Introducing a 30-day acquisition delay (rather than near-instantaneous purchase) reduces iBuyer market share from 5% to below 2% — a reduction of more than 60%. The model mechanism is that the primary iBuyer customers are highly impatient sellers who place extreme value on immediate transactions; even a moderate delay substantially reduces their willingness to accept a price discount.

Q: What happens if iBuyers lose their ability to distinguish between high- and low-quality homes? A: Setting the signal accuracy to zero (α = 0, the “naive intermediary” case) causes iBuyer market share to fall from 5% to just above 1%. Without any quality signal, severe adverse selection forces the intermediary to offer substantially lower prices to break even, which in turn reduces the number of sellers willing to transact.

Q: How much would enabling iBuyers to rent vacant properties during the holding period affect market share? A: The rental-enabled iBuyer counterfactual shows that market share could increase above 7.5 pp from the baseline 5%, because rental income would allow iBuyers to offer higher purchase prices while offsetting carrying costs. This suggests that rental infrastructure or policy changes permitting temporary rentals would substantially expand the scope of dealer intermediation in housing.

Q: How does the model validate itself externally? A: The authors use a difference-in-differences design comparing Phoenix (earlier and larger iBuyer entry) to the other four markets. The model predicts iBuyer entry should reduce average time on market and increase house prices; the DiD results show a 0.5 pp reduction in time on market and a 0.8 pp increase in house prices in Phoenix relative to comparison markets post-entry, consistent with model predictions.

Q: Why did iBuyers suspend operations during the COVID-19 pandemic despite having a contactless technological advantage? A: The model explains the suspension through the liquidity channel: iBuyers’ value proposition depends on quickly reselling acquired properties, not merely on contactless buying. When market liquidity collapsed during lockdowns (transaction volumes fell sharply), iBuyers could not resell properties quickly, making intermediation unprofitable regardless of their purchasing-side technological advantage. As liquidity recovered, iBuyers resumed operations.

Q: What does the model say about Zillow’s exit from iBuying in November 2021? A: In very liquid markets, the iBuyer speed advantage shrinks because homeowners can sell quickly in the traditional market anyway, reducing the discount sellers accept when selling to an iBuyer. With a smaller discount, adverse selection worsens because only sellers with unfavorable private information (knowing their house has problems the algorithm overvalued) choose the iBuyer route. The pandemic-era housing market also featured rapid price appreciation that degraded AVM accuracy trained on historical data, compounding adverse selection. Zillow reported having significantly overpaid for homes, consistent with this mechanism.

Q: Why is dealer intermediation approximately 50% in car markets but near-zero historically in housing? A: The model, applied to car-market parameters, predicts 40–55% dealer intermediation, consistent with observed U.S. car market shares. Three structural differences explain the gap: (i) cars are more homogeneous (year/make/model/mileage sufficiently characterizes value), reducing adverse selection; (ii) cars are mobile and can be transported across markets, increasing effective liquidity; and (iii) cars depreciate primarily through use, so holding a car on a dealer lot incurs lower value loss than leaving a house vacant. Reducing the depreciation rate from the housing calibration (d = 0.02) to a car-like level (d = 0.005) alone raises predicted intermediary market share by about 5 pp.

Q: Does subjective value dispersion (heterogeneity in buyer preferences) play a large role in limiting intermediation? A: While subjective value dispersion plays a significant role in shaping search market equilibrium (affecting match quality and the gains from household-to-household search), the model finds its effect on the overall level of intermediation is comparatively less pronounced than informational asymmetry, market liquidity, or the opportunity cost of vacancy.

Q: What evidence supports the claim that iBuyers use algorithmic pricing? A: Observable property characteristics and ZIP-quarter fixed effects explain over 80% of price variation in iBuyer transactions, compared to only 68% in non-iBuyer transactions. The higher R-squared for iBuyer transactions is consistent with iBuyers relying on measurable, formalizable characteristics rather than soft information (such as odors or neighbor property conditions) that traditional buyers gather through physical visits.

Q: What are the structural limits on iBuyer expansion even with improved technology? A: Even with enhanced pricing technology (lower α), the scope for dealer intermediation remains narrow because strong incentives persist for iBuyers to avoid markets where algorithmic valuation is difficult, such as older and less homogeneous housing stock. The fundamental barriers — heterogeneity, immobility, and high vacancy opportunity cost — cannot be overcome by technology alone, meaning iBuyers are unlikely to reach the ~50% market share seen in automobile dealer markets.

iBuyers: Technology-driven real estate companies (principally Opendoor and Offerpad) that use automated valuation models and online platforms to make near-instantaneous cash offers on homes, functioning as dealer intermediaries who purchase properties onto their balance sheet and resell after a short holding period, thereby providing immediate liquidity to sellers who would otherwise wait 90+ days in the traditional listing process.

Dealer (Balance Sheet) Intermediation: A form of market-making in which an intermediary purchases an asset outright and holds it on its own balance sheet while finding a subsequent buyer, as distinct from matchmaking intermediaries (brokers) who connect buyers and sellers without taking ownership. The intermediary earns a gross spread between purchase and sale prices.

Adverse Selection (in iBuyer context): The problem arising because sellers possess soft private information about their property (odors, hidden defects, neighbor quality) that algorithmic valuation models cannot capture, while traditional buyers can acquire this information through physical visits. Because iBuyers price quickly without visits, they disproportionately attract sellers of unobservably lower-quality homes, as measured in the paper by the calibrated parameter α = 0.35 (the fraction of low-quality homes the intermediary correctly identifies).

Algorithmic Valuation Model (AVM): The pricing technology used by iBuyers to value homes near-instantaneously using observable property characteristics. The paper measures AVM performance by the R-squared of a hedonic regression: over 80% for iBuyer transactions versus 68% for non-iBuyer transactions, with the residual representing information the algorithm misses and traditional buyers discover through visits.

PSALE (Probability of Sale within 3 Months): An ex ante measure of a property’s underlying liquidity, estimated from a probit model on non-iBuyer listings, capturing the probability that a given home sells within three months of listing. The paper uses PSALE as the key liquidity variable; iBuyers are almost entirely absent where PSALE falls below 50%.

Occupancy Cost: The value loss incurred when a house is held vacant on an intermediary’s balance sheet — encompassing both foregone housing service flows (which continue to benefit occupants under traditional listing but are lost under iBuyer ownership) and ongoing maintenance and depreciation costs (calibrated at d = 0.02 per period). This cost distinguishes housing from goods like cars that depreciate primarily through use rather than time.

Gross Spread: The difference between the price at which an iBuyer sells a property and the price at which it purchased that property, expressed as a percentage of the acquisition price. The paper documents a gross spread of approximately 5% (combining the 3.1 pp purchase discount and the 2.2 pp sale premium), which is persistently positive over the sample period.