Presidential Address: Housing Betas: Piazzesi (2025)

What this is. The paper’s core results, the stylized facts on housing betas, and the segmented-markets model that explains the pre-GFC puzzle: enough to know what it found and how, without reading all 34 pages. To replicate or extend it, read the full source at the original.

TL;DR

The paper documents that housing returns had a negative stock-market beta from the postwar period until the Global Financial Crisis, and a positive beta thereafter, while the cashflow growth rates of stocks and housing comove positively throughout. This “housing beta puzzle” is a challenge for representative-agent models, which predict positive return comovement for assets with similar cashflows. A two-type OLG model with segmented stock and housing markets connected through a collateralized bond market resolves the puzzle: homebuyers are poorer per unit of their future housing dividends than stockholders, so they borrow using houses as collateral. Aggregate bad news triggers either a credit demand channel (unconstrained homebuyers cut borrowing sharply while stockholders cut supply only modestly, raising house prices and cutting stock prices) or a credit supply channel (constrained homebuyers are pinned at the collateral limit while stockholders flee to safety, expanding credit supply, cutting the interest rate, and raising house prices as stock prices fall). Both channels produce negative return comovement. Post-GFC shifts (wealthier homebuyers, institutional housing investors, foreign Treasury demand) weaken credit and market segmentation, moving the economy toward positive comovement.

Core results

Magnitudes are as reported; locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Housing betas (slope from regression of 10-year housing returns on 10-year stock returns, two-sided exponential decay kernel) were mostly negative or zero before the GFC and turned strongly positive after it	Figure 2, p. 3109	Housing beta: roughly -0.2 to 0 in 1960s-1990s; peaks near +1 around 2020; levered housing beta even more negative pre-GFC (below -1 during 1980s-1990s)
R2	Cashflow growth rates of stocks and housing comove positively throughout the sample, with correlation 80% pre-GFC and 59% for the full sample	Figure 3, p. 3110	Correlation of 10-year real cashflow growth rates: 80% for 1930-GFC subsample, 59% full sample 1939-2024
R3	Levered housing return has an even more negative beta before the GFC than unlevered housing, because borrowing costs are lower during housing booms	Figure 2, p. 3109; Figure 5, p. 3113	Levered housing beta reaches below -1 during the 1980s-1990s (Figure 2, green line); Panel A of Figure 5 shows real 10-year Treasury returns are negative during the 1950s, 1970s, and 2020s when house prices rise
R4	Homebuyers’ idiosyncratic housing risk is large: cross-sectional standard deviation of annualized capital gains spans -40% to +60% per year in San Francisco and similar volatility in Huntsville, AL	Figure 6, p. 3114	Distribution is right-skewed and more dispersed post-GFC; average capital gain 6.7%/yr (San Francisco) vs 40 bp (Huntsville) over the last decade per CoreLogic data
R5	Proposition 1 (p. 3121): if homebuyers have lower savings per unit of cashflows than stockholders (Assumption A), homebuyers borrow from stockholders in equilibrium; credit market connects the two segmented markets	p. 3121	Qualitative; follows from $f(0) > 0$ under Assumption A: excess credit demand is positive at the no-credit benchmark
R6	Proposition 2 (p. 3122): bad news about the aggregate economy generates negative return comovement. (i) Unconstrained homebuyers: higher uncertainty $\sigma^2$ raises the stock price-dividend ratio and lowers the housing price-dividend ratio, with credit declining; (ii) constrained homebuyers: bad news shifts credit supply down (flight to safety by stockholders), increasing credit, raising house prices, and lowering stock prices	p. 3122	Qualitative proposition; mechanism is the asymmetric sensitivity of levered homebuyers vs. unleveraged stockholders to aggregate uncertainty
R7	Proposition 3 (p. 3125): forces that weaken the credit channel generate positive comovement. Equal savings-to-dividends ratios across types eliminate credit and negative comovement (part i); scaling household savings by $\lambda > 1$ raises both price-dividend ratios (part ii); population growth $n$ reduces the effective discount rate and raises both price-dividend ratios (part iii)	p. 3125-3126	Qualitative; wealthier homebuyers post-GFC, demographic aging, institutional and foreign investors are the candidate empirical counterparts
R8	In the 2000s housing boom, negative comovement is consistent with the data: house prices rose while stock returns declined (Figure 1, p. 3108); both the credit demand channel (laxer collateral, buyer optimism expanding credit demand) and a credit supply shifter (securitization, subprime expansion) were active	Figure 1, p. 3108; §III.B, p. 3124	Annual data 1955-2024 show the shaded house-price boom episodes (1970s, 2000s, 2020s) coincide with stock market slumps

Overall (paper’s conclusion). Representative-agent models predict positive return comovement for assets with similar cashflows; the data show the opposite before the GFC. A segmented-markets model with collateralized credit explains the puzzle. Post-GFC positive comovement reflects weakened credit and segmentation from demographics, institutional investors, and foreign capital.

Theory / model

The paper builds on a two-asset Lucas-tree benchmark and then extends it to a heterogeneous-agent, segmented-markets economy. The motivation for studying stocks and housing jointly draws on Piazzesi and Schneider (2016), who survey the housing and macroeconomics literature. Landvoigt, Piazzesi and Schneider (2015) develop a quantitative predecessor model for the San Diego housing market with segmented markets and credit. Piazzesi and Schneider (2007) model momentum traders in housing with a search framework. Piazzesi and Schneider (2008) document the inflation illusion and credit channel for the 1970s housing boom and stock slump across 12 OECD countries. Jorda, Schularick and Taylor (2019) document a Sharpe ratio near one for aggregate housing returns in U.S. postwar data, used here to contextualize idiosyncratic risk. Mankiw and Weil (1989) linked Baby Boom demographics to the 1970s housing surge, a candidate for post-GFC comovement via higher savings. Iacoviello (2005) studies house prices and borrowing constraints in a monetary business cycle model, cited as a quantitative single-market predecessor.

Benchmark: representative-agent Lucas trees (Section I.C, p. 3109). Aggregate output $Y_t$ grows at log rate (equation 1, p. 3109):

$$g_t := \log Y_t - \log Y_{t-1}, \quad g_t \stackrel{\text{iid}}{\sim} N(\mu, \sigma^2). \tag{1}$$

The representative agent has log utility $\sum_{t=0}^\infty \beta^t \log C_t$. Two long-lived assets pay cashflows $D_t^s$ (stocks) and $D_t^h$ (housing); the Euler equation for asset $i = s, h$ is (equation 2, p. 3110):

$$P_t^i = E_t\!\left[\beta \frac{C_t}{C_{t+1}}\bigl(D_{t+1}^i + P_{t+1}^i\bigr)\right]. \tag{2}$$

Since both assets share the same cashflow growth, the model implies perfectly positively comovving returns, in contradiction to Figure 1. Time-varying discount rates (incorporating Cochrane (2011), Bansal and Yaron (2004), and habit formation) generate positive comovement in valuation ratios and therefore do not resolve the puzzle (p. 3111).

Segmented-markets OLG model (Section III, p. 3115). There are two types of households: fraction $\delta_s$ trade only stocks plus bonds; fraction $\delta_h$ trade only housing plus bonds, with $\delta_s + \delta_h = 1$. In an OLG framework, young households of type $i$ receive labor income $w_{i,t} Y_t$, save their entire income, and consume only when old. Stocks and housing trade only within type. Each young household solves (equation 3, p. 3116):

$$\max E[\log c_{i,t+1}] \quad \text{s.t.} \quad p_{i,t}\theta_{i,t} + b_{i,t} = w_{i,t}, \tag{3}$$ $$c_{i,t+1} = (p_{i,t+1} + d_{i,t+1})\theta_{i,t} + b_{i,t} R,$$ $$\phi\, p_{i,t+1}\theta_{i,t} \geq -R\, b_{i,t},$$

where $\theta_{i,t}$ is the share of the Lucas tree purchased, $b_{i,t}$ is bond holdings, $R$ is the gross interest rate, and the last inequality is the collateral constraint (the loan cannot exceed fraction $\phi$ of the future asset value). Rewriting in portfolio weights $\alpha_{i,t} = p_{i,t}\theta_{i,t}/w_{i,t}$ (equation 4, p. 3117):

$$\max_{\alpha_{i,t}\leq R/(R-\phi)} E_t\!\left[\log\!\left(\tilde{R}_{t+1}^i\, \alpha_{i,t} + R(1-\alpha_{i,t})\right)\right]. \tag{4}$$

The optimal portfolio weight (equation 5, p. 3117) is:

$$\alpha_{i,t} \approx \min\!\left\{\frac{\frac{1}{v^i} + \mu - r}{\sigma^2},\; \frac{R}{R-\phi}\right\}. \tag{5}$$

When the collateral constraint does not bind, the weight equals the Merton weight (Campbell and Viceira (1999)). The Gordon Growth formula for the price-dividend ratio (equation 7, p. 3118) is:

$$v_i = \frac{1}{r - \mu + \sigma^2 \alpha_i}, \tag{7}$$

where $\sigma^2 \alpha_i$ is the risk premium. The credit supply function (equation 8, p. 3118) is obtained from the stock Gordon Growth formula:

$$r_s(Q) = \frac{d_s}{\delta_s w_s - Q} + \mu - \sigma^2 \frac{\delta_s w_s - Q}{\delta_s w_s}, \tag{8}$$

and the credit demand function from the housing Gordon Growth formula (equation 9, p. 3119) is:

$$r_h(Q) = \frac{d_h}{\delta_h w_h + Q} + \mu - \sigma^2 \frac{\delta_h w_h + Q}{\delta_h w_h}. \tag{9}$$

Equilibrium credit $Q^*$ solves $f(Q) = r_h(Q) - r_s(Q) = 0$ (equation 10, p. 3119). When homebuyers hit the collateral constraint, credit demand becomes (equation 11, p. 3120):

$$r_h(Q) = \phi\, \frac{\delta_h w_h + Q}{Q} - 1. \tag{11}$$

Assumption A (p. 3120): homebuyers have lower savings per unit of cashflows than stockholders: $\delta_h w_h / d_h < \delta_s w_s / d_s$.

Proposition 1 (p. 3121): if Assumption A holds, homebuyers borrow from stockholders in equilibrium ($Q^* > 0$).

Proposition 2 (p. 3122): under Assumption A, bad news about the whole economy generates negative return comovement in housing and stock price-dividend ratios, through either a credit demand channel (part i, unconstrained homebuyers) or a credit supply channel (part ii, constrained homebuyers).

Proposition 3 (p. 3125): forces that reduce credit and segmentation move comovement toward positive: equal savings-to-dividends ratios, higher overall savings (part ii), or population growth (part iii) all raise both price-dividend ratios together.

Method

This is primarily a theoretical paper. The model is solved analytically in steady state. Key steps:

1. Impose constant price-dividend ratios in steady state (all shares of aggregate output are constant: cashflows, labor income).
2. Derive the Gordon Growth formulas (equations 7-9) for each asset class from the market-clearing conditions and the optimal portfolio weights.
3. Derive the credit supply function from stockholders’ Gordon Growth formula and the credit demand function from homebuyers’ Gordon Growth formula; show these are respectively upward- and downward-sloping in credit $Q$ (Figure 7, p. 3119).
4. Establish existence and uniqueness of equilibrium using the intermediate value theorem and monotonicity of the excess demand function $f(Q)$ (Appendix A, pp. 3129).
5. Characterize comparative statics via the implicit function theorem, with formal proofs in Appendices B-D (pp. 3129-3133).

The paper builds on overlapping-generations (two-period OLG with constant savings rates) and life-cycle-model (portfolio choice over a finite horizon). The housing beta is measured empirically using a two-sided exponential-decay kernel regression of 10-year real housing returns on a constant and 10-year real stock returns (10% weight on observations five years in the past or future), as described on p. 3108 and displayed in Figure 2.

Empirical specifications

There is no formal econometric specification in the traditional sense; the empirical contribution is measurement and stylized facts. The key estimating procedure is:

Housing beta (R1, R3, Figure 2): The stock-market beta of housing is estimated as the slope from a local regression:

$$\tilde{r}_{t}^h = a + \beta_t^h\, \tilde{r}_{t}^s + \varepsilon_t,$$

where $\tilde{r}_t^h$ and $\tilde{r}_t^s$ are real 10-year geometric mean returns on housing and stocks, the regression is estimated with a two-sided exponential decay kernel (10% weight at horizon 5 years), and $\beta_t^h$ is the time-varying slope. Sample: annual, 1955-2024 (Figure 2, p. 3109).

Cashflow growth correlation (R2, Figure 3): The correlation of real 10-year cashflow growth rates on stocks (S&P 500 dividends) and housing (NIPA housing services expenditure) is computed over the same annual sample 1930-2024. The 10-year rates are geometric means reported per year (p. 3107).

Levered housing return (R3, Figure 5): Levered housing return equals the housing return minus borrowing costs, with borrowing costs proxied by real 10-year Treasury returns scaled by 80% (reflecting the typical down payment) to match the leverage of mortgage-financed housing (p. 3107, p. 3113).

Idiosyncratic capital gains (R4, Figure 6): Cross-sectional distribution of idiosyncratic capital gains on individual houses in San Francisco, CA and Huntsville, AL from CoreLogic individual transaction data. Idiosyncratic gain = individual house gain minus the location-specific average gain over the same holding period (p. 3114).

Datasets used

Dataset	Role in paper	Wiki page
S&P 500 index (Shiller website: shillerdata.com, file ie_data.xls)	Real 10-year stock returns and dividend growth (cashflows); end-of-year values 1930-2024	Shiller data
Financial Accounts of the United States (Fed, table B101/B104)	Value of residential real estate held by households; capital gains computed net of residential fixed investment (table F6); annual 1946-2024	no page yet
NIPA (BEA, tables 2.4.4 line 50, 2.4.5 lines 25 and 47)	Housing cashflows (dollar expenditure on housing services, including imputed rents); price index for nondurables and services; annual 1929-2024	NIPA / FRED
10-year Treasury returns (Shiller website)	Proxy for mortgage borrowing costs (multiplied by 0.8 for typical down payment); annual 1940-2024	Shiller data
American Housing Survey (HUD)	Average homeowner tenure (15.1 yr in 1980, 11.5 yr in 2021), share of buyers with mortgage, share of first-time buyers, mortgage payment as share of income (Figure 4, p. 3112)	no page yet
CoreLogic individual transaction data	Cross-sectional distribution of idiosyncratic capital gains on individual houses in San Francisco, CA and Huntsville, AL (Figure 6, p. 3114)	CoreLogic (licensed)

Sample: primarily annual, U.S., 1930-2024 (returns and cashflows); housing leverage data 1975-2022 (Figure 4).

When to read the full paper

Use the original if you are: studying why housing and stock returns move in opposite directions before 2008; building models of segmented housing and stock markets connected through credit; analyzing how demographics, institutional investors, or foreign capital affect the stock-housing return relationship; or extending the theoretical channels (defaultable debt, idiosyncratic risk, multi-cohort OLG) identified on p. 3128. The appendices (pp. 3129-3133) contain formal proofs of all propositions.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(6). This distillation was extracted by an LLM on 2026-06-03 and is not human-verified or independently reproduced. The paper is paywalled (Wiley VOR terms); extract-only.

Piazzesi, Monika. “Presidential Address: Housing Betas.” The Journal of Finance 80, no. 6 (December 2025): 3103-3136. DOI: 10.1111/jofi.70000. © 2025 the American Finance Association. Paywalled; this page contains only extracted summary and analysis.