Baby Booms and Asset Booms: Francke & Korevaar (2025)

What this is. The paper’s core results, the empirical model, and the mechanism analysis: enough to know what it found and how, without reading all 36 pages. To replicate or extend it, read the full source at the original.

TL;DR

Using centuries of housing transaction data from Amsterdam (1550-1884) and rent data from Paris (1500-1831), Francke and Korevaar show that lagged birth rates are a major and predictable driver of house prices. A high birth rate 25 to 29 years ago, when a large cohort enters prime home-buying years, raises house prices relative to rents by about 4% per percentage-point increase in the five-year birth rate. A high birth rate 60 to 64 years ago, when a large cohort exits homeownership at death or through a move to senior housing, lowers rent-price ratios by a similar magnitude. These effects concentrate in house prices and not in rent prices, pointing to age-concentrated entry into and exit from homeownership rather than general housing consumption demand. The two lags together explain about 18% of total variation in rent-price ratios over 250 years. Mechanism analysis finds that sale probabilities respond sluggishly to demographic demand shocks, and that spatial segmentation between rental and owner-occupied markets contributes to the large price effects.

Core results

Magnitudes and significance are as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Lagged birth rates 25-29 and 60-64 years ago predict rent-price ratios, with the two lags explaining 18.4% of total variation over 250 years	Table II col. 3, p. 3040	Birth rate 25-29 yrs ago: coeff. -4.251*** (SE 1.130); birth rate 60-64 yrs ago: coeff. 4.001*** (SE 0.837); adj. R2 = 0.448
R2	Effect concentrates in house prices, with birth rates 25-29 yrs ago raising house prices and birth rates 60-64 yrs ago lowering them	Table III cols. 1-3, p. 3041	Coeff. on B_{t-25/29} = 4.457*** (SE 1.314); coeff. on B_{t-60/64} = -4.033*** (SE 1.377); adj. R2 = 0.587
R3	Rent prices show little to no response to lagged birth rates in Amsterdam or Paris; rents are not the channel	Table III cols. 4-9, p. 3041	B_{t-25/29} on Amsterdam rents: -0.562 (SE 0.651), insignificant; B_{t-60/64}: -0.862* (SE 0.509); Paris rents: similarly weak
R4	Transaction probability responds to demographic shocks but with a delay, peaking years after price effects, consistent with slow supply adjustment	Figure 5, p. 3047	Birth-rate lag ~37 yrs raises sale probability by 0.1 pp (+4%); price effects peak at lag ~25, sale-probability effects at lag ~37
R5	Spatial segmentation supports the mechanism: young cohorts raise prices in low-homeownership (rental) areas; homeownership-age cohorts raise prices in high-homeownership areas	Figure 6, p. 3049	Young-cohort (teen) lag: ~2% higher house price growth in low- vs high-homeownership areas; early-30s lag: ~1% excess growth in high-homeownership areas
R6	No significant effect on bond or young-cohort dividend yields, confirming the effect is housing-specific and not general asset demand	Table V, p. 3053	B_{t-25/29} on bond yield: 1.555 (SE 1.318), insignificant; on dividend yield: -1.192 (SE 0.792), insignificant; only old-cohort lag shows weak positive dividend yield effect

Overall (paper’s conclusion). Demographics have been a major, predictable driver of house prices relative to rents over multiple centuries. The effect arises because entry into and exit from homeownership are strongly age-concentrated, and because other market participants respond slowly to shifts in ownership demand rather than immediately converting rental units to owner-occupied ones and vice versa. The effect is specific to housing: there is no evidence of similar effects on bond yields and only weak evidence for dividend yields, ruling out a general life-cycle asset-demand explanation.

Theory / model

The paper has no formal dynamic equilibrium model. Instead, it builds on the insight that total housing demand in year $t$ can be written as $D_t = \sum_j \hat{\alpha}_j N_{jt}$, where $N_{jt}$ is the number of individuals aged $j$ and $\hat{\alpha}_j$ is a (constant) age-specific demand weight estimated from cross-sectional data on residents’ housing choices. This is the Mankiw and Weil (1989) framework (p. 3036). The aggregate house price or index $h_t$ then depends on $D_t$ via a simple time-series regression:

$$h_t = \delta_0 + D_t \delta_1 + \varepsilon_t \tag{background}$$

Since the authors do not observe age-specific population counts $N_{jt}$ in the historical setting, they substitute a linear approximation: the change in demand $\Delta D_t$ is a linear function of lagged birth rates $B_{t-\text{lag}}$, so that changes in house prices (in logs) become:

$$\Delta h_t \approx \alpha + \sum_{\text{lag}} B_{t-\text{lag}} \beta_{\text{lag}} + \varepsilon_t$$

Identification logic. The paper uses lagged birth rates as the key predictor rather than current demographic structure, for three reasons stated on p. 3023: (i) lagged birth rates predate the outcomes by decades and so are less likely to be jointly driven by current economic conditions; (ii) migration, which is endogenous to economic opportunity, induces endogeneity in current age structure but not in birth rates from generations ago; and (iii) using lags allows a test for predictability, since birth cohorts and their eventual housing demand are known far in advance. The paper focuses on rent-price ratios (rather than price levels) to further reduce sensitivity to shared confounders: if past birth rates correlate with current economic activity, that would affect house prices and rents similarly, but the ratio difference would remain.

The identification strategy builds on the idea from DellaVigna and Pollet (2007) that predictable demographic demand shifts generate price effects because investors do not fully anticipate them. It also updates the findings of Poterba (2001), who found limited evidence that demographic shifts affect aggregate asset prices; the present paper shows the housing market is the exception, not a general effect across all assets.

No causal identification device (instrument, natural experiment, RDD) is deployed; the strategy is predictive/descriptive using deep historical variation to exploit many decades of pre-demographic-transition fluctuations in birth rates.

Method

The main estimating equation is a time-series predictive regression of five-year log changes in rent-price ratios, house prices, or rents on lagged five-year birth rates (equation 1, p. 3037):

$$y_t - y_{t-5} = \alpha + \sum_{\text{lag} \in L} B_{t - \text{lag}/4 + \text{lag}} \beta_{\text{lag}} + z_t' \gamma + \varepsilon_t \tag{1}$$

where $y$ is the log rent-price ratio $(r - h)$, the log house price index $h$, or the log rent index $r$; $B_{t - \text{lag}/4 + \text{lag}}$ is the total five-year birth rate summed over lags $t-\text{lag}$ through $t-\text{lag}+4$; $z_t$ is a vector of contemporaneous control variables (birth rate, mortality, nuptiality, migration, wage growth, inflation, GDP per capita log change); and $L$ is a set of lag lengths between 15 and 70 years (in five-year steps, corresponding to cohorts aged 15 to 74 at time $t$).

Serial correlation from overlapping five-year differences is addressed by Newey-West standard errors with a lag length of five (p. 3037). The paper estimates equation (1) for various sets $L$: a single lag, two lags (young and old cohort), and all lags jointly. The main reported results use two lags corresponding to birth rates 25-29 years ago (peak homeownership-entry cohort) and 60-64 years ago (peak exit cohort).

The probability-of-sale mechanism is tested using a discrete-time hazard model for the probability that property $i$ sells at holding duration $d = t_i - s_i$ (equations 2a-2b, p. 3046):

$$\Pr(D_{is} = t_i - s_i) = H_{is}(t_i - s_i) S_i(t_i - s_i - 1) \tag{2a}$$ $$\Pr(D_{it} > T - s_i) = S_i(T - s_i) \tag{2b}$$

where $H_{is}(d) = (1 + \exp(-x_{isd}' \beta))^{-1}$ is a logistic hazard function and $S_{is}(d) = \prod_{j=1}^d (1 - H_{is}(j))$ is the survival probability. This is estimated by logistic regression in property-period format.

Empirical specifications

Main specification (Table II, p. 3040; Table III, p. 3041). The baseline uses five-year log changes in the Amsterdam rent-price ratio as the dependent variable, with two birth-rate lags (25-29 yrs and 60-64 yrs). All variables are in five-year differences to address unit-root concerns (unit-root tests reject the null in levels; p. 3038). Six columns vary the control set:

• Column (1): no controls (adj. R2 = 0.178)
• Column (2): demographic controls (birth rate, mortality, nuptiality, migration)
• Column (3): demographic + economic controls (wage growth, inflation, GDP per capita); baseline
• Column (4): baseline + lagged controls matching the birth-rate lag windows
• Column (5): baseline + housing-quality growth control
• Column (6): baseline + interest rate

The coefficients on the two main birth-rate lags are stable across all six specifications: approximately -4 (young cohort) and +4 (old cohort), both significant at 1%.

House prices vs. rents (Table III). The same specification is run separately for Amsterdam house prices, Amsterdam rents, and Paris rents. House prices show large significant effects (coeff. ~4.5 and -4.0). Rents show near-zero and generally insignificant effects, both in Amsterdam and Paris, confirming that the rent-price ratio result is driven by house prices.

Robustness: supply constraints (Table IV, p. 3051). The baseline is interacted with a supply-constrained dummy (SC = 1 before 1668 and after 1855, the two Amsterdam expansion episodes). Interaction effects are insignificant, and the main birth-rate coefficients are unchanged, ruling out that results are driven by periods of constrained supply.

Other assets (Table V, p. 3053). The same two-lag specification is run for five-year changes in Dutch government bond yields and Dutch East India Company dividend yields. The young-cohort lag is insignificant for both. The old-cohort lag shows a weakly significant positive effect only on dividend yields (coeff. 1.525**, SE 0.751), consistent with estate sales rather than general life-cycle asset demand.

Segmentation (Figure 6, p. 3049). Separate house price indices for streets with low, medium, and high homeownership rates (from the 1805 Amsterdam rental census) are estimated via repeat-sales and regressed on the same birth-rate lags. The spatial pattern of coefficients by lag length matches the predicted housing life-cycle: young cohorts raise prices in low-HO (rental) areas, mature cohorts raise prices in high-HO (owner-occupied) areas.

Datasets used

Dataset	Role in paper	Wiki page
Amsterdam housing transaction data (mandatory aldermen registrations, 1620-1811; 19th-century repeat-sales on Herengracht)	Main house price index via Bayesian repeat-sales (Francke 2010); segmentation analysis	Amsterdam housing
Paris repeat-rent indices (Eichholtz, Korevaar, and Lindenthal 2020)	Rent price series for Paris 1500-1831 (new rental contracts only)	Paris rents
Amsterdam repeat-rent index (Eichholtz, Korevaar, and Lindenthal 2020)	Rent price series for Amsterdam 1550-1884	Amsterdam housing
Amsterdam archival civil registers (Amsterdam City Archives, from 1554)	Annual births, deaths, marriages for demographic rates	No page yet
Paris demographic data (historians + official Paris statistics)	Annual births, deaths, marriages for demographic rates 1500-1831	No page yet
Dutch government bond yields (provincial debt pre-1810, national debt post-1810)	Test of general asset-demand mechanism	No page yet
Dutch East India Company (VOC) dividend yields (Golez and Koudijs 2018)	Test of general asset-demand mechanism, 1629-1782	No page yet
1805 Amsterdam rental census (Amsterdam City Archives)	Street-level homeownership rates for segmentation analysis	Amsterdam housing
OECD panel on house prices, rents, demographics (1970-2020)	Modern-context robustness check (Internet Appendix Section VI)	No page yet

Sample: Amsterdam rent-price ratio 1550-1884 (N=256 five-year observations); Paris rents 1500-1831 (N=242-331 observations); probability-of-sale sample covers all Amsterdam repeat-sales pairs 1620-1811. Annual frequency, aggregated to five-year differences for main regressions.

When to read the full paper

Read the original if you are: investigating demographic drivers of house prices or rent-price ratios, building a model of the housing life-cycle and its aggregate price implications, or studying the historical Amsterdam or Paris housing and rental markets. The Internet Appendix (available online) contains the full list of historical data sources (Table IA.V), the age-distribution evidence for the United States (Section II.A), the OECD modern-context replication (Section VI), and further robustness checks including all-lag regressions (Table IA.VI) and the probability-of-sale two-lag results (Table IA.VII).

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(5). This distillation was extracted by an LLM on 2026-06-05 and is not human-verified or independently reproduced. The CC BY 4.0 licence permits mirroring; the verbatim PDF is not hosted in this batch.

Attribution (CC BY 4.0). Francke, Marc, and Matthijs Korevaar. “Baby Booms and Asset Booms: Demographic Change and the Housing Market.” The Journal of Finance 80, no. 5 (October 2025): 3021-3056. DOI: 10.1111/jofi.13480. (C) 2025 The Author(s). Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.