Decomposing Housing wealth across five countries

 The central research focus of the sources, within the context of analyzing "Housing Wealth Across Countries: Determinants and Differences," is the systematic quantification of the drivers of differences in the extensive and intensive margins of housing wealth across advanced economies.

This research is primarily motivated by the observation that homeownership rates and holdings of housing wealth differ immensely across countries, despite housing being the largest asset for most households and a major determinant of wealth inequality. For instance, the sources highlight that while 80% of households in Spain are homeowners, less than half of German households own their residence, a gap that persists across the life cycle.

The goal of the research is to analyze how households accumulate housing wealth over the life cycle by estimating a robust, state-of-the-art life-cycle model. This model features illiquid housing and a discrete–continuous choice between renting and owning a house.

Three Groups of Explanatory Factors

The core methodology employs this estimated model to quantify three key groups of factors responsible for long-run, structural differences in housing wealth across the five advanced economies studied (France, Germany, Italy, Spain, and the United States):

1. House Price Expectations (Beliefs): The model allows for (persistent) differences in house price expectations across individuals and countries. The average long-run growth rate of real house prices ($G$) is matched to historical aggregate data for each country.
2. Institutional Set-up of the Housing Market: This encompasses country-specific factors governing the housing and rental markets, such as maximum loan-value ratios (collateral constraints), costs associated with renting, maintaining, and selling a house, and transaction costs.
3. Preferences: Parameters related to household preferences, such as impatience (time preference rate), the share of housing expenditures in utility ($\omega$), and the bequest motive ($L$), are allowed to vary across households and countries.

Decomposition of Cross-Country Differences

Using a decomposition method, the researchers interpret the differences in housing wealth through the lens of these three factor groups, finding that all three matter, although preferences contribute much less than house price beliefs and housing market institutions.

1. The Extensive Margin (Homeownership Rates)

The extensive margin refers to the household decision of whether to buy or rent a house, captured by the homeownership rate. Differences in homeownership rates are strongly affected by two variables:

• (i) House Price Beliefs: Higher expectations of house price growth make renting less appealing, increasing the share of homeowners. Small differences in long-run house price beliefs are found to be a powerful driver of homeownership, with a 1 percentage point (p.p.) difference resulting roughly in a 15 p.p. difference in the homeownership rate across countries.
• (ii) The Rental Wedge ($\alpha$): Defined as the difference between rents and housing maintenance costs, the rental wedge reflects the quality and segmentation of the rental market. A higher rental wedge makes renting less appealing and increases the share of homeowners. The rental wedge varies significantly, ranging from about 2% in France and the U.S. to nearly 5% in Italy. Quantitatively, a 2 p.p. difference in rental wedges implies roughly a 25–30 p.p. difference in homeownership rates (e.g., between Germany and Italy).

Other factors, while less dominant, also matter, especially for younger households: tighter collateral constraints reduce the homeownership rate for the youngest households.

2. The Intensive Margin (Value of Housing Wealth)

The intensive margin concerns the amount of housing wealth accumulated by homeowners, measured as the mean ratio of housing wealth to income. Differences in this margin are mostly driven by maintenance costs ($\lambda$).

• Maintenance Costs: These costs, which include property taxes and depreciation, reduce homeowners’ return on housing. The estimated maintenance costs for Germany (2.6% of housing wealth) are roughly half those in Spain, France, and the U.S. (around 5% or more). This difference implies that Germany holds higher housing wealth, by a multiple of 2–4 times annual income, compared to these countries.
• Other Factors: A higher utility share of housing preference ($\omega$) in countries like Spain, France, and the U.S. (around 0.30) compared to Germany and Italy (around 0.20) contributes positively to housing wealth accumulation. Furthermore, higher house price growth beliefs encourage existing homeowners to upgrade to larger houses, thereby increasing housing wealth.

The research approach offers a comprehensive comparison by modeling various factors jointly in a "horse race" within a single encompassing model, providing a structural approach that moves beyond reduced-form regressions often used to investigate cross-country wealth differences.

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The relationship between maintenance costs and housing wealth accumulation can be conceptualized like a hidden tax on the asset. If two countries have identical houses and incomes, but one imposes higher ongoing maintenance costs (like property taxes or high repair expenses), the effective financial return on owning the house is lower. Consequently, rational homeowners in the country with higher maintenance costs will choose to purchase smaller houses relative to their income, leading to lower aggregate housing wealth in that country, even if the price of the house itself is the same.

The sources systematically quantify and decompose the drivers of differences in housing wealth across countries into three key groups of explanatory factors: House Price Expectations (Beliefs), the Institutional Set-up of the Housing Market, and Preferences. This analysis uses an estimated life-cycle model applied to micro data from five advanced economies: France, Germany, Italy, Spain, and the United States.

The central finding is that all three groups of factors contribute to explaining cross-country differences in homeownership and housing wealth, although preferences contribute much less than house price beliefs and housing market institutions.

Here is a detailed discussion of the three groups of explanatory factors and their impact on the extensive (homeownership rates) and intensive (value of housing wealth) margins of housing wealth:

1. House Price Expectations (Beliefs)

This group of factors focuses on the role of anticipated house price changes in household decisions. The research allows for (persistent) differences in expectations of house prices across individuals and countries.

Impact on the Extensive Margin (Homeownership) Differences in house price beliefs are found to be a strong driver of differences in homeownership rates across countries.

• Higher expectations of long-run house price growth make renting less appealing, which in turn increases the share of homeowners.
• Quantitatively, the effects are powerful: a 1 percentage point (p.p.) difference in long-run house price beliefs results roughly in a 15 p.p. difference in the homeownership rate across countries.
• These beliefs are key for the decision of whether to buy or rent, and their contribution to explaining gaps in homeownership rates is roughly equal to that of the rental wedge, with both factors mattering throughout the life cycle.
• The spread of house price beliefs ($\tilde{G}$) is identified by the slope or shape of the homeownership profile. A wider dispersion in beliefs leads to a less steep rise in the homeownership profile over the life cycle, as optimistic individuals buy early while others delay.
• Higher house price growth beliefs encourage individuals to purchase a house earlier in their lifecycle to benefit from high returns for longer.

Impact on the Intensive Margin (Value of Housing Wealth) Higher house price growth beliefs tend to increase the amount of housing wealth accumulated by homeowners.

• Existing homeowners who are optimistic about future house price growth are encouraged to upgrade to buy larger houses, leading to higher housing wealth.
• For example, higher house price growth beliefs in Spain and the U.S. (compared to Germany) positively contribute to housing wealth.

2. Institutional Set-up of the Housing Market

This group encompasses country-specific factors governing the housing and rental markets. The key estimated institutional parameters are:

• The Rental Wedge ($\alpha$): The difference between rents and housing maintenance costs ($\alpha \equiv \hat{\alpha} - \lambda$), which reflects the quality and segmentation of the rental market.
• Maintenance Costs ($\lambda$): These are costs associated with maintaining an owned house, including property taxes and depreciation.
• Collateral Constraints ($\delta$): Represented by the maximum loan-value ratio or down payment requirement.
• Transaction Costs ($\phi$): Costs associated with selling a house (linear house selling costs/transfer taxes).

Impact on the Extensive Margin (Homeownership)

• The Rental Wedge is a major factor strongly affecting homeownership rates. A higher rental wedge makes renting less attractive, thereby increasing the share of homeowners. The rental wedge varies significantly, from around 2% in France and the U.S. to almost 5% in Italy. A 2 p.p. difference in rental wedges leads roughly to a 25–30 p.p. difference in homeownership rates (e.g., between Germany and Italy).
• Collateral constraints significantly affect younger households. Tighter collateral constraints (higher down payment requirement, $\delta$) reduce the homeownership rate, particularly for the youngest households. For instance, a 15 p.p. higher down payment requirement in Germany lowers the homeownership rate of households younger than 30 years by 6 p.p. compared to Spain, the U.S., or France.

Impact on the Intensive Margin (Value of Housing Wealth)

• Maintenance Costs are the factor that mostly drives differences in the value of housing wealth of homeowners (measured as mean ratios of housing wealth to income). Maintenance costs reduce the homeowners’ return on housing.
• Estimated maintenance costs for Germany (2.6% of housing wealth) are roughly half those in Spain (4.9%), France (6%), and the U.S. (8.6%). This difference implies that Germany holds higher housing wealth, by a multiple of 2–4 worth of annual incomes, compared to the latter countries.
• The Rental Wedge also matters for the intensive margin due to a selection effect: a higher rental wedge increases homeownership but reduces the average housing wealth because the marginal buyers purchase smaller houses.

3. Preferences

Preference parameters are generally allowed to vary across households and countries. The key estimated preference parameters are:

• Impatience ($\vartheta$ or Discount Factor $\beta$): The rate at which households discount future utility.
• Share of Housing Expenditures in Utility ($\omega$): The weight of housing in the utility function.
• Bequest Motive ($L$): The magnitude of the "warm glow" terminal payout based on final net worth.

Overall Contribution of Preferences While measurable, preferences contribute much less to explaining cross-country differences in homeownership and housing wealth compared to house price beliefs and housing market institutions. The model estimates show that very little preference heterogeneity is needed to explain the homeownership gaps, around 5 p.p. or less.

Specific Impacts

• Housing Preference ($\omega$): The utility share of housing preference in countries like Spain, France, and the U.S. (around 0.30) is higher than in Germany and Italy (around 0.20). This difference is reflected in a positive contribution of this parameter to housing wealth accumulation outside of Germany.
• Bequest Motive ($L$): Affects homeownership, particularly among older households. A weaker bequest motive lowers the homeownership rate in countries like France, the U.S., and Italy. The strength of the bequest motive is identified by the shape of net wealth profiles late in life.
• Impatience ($\vartheta$): More patient households (lower $\vartheta$) accumulate more wealth over their working life. The mean and spread of the log time preference rate are identified by the slope of wealth accumulation and the difference between mean and median net wealth profiles.
• Interaction Factor ($\kappa$): The correlation between the discount rate and house price beliefs is estimated to be strongly negative. This means that more patient households are more optimistic about house prices and are more likely to accumulate housing wealth, while impatient and pessimistic households are more likely to rent.

Key Factor Group	Primary Affected Margin	Key Impact/Mechanism
House Price Expectations	Extensive (Homeownership) & Intensive (Wealth)	Higher expected capital gains make owning more attractive relative to renting (extensive), and encourage existing owners to buy larger houses (intensive).
Housing Market Institutions	Intensive (Wealth)	Maintenance Costs ($\lambda$): Directly reduce the return on housing, leading to lower housing wealth accumulation where costs are high.
Housing Market Institutions	Extensive (Homeownership)	Rental Wedge ($\alpha$): Makes renting less appealing due to market inefficiencies, pushing more households toward ownership.
Housing Market Institutions	Extensive (Homeownership)	Collateral Constraints ($\delta$): Restrict young households' ability to enter homeownership.
Preferences	Intensive (Wealth)	Housing Utility ($\omega$): Directly impacts the preferred size of the house relative to income.

This structural decomposition method provides a first systematic documentation of how these factors drive differences in both the decision to own (extensive margin) and the amount owned (intensive margin) across advanced economies.

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The three explanatory factor groups—Beliefs, Institutions, and Preferences—act like levers determining the composition and size of housing wealth in a country. Beliefs (House Price Expectations) function like a thermostat, setting the general eagerness to own a house; if expectations are high, more people switch from 'renting' to 'owning.' Institutions (like Maintenance Costs) function like a financial gravity field, where high maintenance costs exert a constant downward pull, limiting the size of the houses people can afford to hold. Finally, Preferences act as personalized blueprints, slightly adjusting individual household goals regarding wealth accumulation and desired house size, but playing a secondary role compared to the powerful influence of large institutional and belief differences.

The sources define the extensive margin of housing wealth as the household's decision to buy versus rent, which is quantified by the homeownership rate. Homeownership rates differ immensely across advanced economies; for example, Spain exhibits a rate of 80%, while Germany's rate is less than half that, a substantial gap that persists across the entire life cycle.

The decomposition analysis, which systematically documents the drivers of these differences, finds that all three key explanatory factors (house price beliefs, institutional set-up, and preferences) matter, but preferences contribute much less than house price beliefs and housing market institutions.

The decomposition results for the extensive margin identify two primary factors that are strongly affected by homeownership rates: house price beliefs and the rental wedge.

1. House Price Beliefs (Expectations)

Differences in the expectations of long-run house price growth are a key factor in the decision of whether to buy versus rent.

• Mechanism: Higher expectations of house price growth make renting less appealing, consequently increasing the share of homeowners. Optimistic beliefs encourage individuals to purchase a house earlier in their life cycle to benefit from higher returns for longer.
• Quantification: The relationship between beliefs and homeownership rates is powerful. Across countries, a 1 percentage point (p.p.) difference in long-run house price beliefs results roughly in a 15 p.p. difference in the homeownership rate.
• Context: Mean long-run house price beliefs range from 0% in Italy to 2.8% in France. The sources conclude that small differences in these beliefs are a powerful driver of homeownership. House price beliefs and the rental wedge contribute roughly equally to explaining cross-country gaps in homeownership rates and matter throughout the life cycle.

2. The Rental Wedge (Institutional Set-up)

The rental wedge ($\alpha$) is defined as the difference between rents and housing maintenance costs, reflecting the quality of the rental market and the segmentation between rental and owner-occupied housing markets.

• Mechanism: A higher rental wedge makes renting less desirable, pushing more households toward ownership and thereby increasing the share of homeowners.
• Quantification: The estimated rental wedge varies substantially across the five countries, ranging from around 2% in France and the U.S., to 2.8% in Germany, 3.7% in Spain, and almost 5% in Italy. The difference reflects a less efficient rental market in the latter countries. The model implies that a 2 p.p. difference in rental wedges leads roughly to a 25–30 p.p. difference in homeownership rates (for instance, the difference between Germany and Italy).
• Context: Similar to house price beliefs, small differences in the rental wedge result in large differences in homeownership rates.

3. Other Significant Factors

While house price beliefs and the rental wedge are the dominant drivers of the overall homeownership rate difference, other factors are important, especially for specific age groups:

• Collateral Constraints: These constraints, such as maximum loan-value ratios or down payment requirements, significantly reduce the homeownership rate particularly for the youngest households. Tighter constraints (like the 15 p.p. higher down payment requirement in Germany compared to Spain, the U.S., or France) lower the homeownership rate for households younger than 30 years by 6 p.p..
• Bequest Motive: The strength of the bequest motive affects homeownership, particularly among older households. A weaker bequest motive lowers the homeownership rate in countries such as France, the U.S., and Italy.
• Labor Income Profiles: Steeper labor income profiles, as observed in Germany, reduce the homeownership rate among the youngest households by around 10 p.p., compared to Spain and Italy.

In summary, the substantial cross-country differences in homeownership rates are overwhelmingly explained by the combined effect of a country's long-run expectations regarding house prices and the institutional efficiency of its rental market, both of which govern the relative appeal and feasibility of owning versus renting.

The intensive margin of housing wealth, defined as the value of housing wealth accumulated by homeowners and measured by the mean ratios of housing wealth to income, exhibits substantial differences across countries.

The decomposition results clearly indicate that differences in the intensive margin are mostly driven by maintenance costs related to owning a house, although preferences, house price beliefs, and the rental wedge also play notable roles.

1. Primary Driver: Maintenance Costs ($\lambda$)

Differences in maintenance costs ($\lambda$) are the factor that mostly drives differences in the value of housing wealth of homeowners across the five advanced economies analyzed. Maintenance costs, which include items like property taxes and depreciation, reduce homeowners’ return on housing wealth.

• Quantitative Impact: The estimated maintenance costs for Germany (2.6% of housing wealth) are roughly half the size of those estimated for Spain (4.9%), France (6.0%), and the U.S. (8.6%). They are slightly larger than those estimated for Italy (1.7%).
• Wealth Effect: This cost disparity results in differences in accumulated housing wealth. The lower maintenance costs in Germany imply higher holdings of housing wealth in Germany compared to Spain, France, and the U.S., specifically by a multiple of 2–4 worth of annual incomes.
• Policy Implication: Lower property taxes, which decrease maintenance costs ($\lambda$), are shown to encourage the accumulation of housing wealth (the intensive margin).

2. Secondary Factors (Preferences and Expectations)

Other factors contribute to differences in the intensive margin, although their impact is less substantial than that of maintenance costs.

Housing Preference ($\omega$)

The share of housing expenditures in utility ($\omega$), a preference parameter, affects the chosen size of the house relative to income.

• Cross-Country Difference: Germany (0.186) and Italy (0.210) have a lower share of housing utility ($\omega$, around 0.20) compared to France (0.307), Spain (0.291), and the U.S. (0.296) (roughly 0.30).
• Wealth Effect: This higher preference in the latter countries is reflected in a positive contribution of this parameter to housing wealth accumulation outside of Germany.

House Price Beliefs

Higher expectations of house price growth encourage homeowners to accumulate more housing wealth.

• Mechanism: Higher house price growth beliefs tend to increase the amount of housing wealth in countries like Spain and the U.S. (compared to Germany) because ** existing homeowners upgrade to buy larger houses**.
• Selection Effect: While a selection effect exists (new homeowners drawn into the market may buy smaller-than-average houses, somewhat lowering the mean wealth), the increase in housing wealth among existing homeowners buying larger houses outweighs this effect, resulting in an overall positive contribution to housing wealth from higher price beliefs.

The Rental Wedge ($\alpha$)

The rental wedge (the difference between rents and housing maintenance costs, reflecting rental market inefficiency) affects the intensive margin due to a selection effect.

• Mechanism: A high rental wedge, such as in Italy (roughly twice as large as in Germany), increases the overall homeownership rate (extensive margin), but the marginal buyers entering ownership purchase smaller houses. This inflow of marginal buyers purchasing smaller homes effectively reduces the average housing wealth of all homeowners.

Summary of Effects by Age

The sources note that the strength of the effects of the various factors on housing wealth rises with age. This pattern reflects the fact that housing wealth is a stock that accumulates gradually over the life cycle relative to the household's flow of income.

The sources explicitly document striking cross-country differences in both the decision to own a house (the extensive margin) and the amount of housing wealth accumulated (the intensive margin) across the five advanced economies analyzed: France, Germany, Italy, Spain, and the United States (U.S.).

The central research endeavor is to quantify the structural reasons—specifically differences in expectations, institutions, and preferences—that drive these observed disparities.

1. Differences in the Extensive Margin (Homeownership Rates)

Homeownership rates differ immensely and enormously across the studied countries.

• Germany vs. Spain Contrast: The most frequently cited contrast is between Spain, where 80% of all households are homeowners, and Germany, where less than half of all households own their residence (44.3%).
• Life Cycle Persistence: This striking cross-country difference in homeownership persists over the whole life cycle. For instance, the homeownership gap between Germany and Spain stays around 30 percentage points and does not narrow down with age.
• Overall Rates: Homeownership rates range from a low of 44.3% in Germany to 80.4% in Spain, with Italy (68.2%), the U.S. (66.6%), and France (58.7%) falling in between.

2. Differences in the Intensive Margin (Housing Wealth Holdings)

The sources also observe that homeowners in different countries accumulate substantially different amounts of housing wealth. This is analyzed primarily through the mean ratios of housing wealth to income, which allows for comparison relative to household resources.

• Accumulation over Life Cycle: The mean (gross) housing wealth–income ratios of homeowners generally rise from around 4 to 6 over the life cycle, boosted by the fall of income in retirement.
• Cross-Country Wealth Ratios: Substantial differences persist across countries, with low levels in the U.S. and high levels in Italy, Spain, and France when considering wealth-to-income ratios.
• Mean Housing Wealth (EUR Thousands): In absolute terms (for the sample studied), the U.S. has the highest mean housing wealth (EUR 290.5k), followed by Germany (EUR 231.4k), Italy (EUR 215.2k), France (EUR 214.8k), and Spain (EUR 160.1k).

3. Structural Differences Identified as Determinants

The model decomposes these observed differences based on variations in three structural groups of factors:

A. House Price Expectations (Beliefs)

Differences in house price beliefs are found to be a powerful driver of homeownership.

• Mean Growth: Calibrated mean long-run house price beliefs ($G$) range widely, reflecting historical aggregate growth. The highest belief is in France (2.8%), followed by the U.S. (2.1%) and Spain (2.0%), contrasting sharply with Germany (0.4%) and Italy (0.0%).
• Impact: A 1 percentage point (p.p.) difference in long-run house price beliefs results roughly in a 15 p.p. difference in the homeownership rate. Higher house price growth beliefs encourage accumulation of housing wealth in Spain and the U.S. (compared to Germany) as existing homeowners upgrade to buy larger houses.

B. Institutional Set-up of the Housing Market

Institutional differences explain large portions of both margins, particularly the rental wedge (extensive margin) and maintenance costs (intensive margin).

Factor	Key Cross-Country Differences Noted	Effect
Rental Wedge ($\alpha$)	Ranges from around 2% in France and the U.S. (reflecting more efficient rental markets) to almost 5% in Italy and 3.7% in Spain (reflecting less efficient rental markets).	A 2 p.p. difference in the rental wedge leads roughly to a 25–30 p.p. difference in homeownership rates (e.g., between Germany and Italy).
Maintenance Costs ($\lambda$)	Germany (2.6%) and Italy (1.7%) have roughly half the estimated costs compared to Spain (4.9%), France (6.0%), and the U.S. (8.6%).	The lower costs in Germany imply higher holdings of housing wealth there (intensive margin), by a multiple of 2–4 worth of annual incomes, compared to the U.S., Spain, and France.
Collateral Constraints ($\delta$)	Tighter in Germany (0.35) and Italy (0.40) compared to Spain (0.25), France (0.20), and the U.S. (0.20).	Tighter constraints reduce homeownership rates, particularly for young households.
Labor Income Profiles	Germany and the U.S. have steeply growing labor incomes, compared to flatter profiles in Italy and Spain, where incomes keep rising until later in life.	Steeper labor income profiles reduce the homeownership rate among the youngest households (e.g., by 10 p.p. in Germany compared to Spain and Italy).

C. Preferences

Preference differences, though contributing less overall than expectations and institutions, show measurable variations.

• Housing Utility Share ($\omega$): Germany (0.186) and Italy (0.210) have a lower preference for housing compared to Spain (0.291), France (0.307), and the U.S. (0.296). This lower preference in Germany and Italy is reflected in a positive contribution of this parameter to housing wealth accumulation outside of those countries.
• Bequest Motive ($L$): The bequest magnitude is lowest in France (3.90), intermediate in the U.S. (39.96) and Italy (46.87), and highest in Spain (70.39) and Germany (79.45). A weaker bequest motive lowers the homeownership rate, particularly among older households (e.g., in France, the U.S., and Italy).

The sources conclude that analyzing these structural differences provides a systematic documentation of why homeownership rates and housing wealth levels vary so substantially across advanced economies.