The housing wealth effect: Quasi-experimental evidence

This paper estimates a causal housing wealth effect on consumption using a quasi-natural experiment in Stockholm, Sweden. The identification exploits an unanticipated political decision — announced in September 2007 — to renew the operating contract of Bromma Airport through 2038, reversing a long-standing expectation of closure by 2011. Because the decision resulted from opaque political bargaining and was widely characterized as a political coup by opposition parties, the announcement was genuinely unexpected. The negative externality of continued airport operations (primarily aircraft noise exceeding 70 decibels within a mapped contour) capitalized locally into house prices within one quarter of the announcement. Using difference-in-differences on all single-family house transactions in Stockholm Municipality from 2004 to 2012, the authors estimate a house price decline of 19.4 percent for dwellings within 1,000 meters of the noise contour relative to those farther away (t-statistics above 5; robust to control variables and sample period). Co-op apartment prices show no statistically significant response, consistent with greater structural noise insulation in multi-story concrete buildings.

The consumption outcome is new car purchases, observed at quarterly frequency in a registry-based household panel covering all Stockholm residents, with balance sheet information (loan-to-value ratios, bank deposits, mortgage types) and GIS-located residences. The paper focuses on the intensive margin — the log value of new cars purchased conditional on a purchase — since no effect is found on the extensive margin (probability of buying). A two-sample IV approach yields a short-run elasticity of 0.39: homeowners near the noise contour reduce the value of new cars purchased by 7.7–8.5 log points relative to homeowners farther away. Converting to a marginal propensity for expenditures (MPX): conditional on purchasing a new car, the car MPX is 2.5 cents per dollar of housing wealth lost; scaling by the annual new-car purchase rate of 0.049 per household yields an aggregate new-car MPX of 0.12 cents per dollar per year. Including a symmetry assumption for used cars raises the overall car MPX to 0.38 cents per dollar per year.

Heterogeneity analysis reveals that the collateral channel dominates the pure wealth channel. Homeowners with loan-to-value ratios above 50 percent respond almost twice as strongly as those below (elasticities of 0.526 versus 0.269). Homeowners with below-median bank deposits respond with an elasticity of 0.694, roughly five times larger than those with larger deposits. The financing data show that 47 percent of a new car’s value is financed with credit on average, of which 71 percent takes the form of mortgage debt; however, households with high LTV ratios borrow one-third less per dollar of car value, almost entirely through reduced mortgage use.

A calibrated life-cycle model (quarterly, ages 30–85, Cobb-Douglas preferences over non-durables and cars, long-term fixed-rate mortgage, adjustment costs for cars and mortgages, information friction) replicates the empirical findings. In simulation, a 19.4 percent permanent house-price shock reduces new car values purchased by 6.1 log points on average over the first four quarters, implying an elasticity of 0.31 and a new-car MPX of 0.20 cents per dollar — close to the empirical 0.12 cents and within the 95 percent confidence interval. The model decomposes the response: the collateral effect accounts for 93 percent of the car MPX and 83 percent of the total MPX in the first four quarters; the pure wealth effect accounts for the remainder. The model further shows that full information awareness would roughly double the one-year response, and that smaller shock magnitudes, shorter measurement windows, and crisis-era credit conditions (where more households are already at borrowing limits) each amplify estimated MPXs — helping account for the wide range of estimates (0.12 to 2.3 cents per dollar) in prior literature.

The identification is validated by dose-response monotonicity with distance to the noise contour, placebo tests showing no response for apartment owners or renters, and absence of income effects or differential moving behavior in the treatment group.

Q: What is the quasi-experiment and why is it well-suited for identifying housing wealth effects? A: The Stockholm municipality unexpectedly renewed Bromma Airport’s operating contract through 2038 in September 2007, reversing a broadly held expectation that the airport would close by 2011. The decision emerged from closed-door political negotiations and was denounced as a political coup by opposition parties, making it genuinely unanticipated. Because the shock is geographically contained within the airport’s noise contour, it is unrelated to macroeconomic conditions and unlikely to generate general equilibrium feedback. The authors also verify that no differential income effects, tax changes, or other policies affected the treatment versus control groups over the study window.

Q: How large is the estimated house price effect, and how precisely is it measured? A: Dwellings within 1,000 meters of the noise contour experienced a price decline of 19.4 percent relative to dwellings farther away (baseline estimate, longer sample period). The estimate is highly significant with t-statistics above 5 in all specifications and is robust to the inclusion of rich property-level controls; adding controls changes the pre-crisis estimate only trivially (from -21.4 to -21.3 percent). Co-op apartment prices show no statistically significant response across all specifications, consistent with better structural insulation of multi-story concrete buildings.

Q: What is the main consumption response finding? A: Homeowners near the noise contour reduce the log value of new cars purchased by 7.7–8.5 log points relative to homeowners farther away (reduced form, intensive margin). There is no detectable effect on the extensive margin — the probability of purchasing a new car changes by only 0.029 percentage points per quarter against a baseline of approximately 1.2 percent per quarter. Two-sample IV yields an elasticity of 0.39 (statistically significant at 1 percent), meaning a 1 percent decline in house prices leads to a 0.39 percent reduction in new car values among purchasers.

Q: What does the elasticity of 0.39 imply for the marginal propensity to spend on cars? A: Conditional on purchasing a new car, the car MPX is 2.5 cents per dollar of housing wealth lost (calculated as 0.393 × 19.4% × SEK 250,000 average car value, divided by SEK 774,060 housing wealth loss). Scaling by the annual new-car purchase frequency of 0.049 per household yields an aggregate new-car MPX of 0.12 cents per dollar per year. Assuming an equal response for used cars, the overall car MPX is 0.38 cents per dollar per year. These estimates are substantially smaller than Mian et al. (2013)’s 1.8–2.3 cents per dollar, a discrepancy the model helps explain.

Q: What is the role of the loan-to-value ratio in shaping the consumption response? A: Homeowners with LTV ratios above 50 percent respond almost twice as strongly (elasticity 0.526) as those with LTV below 50 percent (elasticity 0.269). The financing data confirm the mechanism: on average 71 percent of car-purchase borrowing takes the form of mortgage debt, but households with high LTV ratios borrow one-third less per dollar of car value, with the difference almost entirely attributable to reduced mortgage use. This pattern is consistent with binding borrowing constraints preventing high-LTV households from extracting home equity for collateral.

Q: What is the role of liquid savings (bank deposits) in the response? A: Homeowners with bank deposits below the median respond with an elasticity of 0.694, roughly five times larger than homeowners with larger deposits (elasticity approximately 0.139). This heterogeneity is consistent with deposits serving as a buffer stock that allows wealthier households to smooth consumption without altering borrowing behavior after a wealth shock.

Q: What does the quantitative model find about the relative importance of the collateral channel versus the pure wealth effect? A: In the first four quarters following the shock, the collateral effect accounts for 93 percent of the car MPX response and 83 percent of the total expenditure MPX; the pure wealth effect accounts for only 7.5 percent of car MPX and 19 percent of total MPX over the same horizon. Over a longer horizon of 20 quarters, the collateral channel remains dominant at 69 percent of the car baseline, while the wealth effect rises to 32 percent. For non-durable consumption, the short-run collateral effect is 81 percent and the wealth effect is 19 percent.

Q: How does the model match the empirical estimates? A: Simulating a permanent 19.4 percent house-price shock for 200,000 household pairs, the model produces a 6.1 log point average reduction in new car values over the first four quarters, corresponding to an elasticity of 0.31 and a new-car MPX of 0.20 cents per dollar. The empirical estimate is 0.12 cents, and the model value falls within the empirical 95 percent confidence interval. The model also replicates the pattern of no extensive-margin response in the short run and a gradual build-up in the non-durable consumption response (maximum elasticity of 0.079 reached only after ten quarters).

Q: Why is the short-run response concentrated in cars rather than non-durables? A: The paper establishes an intertemporal smoothing mechanism for durables analogous to McKay and Wieland (2021): households delay or bring forward lumpy durable purchases in response to shocks to borrowing capacity. Although cars represent only 5.5 percent of total consumption in the model (Cobb-Douglas expenditure share), they account for 45–72 percent of the total expenditure response in the first four quarters after the house-price shock. The non-durable consumption response builds slowly and reaches its maximum after about ten quarters.

Q: What factors does the model identify as explanations for the wide range of MPX estimates across studies? A: Three factors are identified. First, shock magnitude: larger shocks produce smaller partial-equilibrium MPXs because more households hit borrowing constraints; across shock sizes from -30 to +20 percent, car and total MPXs can range from 1 to 2 cents per dollar. Second, measurement period: short-run (1-year) MPXs exceed long-run (3-year) MPXs, especially for durable goods. Third, the state of the economy: in a crisis-era bust following credit-fueled boom, many more households are constrained when prices fall, amplifying MPXs; Guerrieri and Iacoviello (2017) report car elasticities of 0.24 in the boom phase and 0.49 in the bust phase of the US financial crisis.

Q: What is the role of the information friction in the model? A: Because the quasi-experiment occurred in “normal times” just before the global financial crisis became acute, the authors argue that households were not immediately aware of the house-price shock; they only update their perceived housing wealth when they attempt to adjust their mortgage, trade cars, or receive a random information update. Under full information awareness, the one-year MPX would be approximately twice as large, and the one-year total MPX could be as much as three times as large (with a car MPX of 3 cents per dollar and total MPX well above 6 cents per dollar under full information with small positive shocks). The information friction thus attenuates the estimated MPX relative to a world of full information.

Q: What placebo and robustness tests support the identification? A: Co-op apartment owners show no statistically significant price or consumption response, consistent with their structural insulation from aircraft noise. Renters also show no consumption response. The dose-response test confirms a monotone relationship between distance to the noise contour and both house price and car expenditure effects. Income effects are absent (Figure B.2), and there is no differential probability of moving in either the short or long run. Tax reforms benefited both groups equally and had already been announced before the quasi-experiment.

Q: How does this study’s identification strategy compare to instrumental variable approaches using housing supply elasticity? A: Supply elasticity IV approaches (Mian et al. 2013; Aladangady 2017; Kaplan et al. 2020) rely on regional variation in construction constraints and must assume that consumption demand factors are either observed or uncorrelated with supply elasticity — an assumption critiqued by Davidoff (2016). This paper’s identification exploits an exogenous change in a local negative externality, yielding a geographically granular shock unrelated to macroeconomic conditions and free from general equilibrium feedback. The result is interpretable as a partial equilibrium housing wealth effect in the sense of Berger et al. (2018) and Guren et al. (2020).

Housing wealth effect: The causal effect of a change in housing wealth on household consumption expenditure, decomposed in this paper into a pure wealth channel (change in lifetime resources) and a collateral channel (change in borrowing capacity via home equity).

Marginal propensity for expenditures (MPX): The change in spending per dollar change in housing wealth; distinct from the marginal propensity to consume (MPC) because spending on durables may be lumpy and differ from the flow of consumption services. The paper distinguishes the car MPX conditional on purchase (2.5 cents per dollar), the aggregate new-car MPX (0.12 cents per dollar per year), and the total expenditure MPX.

Collateral channel: The mechanism by which a decline in house prices reduces homeowners’ borrowing capacity — because the house serves as collateral for mortgage debt — thereby tightening credit constraints and reducing spending, independent of any change in permanent income. The model assigns 93 percent of the short-run car MPX to this channel.

Two-sample instrumental variable (TSIV): The empirical strategy of Angrist and Krueger (1992) used here to estimate the consumption elasticity: the house-price first stage is estimated in one sample (transaction data), and the reduced-form consumption effect is estimated in a second sample (household panel), with the IV elasticity computed as the ratio.

Information friction: The assumption in the model that households do not immediately observe the spatial divergence in house prices; they update their perceived housing wealth only when they attempt to adjust their mortgage, trade a durable good, or receive a random information shock. This friction attenuates the short-run consumption response and is calibrated to “normal times” conditions.

Noise contour: The geographic boundary around Bromma Airport within which properties are regularly exposed to noise levels of at least 70 decibels, as adjudicated by the Swedish Land and Environment Court. Properties within 1,000 meters of this contour define the treatment group.

Intertemporal smoothing of durables: The pattern, documented in the model and complementary to McKay and Wieland (2021), whereby households adjust lumpy durable purchases (cars) rapidly in response to changes in borrowing capacity, so that durables account for a disproportionately large share of the total expenditure response in the short run (45–72 percent in the first four quarters despite a 5.5 percent Cobb-Douglas expenditure share).