The median U.S. household holds more wealth in its home than in all other assets combined. As an asset, the home is risky: it is immobile, climate-exposed, and non-diversified. Its price, which constitutes the majority of the household’s wealth, is a reflection of market participants’ expectations about the future of its specific location. In “The Distributional Consequences of Climate Change: Housing Wealth, Expectations, and Uncertainty,” Jeffrey Sun of the University of Toronto explores how changing and uncertain climate expectations have the potential—through housing prices—to dramatically reallocate wealth across space and between the rich and the poor.
Modelling Housing Prices
Rather than imposing a simple assumption about how housing prices react to climate news, the author instead builds a model in which individual households can move and choose how to rent or purchase housing, in a setting where their income, quality of life, and exposure to environmental conditions depend on where they live. Housing prices are then determined endogenously through the interaction of these households within local housing markets. In this way, the model replicates how housing prices are determined in reality.
Over 1700 locations are modelled, covering the contiguous United States. The model combines heterogeneous-agent equilibrium modelling from the macroeconomic literature with high-resolution spatial modelling from the trade and urban literatures, and represents a high water mark in richness among economic models generally. It is made possible by computational innovations developed by the author for the purpose of solving this specific model.
A Keyhole Into Pressing Questions
Will climate change create a reallocation of wealth from hot and flood-prone Louisiana to cool, inland Minnesota? How much will climate skeptics lose out to climate believers who get ahead of the market? How will uncertainty about climate impacts affect house prices and new constructions? This work can begin to shed some light on these concerns, put numbers to stories, and simulate a variety of policy designs.
To examine the role of expectations in shaping housing wealth, insight can be gained from simulating a dramatic shift in climate expectations: What happens when all households go from being completely ignorant of climate change to believing in the most up-to-date IPCC forecasts? In the model, $41bn of housing wealth is effectively reallocated across space immediately, as housing prices fall in areas projected to suffer from heat and flooding, while prices rise in colder, drier inland areas. Over the century following the news, as the climate physically changes, the value of the housing stock in more severely impacted areas falls–and in less severely impacted areas, rises–by over half a trillion dollars.
New Winners and Losers
Another feature of this model is that its population of households is not homogeneous, but consists of households with diverse levels of wealth, income, age, and real estate investments. This allows this work to improve upon the resolution of previous models’ welfare analyses, providing quantified, intuitive insights into how demographic, geographic, and socioeconomic factors can dramatically shape how people feel the impacts of climate change.
The impacts of housing price changes depend not only on where people live, but also on how much housing they own and whether they have other sources of income to fall back on. Older generations are less negatively affected than younger generations overall, as the bulk of climate impacts will not occur within their lifetimes. However, housing price drops affect older homeowners more severely, as they lack the labour income to cushion the loss of wealth.
Analogously to how newer models of international trade often incorporate workers whose livelihoods are tied to particular sectors, challenging previous predictions that trade will make everybody better off, this climate-housing model finds that some policies previously predicted as benefiting everybody can generate losers as well. Encouraging households to relocate away from climate-impacted areas may impose costs on vulnerable populations in the model: when households are able to more easily move out of impacted areas, housing prices in those areas collapse faster, which disproportionately affects older homeowners and landlords.
In addition to the role of average expectations—how severe households think climate impacts will be on average—the model also explores the role of uncertainty—how uncertain households are about their predictions. In the model, climate uncertainty depresses housing prices and discourages new construction. While the reduced prices can actually benefit some households who are rich enough to buy homes, these potential beneficiaries are richer than renters on average. For renters, increased demand and reduced supply of rental housing drive up rents and create a flow of wealth away from poorer renters towards wealthier landlords. In the model, the effect of a level of uncertainty reflecting IPCC projections, compared to perfect foresight of climate impacts, is as regressive as a transfer of $94bn from the poorer half of households to the richer half.