Sustainable intermediation in credit and housing markets – GREENFINHOME

The GREENFINHOME project combines theoretical modeling, empirical analysis, and structural estimation within an integrated research design.

1. Theoretical framework:

The project developed a new tractable model of household housing choice under credit constraints and energy uncertainty. The model distinguishes between two types of housing, green (energy-efficient) and brown (less efficient), and incorporates the dual role of housing as both a consumption good and a financial asset. It demonstrates how wealth differences and borrowing limits lead to systematic sorting across housing types and generate externalities not captured by market prices. The model also allows counterfactual simulations to assess how policies such as green subsidies or improved credit access can change housing outcomes.

2. Empirical analysis:

The empirical component uses rich microdata from Norway that link detailed building characteristics, energy labels, and estimated energy consumption. This dataset enables the project to quantify how energy efficiency affects household energy use and to validate key predictions from the theoretical model. Preliminary results show that A-rated homes consume roughly 25% less energy per square meter than G-rated ones, even after controlling for building and location characteristics. This confirms that green housing delivers real and measurable financial benefits but remains less accessible to constrained households.

3. Structural estimation:

The ongoing final phase of the project involves calibrating and estimating the structural model using the empirical data. This will allow quantification of the externalities identified in the theory and simulation of the welfare and distributional impacts of potential policy interventions.

Throughout, the project employed rigorous econometric and computational techniques, including data harmonization, model simulation, and high-performance computing resources (secured through the Luxembourg partner). The combination of theoretical and empirical methods ensures that policy conclusions rest on robust, quantitatively grounded evidence.

The GREENFINHOME project produced significant scientific and policy-relevant outcomes through its integrated theoretical and empirical approach. It has deepened the understanding of how credit constraints and energy efficiency interact in housing markets, and how financial limitations can hinder the transition toward greener housing.

1. Theoretical results

The project developed a new, tractable model of household housing choice under financial and energy uncertainty. The model captures the dual nature of housing,as both a consumption good and a financial asset,and shows that borrowing constraints can generate persistent inequality in access to green housing. Specifically, wealth-constrained households tend to purchase “brown” homes with lower upfront costs but higher exposure to volatile energy expenses. In contrast, wealthier households can afford “green” homes that act as a hedge against future energy risks.

The model highlights a fundamental market failure: households do not internalize the externalities created by these choices. When constrained households are pushed into riskier housing, the resulting vulnerability affects aggregate welfare but is not priced in market equilibria. Consequently, green housing remains underprovided, and its prices are inefficiently high. These findings provide a theoretical rationale for targeted policy interventions such as green housing subsidies, improved mortgage access, or regulatory incentives for energy-efficient renovation.

2. Empirical validation

The empirical analysis, based on detailed Norwegian data combining energy labels, structural building characteristics, and estimated energy use, confirms key predictions of the model. The results show that energy-efficient buildings deliver substantial savings: homes with an A-level energy label consume, on average, 25% less energy per square meter than G-rated homes, even after controlling for year of construction, location, and building materials. These differences are both statistically significant and economically meaningful.

This empirical evidence validates the model’s core assumption that green housing generates measurable long-term financial gains, but that these gains are unequally distributed due to wealth and credit constraints. Households with limited borrowing capacity cannot easily capture the benefits of energy efficiency, reinforcing financial and environmental inequality.

3. Implementation and dissemination

The project successfully organized two major international workshops held in Luxembourg (2024) and France (2025) which gathered leading scholars and policymakers in finance, housing, and energy economics. These events promoted knowledge exchange, visibility, and cross-institutional collaboration.

Despite administrative delays in acquiring computing resources for the structural estimation phase, substantial progress has been made on data preparation and model calibration. The research team has laid the groundwork for policy simulation

The outstanding feature of the GREENFINHOME project lies in its integrated and interdisciplinary approach to understanding how financial constraints and energy efficiency jointly shape household decisions and market outcomes. Unlike most studies that examine housing, finance, and energy policy separately, GREENFINHOME builds a unified analytical framework that combines theoretical, empirical, and structural methods to explain why the adoption of green housing remains limited despite its long-term benefits.

The project’s originality comes from several elements. First, it connects macroeconomic and microeconomic perspectives by modeling individual housing choices under credit constraints while capturing their aggregate effects on prices, risk exposure, and the supply of energy-efficient housing. Second, it merges the analytical rigor of modern finance theory with the practical relevance of environmental economics, producing results that can inform the design of green mortgage products, tax incentives, and credit policies. Third, it uses detailed administrative and energy data, linking information on building energy labels, structural characteristics, and energy use to empirically validate the theoretical mechanisms.

The collaboration among research teams in France, Luxembourg, and Norway has been another distinctive strength. Each partner contributed complementary expertise. This coordination has enhanced the project’s scientific depth and reinforced its relevance for European policy debates on housing affordability, climate transition, and financial inclusion.

Looking ahead, the project’s future prospects are both scientific and societal. The next step is to complete the structural estimation to quantify welfare effects and simulate the impact of alternative policy reforms. The resulting calibrated model will serve as a decision-support tool for policymakers, helping assess how specific credit or subsidy programs influence both energy efficiency and social equity. Beyond the initial countries, the framework can be extended to other European contexts where energy costs and credit conditions differ, making cross-country comparisons possible.

More broadly, the project opens new research perspectives on the links between green finance, household behavior, and inequality. By quantifying the “green access gap”, that is, the unequal ability of households to participate in the energy transition, GREENFINHOME contributes to the international effort to align financial systems with climate goals. Future research will extend this work to areas such as home renovation, the use of green assets as collateral, and the effects of energy price shocks on household leverage.

In summary, the project’s main contribution is to advance both academic knowledge and policy practice by offering actionable insights for governments, central banks

In this project, we would like to investigate how public policy stimulates sustainable investments and their spillovers on household income, employment and access to credit. To do so, we have obtained access to granular data from Norway about households (including individual-level information about their employment, demographic information, sources of income, and ownership of assets for every Norwegian tax-payer), housing (including detailed transaction and physical characteristics), firms and credit (including loan level transaction information on interest rates, maturity, security and other terms). For our empirical analyses, we plan to rely on causal inference by exploiting heterogeneity in subsidies to sustainable investment projects across Norwegian counties as quasi-natural experiments.