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Area: Policy and financing

Housing policies are usually understood in a narrow manner as social policies targeting ' expensive' housing prices through housing allowances, tax deduction or social housing allocation. However, this definition takes a different approach and draws from a larger body of economic literature to identify the wider array of policies that impact housing markets.

Broadly speaking, housing markets are influenced through fiscal, macroeconomic, prudential and structural policies (Hilbers et al., 2008). These public policies have clear impacts on housing demand and supply and also often create synergies between each other.

Fiscal policies have a stronger impact on income and costs through taxation and subsidies. One of the main fiscal policies with regard to housing is the mortgage interest deduction which reduces user costs for the homeowner and can produce increases in property prices (Poterba, 1984).

Macroeconomic policy regulates the money supply through interest rates. Housing has usually been perceived as a conveyor belt for macroeconomic policy as the expansion of the money supply through low interest rates or quantitative easing has the potential to increase demand during recessions counteracting the procyclical behaviour of financial markets (Muellbauer, 1992).

Prudential policies determine the level of risk associated with lending through Loan-to-Value (LTV) and Debt-to-Income (DTI) ratios. The Global Financial Crisis (GFC) that started in the US in 2008 is usually seen as the failure of prudential policy that resulted in the tightening of loans (Whitehead & Williams, 2017) and  drew renewed attention to housing policy from central banks, policymakers, and economists (Piazzesi et al., 2016).

Structural policies regulate housing supply, this includes planning regulations and environmental standards. For example, research from the US has shown that zoning laws can have a relevant impact on housing affordability by constraining supply (Glaeser & Gyourko, 2002).

While most research is conducted selectively on each of these policy interventions, there are relevant synergies between policy domains that can be identified. These policies usually work in conjunction with each other: lax prudential policies and favourable home ownership taxation together with low interest rates and tight planning controls can lead to higher property prices. Conversely, constrained lending, brick-and-mortar subsidies and higher interest rates are known to mitigate rising house prices.


Glaeser, E. L., & Gyourko, J. (2002). The Impact of Zoning on Housing Affordability. NBER Working Paper 8835, NBER, Cambridge, MA.

Hilbers, P., Hoffmaister, A. W., Banerji, A., & Shi, H. (2008). IMF Working Paper. House Price Developments in Europe: A Comparison. WP/08/211. IMF Working Paper European Department.

Muellbauer, J. (1992). Anglo-German differences in housing market dynamics: The role of institutions and macro economic policy. European economic review36(2-3), 539-548.

Piazzesi, M., S. &, Schneider, M., Arefeva, A., Hoffmann, E., Kermani, A., Lenel, M., Myers, S., Peter, A., Taylor, J., & Uhlig, H. (2016). Housing and Macroeconomics. NBER Working Paper 22354, NBER, Cambridge, MA.

Poterba, J. M. (1984). Tax Subsidies to Owner-Occupied Housing: An Asset-Market Approach. The Quarterly Journal of Economics, 99(4), 729–752.

Whitehead, C., & Williams, P. (2017). Changes in the regulation and control of mortgage markets and access to owner-occupation among younger households. OECD Social, Employment and Migration Working Papers, No. 196, OECD Publishing, Paris.

Created on 01-07-2022 | Update on 28-04-2023

Related definitions

Path dependence

Author: M.Horvat (ESR6)

Area: Policy and financing

Path dependence (or historical institutionalist approach) refers to the idea that the outcomes of a particular situation or process depend on the historical path taken to reach that point. In other words, the current state or future developments of situations are influenced by past decisions, events, or processes, even if those may no longer be the most efficient or rational choices.  As a concept, it provides a valuable framework for analysing historical events, which is primarily concerned with elucidating responses and outcomes in the area of policy change and institutional persistence (Bengtsson & Jensen, 2020). A simplified interpretation of the concept of path dependency states that developments at a specific juncture create inertia in economic, institutional, social and technological progress. Bengtsson & Jensen (2020, p. 15) interpret this concept as the "fundamental causal mechanism in historical versions of institutional theory." In practise, this approach says that a development at a certain point in time sets a direction that either blocks alternative paths or makes them more difficult to achieve at a later point in time. The difference between path dependency analysis and mere "what if" speculation is the understanding of contextual mechanisms that govern historical development, rather than general social theories. In literature, the idea that "the past influences the future" is often only touched upon, leading to misunderstandings. Mahoney (2000) emphasises that a "proper" analysis of path dependency involves understanding change processes, tracing historical events while recognising mutual contingent relationships, and elucidating causal effects that cannot be explained by other events. Contingency refers to the inability of a theory to deterministically or probabilistically predict or explain a particular outcome (Mahoney, 2000, p. 513). In essence, a contingent event has not been predicted within a theoretical understanding of a particular process. Mahoney (2000) emphasises that path dependence should not be confused with a historical explanation that emphasises temporal causal sequences. In the application of path dependency, certain historical outcomes are traced back to relevant earlier events, which are often themselves contingent. There are three key concepts in every path dependence analysis: A single event that is not the product of social forces can significantly affect social outcomes. Contingent events may be temporally distant from the outcomes. The sequence of events is of historical importance and requires a chronological order in the analysis to trace the sequence of outcomes. When applying path dependency as an analytical tool, three core elements are considered: an event (A) that is preferred to an alternative ("critical juncture"), a subsequent decision (B) that connects to A ("focus point"), and the mechanism(s) that explain(s) the impact of A on decisions at B. To identify these mechanisms, it is usually necessary to trace events where no plausible alternatives were chosen. There are two types of path dependence: self-reinforcing sequences and reactive sequences, the latter involving events that are temporarily ordered and causally linked (Mahoney, 2000). In the case of self-reinforcing sequences, the detection of the beginning of the sequence could occur just before a critical turning point. In the phase preceding the critical turning point, different options become viable and processes that influence decisions at that juncture begin to operate. If the conditions in this phase can predict or clarify the outcome of the critical juncture's outcome, the sequence should not be considered dependent on the preceding events. In the case of reactive sequences, it is difficult to determine a point in time that corresponds to the initial conditions because the outcome under investigation may follow an extensive chain of causally related events that can be traced back in time. In other words, it may be difficult to find a starting point of the sequence, as the researcher keeps on going back in time (Mahoney, 2000). The concept of path dependence is of great importance for housing research, especially in the context of housing policy development of post-socialist countries that radically transformed the institutional framework and changed the tenure composition from dominantly public to private homeownership, affecting the future pathways of social housing policy efficiency and wealth distribution (Lux & Sunega, 2020). Despite its potential, path dependency is still underused in housing studies (Bengtsson & Ruonavaara, 2010; Malpass, 2011). It is often applied at the national level, but can also be extended to the municipal and local levels where housing policies are implemented.

Created on 31-08-2023 | Update on 22-10-2023

Performance Gap in Retrofit

Author: S.Furman (ESR2)

Area: Design, planning and building

The performance gap in retrofit refers to the disparity between the predicted and actual energy consumption after a retrofit project, measured in kWh/m2/year. This discrepancy can be substantial, occasionally reaching up to five times the projected energy usage (Traynor, 2019). Sunikka-Blank & Galvin (2012) identify four key factors as contributing to the performance gap: (1) the rebound effect, (2) the prebound effect, (3) interactions of occupants with building components, and (4) the uncertainty of building performance simulation outcomes. Gupta & Gregg (2015) additionally identify elevated building air-permeability rates as a factor leading to imbalanced and insufficient extract flowrates, exacerbating the performance gap. While post occupancy evaluation of EnerPhit—the Passivhaus Institut certification for retrofit—has shown far better building performance in line with predictions, the human impact of building users operating the building inefficiently will always lead to some sort of performance gap (Traynor, 2019, p. 34). Deeper understanding of the prebound effect and the rebound effect can improve energy predictions and aid in policy-making (Galvin & Sunikka-Blank, 2016). Therefore, the ‘prebound effect’ and the ‘rebound effect’, outlined below, are the most widely researched contributors to the energy performance gaps in deep energy retrofit.   Prebound Effect The prebound effect manifests when the actual energy consumption of a dwelling falls below the levels predicted from energy rating certifications such as energy performance certificates (EPC) or energy performance ratings (EPR). According to Beagon et al. (2018, p.244), the prebound effect typically stems from “occupant self-rationing of energy and increases in homes of inferior energy ratings—the type of homes more likely to be rented.” Studies show that the prebound effect can result in significantly lower energy savings post-retrofit than predicted and designed to achieve (Beagon et al., 2018; Gupta & Gregg, 2015; Sunikka-Blank & Galvin, 2012). Sunikka-Blank & Galvin’s (2012) study compared the calculated space and water heating energy consumption (EPR) with the actual measured consumption of 3,400 German dwellings and corroborated similar findings of the prebound effect in the Netherlands, Belgium, France, and the UK. Noteworthy observations from this research include: (1) substantial variation in space heating energy consumption among dwellings with identical EPR values; (2) measured consumption averaging around 30% lower than EPR predictions; (3) a growing disparity between actual and predicted performance as EPR values rise, reaching approximately 17% for dwellings with an EPR of 150 kWh/m²a to about 60% for those with an EPR of 500 kWh/m²a (Sunikka-Blank & Galvin, 2012); and (4) a reverse trend occurring for dwellings with an EPR below 100 kWh/m²a, where occupants consume more energy than initially calculated in the EPR, referred to as the rebound effect. Galvin & Sunikka-Blank (2016) identify that a combination of high prebound effect and low income is a clear indicator of fuel poverty, and suggest this metric be utilised to target retrofit policy initiatives.   Rebound Effect The rebound effect materializes when energy-efficient buildings consume more energy than predicted. Occupants perceive less guilt associated with their energy consumption and use electrical equipment and heating systems more liberally post-retrofit, thereby diminishing the anticipated energy savings (Zoonnekindt, 2019). Santangelo & Tondelli (2017) affirm that the rebound effect arises from occupants’ reduced vigilance towards energy-related behaviours, under the presumption that enhanced energy efficiency in buildings automatically decreases consumption, regardless of usage levels and individual behaviours. Galvin (2014) further speculates several factors contributing to the rebound effect, including post-retrofit shifts in user behaviour, difficulties in operating heating controls, inadequacies in retrofit technology, or flawed mathematical models for estimating pre- and post-retrofit theoretical consumption demand. The DREEAM project, funded by the European Union, discovered instances of electrical system misuse in retrofitted homes upon evaluation (Zoonnekindt, 2019). A comprehensive comprehension of the underlying causes of the rebound effect is imperative for effective communication with all retrofit stakeholders and for addressing these issues during the early design stages.   Engaging residents in the retrofit process from the outset can serve as a powerful strategy to mitigate performance gaps. Design-thinking (Boess, 2022), design-driven approaches (Lucchi & Delera, 2020), and user-centred design (Awwal et al., 2022; van Hoof & Boerenfijn, 2018) foster socially inclusive retrofit that considers Equality, Diversity, and Inclusion (EDI). These inclusive approaches can increase usability of technical systems, empower residents to engage with retrofit and interact with energy-saving technology, and enhance residents’ energy use, cultivating sustainable energy practices as habitual behaviours. Consequently, this concerted effort not only narrows the performance gap but simultaneously enhances overall wellbeing and fortifies social sustainability within forging communities.

Created on 08-09-2023 | Update on 01-12-2023


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Alsaeed, M., Hadjri, K., & Nawratek, K. (2024, March). A comparative analysis of UK sustainable housing standards. In 7th Residential Building Design & Construction Conference, Pennsylvania, USA.

Posted on 04-12-2023



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