7. Reconciling Housing and the Environment

A residential structure generates various pecuniary and negative externalities across its life cycle. First, it implies the use of land, which in many cases is relatively scarce and may have other productive uses. Its construction requires materials and energy that generate greenhouse gas emissions and other environmental pollutants. Globally, urban land area is projected to rise nearly five-fold, to almost 3 million km2 by 2050 (Angel et al., 2011[1]), and 70% of the world population is expected to live in these areas. To meet the increasing housing demand globally, the construction sector is expected to more than double between 2017 and 2060, along with its use of materials. This expansion is set to lead to almost 84 Gt of construction materials use per year in 2060 (OECD, 2019[2]).

Once a structure is built, it continues to have environmental impacts through energy and water consumption on the one hand, and waste and sewerage creation on the other. Many countries have improved housing-related energy efficiency, as evidenced by declining residential energy consumption per capita (Figure 7.1 , Panel A). Exceptions are most Eastern European countries as well as Brazil, Italy, Spain and Finland. Countries that achieved small reductions in energy intensity however saw overall energy consumption increase on the back of rising population, a trend that is set to continue on unchanged housing and energy policies. The bulk of the energy consumption of the residential sector originates comes from heating, which explains why countries exposed to cooler temperatures usually exhibit higher per capita energy consumption. Still, there are large discrepancies in terms of how heating degree days, an indicator for the intensity and duration of cold temperatures, translate into residential energy consumption per capita (Figure 7.1 , Panel B). In some countries that consume a lot of energy relative to what the number of heating degree days would suggest, the use of air conditioning explains a good chunk of energy consumption (United States, Australia, Canada). Another key determinant of high energy consumption seems to be the size of dwellings. Indeed, the United States leads the country ranking in terms of floor area per capita followed by Canada and Denmark, all countries that exhibit a residential energy intensity above the average for a given number of heating degree days.

Including the indirect emissions from power generation are considered, buildings are responsible for nearly 30% of global energy-related CO2 emissions. In absolute terms, buildings-related CO2 emissions rose to an all-time high of 9.6 GtCO2 in 2019 (IEA, 2020[3]). While the residential sector's carbon intensity strongly correlates with energy intensity (Figure 7.2), differences in the energy mix explain a significant part of cross-country differences in the carbon footprint per capita. Countries with a high share of low-carbon energies (i.e. nuclear and renewables) achieve a much lower per capita carbon footprint for the same per capita energy consumption. Countries that stand out in this respect are France with a high share of primary energy from nuclear (37% in 2019), Sweden with both a high percentage from nuclear (27%) and renewables (42%) and Brazil that displays the highest share from renewables (45%) mainly from hydroelectric power (28%).

1. Residential activities are also responsible for 44% of fine particulate matter (PM2.5) emissions on average across OECD countries (Figure 7.3).1 Housing is a critical source of PM2.5, especially in Central and Eastern European countries, due to the relatively high proportion of solid fuels, notably wood and coal, in residential heating (Karagulian et al., 2015[4]). PM2.5 is the air pollutant that poses the greatest risk to health globally, and critical exposure to these particles considerably increases the risk of respiratory and cardiovascular diseases. Exposure to PM2.5 concentration is positively correlated with the density of urban areas (Borck and Schrauth, 2021[5]). Mean exposure to PM2.5 emissions is decreasing in most OECD countries (Figure 7.4) – due to optimised combustion processes (in industry and in residential heating), a decrease of coal in the energy mix, and lower emissions from transport and agriculture – but still remains high and above the 10 μg/m3 recommended by WHO (OECD, 2020[6]).

2. The housing sector also generates environmental impacts related to the transport activity that it engenders. In general, a less accessible location implies greater reliance on private cars and a larger environmental footprint. The relationship between environmental quality and the housing sector is bidirectional, as the former also has implications for the latter. Proximity to environmental amenities is an important determinant of housing demand, and the elasticity of property values with respect to the quality of environmental amenities is generally greater than one (Kuethe and Keeney, 2012[7]; Wang et al., 2015[8]). Finally, urban growth is often characterised by a scattered, low-density development pattern known as urban sprawl, which is associated with multiple environmental externalities, social inefficiencies and car dependence (OECD, 2018[9]). The loss of biodiversity is among the most pressing global environmental challenges related to urbanisation. Figure 7.5 illustrates the percentage of tree cover, grassland, wetland, shrubland and sparse vegetation converted to cropland or artificial surfaces from 1992 to 2015 in functional urban areas. The results suggest large discrepancies across countries.

The general aim of environmental policies in urban areas is to reduce the environmental externalities of urban development, such as greenhouse gas emissions and other pollutants generated by buildings and transport. Other interventions aim at limiting land-use change, preserving open space and protecting biodiversity. Such policies can affect the functioning of housing markets through their impacts on supply and demand for housing, and therefore on housing prices and affordability. The impact of environmentally-related policies on housing supply is twofold. In the long term they may induce more gradual changes in urban form and other factors that affect supply and housing prices.

3. The interactions between environmentally related policies and housing markets are complex (Figure 7.6). Land-use and transport policies may impact either housing demand or housing supply, or both, whereas construction regulations mainly influence housing supply. Policies surrounding construction practices and energy efficiency mainly influence housing supply. Non-environmentally related policies can also have impacts on environmental quality through their impacts on housing markets. In turn, environmentally related policies that impact housing supply and demand will also impact housing prices and affordability.

Policies also interact with fundamental drivers of housing supply, including the cost of land, renovation/site improvement, labour and materials, as well as finance, administration and marketing. In addition to the cost of these inputs, the price of the existing stock of dwellings, and the technologies used in construction also impact housing supply. Insofar as the housing supply is elastic to the availability of land and other factors, any policy affecting these factors will have an impact on the housing market via changes in housing supply. The extent to which environmentally related policies will impact housing supply in specific areas depends on local conditions that determine how supply responds to changes in these factors.

Environmentally related policies can impact housing demand by affecting accessibility of jobs, economic centres, and environmental and other amenities. For instance, policies that make an area more accessible to public transport and soft mobility, as well as less exposed to traffic congestion will render it more attractive as a residential location and will consequently serve to raise housing and land prices in that area. In addition, other powerful factors drive housing demand, such as demographics (e.g. population growth, family size and age composition, net migration), income, the user cost of capital, the availability of credit, consumer and investor preferences, and the prices of substitutes and complements to housing. The analysis of the impacts that follows considers these factors as fixed, thus the reported impacts should be interpreted as assuming that all else remains equal.

Land-use policies must be carefully designed to achieve their environmental objectives without inducing substantial welfare losses in the housing market. Land-use policies play an important role in shaping urban form, which has direct as well as indirect environmental implications. Environmentally related land-use policies seek to mitigate the negative externalities of the residential sector via several approaches, including managing growth, reducing the environmental impacts of existing development, and preserving open space (Table 7.1 and Table 7.2). In addition to reducing the negative environmental externalities of urban areas, land-use policies also aim to foster social cohesion and security, protect public health and safety, secure property rights and improve the functioning of housing markets, capture the value accruing from public sector investments and raise revenues to finance continued infrastructure provision (UNECE, 2008[10]; Silva and Acheampong, 2015[11]).

Despite their generally positive environmental impacts, land-use policies have substantial distortionary impacts on the functioning of the housing market in urban areas. For instance, the economic benefits of greenbelts include the higher amenity value of protected land and fiscal savings from more efficient provision of public services and infrastructure. However, greenbelts can also give rise to economic side-effects such as rising housing costs and social pressure if housing supply within the area is not able to accommodate growing demand (see Box 7.1; Glaeser and Kahn, 2008). Furthermore, although they often result in improvements in local environmental quality, the net environmental impacts of these types of measures are not always positive. Net environmental impacts can in fact be negative in cases where the urban area located within the containment zone is not able to accommodate additional development. This may occur, for example, when an urban growth boundary coexists with stringent maximum building height restrictions. For instance, leapfrog development may create a scattered development pattern (Vyn, 2012[36]) that increases the social cost of public service provision. Car dependency and increased CO2 emissions are among the most important consequences of such development (Matteucci and Morello, 2009[37]).

Therefore, environmentally motivated land-use policies must be carefully designed to achieve their environmental objectives without inducing substantial welfare losses in the housing market. Ensuring an adequate amount of developable area within an urban perimeter and periodically reevaluating the boundaries defined by urban containment measures, for example, can prevent housing supply from becoming inelastic and mitigate the negative impacts of containment policies on house prices (Silva and Acheampong, 2015[11]; Ball et al., 2014[13]; Bengston and Youn, 2006[16]; Blöchliger et al., 2017[38]). Similarly, the net environmental impacts of zoning regulations are unclear, as considerable diversity exists regarding specific zoning mechanisms and the contexts in which they are implemented.

Maximum building height restrictions are among the most common regulatory mechanisms worldwide, with considerable impacts on the housing market and the environment. For instance, maximum building height restrictions are often invoked to protect historical buildings in city centres and to maintain non-market attributes, such as visibility, primarily in suburban areas. As such, they often confer social benefits, which may increase residential satisfaction (Brown, Oueslati and Silva, 2016[39]) and raise land and house prices. Flexible building height restrictions are a particularly effective instrument to prevent population density from reaching levels that are socially detrimental, such as in areas where the spatial concentration of air pollutants is high (Schindler and Caruso, 2014[40]). In spite of this, widespread building height restrictions can have severe adverse effects in the markets of land and housing, as well as on the environment. When deployed without sufficient justification, this type of zoning policy contributes to excessive sprawl, generates additional congestion and emissions whose social cost may well exceed 2% of household income (Bertaud and Brueckner, 2005[41]; Tikoudis, Verhoef and van Ommeren, 2018[42]).

Other measures, while theoretically efficient, are not in widespread use due to practical issues related to their implementation. For example, performance zoning, which requires properties to meet certain standards of environmental performance, allows for flexibility in how developers achieve environmental outcomes. However, this type of zoning is more difficult to administer than more classic approaches based on simpler metrics, such as how a property is used and its physical characteristics (Wilson et al., 2018[43]; Frew, Baker and Donehue, 2016[44]; Baker, Sipe and Gleeson, 2006[19]).

A number of environmentally related policies and measures target construction processes and energy efficiency. Such policies aim to promote or impose durable building design, recycling of construction and demolition waste, energy efficiency standards and the use of renewable energy (Table 7.3 and Table 7.4). In general, environmentally related construction and energy efficiency policies do not have a considerable impact on housing supply, but they affect house prices primarily via their impacts on construction and maintenance costs. Subsidies for energy-efficiency upgrading can ease adverse near-term impacts on affordability but are likely to be neutral over the long term as the value of the improvement gets capitalised in the dwelling price (Taruttis and Weber, 2020[46]). One large-scale example is Italy’s “Superbonus 110” programme, which provides a tax reduction equal to 110% of the expenses made by households to improve the energy efficiency of their homes.2 The COVID-crisis is likely to alter workplace and housing preferences bringing about challenges and opportunities for the environmental policy agenda (Box 7.2).

Measures that rely on voluntary engagement, such as some benchmarking efforts and information campaigns to encourage behaviour change also have a role to play. Benchmarking measures, for example, have been found to yield 2-14% energy savings across 8 studies in the United States (Karatasou, Laskari and Santamouris, 2014[58]; Mims et al., 2017[51]). The programmes that yielded these reductions relied on providing owners with information on how the emissions from their buildings compare with similar ones and also on concrete measures that can be taken to reduce emissions.

Transport policies can have a long-term impact on housing markets to the extent that they alter the desirability of different residential locations, primarily through their impact on travel time and costs. Transport policies may also affect local levels of air pollution, noise and traffic accidents. While transport policies can significantly affect the demand for housing and property prices across space, they can also impact housing supply insofar as they shape the investment decisions of residential developers.

Several environmentally related transport policies that regulate traffic-related externalities have an impact on urban form and house prices (OECD, 2018). These include a series of market-based instruments: pricing of road use, either with a flat kilometre tax or with charging schemes based on a cordon surrounding the central business district; pricing of on-street parking and public transport services; and motor fuel taxes. Regulatory mechanisms include various forms of urban vehicle access regulations, such as low emission zones, i.e. areas in which entry of vehicles is regulated based on their emission profile. Finally, the provision of infrastructure for public transport, walking and cycling also have clear implications for the environment and a simultaneous impact on demand for housing (Table 7.5 and Table 7.6).

Evidence on the impact of transport policies on housing markets is well documented. For instance, simulations for cities with relatively monocentric structures find that common pricing schemes substantially increase property prices and rents closer to central business district areas, while property values and rents in remote areas generally decrease (Verhoef, 2005[87]; Tikoudis, Verhoef and van Ommeren, 2015[88]). To some extent, these findings also apply to polycentric cities with multiple business districts. For instance, pricing traffic with a cordon toll surrounding the inner core of a polycentric city can result in housing costs changing from -4% to +12% (Tikoudis and Oueslati, 2020[89]). These changes largely correlate with house and land prices prior to policy implementation, implying that larger capital gains are expected in the most expensive areas, while smaller or negative capital gains are anticipated in less expensive ones. These results suggest that the distributional impacts of road pricing that materialise through the housing market are substantial and need to be carefully considered in policy design. However, despite their substantial effects on housing costs, urban road pricing generates aggregate welfare gains. Once road charges are aligned with the volume of traffic externalities and streamlined to account for interactions with the rest of the fiscal system, these welfare gains can be considerable.

Fuel taxes also affect house prices. As in the case of a flat kilometre tax, the price effect of the two instruments are at first identical, since in the short run the fuel consumption of private vehicles is fixed. Consequently, fuel tax increases in the short run generally have the effect of inflating property prices in locations of high accessibility since the tax makes travelling more expensive. In general, road pricing and fuel taxes encourage compact urban forms (Creutzig et al., 2015[90]). However, the disincentive created by a fuel tax will gradually subside over time as increasingly fuel-efficient vehicles become used.

Soft mobility and public transport infrastructure have a positive effect on property values. Opinion surveys show a substantial willingness-to-pay for walking and biking infrastructure, as this type of infrastructure can increase accessibility to public transport (Yang et al., 2018[91]). Making public transport more accessible, especially by promoting transit-oriented development, has been empirically found to have a positive effect on house prices (Bartholomew and Ewing, 2011[92]). As a result, investments in public transport and soft mobility can result in higher local property values.

Many policies targeting land-use and housing markets have an impact on the environment. Given that these impacts can be considerable, they need to be taken into account in the design of housing refom packages (Table 7.7 and Table 7.8).

Ad-valorem (based on market value) property taxes have two important environmentally related functions (Chapter 8). First, they increase the overall cost of housing and thus may reduce the demand for residential floor space. In this sense, ad-valorem taxes could foster compact development, given country-specific context and circumstances. More compact development implies economies of density and saves resources by shortening travel distances and reducing transport-related externalities. Second, ad-valorem taxes impose a burden per unit of surface that is higher for dwellings located in areas where land is more expensive. Therefore, they could have a long run centrifugal impact on development patterns, redirecting development to peripheral areas. The environmental impact of the latter can be positive only to the extent that this redirection does not increase car use and exacerbate congestion. This could be the case in polycentric urban environments, where remote areas with relatively low property values may lie close to local job hubs that can provide an employment alternative to the central business district.

Property taxation can also be leveraged to reduce the environmental impacts of development. “Green” property taxes seek to incorporate the full cost of externalities arising from development, and environmentally oriented preferential property taxes can encourage property owners to preserve environmental amenities (Brandt, 2014[32]). Split-rate property taxes are characterised by higher tax rates on the value of land than on the value of buildings and other property improvements (OECD, 2021[94]). By encouraging the development of underdeveloped land, split-rate taxes reduce development pressure at the rural-urban fringe (Banzhaf and Lavery, 2010[93]) and provide further incentive to redevelop urban brownfields. Land taxes also serve to encourage homebuilding where it is most needed if land-use rules are compatible with such development (Chapter 8). They could however also induce construction in areas of high environmental value, for instance those close to ecologically sensitive areas. For this reason, they can be used in conjunction with other regulatory or market-based instruments designed to discourage development in environmentally sensitive areas (OECD, 2018[9]). Location-efficient mortgages constitute another class of instruments with important environmental implications. They can target direct spatially varying environmental externalities, since they can incentivise home purchase in areas where population density lies below socially optimal levels. Location-based mortgages possess plenty of theoretical appeal, but have little precedent demonstrating their effectiveness due to shortcomings in implementation and design (Chatman and Voorhoeve, 2010[95]; Kaza et al., 2016[96]).

Other policies that have a bearing on housing markets can also have an impact on the environment. Policies that seek to increase home ownership and revitalise declining rural areas (e.g. the French “one house for 1000 euros” initiative) can reduce house prices but also contribute to scattered development and urban sprawl. Housing finance regulations could also have an environmental impact in addition to the efficiency considerations discussed in Chapters 3 and 4. For instance, the gradual relaxation of regulations governing low collateral mortgages (sub-prime) played a substantial role in the Great Financial Crisis, and it also affected the spatial distribution of dwellings acquired with sub-prime loans, which were located typically in low-income and predominantly minority neighbourhoods (Rosenblatt and Sacco, 2018[97]; Gerardi and Willen, 2008[98]). The extent to which the sub-prime boom contributed in urban sprawl has not yet been examined empirically.

Housing and environmentally related policies are often administered by different levels of government and jurisdictions, and therefore they need to be coordinated appropriately to achieve their intended objectives. Without coordination, different jurisdictions face incentives to implement taxes and charges above socially optimal levels, especially when they can use the generated revenue to the benefit of local residents (Phillips, 2020[99]). For example, jurisdictional differences in urban containment policies can create incentives for leapfrog development and spatially scattered development. Similarly, differences in property taxes across municipalities can be harmful for the environment and lead to negative distributional outcomes (Banzhaf and Walsh, 2008[100]).

Environmental policies often have diverging impacts on the environment and housing markets. While many environmental policies improve environmental quality in targeted areas, they can involve trade-offs in the residential sector, notably with respect to housing affordability. The opposite can also be true, as several instruments that appear ineffective have plenty of positive by-products. Also, policies can be both beneficial and detrimental in all its objectives, depending on the stringency of the corresponding policy instruments.

Reliable evaluations of alternative policy instruments requires welfare calculations that monetise their various costs and benefits across sectors. A cost-benefit approach can help policymakers to rank competing policies whose primary objectives are the same but whose mechanisms and implications may differ. Such an endeavour is to a large extent context specific and resource intensive, and as such goes beyond the scope of this chapter.

In spite of variation in policy measures and their impacts, a series of general insights can nevertheless be drawn from the evidence gathered. The first is that the net environmental impacts of many environmentally motivated land-use policies are in fact indeterminate, due in part to variations in their stringency and the diverse secondary effects they can entail in terms of development, energy use and transport activity. Similarly, some housing market policies can have negative impacts on affordability insofar as they raise housing costs without yielding substantial additional social value. Thus, in many cases governments should re-evaluate the stringency of some housing policies in light of the negative secondary effects they may create.

In contrast to regulatory land-use interventions, investments in public transport and soft mobility increase the social value of land and housing, rather than simply raising housing costs. Although such policies can render housing more expensive, the higher property values they generate reflect local benefits (e.g. increased accessibility) and the fact that certain local externalities are internalised. As long as investment costs are reasonable and willingness-to-pay for the advantages they afford is substantial, the social benefit of investments in public transport and soft mobility infrastructure should be expected to be positive.

A number of environmentally related market-based mechanisms that attempt to correct for the externalities of urban development can impact house prices. Once these externalities are incorporated into market prices via such mechanisms, the resulting adjustments in property prices will reflect improvements in accessibility or environmental quality. Policy makers should evaluate this type of strategy against policy-driven cuts in housing supply, which can have a similar negative impact on housing costs without necessarily increasing the social value of the existing housing stock. For this reason, housing price adjustments should not be the primary concern in policy reforms that attempt to mitigate the considerable social cost of some types of externalities. There are certain exceptions to this rule, the most important being the case in which environmental taxation causes house price adjustments with large distributional effects. In these cases, tailored compensation mechanisms can support social objectives such as poverty reduction, inclusive growth, and reduced inequalities.

References

[35] Allen, L. (2018), The effect of tax increment financing development on housing affordability in Houston, Texas, University of Texas at Austin, http://dx.doi.org/10.15781/T2C82518C.

[1] Angel, S. et al. (2011), Making Room for a Planet of Cities, Lincoln Institute of Land Policy, http://www.lincolninst.edu (accessed on 4 June 2020).

[19] Baker, D., N. Sipe and B. Gleeson (2006), “Performance-Based Planning”, Journal of Planning Education and Research, Vol. 25/4, pp. 396-409, http://dx.doi.org/10.1177/0739456X05283450.

[13] Ball, M. et al. (2014), “Urban Growth Boundaries and their Impact on Land Prices”, Environment and Planning A: Economy and Space, Vol. 46/12, pp. 3010-3026, http://dx.doi.org/10.1068/a130110p.

[93] Banzhaf, H. and N. Lavery (2010), “Can the land tax help curb urban sprawl? Evidence from growth patterns in Pennsylvania”, Journal of Urban Economics, Vol. 67/2, pp. 169-179, http://dx.doi.org/10.1016/j.jue.2009.08.005.

[100] Banzhaf, H. and R. Walsh (2008), “Do People Vote with Their Feet? An Empirical Test of Tiebout’s Mechanism”, The American Economic Review, Vol. 98/3, pp. 843-863.

[57] Bardhan, A. et al. (2014), “Energy efficiency retrofits for U.S. housing: Removing the bottlenecks”, Regional Science and Urban Economics, Vol. 47/1, pp. 45-60, http://dx.doi.org/10.1016/j.regsciurbeco.2013.09.001.

[61] Barr, J. and T. Tassier (2020), Are Crowded Cities the Reason for the COVID-19 Pandemic?, Scientific American Blog, https://blogs.scientificamerican.com/observations/are-crowded-cities-the-reason-for-the-covid-19-pandemic/ (accessed on 4 June 2020).

[92] Bartholomew, K. and R. Ewing (2011), “Hedonic Price Effects of Pedestrian-and Transit-Oriented Development”, Journal of Planning Literature, Vol. 26/1, pp. 18-34, http://dx.doi.org/10.1177/0885412210386540.

[30] Been, V. (2005), Impact Fees and Housing Affordability Impact Fees and Housing Affordability.

[16] Bengston, D. and Y. Youn (2006), “Urban Containment Policies and the Protection of Natural Areas: The Case of Seoul’s Greenbelt”, Source: Ecology and Society, Vol. 11/1, http://dx.doi.org/10.2307/26267777.

[41] Bertaud, A. and J. Brueckner (2005), “Analyzing building-height restrictions: Predicted impacts and welfare costs”, Regional Science and Urban Economics, Vol. 35/2, pp. 109-125, http://dx.doi.org/10.1016/j.regsciurbeco.2004.02.004.

[38] Blöchliger, H. et al. (2017), “Local taxation, land use regulation, and land use: A survey of the evidence”, OECD Economics Department Working Papers, No. 1375, OECD Publishing, Paris, https://dx.doi.org/10.1787/52da7c6a-en.

[5] Borck, R. and P. Schrauth (2021), “Population density and urban air quality”, Regional Science and Urban Economics, Vol. 86/January, pp. 1-24.

[32] Brandt, N. (2014), “Greening the Property Tax”, OECD Working Papers on Fiscal Federalism, No. 17, OECD Publishing, Paris, https://dx.doi.org/10.1787/5jz5pzw9mwzn-en.

[39] Brown, Z., W. Oueslati and J. Silva (2016), “Links between urban structure and life satisfaction in a cross-section of OECD metro areas”, Ecological Economics, Vol. 129, pp. 112-121, http://dx.doi.org/10.1016/j.ecolecon.2016.05.004.

[31] Byrne, J. and K. Zyla (2016), “Climate Exactions”, 75 Maryland Law Review, Vol. 758, https://scholarship.law.georgetown.edu/facpub/1668http://ssrn.com/abstract=2765191 (accessed on 3 June 2020).

[59] Carrns, A. (2020), “Home Prices Are Rising, Along With Post-Lockdown Demand”, The New York Times, https://www.nytimes.com/2020/06/05/your-money/houses-prices-coronavirus.html?action=click&module=News&pgtype=Homepage (accessed on 11 June 2020).

[20] Carroll, T. et al. (2009), Analysis of the Impacts of Transferable Development Rights Programs on Affordable Housing.

[52] Cerin, P., L. Hassel and N. Semenova (2014), “Energy Performance and Housing Prices”, Sustainable Development, Vol. 22/6, pp. 404-419, http://dx.doi.org/10.1002/sd.1566.

[95] Chatman, D. and N. Voorhoeve (2010), “The transportation-credit mortgage: a post-mortem”, Housing Policy Debate, Vol. 20/3, pp. 355-382, http://dx.doi.org/10.1080/10511481003788786.

[76] Chen, Y. et al. (2019), “The impact on neighbourhood residential property valuations of a newly proposed public transport project: The Sydney Northwest Metro case study”, Transportation Research Interdisciplinary Perspectives, Vol. 3, p. 100070, http://dx.doi.org/10.1016/j.trip.2019.100070.

[90] Creutzig, F. et al. (2015), “Global typology of urban energy use and potentials for an urbanization mitigation wedge”, PNAS, Vol. 112/20, pp. 6283-6288, http://dx.doi.org/10.1073/pnas.1315545112.

[56] de Feijter, F., B. van Vliet and Y. Chen (2019), “Household inclusion in the governance of housing retrofitting: Analysing Chinese and Dutch systems of energy retrofit provision”, Energy Research and Social Science, Vol. 53, pp. 10-22, http://dx.doi.org/10.1016/j.erss.2019.02.006.

[34] Dzigbede, K. and R. Pathak (2019), TAX INCREMENT FINANCING AND ECONOMIC DEVELOPMENT, The Brookings Institute.

[75] Efthymiou, D. and C. Antoniou (2013), “How do transport infrastructure and policies affect house prices and rents? Evidence from Athens, Greece”, Transportation Research Part A: Policy and Practice, Vol. 52, pp. 1-22, http://dx.doi.org/10.1016/j.tra.2013.04.002.

[70] El-Geneidy, A., D. van Lierop and R. Wasfi (2016), “Do people value bicycle sharing? A multilevel longitudinal analysis capturing the impact of bicycle sharing on residential sales in Montreal, Canada”, Transport Policy, Vol. 51, pp. 174-181, http://dx.doi.org/10.1016/j.tranpol.2016.01.009.

[66] Eliasson, J. and L. Mattsson (2001), Transport and Location Effects of Road Pricing: A Simulation Approach, https://www.jstor.org/stable/20053883 (accessed on 3 June 2020).

[44] Frew, T., D. Baker and P. Donehue (2016), “Performance based planning in Queensland: A case of unintended plan-making outcomes”, Land Use Policy, Vol. 50, pp. 239-251, http://dx.doi.org/10.1016/j.landusepol.2015.10.007.

[22] Furman Center for Real Estate and Urban Policy (2014), Unlocking the right to build: Designing a more flexible system for transferring development rights, http://www.nyc.gov/ (accessed on 2 June 2020).

[77] Gallo, M. (2018), “The Impact of Urban Transit Systems on Property Values: A Model and Some Evidences from the City of Naples”, Journal of Advanced Transportation, http://dx.doi.org/10.1155/2018/1767149.

[82] Gan, H. and Q. Wang (2013), “Emissions Impacts of the Park-and-Ride Strategy: A Case Study in Shanghai, China”, Procedia - Social and Behavioral Sciences, Vol. 96, pp. 1119-1126, http://dx.doi.org/10.1016/j.sbspro.2013.08.128.

[98] Gerardi, K. and P. Willen (2008), Subprime Mortgages, Foreclosures, and Urban Neighborhoods, Federal Reserve Bank of Boston Public Policy Discussion paper, http://www.bos.frb.org/economic/ppdp/index.htm. (accessed on 12 June 2020).

[28] Gilderbloom, J., M. Hanka and J. Ambrosius (2009), “Historic preservation’s impact on job creation, property values, and environmental sustainability”, Journal of Urbanism: International Research on Placemaking and Urban Sustainability, Vol. 2/2, pp. 83-101, http://dx.doi.org/10.1080/17549170903056821.

[85] Haller, M. et al. (2007), “Economic costs and environmental impacts of alternative fuel vehicle fleets in local government: An interim assessment of a voluntary ten-year fleet conversion plan”, Transportation Research Part D: Transport and Environment, Vol. 12/3, pp. 219-230, http://dx.doi.org/10.1016/j.trd.2007.02.001.

[27] Haninger, K., L. Ma and C. Timmins (2017), “The Value of Brownfield Remediation”, http://dx.doi.org/10.1086/689743.

[50] Heeren, N. et al. (2015), “Environmental Impact of Buildings - What Matters?”, Environmental Science and Technology, Vol. 49/16, pp. 9832-9841, http://dx.doi.org/10.1021/acs.est.5b01735.

[60] Hughes, C. (2020), “Coronavirus Escape: City to Suburbs”, The New York Times, https://www.nytimes.com/2020/05/08/realestate/coronavirus-escape-city-to-suburbs.html (accessed on 5 June 2020).

[3] IEA (2020), Tracking Transport 2019, https://www.iea.org/reports/tracking-transport-2019.

[53] Im, J. et al. (2017), “Energy efficiency in U.S. residential rental housing: Adoption rates and impact on rent”, Applied Energy, Vol. 205, pp. 1021-1033, http://dx.doi.org/10.1016/j.apenergy.2017.08.047.

[48] Jeddi Yeganeh, A., A. McCoy and S. Hankey (2019), “Green Affordable Housing: Cost-Benefit Analysis for Zoning Incentives”, Sustainability, Vol. 11/22, p. 6269, http://dx.doi.org/10.3390/su11226269.

[18] Jepson, E. and A. Haines (2014), “Zoning for Sustainability: A Review and Analysis of the Zoning Ordinances of 32 Cities in the United States”, Journal of the American Planning Association, Vol. 80/3, pp. 239-252, http://dx.doi.org/10.1080/01944363.2014.981200.

[4] Karagulian, F. et al. (2015), “Contributions to cities’ ambient particulate matter (PM): A systematic review of local source contributions at global level”, Atmospheric Environment, Vol. 120, pp. 475-483, http://dx.doi.org/10.1016/j.atmosenv.2015.08.087.

[58] Karatasou, S., M. Laskari and M. Santamouris (2014), Models of behavior change and residential energy use: A review of research directions and findings for behavior-based energy efficiency, Taylor and Francis Ltd., http://dx.doi.org/10.1080/17512549.2013.809275.

[96] Kaza, N. et al. (2016), “Housing Policy Debate Location Efficiency and Mortgage Risks for Low-Income Households”, http://dx.doi.org/10.1080/10511482.2016.1159972.

[24] Kelly, J. (2015), Sustaining Neighborhoods of Choice: From Land Bank(ing) to Land Trust(ing), Notre Dame Law School Legal Studies Research Paper No. 1520, https://scholarship.law.nd.edu/law_faculty_scholarship/1207 (accessed on 3 June 2020).

[64] Kholodilin, K. (2020), Housing policy during COVID-19 crisis: Challenges and solutions, https://rpubs.com/Konstantin_Xo/605805 (accessed on 14 May 2020).

[73] Knittel, C. and R. Sandler (2013), “The Welfare Impact of Indirect Pigouvian Taxation: Evidence from Transportation”, NBER Working Paper Series, http://dx.doi.org/10.3386/w18849.

[47] Kontokosta, C., V. Reina and B. Bonczak (2020), “Energy Cost Burdens for Low-Income and Minority Households: Evidence From Energy Benchmarking and Audit Data in Five U.S. Cities”, Journal of the American Planning Association, Vol. 86/1, pp. 89-105, http://dx.doi.org/10.1080/01944363.2019.1647446.

[29] Krizek, K. (2003), Transit Supportive Home Loans: Theory, Application, and Prospects for Smart Growth.

[79] Krizek, K. and P. Johnson (2006), “Proximity to Trails and Retail: Effects on Urban Cycling and Walking”, Journal of the American Planning Association, Vol. 72/1.

[7] Kuethe, T. and R. Keeney (2012), “Environmental Externalities and Residential Property Values: Externalized Costs along the House Price Distribution”, Land Economics, Vol. 88/2, pp. 241-250.

[49] Listokin, D. and D. Hattis (2005), “Building codes and housing”, Cityscape, Vol. 8/1, pp. 21-67, http://dx.doi.org/10.2307/20868571.

[67] Litman, T. (2020), “Parking Requirement Impacts on Housing Affordability”, http://[email protected] (accessed on 3 June 2020).

[15] Mathur, S. (2014), “Impact of Urban Growth Boundary on Housing and Land Prices: Evidence from King County, Washington”, Housing Studies, Vol. 29/1, pp. 128-148, http://dx.doi.org/10.1080/02673037.2013.825695.

[37] Matteucci, S. and J. Morello (2009), “Environmental consequences of exurban expansion in an agricultural area: The case of the Argentinian pampas ecoregion”, Urban Ecosystems, Vol. 12/3, pp. 287-310, http://dx.doi.org/10.1007/s11252-009-0093-z.

[81] Matute, J. et al. (2016), Toward Accurate and Valid Estimates of Greenhouse Gas Reductions from Bikeway Projects, UCLA and Caltrans.

[83] Meek, S., S. Ison and M. Enoch (2008), “Role of Bus‐Based Park and Ride in the UK: A Temporal and Evaluative Review”, Transport Reviews, Vol. 28/6, pp. 781-803, http://dx.doi.org/10.1080/01441640802059152.

[86] Melaina, M. et al. (2013), Alternative Fuel Infrastructure Expansion: Costs, Resources, Production Capacity, and Retail Availability for Low-Carbon Scenarios, Transportation Energy Futures Series, Prepared for the U.S. Department of Energy by National Renewable Energy Laboratory, Golden, CO.

[51] Mims, N. et al. (2017), Evaluation of U.S. Building Energy Benchmarking and Transparency Programs: Attributes, Impacts, and Best Practices.

[84] Mingardo, G. (2013), “Transport and environmental effects of rail-based Park and Ride: Evidence from the Netherlands”, Journal of Transport Geography, Vol. 30, pp. 7-16, http://dx.doi.org/10.1016/j.jtrangeo.2013.02.004.

[33] Morris, M. (2000), Incentive zoning: Meeting urban design and affordable housing, American Planning Association Planning Advisory Service Report Number 494.

[94] OECD (2021), Making Property Tax Reform in China Happen: A Review of Property Tax Design and Reform Experiences in OECD Countries, OECD Publishing, forthcoming.

[45] OECD (2020), Decarbonising Urban Mobility with Land Use and Transport Policies: The Case of Auckland, New Zealand, OECD Publishing, Paris, https://dx.doi.org/10.1787/095848a3-en.

[6] OECD (2020), Environment at a Glance 2020, OECD Publishing, Paris, https://dx.doi.org/10.1787/4ea7d35f-en.

[62] OECD (2020), Environmental health and strengthening resilience to pandemics - OECD, https://read.oecd-ilibrary.org/view/?ref=129_129937-jm4ul2jun9&title=Environmental-health-and-strengthening-resilience-to-pandemics (accessed on 14 May 2020).

[63] OECD (2020), From containment to recovery: Environmental responses to the COVID-19 pandemic - OECD, https://read.oecd-ilibrary.org/view/?ref=126_126460-1tg1r2aowf&title=From-containment-to-recovery_Environmental-responses-to-the-COVID-19-pandemic (accessed on 14 May 2020).

[2] OECD (2019), Global Material Resources Outlook to 2060: Economic Drivers and Environmental Consequences, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264307452-en.

[9] OECD (2018), Rethinking Urban Sprawl: Moving Towards Sustainable Cities, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264189881-en.

[21] Otto, K. (2010), Smart Growth through the Transfer of Development Rights: A selection of TDR case studies with relevance for the preservation of farmland, open space and other natural resources in New Jersey, New Jersey Future.

[69] Pelechrinis, K. et al. (2017), “Economic impact and policy implications from urban shared transportation: The case of Pittsburgh’s shared bike system”, PLOS ONE, Vol. 12/8, p. e0184092, http://dx.doi.org/10.1371/journal.pone.0184092.

[99] Phillips (2020), “Decentralisation and inter-governmental relations in the housing sector”.

[71] Qiu, L. and L. He (2018), “Bike Sharing and the Economy, the Environment, and Health-Related Externalities”, Sustainability, Vol. 10/4, pp. 1-10.

[17] Quigley, J., L. Rosenthal and R. Quigley (2005), The Effects of Land Use Regulation on the Price of Housing: What Do We Know? What Can We Learn? The Effects of Land Use Regulation on the Price of Housing: What Do We Know? What Can We Learn? Cityscape 69.

[72] Rodriguez, J. (2013), “Effect of High Gasoline Prices on Low-Density Housing Development”, Leadership and Management in Engineering, Vol. 13/3, pp. 131-143, http://dx.doi.org/10.1061/(ASCE)LM.1943-5630.0000225.

[97] Rosenblatt, P. and S. Sacco (2018), “Investors and the Geography of the Subprime Housing Crisis”, Housing Policy Debate, Vol. 28/1, pp. 94-116, http://dx.doi.org/10.1080/10511482.2016.1242021.

[65] Rouhani, O. (2016), “Next Generations of Road Pricing: Social Welfare Enhancing”, Sustainability, Vol. 8/3, p. 265, http://dx.doi.org/10.3390/su8030265.

[68] Safirova, E. et al. (2006), Congestion Pricing Long-Term Economic and Land-Use Effects Congestion Pricing: Long-Term Economic and Land-Use Effects, http://www.rff.org (accessed on 3 June 2020).

[40] Schindler, M. and G. Caruso (2014), “Urban compactness and the trade-off between air pollution emission and exposure: Lessons from a spatially explicit theoretical model”, Computers, Environment and Urban Systems, Vol. 45, pp. 13-23, http://dx.doi.org/10.1016/j.compenvurbsys.2014.01.004.

[11] Silva, E. and R. Acheampong (2015), “Developing an Inventory and Typology of Land-Use Planning Systems and Policy Instruments in OECD Countries”, OECD Environment Working Papers, No. 94, OECD Publishing, Paris, https://dx.doi.org/10.1787/5jrp6wgxp09s-en.

[14] Staley, S., J. Edgens and G. Mildner (n.d.), A Line in the Land: Urban-growth Boundaries, Smart Growth, and Housing Affordability.

[26] Sullivan, K. (2017), “Brownfields Remediation: Impact on Local Residential Property Tax Revenue”, Journal of Environmental Assessment Policy and Management, Vol. 19/3, p. 1750013, http://dx.doi.org/10.1142/S1464333217500132.

[46] Taruttis, L. and C. Weber (2020), Estimating the impact of energy efficiency on housing, https://www.econstor.eu/bitstream/10419/224582/1/vfs-2020-pid-39805.pdf.

[89] Tikoudis, I. and W. Oueslati (2020), “MOLES: A New Approach to Modeling the Environmental and Economic Impacts of Urban Policies”, Computational Economics, pp. 1-50, http://dx.doi.org/10.1007/s10614-019-09962-3.

[42] Tikoudis, I., E. Verhoef and J. van Ommeren (2018), “Second-best urban tolls in a monocentric city with housing market regulations”, Transportation Research Part B: Methodological, Vol. 117, pp. 342-359, http://dx.doi.org/10.1016/j.trb.2018.08.014.

[88] Tikoudis, I., E. Verhoef and J. van Ommeren (2015), “On revenue recycling and the welfare effects of second-best congestion pricing in a monocentric city”, Journal of Urban Economics, Vol. 89, pp. 32-47, http://dx.doi.org/10.1016/j.jue.2015.06.004.

[10] UNECE (2008), Spatial planning: Key Instrument for Development and Effective Governance with Special Reference to Countries in Transition, United Nations, Geneva, https://www.unece.org/fileadmin/DAM/hlm/documents/Publications/spatial_planning.e.pdf (accessed on 5 June 2020).

[54] US DOE (2020), Energy Efficiency Policies and Programs, https://www.energy.gov/eere/slsc/energy-efficiency-policies-and-programs (accessed on 14 May 2020).

[55] US DOE (2020), Property Assessed Clean Energy Programs, Office of Energy Efficiency and Renewable Energy, https://www.energy.gov/eere/slsc/property-assessed-clean-energy-programs (accessed on 3 June 2020).

[25] US EPA (2011), Air and Water Quality Impacts of Brownfields Redevelopment: A Study of Five Communities.

[87] Verhoef, E. (2005), “Second-best congestion pricing schemes in the monocentric city”, Journal of Urban Economics, Vol. 58/3, pp. 367-388, http://dx.doi.org/10.1016/j.jue.2005.06.003.

[36] Vyn, R. (2012), “Examining for Evidence of the Leapfrog Effect in the Context of Strict Agricultural”, Land Economics, Vol. 88/3, pp. 457-477, https://www.jstor.org/stable/23272622 (accessed on 12 June 2020).

[78] Wang, L. et al. (2018), “The impacts of transportation infrastructure on sustainable development: Emerging trends and challenges”, International Journal of Environmental Research and Public Health, Vol. 15/6, http://dx.doi.org/10.3390/ijerph15061172.

[8] Wang, Y. et al. (2015), “Impact of urban landscape and environmental externalities on spatial differentiation of housing prices in Yangzhou City”, Journal of Geographical Sciences, Vol. 25/9, pp. 1122-1136, http://dx.doi.org/10.1007/s11442-015-1223-6.

[23] Whitaker, S. and T. Fitzpatrick (2016), “LAND BANK 2.0: AN EMPIRICAL EVALUATION”, Journal of Regional Science, Vol. 56/1, pp. 156-175, http://dx.doi.org/10.1111/jors.12206.

[43] Wilson, L. et al. (2018), Quantifying the Urban Experience: Establishing Criteria for Performance Based Zoning, http://hay-stack.s3-website-us-east-1.amazonaws.com/#!/?set=PerformanceBasedZoning (accessed on 2 June 2020).

[12] Wu, J. and W. Oueslati (2016), “How does urbanization affect the economy and the environment? Policy challenges and research needs”, International Review of Environmental and Resource Economics, Vol. 10/1, pp. 1-35, http://dx.doi.org/10.1561/101.00000081.

[91] Yang, L. et al. (2018), “Walking accessibility and property prices”, Transportation Research Part D: Transport and Environment, Vol. 62, pp. 551-562, http://dx.doi.org/10.1016/j.trd.2018.04.001.

[74] Yiu, C. and S. Wong (2005), “The effects of expected transport improvements on housing prices”, Urban Studies, Vol. 42/1, pp. 113-125, http://dx.doi.org/10.1080/0042098042000309720.

[80] Zahabi, S. et al. (2016), “Exploring the link between the neighborhood typologies, bicycle infrastructure and commuting cycling over time and the potential impact on commuter GHG emissions”, Transportation Research Part D: Transport and Environment, Vol. 47, pp. 89-103, http://dx.doi.org/10.1016/j.trd.2016.05.008.

Notes

← 1. Similarly, Karagulian F. et al., (2017[219]) find that the residential sector (heating/cooling of buildings and equipment/lighting of buildings and waste treatment) accounts for 37% of PM2.5 emissions globally.

← 2. The programme covers expenses incurred between 1 July 2020 and 30 June 2022 (cf. https://www.efficienzaenergetica.enea.it/detrazioni-fiscali/superbonus.html) .

Metadata, Legal and Rights

This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Extracts from publications may be subject to additional disclaimers, which are set out in the complete version of the publication, available at the link provided.

© OECD 2021

The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at http://www.oecd.org/termsandconditions.