Brick by Brick

9. Upgrading the Evidence Base

Abstract

Housing is a multifaceted policy area, where evidence-based policy reform requires a range of indicators on both outcomes and instruments. Data gaps are particularly relevant in three areas: house prices, vulnerability in access to housing, and local land-use regulations. Filling these data gaps would bring large benefits by helping to better inform housing policy choices.

Main lessons and areas for future progress

International statistical standards have been developed over the last decade or so for the compilation of house price indices. All OECD countries now release internationally comparable statistics to monitor house price developments at the national level. Nevertheless, nine OECD countries still do not provide any statistics to monitor house price developments at the regional or city level.

Rigorous, internationally comparable measurement of house price levels is much less advanced. Doing so would help to assess barriers to labour mobility, understand the financial challenges faced by households living in different areas, and design economic policies at regional level. This area would benefit from pilot projects in some countries, which could then be used as a basis for the development of international statistical guidelines. A statistical agenda for progress could include:

• Compile house price indices at subnational level in line with international statistical standards.

• Extend the coverage of house price indices as much as possible to cover all dwelling types and vintages.

• For the compilation of house price statistics in urban areas, rely on the concept of Functional Urban Area.

• Start developing statistics on house price levels, at both national and subnational levels.

Monitoring housing outcomes among vulnerable households, particularly in view of the heightened economic vulnerability brought on by the COVID‑19 pandemic, is another critical undertaking. While the OECD Affordable Housing Database provides data on housing and affordable housing outcomes, as well as evictions and homelessness, considerable gaps remain, due in part to definitional and methodological challenges. A statistical agenda for progress could include:

• Improve the monitoring of evictions.

• Incorporate questions relating to evictions in regular national and international housing surveys.

• Collect homelessness data on a regular basis while extending geographic coverage.

• Integrate different data sources on homelessness (e.g. administrative and/or survey data; health and homelessness data).

Land-use planning is important to make cities attractive, sustainable and productive. Yet, land-use regulations can restrict housing supply and contribute to higher housing costs especially in the most expensive cities. Little systematic data exists on land-use regulations, partly because of their complexity and also because they are predominantly the responsibility of local governments. The OECD aims at developing internationally comparable measures of land-use regulations by collecting data from local governments on:

• Regulations on the type of use of land.

• Regulations on building densities (building footprint, floor space, height, etc.).

• Information on the permitting process.

Collect more data on house price trends and levels across countries

House price indices measure developments over time taking account of quality changes

One of the lessons from the Global Financial Crisis is the need to identify gaps in areas, including housing markets, where better, more internationally comparable data could help to identify the build-up of imbalances at an earlier stage. Indeed, in 2009 the G20 Finance Ministers and Central Bank Governors endorsed 20 recommendations to address data gaps revealed by the global financial crisis. This G20 Data Gaps Initiative led to the development of international statistical standards for the compilation of house price indices (ILO et al., 2013[1]). Currently all G20 countries except Argentina release at least one official index compiled according to these guidelines and representative for house price developments in their country. The OECD collects this information at quarterly frequency and makes it freely available online through its OECD.Stat service.

House price indices are index numbers measuring the rate at which the prices of residential properties purchased by households change over time. These indices adjust for any quality difference between dwellings sold in the current period, relative to the reference period. In other words, they aim at measuring pure price changes. They cover both new and existing dwellings whenever possible, independently of their final use (to live in or for rent). These prices include the price of the land on which residential buildings are located.

Coverage is extensive at the national level

At least one house price index compiled according to international statistical standards and covering the country as a whole is available for each of the 37 OECD member countries (Table 9.1). Nevertheless, only 32 OECD countries compile national-level indices that are representative for all dwelling types and vintages at the same time. The five countries for which some dwelling types or vintages are not covered by the overall national index are Canada, Greece, Korea, Switzerland and United States. For the United States, for example, the most representative index at the national level only covers single-family and existing dwellings, which might be problematic to capture house price developments in urban areas where multi-family dwellings are predominant.

Table 9.1. Availability of house price indices for the 37 OECD member countries

	Any dwelling vintage	All dwelling vintages together	New dwellings	Existing dwellings
National level
Any dwelling type	37
All dwelling types together		32	25	26
Single-family		9	5	9
Multi-family		9	4	9
Subnational level (regions and/or cities)
Any dwelling type	28
All dwelling types together		16	8	12
Single-family		10	4	6
Multi-family		10	3	7

Note: For each cell, the above table indicates the number of OECD countries for which the corresponding house price indices are available. In this Table, we only consider subnational house price indices that are available for unsegmented geographical areas. For example, we do not include indices covering cities located in different parts of a country and aiming at capturing house price developments in urban areas.

Source: OECD database on national and regional house price indices.

Coverage has expanded but remains more limited at the regional level

In addition, 28 OECD countries compile at least one house price index at the subnational level, and 16 of them provide subnational indices covering all dwelling types and vintages together (Table 9.1). Nevertheless, some time series are very short. For example, regional indices for Israel only start in 2018. Among the nine countries that do not provide house price indices for individual regions or cities (Belgium, Czech Republic, Estonia, Germany1, Latvia, Luxembourg, New Zealand, Portugal and Slovak Republic), there are some large countries for which, based on the evidence observed in other OECD countries, such indices would likely reveal heterogeneity in house price developments (OECD, 2020[2]).

There is considerable scope for improving the international comparability of regional house price statistics. The available statistics follow the definition of administrative regions within countries and usually allows for a straightforward mapping with internationally agreed regional classifications such as Eurostat’s NUTS classification and the OECD TL classification, as well as the mapping with other available regional statistics. Nevertheless, the delineation of cities for the compilation of specific house price indices is less straightforward, and country practices are not standardised. Tracking house price developments within Functional Urban Areas (FUAs), ideally with a distinction between the city (i.e. ‘core’) and the commuting zone, would ensure comparability across countries and be most useful for economic analyses. Dijkstra et al. (2019[3]) define FUAs and introduce the joint methodology of the European Union and the OECD to delineate these areas. The 2020 UN Statistical Commission recently endorsed FUAs as a delineation method for international comparisons. So far, even though proxies may be available, no official statistical agency explicitly relies on the FUA concept to define the geographical area underlying house price indices, but this practice should be encouraged.

House price levels are a key area for future work

While house price indices are designed to measure house price developments over time in a given geographical area, they do not allow for comparing house price levels across geographical areas. Similarly, Consumer Price Indices (CPIs) allow for measuring inflation (i.e. how consumer prices develop over time), but only Purchasing Power Parities (PPPs) allow for comparing price levels across space.

Statistics that would be similar to PPPs and allow comparing house price levels across space are typically not available from official statistical agencies.2 Nevertheless, this information would be key to assess barriers to labour mobility and financial challenges faced by households living in different areas, as well as for the development of regional economic policies. The 2019 OECD Regional Outlook (OECD, 2019[4]) emphasises that the geographical patterns of public discontent are closely related to the degree of regional inequalities and that policies to address public discontent need to have a place-based dimension. House price level differentials across regions precisely contribute to regional inequalities and statistics on this issue could contribute to the design of regional economic policies.

Ideally, statistics on house price levels need to reflect the specific nature of dwellings in the housing stock of each geographical area (e.g. the fact that detached houses with a garden are more common in rural areas than in urban areas). Even though elementary house prices could be collected from observed transactions or valuations, as for the compilation of house price indices, the weighting scheme would depend on the characteristics of the dwelling stock in the region, and the underlying information on the dwelling stock would come from the Census or other types of administrative registers. Even though such stock-based weights can also be used for the compilation of house price indices, they are less common than transaction-based weights in that case.

Future development of official statistics on house price levels include:

• Compiling, and regularly updating, statistics on the characteristics (quality, size, age) of the dwelling stock.

• The choice of the level at which weights should be introduced given the heterogeneity of house prices in a given area. Note that the use of weights only makes a difference if the characteristics and the price of houses in a given area are sufficiently heterogeneous. If they were all the same, there would be no difference between transaction-weighted, stock-weighted and unweighted house price statistics.

• The analysis of possible discrepancies between the evolutions of house price levels and indices. They can be related to the level at which weights are introduced, the use of different weighting schemes (transaction- or stock-based), or the use of different compilation methodologies (e.g. stratification vs. hedonic methods).

• The granularity of house price levels that can be compiled given the observed number of transactions.

• The possibility to use asking prices collected from real-estate agency websites for the compilation of house price statistics.

• A concrete way of prompting progress would be to explore these research questions in pilot countries. The lessons learnt could be used for the subsequent development of international statistical guidelines.

A measurement agenda for house price statistics is emerging

The above described state of play suggests the following measurement agenda in the area of house price statistics:

• Compile house price indices at the subnational level in line with international statistical standards, giving priority to geographical areas in which house price developments are suspected to be the most different from the national average. Provide the longest possible time series in order to facilitate economic analysis.

• Extend the coverage of house price indices as much as possible to cover all dwelling types (single- and multi-family dwellings) and vintages (new and existing dwellings). Even though separate indices may be compiled for different dwelling types and vintages, some indices should also cover all of them at the same time.

• For the compilation of house price statistics in urban areas, rely on the concept of Functional Urban Area and distinguish the city and the commuting zone whenever possible.

• Start developing statistics on house price levels, at both national and subnational levels. This information would be key to assess barriers to labour mobility and financial challenges faced by households living in different areas, as well as for the development of regional economic policies.

Better assess housing vulnerability among households

Develop more robust data on evictions

Data on evictions – defined as the involuntary removal of people from their homes involving a judicial process in courts or other litigating bodies – is piecemeal in OECD countries (OECD, 2020[5]). This section refers exclusively to evictions among tenant households, although, as discussed below, evictions may also occur among owner-occupied households. To begin with, the formal evictions process is complex and can vary across and even within countries. It generally involves three phases: phase 1) the landlord initiates the formal eviction process by filing an application to evict a tenant (which may lead to the litigating bodies summoning both parties to court); phase 2) the litigating body formally orders possession of the rental dwelling and issues an eviction notice, or declines the initial eviction request; and phase 3) the tenant household is physically evicted from the dwelling through the execution of the court order (either with or without executive force).

Not all households who receive a notice to quit or a repossession letter are ultimately evicted; for example, households may be able to pay their rent arrears in order to avoid eviction. On the other hand, some tenants may not be aware that eviction orders or notices do not necessarily need to result in formal evictions, leading them to leave their dwelling prematurely. In Finland for example, for about 39% of all scheduled physical repossessions, bailiffs find a dwelling already vacated by the household (Valtakunnanvoudinvirasto, 2020[6]). In addition, only data on formal, legal evictions are typically available; data on informal or evictions (without judicial proceedings) are much rarer (Kenna et al., 2016[7]).

A number of challenges hamper the collection, analysis and cross-country comparison of these data:

• There are important differences in how evictions are reported across countries. Jurisdictions may provide data at three different stages of evictions proceedings. Overall, information on initiated eviction processes (phase 1) is more prevalent than information on eviction orders (phase 2) or instances of actual physical eviction (phase 3). Moreover, each phase has a different magnitude – that is, not all started proceedings lead to eviction notices, and not all notices lead to actual evictions, further complicating international comparison.

• Data are hard to acquire. Evictions data are not always public, given their sensitivity as well as concerns over the end-use of the data (e.g. when they may be used during the letting process by landlords to screen potentially high-risk tenants). Sources vary within and across countries (e.g. court records and bailiff statistics, figures from (public) housing providers, surveys conducted by academic or community groups, or one-off reports), rendering data collection challenging and comparison difficult. Data may also be available only at local or regional levels (as in the United States, for instance) and are not always available in electronic form. Court records from different jurisdictions provide varying levels of detail and are not readily comparable (Eviction Lab, 2018[8]).

• Data are often incomplete. Evictions data may only cover a subsector of the housing market, as in the Netherlands and New Zealand, where they only cover evictions from social rental dwellings or in Germany, where they also include commercial rentals. In France, actual physical evictions are only reported if they involve police enforcement, which is likely to underestimate the actual number of evictions significantly. In Portugal, data are only available for special eviction proceedings under the National Rental Board (Balcão Nacional do Arrendamento), covering an estimated one-third of all eviction processes, most of which are handled in civil courts.

• In some cases, such as for Austria, it is not possible to differentiate between tenant evictions and repossessions of mortgages (see below), which complicates comparison across countries. Further, very little information is provided on the characteristics of households involved in the eviction process, making it difficult to analyse potential drivers of evictions, and whether they are more prevalent among some groups relative to others.

The OECD Affordable Housing Database provides comparative data on evictions among tenant households for many OECD countries (Indicator HC3.3 in OECD (2020[5])).Homeowners can also face eviction following default on their mortgage payments. Mortgage foreclosures are typically initiated by the banks that issued the loan. The data collection on the mortgage foreclosure process faces similar caveats as evictions from rental dwellings.

Homelessness is challenging to measure and compare across countries

Homelessness data are hard to come by and difficult to compare across countries. The first major challenge is that there is no internationally agreed upon definition of homelessness, and countries do not define or count the homeless population in the same way. For instance, in 13 OECD countries, the definition of homelessness is restricted to people living on the streets or in public spaces (i.e. “sleeping rough”), and/or living in shelters or in other emergency accommodation. Meanwhile, 10 OECD countries apply a broader definition that also includes people who are living in hotels and are doubled up with friends and family (OECD, 2020[9]). Nevertheless, there have been efforts at standardisation through a common typology at European level (ETHOS light) (Table 9.2).

Table 9.2. A harmonised typology of homelessness: ETHOS Light

	Operational category	Living situation
1	People living rough	Public spaces / external spaces
2	People in emergency accommodation	Overnight shelters
3	People living in accommodation for the homeless	Homeless hostels
		Temporary accommodation
		Transitional supported accommodation. Women’s shelters or refugee accommodation
4	People living in institutions	Health care institutions
		Penal institutions
5	People living in nonconventional dwellings due to lack of housing	Mobile homes
		Non-conventional buildings
		Temporary structures
6	Homeless people living temporarily with family and friends	Conventional housing, but not the person’s usual place of residence

Note: ETHOS Light is a streamlined version of the European Typology of Homelessness and Housing Exclusion (ETHOS).

Source: Adapted from European Commission (2007[10]).

Beyond definitional differences, countries’ data collection efforts differ in their method, scope and frequency. Homelessness is by its very nature a difficult circumstance to assess, as an individual’s experience of homelessness may be more or less visible to public authorities and support institutions, and thus hard to capture in official statistics. Data collection methods vary, most commonly relying on point-in-time estimates (such as annual street counts like the city of Paris’ Nuit de la solidarité, conducted on a given day of each year), administrative data (such as registries from shelters and local authorities), or a combination of both. Each method provides only a partial picture of homelessness, and fails to effectively capture the “hidden homeless” who do not appear in official statistics, because they do not seek formal support, or they seek temporary shelter with family or friends, or live in their car. Such hidden homelessness is likely to be more prevalent among women, youth and vulnerable groups outside the scope of homelessness surveys (OECD, 2020[9]).

Incomplete geographic coverage and limited frequency and consistency of data collection represent additional methodological challenges. For instance, some national data only cover the largest municipalities or the biggest region or city; jurisdictions may collect data on a monthly, quarterly, annual, bi-annual basis – or without any regularity at all (OECD, 2020[5]).

The OECD, through its Questionnaire on Affordable and Social Housing (QuASH), regularly collects homelessness data in OECD, Key Partner and European Union countries, in line with each country’s national statistical definition. National statistics are reported in the OECD Affordable Housing Database (Indicator HC3.1), along with an indication of the definition and categorisation for each country.

In light of the increasing homelessness rate in around one-third of OECD countries prior to COVID-19, as well as potentially heightened vulnerability of many households due to the pandemic improving data collection on homelessness should be a priority (OECD, 2020[9]). Depending on the country, this could imply more regular data collection, the integration of different homelessness data sources, as well as efforts to expand the methodological toolbox to collect data. Innovative approaches to link administrative and survey data can provide a more comprehensive understanding of the challenges and needs of different homeless populations. For instance, researchers in Scotland (United Kingdom) linked homelessness and health datasets to find that at least 8% of the Scottish population in mid-2015 had experienced homelessness at some point in their lives – a much larger share than expected (Waugh et al., 2018[11]). More widespread use of the ETHOS Light typology could also facilitate cross-national comparison of homelessness estimates and trends.

A measurement agenda is called for to improve the evidence base on eviction and homelessness

Progress along the following objectives would help better assess and monitor housing vulnerability:

• Improve the monitoring of evictions, including, where feasible, introducing a national system to monitor evictions. Data should include information on the different stages of the evictions process as well as household and dwelling characteristics.

• Incorporate questions relating to evictions in regular national and supra-national housing surveys.

• Collect homelessness data on a regular basis, extending geographic coverage as much as possible in order to capture trends across cities, regions as well as more rural areas. As feasible, report homelessness statistics along the lines of the ETHOS Light categorisation in order to facilitate comparison across jurisdictions.

• Expand the methodological toolbox to integrate different data sources (e.g. administrative and/or survey data; health and homelessness data) to better understand the needs and challenges of the homeless population.

Measure local land-use regulations

Land-use regulations are used to address a wide range of policy objectives. They aim at protecting residents from hazards and nuisances, ensuring adequate infrastructure capacity and public transport provision, creating attractive neighbourhoods and reducing segregation as well as preserving the environment and the built heritage. Yet, by imposing restrictions on how land can be developed, land-use regulations can make housing supply less responsive to housing demand, thereby increasing house prices (Chapter 4).

Land-use regulations are complex documents that are not often suited for statistical processing and no official statistics are collected on them. In the absence of official data, researchers have attempted to fill the information gap by launching surveys on local governments (Gyourko, Saiz and Sumers, 2008[12]) or by using proxy measures (Ganong and Shoag, 2017[13]). Yet, these alternative methods have important limitations: current survey-based measures are often limited in their geographical extent, while proxy-based measures are of unknown accuracy and do not provide any details on how land-use regulations restrict residential development.3 Moreover, the available data is generally not comparable across countries.

The lack of internationally comparable data on local land-use regulations holds back our understanding of their impact on housing outcomes. In particular, it makes it difficult to develop insights on how to reform planning systems to encourage housing construction without creating detrimental effects on other important objectives of the planning system, such as preventing sprawl. To improve the evidence base, the OECD has started to collect internationally comparable data on land-use regulations from local governments.

Measuring land-use regulations poses significant challenges

Land-use regulations are complex sets of rules. They generally include qualitative and quantitative rules as well as map-based regulations, whose importance differs across countries. Moreover, they frequently leave substantial scope for discretionary decisions by local planning officials. Their complexity and discretionary nature poses substantial challenges in their quantification.

• Land-use regulations can vary considerably across local jurisdictions within a country. Throughout the OECD, land-use planning is predominantly the responsibility of subnational governments and in particular of local governments. While many countries impose national frameworks that guide the application of land-use regulations, local governments nevertheless have high discretion in deciding if and how they use the instruments at their disposal. As a consequence, any accurate measure of land-use regulations needs to reflect their geographical diversity.

• Land-use regulations are often ambiguous. Regulations that prevent housing construction on undeveloped land, such as agricultural land and forests, are often nuanced and difficult to quantify: for example, they can sometimes allow infill development in the proximity of existing buildings, but not leapfrogging development away from built-up areas. Additionally, many countries use mixed-use zoning that imposes restrictions on residential development, complicating the measurement of these regulations: for example, mixed-use zoning can impose upper/lower limits on the share of residential floor space or permit residential development only in certain locations (e.g. facing away from large roads).

• Additional data is needed to quantify building density restrictions and it is often difficult to access. Land-use regulations to control building density, such as height limits, floor-area-ratios, or minimum lot sizes, are tightly linked to the density of the existing building stock. If existing buildings are built up to the permitted limits, no further densification is possible. In contrast, if they remain well below permitted limits, additional housing units can be created through densification. Yet, the cadastre data that is required to measure the existing building stock is difficult to access and often not available in fully digitised form. This makes it impossible to compare permitted densities to the actual building stock in a large number of cities. Moreover, regulations on building density contain context-specific elements in many countries, such as regulations that prevent buildings from visually dominating or shading adjacent properties. Whether a new development meets such criteria is decided in case-by-case evaluations of planning officials.

• Planning policies can impose a wide range of requirements as a prerequisite for obtaining a permit to develop housing, and need to be considered in the quantification of land-use regulation. Typical examples are requirements that specify the building type, such as single-family housing, row housing and perimeter block housing. Architectural requirements are also common and often include specifications on façade design and roofing. Heritage protections limit the scope to demolish or alter existing buildings in historic parts of cities. Building code regulations aimed at the safety of buildings can reduce the usable floor space of buildings and increase construction costs. Some countries impose even more requirements. For example, some local governments stipulate minimum and/or maximum internal size requirements for individual dwelling units or require that housing is reserved for specific populations, such as elderly or disabled people.

• Land-use regulations often include clauses applied infrequently on a case-by-case basis. For example, in several countries, land-use regulation features the provision of rental housing at below-market prices. The infrequency and specificity of these clauses make it hard to collect representative data.

• The frequency with which land-use plans are updated should also be considered part of land-use regulations. All else equal, a higher frequency of updates usually indicates less restrictive land-use regulations. If a plan contains strict regulations, but is often updated or replaced at the initiative of developers to permit additional development, it is much less restrictive in practice than a similar plan that remains in place for a long time without modifications. Thus, an accurate measure of land-use regulations also needs to take into account how plans are revised. In particular, in countries where plans are frequently revised, the revision process can be more significant in determining the restrictiveness of planning regulations than the content of the plan at a single point in time.

Towards internationally comparable data on local land-use planning policies

The OECD aims at collecting data from local governments to provide the first internationally comparable measure of land-use regulation. The first step of this initiative is taking place in the Czech Republic, where the OECD in cooperation with the Ministry for Regional Development surveyed almost 2 000 municipalities. All municipalities within functional urban areas of more than 50 000 inhabitants have been covered by the survey as well as a set of additional municipalities that the Ministry for Regional Development considers of particular importance for the Czech housing market.

The survey collects information on local land-use regulations as well as other relevant aspects of local housing policies. It focuses primarily on regulations contained within local master plans (e.g. density and use regulations) and covers the content of more detailed regulatory plans (e.g. architectural requirements) to a lesser degree. It covers three main elements of land-use planning policies:

• Zoning by land-use;

• Building density regulations;

• The permitting process.

To put the data on land-use regulations into context, further information is collected on the existing housing stock, housing prices, municipally-owned housing, and housing construction. The survey aims at collecting data that is internationally comparable while still capturing all important aspects of local land-use policies. Thus, it contains sections that are designed to be applicable across countries as well as sections that are targeted specifically to the Czech context.

Two main constraints limit the amount of information that can be collected by the survey. First, to limit the administrative work of the responding local governments, the questionnaire has been restricted to the most important land-use planning regulations. Second, local governments that respond to the questionnaire face many of the conceptual challenges discussed above. For example, local governments are unlikely to have an exact assessment of the degree to which their density regulations leave scope for additional residential development.

Despite these challenges, the survey is likely to provide a range of new insights into local land-use and housing policies. For many countries, it will provide the first systematic measure of local land-use policies. Even in countries where such measures exist already, the data collected by the survey is likely to be the first measure derived from official sources. Moreover, as the survey is rolled out across different countries, it will provide the first robust basis for statistical comparisons of land-use regulations across different countries. It will allow developing insights on how to reform planning systems to encourage housing construction without creating detrimental effects on other important objectives of the planning system. As the OECD plans to increase the number of countries covered by the survey, it is seeking to continue to work with interested governments.

References

[3] Dijkstra, L., H. Poelman and P. Veneri (2019), “The EU-OECD definition of a functional urban area”, OECD Regional Development Working Papers, No. 2019/11, OECD Publishing, Paris, https://dx.doi.org/10.1787/d58cb34d-en.

[10] European Commission (2007), ETHOS Light: European Typology of Homelessness and Housing Exclusion, Measurement of Homelessness at European Union Level, https://www.feantsa.org/download/fea-002-18-update-ethos-light-0032417441788687419154.pdf (accessed on 8 October 2019).

[8] Eviction Lab (2018), Methodology Report v.1.1.0 (5/7/18), Princeton University, https://evictionlab.org/docs/Eviction%20Lab%20Methodology%20Report.pdf (accessed on 11 June 2020).

[13] Ganong, P. and D. Shoag (2017), “Why has regional income convergence in the U.S. declined?”, Journal of Urban Economics, Vol. 102, pp. 76-90, https://doi.org/10.1016/j.jue.2017.07.002.

[12] Gyourko, J., A. Saiz and A. Sumers (2008), “A New Measure of the Local Regulatory Environment for Housing Markets: The Wharton Residential Land Use Regulatory Index”, Urban Studies, Vol. 45/3, pp. 693-729, https://doi.org/10.1177/0042098007087341.

[1] ILO et al. (2013), Handbook on Residential Property Prices Indices, https://ec.europa.eu/eurostat/documents/3859598/5925925/KS-RA-12-022-EN.PDF.

[7] Kenna, P. et al. (2016), Pilot project - Promoting protection of the right to housing - Homelessness prevention in the context of evictions, European Union, http://dx.doi.org/10.2767/463280.

[5] OECD (2020), Affordable Housing Database, http://oe.cd/ahd.

[9] OECD (2020), Better data and policies to fight homelessness in the OECD. Policy Brief on Affordable Housing, OECD Publishing, Paris, http://oe.cd/homelessness-2020. (accessed on 16 March 2020).

[2] OECD (2020), Statistical Insights: Location, location, location - House price developments across and within OECD countries, http://www.oecd.org/sdd/prices-ppp/statistical-insights-location-location-location-house-price-developments-across-and-within-oecd-countries.htm (accessed on 15 July 2020).

[4] OECD (2019), OECD Regional Outlook 2019: Leveraging Megatrends for Cities and Rural Areas, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264312838-en.

[6] Valtakunnanvoudinvirasto (2020), Ulosotto Suomessa: Ulosottolaitoksen tilastoja vuodelta 2019, https://valtakunnanvoudinvirasto.fi/material/attachments/vvv2/vvvliitteet/PcgwTXKsp/Ulosotto_Suomessa2019.pdf.

[11] Waugh, A. et al. (2018), Health and Homelessness in Scotland: Research, https://www.gov.scot/publications/health-homelessness-scotland/ (accessed on 29 October 2019).

Notes

← 1.  Germany (Destatis) provides house price indices for four separate subnational groupings: cities not attached to a district, urban districts, densely populated rural districts, and sparsely populated rural districts. Even though these groupings are very relevant to assess the existence of an urban/rural divide in house price developments, they do not relate to unsegmented geographical areas, which is why we do not consider them as “regions”.

← 2.  Note that PPPs cover all types of goods and services that are consumed, invested or exported in an economy. Therefore, specific PPPs are compiled for dwellings and other investment goods. Nevertheless, only new dwellings, and only a handful of dwelling types with very precise characteristics to allow for international price comparisons, are taken into account for the compilation of PPPs. Moreover, even though Costa et al. (2019_[205]) is a recent attempt in the literature to compile regional PPPs for a few countries, most statistical agencies only compile PPPs at national level. For these reasons, the available PPPs do not allow comparing house prices across regions within the same country, nor taking into account the specific nature of dwellings in the housing stock of each region. For a description of how PPPs for construction goods are compiled in the Eurostat-OECD area, see Chapter 11 in Eurostat, OECD (2012_[204]). For a recent attempt in the literature to estimate national house price levels for 40 countries, see Bricongne, Turrini and Pontuch (2019_[49]).

← 3.  See Lewis and Marantz (2019_[212]) for an overview of attempts to measure local land-use regulations in California.