The government has implemented policies to curb speculative demand for new apartments and for apartments scheduled for reconstruction, as that has been a main factor behind the rise in housing prices in the SMA. It has tightened the criteria for top-priority subscribers for new apartment purchasing rights, while limiting their resales by imposing a higher capital gains tax on them. The government has also tightened the requirements for apartment reconstruction, by for example revising the safety criteria for approving reconstruction projects.

outstrips that in income, and Korea's household debt-to-nominal GDP ratio (household credit statistics basis) has thus continued to increase, from 73.0% at end-2014 to 81.8% at end-2016 and 83.8% at end-2017.

#### Tax Policies

The taxation of housing investment has been strengthened, to curb demand for investment purposes and to heighten tax fairness. The government has raised the transfer income tax on owners of multiple homes, which became effective from April 2018, and imposed levies on excess earnings from reconstruction in January 2018. Discussions on revising the property holding tax on owners of multiple homes and expensive homes are also ongoing. This could be a more effective means of controlling housing demand for investment.

#### 4.2 Tailored Responses

#### By Region

The target regions for the tighter regulations are sub-divided into speculative investment zones, overheated zones and adjustment-required zones, depending upon their respective degree of housing price rises and overheating. Each zone is subject to a different level of regulation.13 The Gangnam districts in Seoul were designated as speculative investment zones, and are subject to the strongest regulations. Other parts of the SMA and the provincial cities are subject to relatively relaxed regulations.

#### By Household

The level of regulations applied to households varies depending upon whether they own multiple homes, or have taken out home mortgage loans. Households that own multiple homes are subject to a 10–20pp transfer tax add-on. Households owning only single housing units benefit from tax exemptions only after they have lived in their homes for two or more years. When a household with an existing home mortgage loan seeks an additional loan for a housing purchase, the applicable LTV and DTI ratios will be tightened by 10pp each.14

#### By Housing Demand

Housing demand is categorized into demand for speculative and for residential purposes. The government strictly regulates speculative demand, while providing active support for residential demand. Speculative demand is strictly controlled through tighter screening of the financing methods involved and checking on the owner-occupancy status of the borrower. On the other hand, LTV and DTI ratios are relaxed for low-income households purchasing homes for residential purposes, in order to alleviate their financing difficulties.

13See appendix for a detailed explanation.

14When a home mortgage loan borrower purchases an apartment in one of the overheated regions, the borrower will be subject to tighter LTV and DTI ratios (30% instead of 40%).

#### 5 Policy Evaluation

Thanks to the government's comprehensive and tailored policy responses, the growth in household debt has slowed since the second half of 2017, and the housing market in the SMA has been gradually stabilizing in 2018. The rate of household debt growth was 8.1% (at year-end) in 2017, down significantly from the figures of around 10% in 2015 and 2016, in line with the tightening of the LTV and DTI limits and the other policy measures implemented in 2017 (Fig. 16.8a).

Despite the government's announcement of its housing market stabilization plan in August 2017, and its partial implementation thereof, housing prices in the SMA continued to rise for some time. In 2018, however, they have shown slower growth as the effects of the government's policies have begun to take hold (Fig. 16.8b).

For long-term housing market stability it should be noted, however, that demandreduction policies alone cannot stabilize the housing market in the SMA. There is a structural imbalance between the limited supply of new housing in that area, and the continually growing demand for it.

*Source: Bank of Korea (2017) Source: Korea Appraisal Board (2018)*

Fig. 16.8 (a) Volume and growth rate of household debt. Note: Quarter-end basis; year-on-year. (b) Growth rate of apartment prices in the SMA. Note: Week-on-week

#### Appendix

Table 16.1 Housing market regulations by region

Note: As of May 2018; designation of speculative investment zones was repealed in April 2018 upon the introduction of transfer tax add-on in adjustment-required zones Source: FSC 2017a, b

#### References

Bank for International Settlements. (2018). Residential property prices: Selected series (nominal and real). Accessed July 1, 2018, from https://www.bis.org/statistics/pp\_selected.htm

Bank of Korea. (2017). Financial Stability Report 2017(6).

IMF. (2017). Global Housing Watch 2017(Q1).

Korea Appraisal Board. (2018). Accessed July 1, 2018 from http://www.kab.co.kr/kab/home/eng/ main.jsp

Ministry of Land, Infrastructure and Transport. (2018). Accessed July 1, 2018 from http://stat.molit. go.kr

Oh, K., Kim, S., Yoon, J., Ahn, S., & Kwon, D. (2017). Impact of population aging on the housing market. Bank of Korea 2017-25.

Statistics Korea. (2018). Accessed July 1, 2018 from http://kostat.go.kr/

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Chapter 17 House Prices and Financial Stability: An Australian Perspective

Michele Bullock and David Orsmond

## 1 Introduction

As in a number of countries, housing prices in Australia have increased at a rapid pace over the past couple of decades, particularly in the major cities. These price rises have raised a range of concerns, including for housing affordability. As a result, there has been much debate about the types of policies that could ease the growth in housing prices.

While acknowledging the broader societal impacts of high and rising housing prices, the Reserve Bank of Australia (RBA) has not seen managing the pace of housing price rises per se as within its policy remit. Rather, the RBA's concern has been that the associated build-up in household debt and other dynamics in the housing sector have raised concerns about the health of borrowers' balance sheets and, therefore, financial stability. If realised, these risks could have a detrimental impact on the broader economy. Like many other countries, the RBA and other policymakers have considered various ways to address these risks. This chapter outlines this approach and recent experiences.

#### 2 The Housing Market in Australia

Australia is a large country geographically but its population is concentrated in a small number of cities. From a population of close to 25 million, around 15 million live in the largest five cities, with 10 million people in Sydney and Melbourne alone.

M. Bullock

D. Orsmond (\*)

© The Reserve Bank of Australia 2019

Financial System Group, Reserve Bank of Australia, Sydney, NSW, Australia e-mail<EMAIL_ADDRESS>
Financial Stability Department, Reserve Bank of Australia, Sydney, NSW, Australia e-mail<EMAIL_ADDRESS>
R. Nijskens et al. (eds.), Hot Property, https://doi.org/10.1007/978-3-030-11674-3\_17

In the decade to mid-2017, the combined population of the five largest cities grew by 22%, accounting for around three-quarters of Australia's population growth, which was one of the fastest within the OECD. It is unsurprising then that there has at times been pressure on the housing stock, particularly in Sydney and Melbourne.

Housing prices in Australia have risen substantially since the 1980s. Part of this reflects the strong growth in the economy and household incomes over the past few decades. But housing prices have also increased relative to incomes. The factors driving this upward shift in the relative price of housing were primarily twofold (Kohler and van der Merwe 2015; Reserve Bank of Australia 2003, 2014). First, the decline in average nominal interest rates as a result of the structural decline in inflation in the 1990s meant that borrowers could service larger loans out of their current income than they had been able to in the past. Second, deregulation of the financial system reinforced this trend by removing many of the previous constraints on credit supply to households. By the mid-2000s, this structural transition seemed largely complete and the average housing price settled at around four times average household disposable incomes (Fig. 17.1).

In more recent years, however, housing prices have grown quite strongly again, particularly in Sydney and Melbourne (Fig. 17.2). Low interest rates, relatively strong population growth and interest by investors (foreign and domestic) all contributed to a strong demand for residential property. Coupled with zoning

Fig. 17.1 (a) Dwelling prices (yearly means, \$000 s) (b) Price-to-income ratios. Note: CoreLogic data is commercial and not for publication or further distribution without approval. Graphs using this data can be published with appropriate attribution

Fig. 17.2 Housing price growth (6-month-ended annualised; seasonally adjusted). Note: CoreLogic data is commercial and not for publication or further distribution without approval. Graphs using this data can be published with appropriate attribution

restrictions and other supply constraints, this resulted in housing prices increasing by around 20% a year in Sydney and Melbourne in 2013 and 2014 (Hsieh et al. 2012). While the pace of growth of overall housing credit was more modest than in previous housing cycles, investor credit was starting to rise rapidly. Housing price growth eased in 2015 but increased again through 2016 and 2017. By the end of 2017, the average price was around five times average household incomes. In contrast, the slowdown in the mining investment boom saw housing price declines in Perth and, to a lesser extent, Brisbane, which are both located in the more miningintensive states of Australia.

These developments have generated a lot of attention. Housing affordability has consistently been a topical issue in Australia, with much discussion around issues such as how to ease constraints on supply, policies to assist first home buyers, tax incentives for investors and the impact of foreign buyers on the housing market.1 The RBA's concerns, however, have been focused on the potential implications of broad developments in the housing sector for financial stability and macro-financial risks. There have been a couple of elements to this.

First, the rise in housing prices has been accompanied by a rise in the ratio of household debt to income. After stabilising at around 120% of income from the mid-2000s, the household mortgage debt ratio has since risen to 140% (Fig. 17.3).

1 For the RBA submissions to the various inquiries into housing and housing finance over the past decade that outline these issues, see https://www.rba.gov.au/publications/submissions/housingand-housing-finance/

*Source: National central banks and statistical offices; RBA (2018); OECD (2018); Thomson Reuters; ABS (2018); Bank of England (2018); Statistics Canada (2018); Statistics Netherlands (2018); US Federal Reserve (2018)*

Fig. 17.3 Household mortgage debt-to-income ratio

This ratio is high relative to many other (but not all) advanced economies and raises issues about the resilience of household balance sheets in the event of an adverse shock to the housing sector and/or the economy.

Second, the Australian financial system is heavily weighted towards housing lending. Over the past decade, the largest Australian banks (which account for around 80% of the assets of the deposit-taking institutions) have steadily increased their exposure to housing so that around 60% of banks' lending is now for mortgages (Fig. 17.4). There have also been concerns that strong competition among mortgage lenders has resulted in lending standards becoming too relaxed. While stress tests indicate that banks are sufficiently well capitalised to handle a rise in loan delinquencies combined with a large fall in housing prices, a substantial downturn in the housing market could have implications for the economy more broadly.

These risks might be realised in a number of ways. First, while households often have sizeable pre-payment buffers, the high debt-to-income ratio can make them vulnerable to an adverse economic shock, with implications for household consumption. Households might also become forced sellers, resulting in downward pressure on housing prices and possibly inducing further selling. In addition, there has been a significant increase in the amount of interest-only lending, which can leave households with higher debt than for principal-and-interest loans and, in the event of sharp downturn in the housing market, possibly with negative equity. These developments would also weigh on banks' asset performance and profitability.

Second, investor activity could amplify the housing cycle. Unlike in many other countries, most of the rental stock of housing in Australia is owned by households as an investment, which boosts the level of household debt. Through 2013 and 2014,

Fig. 17.4 (a) Banking system concentration (market share of the five largest banks) (b) Mortgage lending concentration

as the housing market was picking up, growth in lending to housing investors was rising by over 10% a year. In Australia, the tax deductibility of interest on investment loans and favourable rate of capital gains tax encourage households to borrow to purchase investment properties and not to pay down the principal. The substantial growth in investor lending therefore raises concerns about possible pro-cyclical behaviour of housing investors—the more housing prices rise, the more investors borrow to invest, potentially pushing up housing prices and debt levels further. If the housing market then turns down, and the prospect of capital gains diminish, these investors might sell ahead of expected price falls, amplifying the housing cycle (Reserve Bank of Australia 2017). This would have wider flow-on effects to owneroccupier households, which would find the price of their most important asset falling while they have a high level of debt.

Finally, the residential development cycle might also exacerbate the housing cycle, particularly for medium- and high-density apartment buildings, whose share has increased in recent years from around one-quarter to one-half of the new housing stock. High prices typically bring forth a supply response. But the lag in supply of new apartments coming on stream can mean that by the time it does, there is an oversupply. If the oversupply is significantly large, it would have implications for developers, and hence the performance of banks' property books. It would also have implications for households if they have contracted to purchase property in advance of construction on the expectation of making a subsequent capital gain. Again, this would have implications for bank balance sheets.

#### 3 The Policy Response

In Australia, prudential regulation of financial institutions is the responsibility of the Australian Prudential Regulation Authority (APRA). APRA is tasked with protecting the interests of depositors, policyholders and superannuation (pension) fund members by maintaining the safety and soundness of financial institutions. Its mandate also requires it to promote financial stability more broadly. APRA has the usual regulatory and supervisory tools at its disposal to achieve this, but can also use these tools for what are now often-termed 'macroprudential' purposes.

The RBA has responsibility for financial stability more broadly. The RBA interprets this to mean that it should aim to minimise the possibility that instability in the financial sector or broader macro-financial risks threaten the real economy. The RBA does not, however, have any specific tools that it can use to deal with financial stability issues, though in stress periods it can provide liquidity to solvent institutions. While its monetary policy framework provides the RBA with the flexibility to set policy to achieve its broad objectives over time, including financial stability, addressing systemic risk typically involves identifying and communicating risks and working with other financial regulators to address them. The Council of Financial Regulators (which comprises the RBA, APRA, the Australian Securities and Investments Commission (ASIC) and the Australian Treasury) plays an important role by providing a forum for the sharing of information and views in this regard. It meets quarterly, although ongoing interactions are undertaken more frequently at all levels of these agencies. The Council has no powers of its own. So if stability risks are building, it is usually APRA that has the most appropriate tools to deal with not only risks to individual institutions but also risks to the system as a whole.

The financial stability risks associated with the recent increase in household indebtedness and housing prices generated a number of policy responses. As has historically been the case, the main approach by the regulators was to focus on setting appropriate lending standards. In response to the emerging risks in the housing sector, at end 2014 APRA announced that it would increase its supervisory oversight on mortgage lending (APRA 2014). Specifically, it indicated that it would be paying increased attention to higher-risk mortgage lending such as high loan-toincome loans, high loan-to-valuation (LVR) loans, interest-only loans to owner occupiers and loans with very long terms. It set out clear prudential expectations that lenders' serviceability tests for new borrowers must incorporate an interest rate buffer that was comfortably above (by at least 2 percentage points) the prevailing loan product rate. APRA also indicated that the prevailing strong growth in lending to property investors was a particular area of concern and set a new benchmark where growth in an institution's portfolio of investor loans of more than 10% would be an 'important risk indicator for APRA supervisors in considering the need for further action'. At the same time, ASIC took steps to reinforce the 'responsible lending' obligations that cover all lenders and ensure that loan serviceability is assessed to ensure that new borrowers do not overstretch their financial capacity to purchase property or need to rely on expectations of future increases in housing prices to enable them to do so.

These policy steps were primarily designed to dampen the most risky competitive pressures and encourage banks to lend more prudently. But they also helped to improve the resilience of household balance sheets, essentially reducing the maximum loan size and ensuring that the borrowers that received loans were in the best position to service the loans thereafter. Growth in lending to property investors subsequently fell from over 10% to less than 3% annually while growth in credit to owner-occupiers actually increased (Fig. 17.5). The share of loans at LVRs of over 90% declined from 12% to 7%. Growth in housing prices also eased.

Nevertheless, by 2016, housing activity was picking up again in Sydney and Melbourne, household debt was still rising faster than incomes, and growth in lending to property investors had started to rise again. So in early 2017, APRA, with the support of the Council of Financial Regulators, introduced further measures to bolster residential mortgage lending practices (APRA 2017). Apart from tightening somewhat further its 2014 guidelines on lending standard expectations, APRA indicated that it expected financial institutions to limit the flow of new interest-only lending for housing to 30% of their new lending (Fig. 17.5). To limit the risk of leakage, APRA also noted that it would be carefully examining trends in the level of warehouse funding by banks to non-bank lenders.

*Source: APRA (2018); RBA (2018)*

Fig. 17.5 (a) Housing credit growth (6-month-ended annualised) (b) Interest-only loan approvals share. Note: Dashed vertical lines in both panels of graph positioned in alignment with January 2015 and March 2017

Since then, risks in the housing market have again eased. The share of new housing lending that is on interest-only terms has declined to around 16%, well below the benchmark. There has also been less investor activity relative to owner occupiers and some signs of an increase in first home buyer activity. APRA has recently announced that the quality of new lending has improved to the extent that it will be able to remove the investor loan growth benchmark for individual institutions that meet certain requirements (APRA 2018). And while not a target in itself, growth in housing prices in Sydney and Melbourne has declined substantially, and in Sydney's case prices have even fallen a little in recent months.

## 4 Reflections

The Australian approach to addressing financial stability concerns has been heavily weighted towards setting appropriate prudential expectations, with relatively little use of prescriptive quantitative targets. While not an exhaustive list, there are four key issues that the recent experience has raised.

#### 4.1 How Is Effectiveness Evaluated?

It is not always straightforward to measure the effectiveness of what have come to be known globally as 'macroprudential policies.' In Australia's case, the goal was to address financial stability risks by ensuring that banks and households were being appropriately prudent in their lending and borrowing decisions. But this broad policy goal is very difficult to measure with a high degree of accuracy. Instead, we can look to the specific impact of the measures taken—for example, the incidence of high-risk lending, pace of growth in investor loans and share of interest-only loans. By these metrics, the policies were apparently effective. But quantitative measures are difficult to calibrate and assess ex-post and in some cases have had only temporary effects, especially as other policies have continued to evolve. The benchmark on growth in investor lending introduced in 2014, for example, initially resulted in a sharp decline in the growth rate, but by early 2017 the growth rate was rising again. The 30% benchmark on the share of new interest-only loans introduced in 2017 was also materially overshot. But there appears to be continued demand for interest-only loans and there are signs that banks are looking to expand this category of lending again. More broadly, the global experience of the directed credit regimes of the 1960s and 1970s suggests that borrowers and lenders have an incentive to try to find ways to circumvent such controls. Noting this, the benchmark measures that have been used in Australia were always intended to be temporary while APRA put in place more permanent guidelines to address the associated concerns about lending standards.

Whether these measures had an impact on the growth of housing prices is unclear. Certainly, housing price growth in both Sydney and Melbourne eased substantially following the APRA and ASIC measures that were introduced. But loan approvals and price growth had already started to ease prior to the 2017 measures. And there is evidence that the price declines have been more evident in higher priced properties over the past year—a segment that would be expected to be least affected by the portfolio benchmark measures as investors are more likely to purchase cheaper apartments than detached housing.

#### 4.2 Are Geographical Restrictions Helpful?

In Australia, we did not implement geographic-specific policies to shore up financial stability concerns, though some individual banks decided to restrict lending to certain postcodes. While it was mainly Sydney and Melbourne where housing conditions were most buoyant, APRA acted to improve mortgage lending standards across the board. One argument for implementing geographical restrictions could be that the banks would meet the new guidelines by restricting lending everywhere except for Sydney and Melbourne, disproportionally affecting (say) the Brisbane apartment market (that was not experiencing sharp price rises) and exacerbate the price falls in Perth. This has not turned out to be the case. But more broadly, we are sceptical about the value of geographic policies. Apart from the fact that it looks increasingly like directed lending, other countries' experiences suggest that there are difficulties in calibration and in implementation of geographic constraints over the longer run (Reserve Bank of New Zealand 2016). And as long as the large cities continue to grow, there will be a continuing demand for new housing and, if anything, a challenge for supply to keep up with that demand. This suggests that policies directed at lending in specific areas are likely to be successful in restraining housing price growth only in the short term.

#### 4.3 Are There Undesirable Consequences?

An important challenge is to respond to systemic risks in a manner that strikes a reasonable balance between facilitating the ongoing provision of credit to the economy while managing the risks and other flow-on effects, including during the transition from one equilibrium to another (less risky) one. As seen in other countries, the use of quantitative restrictions to curtail the provision of credit to borrowers perceived to be systemically risky can have undesirable consequences. These include adverse effects on competition and distributional considerations. In Australia, some small and regional lenders have argued that the benchmark on the growth of investor lending has entrenched market shares (COBA 2017). Further, the various restrictions constrain the size of a loan marginal borrowers can get, including some first home buyers, and so have equity and distributional implications, although in doing so they make the debt more manageable for these households. Another group of borrowers that could be adversely affected by the tighter lending standards are investors that paid deposits on off-the-plan housing on the assumption that they would be able to get finance on prevailing credit terms, and now find that they are unable to do so.

#### 4.4 How Are These Policies Likely to Evolve?

Like many other countries, the use of quantitative benchmarks to bolster financial stability does not have a long history in Australia and there are still many lessons to learn. In principle, these types of tools have the potential to contain a range of financial stability risks, and the early evidence is that they can be effective, at least for a while. But in practice, their use poses a range of challenges including calibration, selecting the most effective tool for the specific risk, containing potential leakages, ensuring on-going political support, and knowing how to effectively integrate them into the more traditional supervisory and monetary policy frameworks. These issues are a challenge for all countries (including Australia) and hence it is likely that their operational practices and governance frameworks will continue to evolve.

#### References

#### Graph Data Sources

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# Chapter 18 Exploding House Prices in Urban Housing Markets: Explanations and Policy Solutions for the Netherlands

Christian Lennartz, Barbara Baarsma, and Nic Vrieselaar

## 1 Introduction1

Strong house price growth is by no means a post-crisis phenomenon but has been at the center of economic and societal transformations in almost all advanced economies since the 1980s (Aalbers 2015; Ansell 2014). For a given period it seemed that the global boom in house prices had finally come to a halt in and through the Global Financial Crisis (GFC) of 2008. Yet, 10 years after these events, housing markets are following a strong upward trend again, making the crisis years almost look like a glitch in the data.

However, the defining forces of house price appreciation have shifted substantially in recent years. Before 2008, price growth across the globe was largely carried by the easing of finance conditions, i.e. higher loan-to-value (LTV) and loan-toincome (LTI) ratios for individual borrowers, as well as the expansion of interestonly mortgages, mortgage guarantee funds, mortgage tax deductibility, and lower capital requirements for mortgage lenders (Cerutti et al. 2017; Schwartz 2012; Martin 2011; Schnure 2005). In the current boom cycle, other factors seem to have become more relevant.

Next to the extremely low (mortgage) interest rates, house price appreciation has become increasingly defined by fundamental demographic and economic parameters: the growth in the number of households relative to sluggish new supply, catch-

At the time of writing, Christian Lennartz and Barbara Baarsma worked at RaboResearch.

1 Reprinted with permission of Rabobank. Previously published as Lennartz et al. (2018), Exploding house prices and imploding affordability in urban housing markets, Explanations and policy solutions for the Netherlands, Rabobank, June.

C. Lennartz (\*) · B. Baarsma · N. Vrieselaar RaboResearch Rabobank, Utrecht, Netherlands

e-mail<EMAIL_ADDRESS>
up growth in jobs and job-related income, and the demand of (buy-to-let) investors pursuing high returns on housing property investments (e.g. Hekwolter of Hekhuis et al. 2017; Lennartz 2017). As a result of the rebalancing from a finance-driven towards a more fundamentals driven housing environment, market outcomes have become more regionalized, implying a growing divergence between hot property markets in major cities and less dynamic markets in more peripheral regions.

In this chapter we analyze these post-crisis developments for the Netherlands. Doing so, we establish a basis for a coherent policy discussion of how to mitigate the risks of extreme house price and excessive rent growth in urban housing markets. We propose five detailed measures to achieve this goal: [I] massively increase new housing production; [II] focus on affordable housing in the production of new homes; [III] increase the turnover rate of existing homes; [IV] better integrate housing and infrastructure/mobility policies; [V] penalize speculative housing investments.

## 2 The Specifics of Pre- and Post-crisis Housing in the Netherlands and Its Major Cities

Generally, house prices in the Netherlands have moved in line with the bulk of Western countries in the past 40 years. Starting in the early 1980s and ending with the Global Financial Crisis, the Netherlands experienced a sustained period of strong house price growth (see Fig. 18.1). This was followed by a prolonged market downturn between 2009 and 2013. Most strikingly, the recession on the housing market had—with the exception of Spain, Ireland and Denmark—run much deeper and for much longer than in most other countries.

*Source: Bank for International Settlements (2018)*

Fig. 18.1 Nominal house prices

One explanation here is that due to high average loan-to-value mortgages a large proportion of mortgaged homeowners saw the value of their home decrease below their outstanding mortgage debt.2 This meant that households had become more likely to repay their mortgage and less likely to consume, which in turn had a negative impact on the wider economy, implying lower growth and higher unemployment.

In reverse, the economic recession was further aggravated trough fiscal consolidation (see Heimberger 2017), which in turn had a negative impact on housing demand. Moreover, with high private (mortgage) debt having become a wearing factor for the stability of the financial system, the Dutch government implemented pro-cyclical macroprudential policies (lowering the maximum LTV, implementing binding LTI norms) in the early crisis period, which further suppressed demand for housing (Tu et al. 2017). Meanwhile, these patterns in price developments were also followed by national-level developments in housing affordability. Figure 18.2 illustrates that while the house price boom in the past 3 years has deteriorated affordability, relatively speaking housing costs in the owner-occupied sector still fall short of their 2008 levels.

Yet, the most noticeable feature of the post-crisis period is the way price recovery has differed across cities and regions. Even though the regulatory and institutional context is defined at the national level, local and regional housing markets have come to operate under different socio-economic and demographic conditions and

Fig. 18.2 Price-to-income ratio. Note: Price-to-income ratio is defined as the nominal house prices divided by nominal disposable income per head. Net household disposable income is used. The population data come from the OECD national accounts database. The long-term average is calculated over the whole period available when the indicator begins after 1980 or after 1980 if the indicator is longer. This value is used as a reference value. The ratio is calculated by dividing the indicator source on this long-term average, and indexed to a reference value equal to 100

2 For a good illustration of how severe the problem of underwater mortgages was, the reader is referred to Fig. 1 in Hekwolter of Hekhuis et al. 2017 (p. 33).

Fig. 18.3 House price indices of the four largest Dutch cities

with varying price dynamics. To be more precise, regional house price data reveals a sharp and growing contrast between the major cities and city regions (Amsterdam, Utrecht, Rotterdam and The Hague inside the Randstad region (see Fig. 18.3), as well as Eindhoven, Groningen and Maastricht outside of it) and more peripheral housing markets.

Indeed, house prices in urban markets have long moved past their pre-crisis levels, with houses in Amsterdam as the most extreme case, now being 30% more expensive than in 2008 and a whopping 55% since they bottomed out in 2013. Meanwhile, house prices are still below their pre-crisis levels in more rural and less urbanized regions, particularly in those areas which are dealing with population decline and rapidly aging societies.

Secondly, Fig. 18.4 shows that in comparison to other major cities in Europe, the Dutch capital seems to be a particularly hot property market. While Amsterdam in absolute terms is on average (just like Dublin and Copenhagen) still relatively close to the national price level, the process of house price divergence since 2014 is only matched by the London/UK case.3 Where in 2006 the average house in Amsterdam was only about 1.1 times more expensive than the average house in the Netherlands as a whole,4 this ratio had increased to 1.5 by the end of 2016. Meanwhile, the price gap in Dublin, Copenhagen, and Stockholm had barely changed over the same period.

Whether the growing divergence between major cities and peripheral regions is a temporary phenomenon or a permanent structural shift in in the Dutch housing context is, of course, a valid question. Figure 18.3 reveals that Amsterdam experienced a major price correction in the mid-2000s after a period of unprecedented and unparalleled house price growth in the late 1990s. Accordingly, one could argue that

3 For more capital/country pairs see IMF (2018).

4 Admittedly, this does not control for differing compositions of the housing stocks.

Fig. 18.4 Ratio of median house price in capital city to median house price in the whole country

in the current housing market cycle it is also a matter of time before house price developments in Amsterdam and other regions will level out again, be it through a relative fall in prices in Amsterdam (and other cities) or through faster growth in peripheral regions. In the following we will explicate why this is not the most likely outcome in the years to come.

#### 3 Explaining the Emergence of Hot Property Markets

The emergence of hot residential property markets in the Netherlands can, by and large, be explained by three factors. Firstly, following the aforementioned economic and housing market downturn, housing production had fallen to an all-time low. Indeed, while in the years before the GFC the housing stock in the Netherlands had grown by more than 80,000 dwellings per year, this number dropped to 45,000 units between 2011 and 2014. Most strikingly, this drop materialized more rapidly and most strongly in urban regions (CBS 2018). Given that building land in these areas is scarcer and thus more expensive (See Fig. 2 in Hekwolter of Hekhuis et al. 2017), developers are seemingly more reluctant to keep investing in these areas, resulting in a relatively stronger backlog of new dwellings in relation to new households.

Secondly, following the restructuring towards a knowledge-based economy in the past decades, larger cities have increasingly become the economic powerhouses of the country. Even before the crisis this had led to positive (internal and external) migration rates of younger and highly-educated adults. After the crisis, this trend has accelerated considerably, also because knowledge-intensive sectors have fared better and recovered faster than more traditional and spatially less concentrated sectors as construction and retail. As a result, the demand of private households for rental and owner-occupied units in and around these cities has grown much stronger than elsewhere in the post-crisis period.

Finally, the housing landscape in the Netherlands has changed massively through the emergence of a new class of domestic5 buy-to-let private investors.6 Given the low borrowing costs and strong price appreciation since 2014, residential real estate has generally attracted increasing demand from small-scale (buy-to-let) landlords and more professional (and larger) investors alike. Here, investors specifically channelled their financial resources into urban housing markets such as Amsterdam, Eindhoven, and Maastricht, i.e. localities with the highest (possible) rent increases and price growth due to stronger imbalances between supply and demand (e.g. Hekwolter of Hekhuis et al. 2017). Within this process, buy-to-let investors have contributed to increasing prices in two ways. By turning owner-occupied units into rental properties they have significantly contributed to the scarcity of owner-occupied units in urban areas. Additionally, newly purchased buy-to-let properties were most often offered in the unregulated rental sector,7 a sector which has seen tremendous rent increases, particularly in the larger cities (CBS 2017a).8 This in turn has allowed for higher purchase prices by investors, which then were used to justify further rent increases to meet target returns—and so the cycle continued.

5 Although this has become a topic of interest in the Dutch media, there is no clear evidence of increased activities of foreign buyers with the exception of international pension funds buying residential complexes of housing associations (Capital Value 2018).

6 See Fields (2018) for a good overview of the forces behind this process.

7 The Dutch rental market generally consists of two sectors: a regulated sector in which initial rents are stipulated by a dwelling quality and location-based point valuation system. Currently, the deregulation threshold stands at €710 net rent per month. Above this threshold initial rents are fully deregulated and freely negotiated between landlords and tenants. Likewise, rent increases are only regulated below the €710 threshold, where the central government determines maximum rent increases once a year. In the deregulated sector rent increases are not controlled, yet they can only be increased once per year. Furthermore, a specific trait of the Dutch rent regime is that access to regulated rental dwellings owned by non-profit housing associations is based on disposable household income (between €36,000 and €43,000 depending on household composition) and waiting time, while regulated units owned by private parties are not bound to this rule.

8 Here, the specifics of the rent regime plays a key role. Since the point-based valuation system is partially based on house values, which in turn are based on current market values, rising house prices imply that rents can become deregulated much quicker.

#### 4 Policy Solutions for Hot Property Markets

With house prices and rents increasing sharply in the past couple of years, and deteriorating affordability and accessibility becoming a threat to the stability of the Dutch housing market and the economy as a whole (see e.g. DNB 2018a), housing policy has become a central theme in local and national policy debates. In the following we seek to lay out coherent policy solutions to what were argued the underlying causes of the emerging housing crisis in the Netherlands.

#### 4.1 Macroprudential Policy

It is necessary to first deal with the 'elephant in the room': macroprudential policy. In a country where the maximum mortgage loan still stands at 100% of home value, tightening mortgage lending might be the most obvious way of reducing demand of prospective house buyers, and through this lowering aggregate prices. Recent policy shifts in Sweden and Australia suggest that more restricted access to mortgage credit may indeed have a direct and measurable impact on house prices, at least in the short run (see Bloomberg 2018a; SMH 2018). However, we would argue that macroprudential policies are not the most useful solution to the problem in the current Dutch housing environment.

Firstly, tighter lending norms were already implemented several years ago. While the maximum LTV ratio has been reduced from more than 120–100% in 2018 which, admittedly, is still very high by international standards—it is particularly the more stringent LTI norms, which are becoming a limiting factor to exuberant mortgage lending and, accordingly, endless house price excesses (see also Tu et al. 2017). Secondly and related to this, the primary goal of macroprudential policy is to guarantee the stability of the financial system (Galati and Moessner 2017; and Kuttner and Shim 2012). Yet, in the recent boom cycle the growth in aggregate mortgage debt has lagged behind house price growth (see Fig. 18.5).

This brings up the question whether macroprudential policies would then not be utilized for targeting a problem which they are not designed for. In a similar vein, while macroprudential policies affect all prospective homebuyers, the problem of escalating prices is, as described, concentrated in the country's major urban areas. The fact that LTV and LTI limits are by design countrywide rules means that there is a risk of making the situation worse for first-time buyers in peripheral markets. And indeed, first-time buyers do borrow relatively more in these areas (average LTV is 95%) than first-time buyers in major cities (90%—see Hekwolters of Hekhuis et al. 2017).

Finally, macroprudential policies alone would do little to nothing about the problem of sharply increasing rents in the deregulated rental sector. In fact, given that buy-to-let properties are most often not purchased with high LTV ratios and do not fall under the current LTI regime, a further tightening of lending conditions

Fig. 18.5 Mortgage debt versus house prices

would imply that first-time buyers fall further back behind the purchasing power of private investors.

#### 4.2 Structural Urban and National Housing Policies

In short, although there is little doubt that macroprudential policies would lead, at least temporarily, to a fall in house prices, they are not effective in targeting the underlying causes of urban housing problems. From our viewpoint a mix of structural urban and national (housing) policies are much more suited for reaching this goal. These policies consist of five building blocks.

#### I. Massively Increase Housing Supply in Urban Areas

It was argued above that a shortage in new housing units is a key driver of the growing imbalances between supply and demand in the past 4 years. Yet, housing shortages are not a new phenomenon. In the second half of the Twentieth century, population growth and the demand for new housing was much stronger than in the current housing cycle. The solution to this specific problem at the time was a much more active role by the central government in the production of new housing through, for instance, bricks-and-mortar subsidies and a more direct role in urban and regional planning (see also Boelhouwer and Priemus 2014).

Currently, the Dutch government has given itself a coordinating part at best. However, the problem with this is that individual municipalities often lack the means and the willingness to spur production within their municipal boundaries (see also point IV). More precisely, since selling land at maximum prices is an essential source of income for local administrations (CBS 2017b), there is little incentive to sell many land positions simultaneously, since this would lower prices significantly. To overcome this blockade, the central government could reimburse city administrations either through direct subsidies on land sales or by rebalancing their income structure altogether, where the exploitation of land positions would receive a smaller role than it has now. Additionally, urban planners are increasingly dealing with voter backlash and Nimbyism against housing construction in inner-city areas (Hankinson 2018). By giving the central government a stronger role in issuing new housing locations, it could function as a scapegoat for the upzoning process. This would allow for taller buildings, greater densities and higher pace in land or building conversion projects (see also Metcalf 2018).

A final point here relates to the fact that potential building land, especially in outer-city locations, is not necessarily owned by public administrations but by private developers themselves. The underlying problem remains the same. While the willingness to build more housing appears to be high at the moment, private developers have little incentive to develop all of their locations simultaneously, as this would lead to a fall in sales prices and thus imply lower returns (Miles 2018). Since appropriation is time-consuming and costly, governments should address this problem by increasing property taxes on inactive land holdings.

#### II. New Production Should Match the Need for Affordable Housing

Where most newly constructed inner-city dwellings are small-sized apartments for single-person households or multi-person units in the high-end segment, it becomes increasingly apparent that just building a lot of new units is not enough. Rather, to keep urban housing markets diverse and affordable, planning authorities need to ensure that the new housing stock does not only satisfy the influx of higher-income households, but that it also covers the housing needs of (new) lower- and middle income households. Given the fact that in the two major cities of the country, Amsterdam and Rotterdam, regulated rental housing (<€710 net rent per month) already makes up more than 50% of the housing stock—and is thus significantly larger than the share of low and lower-middle income households,9 this might seem a rather unnecessary prerequisite for housing policy. However, the crucial distinction here is between existing and readily available affordable units, where the former are abundantly available and the latter extremely scarce.

The main solution that is widely proposed for creating a more affordable and accessible rental housing sector is the expansion of the 'mid-level' market segment in the rental sector, which are deregulated dwellings with a net rent of between €710 and €950 to €1000 per month. While there certainly is growing demand for these dwellings, the discussion misses two essential points. First, rather than ensuring that

9 For most recent numbers on the distribution of the housing stock the reader is referred to the report 'Housing in the Metropolitan Region Amsterdam 2017' (Booi et al. 2018).

these units are objectively affordable in relation to their occupiers' incomes, the 'mid-rent' segment only assumes affordable rents (see also Van Middelkoop and Schilder 2017). Second, there are no guarantees under the current rent regime that mid-level rental dwellings are not being pushed into the high-rent segment with the arrival of a new tenant.10

In our view, a more effective way of producing permanently affordable dwellings for middle-income households is through land policies and one new and coherent regulation regime that applies to the entire rental market. If municipalities for example sell land at reduced prices, or, alternatively, use ground lease arrangements to split the costs of land from the costs of building structures (see e.g. Kenny et al. 2018), developers should be obliged to (partially) construct new dwellings for lowerto middle-income single-person and family-households. Initial rents should take into account both dwelling properties as well as the income of its occupier, while annual rent increases should be controlled for a time-specific period, even in the case of tenancy change. The system of local reference rents to determine initial rents and rent increases as applied in Germany for instance, has proven to be an effective policy measure in that regard and could possibly be a leading principle in re-regulating the Dutch rental sector as well.

#### III. Increase Turnover Rate to Achieve a Better Allocated Housing Stock

Building new (affordable) homes is the key ingredient for a better balance between housing supply and demand. Nonetheless, prices and rents are, at least in the short run, more susceptible to changes in the stock for existing homes. One of the core problems of hot property markets is that they reduce the willingness and ability to move house because the additional utility of a new rental or owner-occupied unit most often does not justify its additional costs. Particularly in the rental market this tends to be a problem. While there is little incentive to move from a regulated to an unregulated rental dwelling per se, the growing divergence between the two sectors means that even if a move to a (better) or larger unregulated dwelling was desired, it has become more difficult to realize this wish. The solution to this problem most likely lies in a more balanced housing stock with a sufficient number of dwellings in all segments across the rental and owner-occupied spectrum, and thus will take many years to realize.

Nevertheless, governments could think about short-term solutions to achieve a more optimal allocation of the housing stock. A more penalizing approach would be the introduction of income-based rents and changing permanent into temporary contracts for tenants who, given their household income and household size,

10Similarly, critics of the sole focus on expanding the mid-level segment have proposed the alternative idea of increasing the current deregulation threshold (to €1000 per month). However, this proposal misses that this would further deteriorate the chances of newcomers and market outsiders to find accessible and affordable housing in cities—after all, the largest share of the deregulated housing stock is not only allocated based on household income but also on time accumulated on waiting lists. Moreover, it would, most likely, push investments by private developers further down the road, meaning that they would probably seek to establish a mid-rent segment that would start at much higher rents per month, e.g. €1000–€1200 per month.

severely overconsume social rental housing.11 Since there are legal limitations to implementing such policies, incentivizing moves might be a more effective way forward. Amongst other options, local policy makers could think about setting up funds to cover the transaction costs of house moves or by giving rent credits to tenants and discounts on property taxes to owner-occupiers if they move to a more suited dwelling. In contrast, overcrowding as a coping strategy to avoid unaffordable rental costs could be averted by extending housing allowances to the deregulated sector.12 This would mean that households who move to larger and often more expensive units would not lose their full subsidy automatically.

#### IV. Better Integration of Housing and Infrastructure Policies

In the current housing landscape in the Netherlands lower- and lower-to-middle income households are heavily concentrated in inner-city locations.13 While there is an ongoing process of a displacement of poorer households to less desirable outercity locations—in a recent study by Hochstenbach and Musterd (2018) this was dubbed as the suburbanization of poverty—there is still a case to be made for a more equal spread of different income classes across city regions.

On the one hand, this would require the production of new housing being set at the regional level. However, to guarantee that local planners do not seek to implement policies which would welcome higher-income but fend off lower-income households, oversight and coordination should potentially be given to national authorities. On the other hand, a regional approach would require better coordination and integration of housing and public infrastructure/transport policies.

In the Netherlands, commuting distances are strongly influenced by income and educational level (CBS 2016), meaning that the higher socio-economic stratum commutes significantly longer distances, while lower-income households tend to stay closer to their work places. If (public) transportation became cheaper, not living in inner-city locations would potentially become more attractive to all income classes. From a policy point of view this would not only include infrastructural projects that would particularly strengthen sustainable public transport projects but could also be achieved through partially replacing housing allowances with mobility allowances.

#### V. Penalize Speculative Buy-to-Let Investments but Incentivize Build-to-Let

Recent evidence shows that there is a relatively straightforward and effective way in limiting the influence of domestic and foreign investors in urban housing markets:

11These would be regulated rental housing units that are accessed through the income- and waitingtime based allocation system.

12Under current law housing allowances can only be obtained for regulated dwellings.

13See Booi et al. (2018) for the case of Amsterdam.

a substantial increase of stamp duty taxes on secondary property purchases.14 Currently, the stamp duty tax (overdrachtsbelasting in Dutch) in the Netherlands stands at 2% of the purchasing price for all types of buyers. One possible avenue would be to increase it to the pre-crisis level of six per cent for the purchase of a second home and to-let dwellings. The rationale behind this measure is that it would make investments of speculative buy-to-let investors15 and buy-to-leave buyers16 much less attractive and would also tilt the level playing field back towards younger first-time buyers. However, long-term private investors who turn owner-occupied units into affordable rental units may be exempted from this rule. As it is often the first destination of younger newcomers in urban housing markets, affordable private rental dwellings serve a crucial function for spatial and economic mobility. This means that fending off private investors should not be a goal in itself; rather, policy makers should devise a system in which private investors do not solely seek to invest in the higher segment of the free rental market. As argued above this could be achieved through a system of a more comprehensive rent regulation regime that serves the interests of both landlords and tenants, as well as more targeted subsidies for new (affordable) housing.

#### 5 Conclusions

Extreme house price growth and increasing unaffordability in the owner-occupied and private rental sector pose some of the greatest challenges in contemporary urban housing markets. While it has been argued elsewhere that hot property markets will cool off—to put it mildly—once interest rates rise to higher levels again, this contribution has argued that more coordinated policy interventions at both the local and national level are required. The potential problem with not acting quickly and decisively is manifold: It increases the risk of local bubble formations and severe housing market busts, it undermines the stability of the financial system and economic growth, it increases wealth inequality, and it weakens social cohesion within cities and across countries.

We argued that a coherent housing and planning policy approach would entail five key components. A massive increase of housing supply; the production and preservation of affordable housing; higher turnover rates to reach a better allocation

14Indeed, right after such a measure was introduced in London, prices started to fall relatively to the rest of the country (Bloomberg 2018b).

15That is private investors who purchase properties, refurbish and split them into smaller units, only to rent them out for high prices in the deregulated sector.

16That is buyers who do not seek to rent out the dwelling in the first place, but just use it to store their wealth.

of the housing stock; a better integration of housing and mobility/infrastructure policies; and finally, a significant reduction of speculative buy-to-let and buy-toleave transactions.

We end this contribution on one specific qualification: While the policy details described in this contribution are specific to urban housing markets in the Netherlands, we would argue that the broader strokes also apply to hot property markets elsewhere. Recent studies have suggested that housing markets in major cities have become globally connected and thus increasingly function along the same basic premises (Aalbers 2015; IMF 2018). This then does not only suggest that policy solutions might even be sought at the supranational level, but it also means that international policy learning has become more meaningful in making national and local housing policies more robust and effective. This contribution hopes to be a testimony to this claim.

#### References

ts¼1524665374837

CBS. (2018). https://opendata.cbs.nl/statline/#/CBS/nl/dataset/70072ned/table?

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**4. Media characteristics**

Soil is used as media for the recharge in the physical model developed. The media characteristics such as permeability, porosity, grain size, specific gravity, and density are the parameters determined.

The table indicates the.

*Study on the Impact of Artificial Recharge on Treated Domestic Sewage DOI: http://dx.doi.org/10.5772/intechopen.109868*

From the results, it was observed that there is a considerable reduction in the pollutant concentration from inlet to outlet which shows the impact of artificial recharge. Further, it is observed that the pollutant removal efficiency is higher in soil media than in sand media. Hence for the prediction analysis, the properties of soil are considered in the simulation model (COMSOL Multiphysics) [7].


**Acknowledgements**

The author greatly acknowledges President Hiroyuki Okada of Shinko Sangyo, Co. Ltd., Takasaki, Gunma, Japan for his financial support and Dr. Teiji Okayasu who was one of collaborators in Dokkyo Medical University for his excellent computer analyses.

*Cheminformatics and Its Applications*


*4.1. Missions*

We conceived three missions in which robot swarms operate in arenas surrounded by modular RGB blocks: STOP, AGGREGATION, and FORAGING. STOP and AGGREGATION are adaptations we make from the equivalent missions proposed by Hasselmann et al. to study Gianduja [14]. FORAGING is an abstraction of a foraging task, in a *best-of-n* fashion [63]—similar to the experiment of Valentini et al. [64]. The performance of the swarm is evaluated according to a mission-dependent objective function. We selected these missions because we conjecture that, to successfully perform them, the robots need to identify, process and/or transmit color-based information. In all missions, the time available to the robots is *T* = 120 s. The RGB blocks are arranged in walls that display colors on a per-mission basis—each RGB block is 0.25 m length and can display the colors red, green and blue {*R*, *G*, *B*}. In the context of these missions, when we reference to colored walls we imply that the RGB blocks arranged in the wall display the named color—for example, *"the green wall"* stands for a wall in which the RGB blocks composing it display the color green. In the following, we describe the scenario, the objective function, and the role of the colors for each mission. Figure 2 shows the arenas for the three missions.

**Figure 2.** Set-up of the simulated (top) and real arena (bottom) for the missions STOP (left), AGGREGATION (center), and FORAGING (right). The images show an example of the initial position of the robots.