House Prices, Household Debt, and Financial Stability Risks in New Zealand1
House prices and household debt have increased rapidly in New Zealand over the past two decades. This paper traces the evolution of New Zealand house prices and household debt in an international perspective, analyzes long-term fundamental determinants of house prices and assesses macroeconomic risks stemming from the rapid increase in house prices and household debt.
1. Over the past 25 years, house prices have increased strongly in New Zealand. As house prices have increased faster than disposable income, household indebtedness as percent of disposable income has increased from 56 percent in 1990 to 154 percent in 2014 (chart). House prices have risen faster than the OECD average but broadly in line with comparator countries (chart).
2. Rising house prices and household debt have implications for financial stability and macroeconomic risks. In view of the strong price increases, many common measures of housing valuation based on deviation from long-run historical trends, such as price-to-rent and price-to-income ratios, suggest overvaluation of about 20-40 percent (see Figure 1 and OECD, 2015; IMF Housing Watch, 2014). While international comparisons of house prices are fraught with difficulty, the household debt-to-income ratio is a key variable from a financial stability and macroeconomic risk perspective as this reflects the risks borne by households and the possible amplification of house price declines to the macro economy through wealth, investment, bank balance sheet and confidence effects (see Hunt, 2014, 2015 and Debelle, 2004).2
Figure 1.Rapid House Price Growth
Sources: OECD, and IMF Staff calculations.
Household Debt and Interest Rates
Real House Prices vs OECD
3. This paper analyzes long-run trends in house prices and household debt in New Zealand. The key findings are that economic fundamentals such as financial liberalization, lower interest rates, demographics and supply constraints are important factors in the large run up in house prices. Although higher house price and household debt can largely be explained, it still has implications for financial stability. The paper also discusses the macroeconomic risks arising from higher house prices and debt.
What Explains the Rise in House Prices and Household Debt?
4. Financial liberalization and disinflation have impacted equilibrium levels of house price and household debt. Following the structural reform in the 1980s, financial liberalization, lower inflation and interest rates have facilitated easier access to credit. Lower interest rates have also increased the serviceability of higher levels of debt, leading to higher levels of indebtedness and higher house prices relative to incomes. As nominal (and real) interest rates have declined over a sustained period, household debt as a share of disposable income has increased (see chart). Housing demand has also been boosted through lower interest margins of mortgage banks, and increased finance availability. For instance, OECD (2011) finds that 30 percent of the house price increases in OECD countries can be attributed to financial deregulation (e.g. lower down payment requirement is associated with higher homeownership among previously credit constrained households). In the context of Australia, Ellis (2005, 2013) argues that financial deregulation led to greater mortgage market competition and product innovation.
Household Debt and Real Mortgage Rates
Sources: RBNZ and IMF Staff Calculations.
5. House prices and economic fundamentals. An analytical way of looking at the equilibrium level of house prices is to model and estimate the main driving sources of house prices in a period where the transition to lower interest rates was largely complete (e.g. 2000-14). This approach can include both fundamental economic demand and supply factors, and then calculate the gap between the actual house prices and their predicted values from the economic model.
6. Analytical approaches to assess the level of house prices.
Economic fundamentals. Using time-series data, changes in equilibrium real house price changes are modeled as a function of real disposable income, working-age population, equity prices, and the level of short- and long-term interest rates, aiming to capture major demand factors (see Igan and Loungani, 2012, and Appendix 1 for further details). As discussed above, housing and financial markets have changed significantly over this period owing to structural reform in the 1980-90s, making property market developments in the 1970-80s less informative. A more appropriate time period for estimation, arguably, is using data from 2000 onwards, a period where the transition to lower interest rates and financial liberalization has been completed. Using this shorter time frame suggests that house prices are around 18 percent stronger than consistent with these economic fundamentals.
User cost approach. Another method to assess house prices is to apply the concept of user cost of housing. The user cost approach compares the relative costs of owning a home versus renting it by adding up the discounted costs of each alternative over the period for which a house is expected to be owned (see Appendix 2 for details). User costs are affected by a range of factors, including the direct cost of owning a home such as the real interest rate (after tax deductions), operating and maintenance costs, property taxes. The cost of owning a home is also affected by expectations of future house prices, but also significantly by the tax system treatment of housing. In New Zealand, housing is a tax-advantaged asset and owner-occupied housing is exempt from the capital gains tax and the tax treatment of investment in rental property, and particularly from highly geared investment, also imply significant incentives for housing investment (see Spencer 2015, and Selected Issues Paper on Tax Reform). Largely following the calibration in Fox and Tulip (2014), the user cost approach results in overvaluation of around 3 percent. However, the results of the user cost model are very sensitive to changes in the assumption of real mortgage rate variability and expectations of future house price increases.
7. How can one relate household debt to house prices? The link between the real interest rate and the debt/income ratio can be illustrated in a general equilibrium framework (see, for example Walentin, 2013), where lower real rates support a higher debt ratio. One can also extend the user cost model, discussed above, to illustrate the effects of lower real interest rates on household debt (see chart and Appendix 2 for details). Using this approach, a permanent decline in real interest rates of one percent suggests a change in the debt ratio in the range of 10-20 percent although the effects are non-linear (see chart). Thus, in this highly stylized context, the real interest rate assumption has considerable implications for the equilibrium level of indebtedness (see also Ellis, 2005).
Household Debt Service to Disposable Income
Sources: RBNZ and IMF Staff calculations.
8. The trends of lower interest rates, higher house prices and household debt are closely interrelated. However, the fact that higher house prices and household debt can largely be explained does not imply that it is sustainable in the long term. Sustainability would depend on the evolution of variables such as the real interest rate, income growth and expectations of real appreciation.
Population growth and housing supply
9. Supply factors are important in house price dynamics in New Zealand and housing supply has not kept up with demand (see RBNZ, 2011; Spencer, 2015). New Zealand’s population has grown rapidly since 2000, and much faster than the OECD average. However, residential investment has remained stable below 5 percent of GDP for much of the past decade, and below OECD average (see text chart).
10. New Zealand’s population is highly urbanized, with 40 percent in the two largest cities. As city prices are typically higher than rural, countries with high degrees of urbanization tend to have higher house prices on average (see Ellis and Andrews, 2001). Supply of housing tends to be inelastic as geographic conditions, such as limited available land for high density housing and lack of infrastructure can restrict housing supply in certain areas, causing house prices to increase rapidly. This is particularly the case in Auckland, where the population-to- dwelling ratio is higher than the rest of the country. The supply response to higher house prices has also been relatively slow in New Zealand (see OECD, 2011), but there are signs that housing completions have increased recently.
Population Growth, 2000-14
Sources: OECD, Haver Analytics, and World Development Indicators.
11. Reflecting rapid population growth and geographical constraints that limit the supply response, property prices in Auckland have risen sharply. Through December 2015, property prices increased by 22.5 percent in Auckland, compared with 14.2 percent for the country as a whole (Corelogic, 2016). Investor activity has been a key driver of house market activity (see Skilling, 2015) and new investor loans have grown strongly recent years, with low interest rates and strong competition among lenders stimulating investor lending growth, especially in Auckland. Parker (2015) lists planning requirements, fragmented ownership, cost of construction, and infrastructure as factors that have limited the supply response. Net migration and foreign demand for housing in Auckland is also a factor that may have supported house price increases and rising price-to-income ratios (see Spencer 2015).
New Zealand Population is Highly Urbanized
Source: UN Demographic Yearbook.
1/ Measured as urban agglomeration.
12. A slow supply response to rising demand in some areas mitigates house price overvaluation concerns, but does not rule out large adjustments. While supply constraints do suggest that equilibrium property prices have risen, they do not rule out that demand is excessive, nor that it could fall sharply. House prices in New Zealand have varied by more than can be explained by the relatively stable deviation between population and housing supply. The UK, for example, had little supply response in the housing boom of the 2000s, but still saw a 20 percent fall.
Sources: Statistics New Zealand and RBNZ.
Sources of Private Sector Credit
Banking Sector: Assets
Can the banking system withstand a housing downturn?
13. The banking system is concentrated, with housing loans as the largest asset. Private sector credit is primarily intermediated via the banking system, especially the big-4 banks. The largest four banks provide 87 percent of the total banking sector credit. Housing credit constitute nearly half of total assets.
14. Asset quality
Asset quality remains strong. Non-performing housing loans have been historically, and remain, low (chart). Mortgage loans are full-recourse, which implies that the mortgage holder is legally responsible for the loan amount regardless of default or repossession of the property by the lender.
High LTV ratios have declined. The average loan-to-value ratio stands at about 55 percent and it has been declining from 60 percent of the past decade. With the introduction of LTV measures in 2013, the proportion of high LTV lending has declined (Figure 2). Dunstan and Skilling (2015a) discuss financial stability risks related to commercial property in New Zealand and find that risks have declined since the GFC owing to less leverage.
Figure 2.Financial Sector and Household Balance Sheets
Sectoral Non-Performing Loans
Households have built up substantial wealth with net financial wealth amounting to 300 percent of disposable income, although a large fraction of the wealth is concentrated in housing.
15. Funding. Although the core funding ratio has increased since the GFC, the banking system remains dependent on (offshore) wholesale funding. Given the prevalence of floating rate loans (see Figure 2), higher wholesale funding costs would be passed on to mortgage holders (see discussion of implications in stress tests below).
16. There is no sign of a generalized credit boom and estimates of credit gaps are small. Using financial gap estimates from the Borio et al. (2013) methodology, yield small gaps, although in some specifications, house prices appear to have an impact on the output gap measures – (see chart). However, some specific areas of concern have emerged as investor credit has picked up sharply lately, largely focused in Auckland (see Skilling, 2015b; Spencer 2015).
Core Funding Ratio
House Prices Appear to Impact the Output Gap
Source: IMF staff estimates.
1/ Following Borio et. al. 2013.
2/ Demeaned (avg 1995-2015), inverted right scale.
17. In collaboration with the Australian Prudential Regulatoion Authority, RBNZ conducted a severe stress test of the New Zealand banking system, focused on housing and higher interest rates (see RBNZ, 2014).
House price bust (Scenario A). A housing market decline, prompted by a sharp slowdown in China, where New Zealand GDP growth declines to -4 percent, unemployment increases to over 13 percent and house prices fall by a cumulative 40 percent, with a marked fall in Auckland.
Higher interest rates (Scenario B). In the face of strong growth and emerging inflation, the RBNZ raises the cash rate significantly. Global growth subsequently weakens and a sharp drop in commodity prices leads to increased uncertainty and volatility in financial markets. This leads to higher unemployment and higher borrowing in New Zealand and a significant fall in house prices. Rising global bank funding costs increase lending rates by a further 200 basis points, resulting in floating mortgage rates peaking at 11-12 percent.
18. The banking sector would remain solvent, but unlikely to function well. In each scenario banks face an increase in funding costs, decline in credit quality and credit losses, with a significant adverse impact on profitability and declines in capital ratios. Losses on residential mortgages accounted for around one-third of total credit losses. These aggregate losses contributed to a material decline in the capital ratio of the banking system. While all banks remained above the minimum CET1 capital requirement, almost all banks would use capital conservation buffers and face constraints on dividend and bonus payouts. In such circumstances banks would face funding constraints and likely curtail lending. This would likely exacerbate an already extremely difficult macroeconomic situation.
19. Even abstracting from the impact on banks, a sharp fall in house prices would likely have major macroeconomic effects. This would operate through many channels (see Hunt, 2015 and Debelle, 2004). For example:
Wealth effects: households would cut consumption as their housing wealth falls. Smith (2007) finds wealth effects in New Zealand primarily among older households. Dvornak and Kohler (2003) find wealth effects of around 3 cents on the dollar in Australia.3
Investment: Investment and employment in housing would decline. Although average dwelling investment has been range bound, between, 4-5 percent of GDP over the past decades, a slowdown would be expected to have an adverse impact economic activity.
Following Igan and Loungani (2012), real house price changes are modeled as a function of changes in affordability, real disposable income per capita, working-age population, equity prices, and the level of short- and long-term interest rates. The following quarterly regression is estimated for the period 2001-2014:
where AHPI is the change in real house prices over the last quarter (capital cities), A is affordability level of housing in the previous period, measured by (the log of) the ratio of house prices to income per capita; ΔYPC is the change in real income per capita over the last quarter; A WAP is the change in working-age population over the past year; Asp is the change in stock prices over the year before last and
The regression equation is estimated using ordinary least squares (OLS).
|Source||SS||df MS||Number of||60|
|Model||0.018762||6.003126999||Prob > F||0|
|hpi||Coef.||Std. Err. P>t||[95% Conf. Interval]|
The explanatory variables generally have the expected sign and are statistically significant. Affordability is negatively related to the change in prices and change in income per capita enters the equation with a positive sign. There is also a positive and significant relation between house price changes, equity prices, construction costs, and population growth. On the interest rates, there is a positive coefficient on short term interest rates and a negative sign on long-term interest rates. A positive relationship may emerge if higher short-term term rates signals an improved economic outlook which may stimulate housing markets.
To arrive at an estimate of overvaluation, it is assumed that house prices were in equilibrium in 2000 (after the transition to lower inflation and interest rates) the house price index is set to 100. Using the predicted house price changes from the regression analysis, index values are computed from that date onward. To assess whether house prices are in line with the economic fundamentals of the model, the actual index value is compared to the predicted one and the difference between the two values labeled as the estimated price gap.
User cost equilibrium in the housing market occurs when the expected cost of owning a house equals that of renting. In this context, overvaluation is defined by the actual price being greater than that calculated with the user cost. In equilibrium (using the definitions in Fox and Tulip, 2014):
where P* is the “fundamental” value of housing; r is the real interest rate; c is running costs such as repairs and insurance as proportion of price; s is transactions costs averaged over the period of ownership as proportion of price and π is the expectation of real appreciation on a constant quality basis.
|M||Rental payments as a share of income||0.2||Source:||Fox and Tulip (2014)|
|LTV||Average loan-to-value ratio||0.56||Source:||RBNZ|
|c||Running costs such as repares insurance as a percentage of price||0.015||Source:||Fox and Tulip (2014)|
|d||Depreciation||0.011||Source:||Fox and Tulip (2014)|
|s||Average transaction costs||0.007||Source:||Fox and Tulip (2014)|
|change in constant quality prices||0.017||Source:||Fox and Tulip (2014)|
|r||Real mortgage rate||[0.02-0.14]|
Following the approach in Alsterlind et al (2014), one can link the user cost model to household debt,
Assuming households aim to maintain a constant loan to value ratio,
With the user cost model, one can obtain a link between user cost and the long-term debt ratio:
The chart in Figure 3 is computed with the assumption that renters spend about 20 percent of their income on housing, and an average loan-to-value ratio of 56 percent.
AlsterlindJ. and U.HolmbergK.JonssonB.LagerwallJ.Winstrand (2014) “Memorandum 6 – Risks to the Macroeconomy and financial stability arising from the households’ debts and housing prices”,MimeoSveriges Riksbank.
BorioC.et al (2013) “Rethinking potential output: embedding information about the financial cycle” BIS Working Paper No 404.
CalderaSanchez and Johansson (2011) “The Price Responsiveness of Housing Supply in OECD Countries” OECD Economics Department Working Papers No. 837.
Corelogic (2016) “December 2015 Property Value Map” http://www.corelogic.co.nz/.
DebelleG. (2004) ‘Macroeconomic Implications of Rising Household Debt’BIS Working Papers No 153.
DunstanA. and HSkilling (2015a) “Commercial Property and Financial Stability” RBNZ Bulletin March.
DunstanA. and HSkilling (2015b) ‘Were mortgage borrowers taking on more risks prior to the introduction of the LVR speed limit?’Reserve Bank of New Zealand Analytical Notes AN2015/02.
DvornakN. and M.Kohler (2003) “Housing Wealth, Stock Market Wealth and Consumption: A Panel Analysis for Australia” RBA RDP 2003-07.
EllisL. (2005) ‘Disinflation and the Dynamics of Mortgage Debt’in Investigating the Relationship between the Financial and Real Economy BIS Papers No 22
EllisL. (2013) Housing and Mortgage Markets: The Long Run, the Short Run and Uncertainty in Between; RBA Address to Citibank Property Conference (4/23/2013).
EllisL. and DAndrews (2001) ‘City Sizes, Housing Costs, and Wealth’RBA Research Discussion Paper No 2001-08.
HuntC (2014) “Household Debt: A Cross-Country Perspective” RBNZ Bulletin October.
HuntC (2015) “Economic Implications of High and Rising Household Debt” RBNZ Bulletin March.
Igan and Loungani (2012) “Global Housing Cycles,” IMF Working Paper12/217.
OECD (2011) “Housing and the Economy, Policies for Renovation,” Chapter 4 in Economic Policy Reforms 2011: Going for Growth.
OECD (2015) Focus on Housinghttp://www.oecd.org/eco/outlook/focusonhouseprices.htm
ParkerC. (2015) “Housing Supply, Choice and Affordability” Auckland Council.
RBNZ (2011) “Submission to the Productivity Commission inquiry on Housing Affordability” Reserve Bank of New ZealandAugust2011
RBNZ (2014) “Stress Test of the New Zealand Banking System” FSRNovember.
SkillingH. (2015) “A Deeper Look at Recent Housing Market Trends: Insights from Unit-Record Data”,Reserve Bank of New Zealand AN2015/06.
SmithM (2007) “Microeconomic analysis of household expenditures and their relationship with house prices” Reserve Bank of New Zealand: Bulletin Vol. 70 No. 4December2007
SpencerG. (2015) “Action needed to reduce housing imbalances” Speech delivered to the Chamber of Commerce in Rotorua March.
WalentinK. (2013) “Housing Collateral and the Monetary Transmission Mechanism” Scandinavian Journal of Economics.
Prepared by Dan Nyberg.
Using differences from averages across countries to derive estimates of over- or under-valuation can be problematic. One reason is that it assumes that the starting period was an equilibrium. Moreover, comparisons of price-to-income ratios are difficult owing to different national definitions of housing coverage and household disposable income.
Windsor et. al (2013) find that the removal of credit constraints (consumption rises with home prices due to households' ability to borrow more, given more valuable collateral), and the related buffer-stock savings argument (higher home prices act as a form of precautionary savings for low-saving households, allowing them to increase spending) are key channels through which house prices affect spending
Data sources are described in Igan and Loungani (2012).