2 Impact of the factors of supply

Source: Lees (2017).

A more competitive land market will increase competition between locations across a city and across different land uses. The decrease in the market power of landowners decreases land values. To achieve this, it is required that local governments promote development permission for both brownfield and greenfield developments, particularly in areas of high demand with better access to jobs. The results of a recent study by the Chief Economist Unit of Auckland Council confirm that more up-zoning (i.e. allowing greater housing density) is required to accommodate for the demand in areas closer to the city centre (Norman et al., 2021). The study does not account for the costs of supply in different locations.

In a comprehensive study of the costs and benefits of the NPS-UD, PwC (2020) estimated direct and indirect impacts of intensification, minimum car parking requirements, and local government’s strategic planning requirements. Their results suggest that the benefit to cost ratio (BCR) of achieving higher responsive housing supply (to price changes) across New Zealand cities is between four and seven.²⁸

MRCagney et al. (2016) provides a comprehensive cost benefit analysis for policy options for the NPS-UDC. They investigated the impact of planning constraints including general zoning restrictions, MUL, building height limits, minimum parking requirements, apartment etc. Their results suggest that a less restrictive approach to urban planning that enabled sufficient supply to housing would reduce the rate of house price inflation by 50 percent with net benefits of $1.4 billion to $10.7 billion over the 2001-2013 period.

Source: Cooper & Namit (2021).

Grimes & Aitken (2006) studied determinants of new housing supply and the impact of supply responsiveness on price dynamics. Their results show that a 1 percent increase in house prices relative to total development costs raises new house supply with an elasticity of between 0.5 and 1.1 percent. Since the study has controlled for land prices, the estimated low response of the housing supply (to price increases) is likely driven by regulatory restrictions in the housing market. The authors also note that land prices have a strong impact on new house construction. A 1 percent increase in land prices is estimated to lift total development costs by 0.33 percent, which in turn leads to a 0.37 percent decrease in supply of houses. The data used is a quarterly dataset of median house prices for New Zealand over the period of 1991Q1 to 2004Q2 covering 73 Territorial Local Authorities from QVNZ. Given the lack of granularity of their data, the study does not provide more information about potential variations across location.

Greenaway-McGrevy (2018) assessed the impact of land use regulation on house prices in the Auckland region. They used a difference-in-difference (DiD) method and a unit level dataset of house sales for their assessment. The authors compared house price sales for property pre and post the Auckland Unitary Plan to test price effects of up-zoning (i.e., relaxing a restriction on site development). Their results suggest that up-zoning generated a significant increase in prices for underdeveloped properties relative to highly developed properties and properties that were not up-zoned; up-zoning in the most intensive residential zone generated a premium of 22.2 percent.

Parker (2015) uses the results of Bertaud, (2014) to show that Auckland’s population density is significantly higher in the CBD and almost flat in other areas. He discusses that the impacts of planning constraints are reflected by the non-continuous rate of population density in Auckland compared to other metropolitan areas (for illustration of the non-continuous density in Auckland see Figure 22). He argues that planning constraints that cause the need to build on progressively, more difficult sites lead to lower construction productivity. Additionally, planning constraints cause difficulties in buying large land areas for efficient scale development. Changes in population/dwelling density over time could provide more information about potential impact of policies. We will discuss this further in the next section, to provide an understanding of the potential impact of the RMA.

Grimes & Mitchell (2015) conducted interviews with 16 developers, providing information on 21 developments across Auckland to assess the costs of the rules and regulations (as perceived by developers).³⁰ Their results suggest that building height limits and balcony requirements can each have costs impacts of over $30,000 per apartment. The council’s desired mix of typologies and increased minimum floor to ceiling heights can each add over $10,000 per apartment. For the residential section and standalone dwellings, infrastructure contributes not related to a specific development cost at around $15,000. These includes costs such as extended consent process, section size requirements, and other urban design considerations. They also assessed the potential loss in development capacity³¹ from council’s rules and regulations. Their results suggest a median loss in capacity of 22 percent (for the developments that proceeded).

Norman et al. (2021) used GIS mapping to illustrate that a higher land value correlates with high density housing areas. Assuming demand is highest at the city centre, they show that the Auckland Council’s zoning is not consistent with where demand is the highest.

Cavalleri, et al. (2019) use a stock-flow type model of supply and demand using panel data from 25-countries, between 1980Q1 and 2017Q4. They used an index as a proxy for land-use restrictiveness.³² Their findings suggest that: regulations that restrict housing tend to result in more vacant houses; regulations exacerbate mismatches between supply and demand; rent controls reduce the responsiveness of housing supply to demand pressures (though the effect is small); and that limits to urban expansion (geographic and regulatory) reduce incentives for new construction. > Mayer & Somerville, (2000) used quarterly data from a panel of 44 U.S. metropolitan areas between 1985 and 1996 to study the impact of house prices and costs on new housing construction. Their results suggest that metropolitan areas with more extensive regulations can have up to 45 percent fewer housing starts and price elasticities 20 percent lower than those in less-regulated markets. Also, their results suggest that a 1 percent increase in house prices temporarily increases new construction by 15 percent over current and following 5-quarters. The results of their modelling suggest that the regulations that lengthen the development process have an uneven temporal effect on supply elasticity.

Source: Parker (2021). The horizontal axis shows distance from the city centre and the vertical axis is the price of land.

The prominent study of Grimes & Liang (2009) on the impact of the MUL on land prices has been cited widely as an indicator cost of regulation. We consider MUL regulation as an environmental regulation as its successor, the Rural Urban Boundary (RUB) under the Auckland Unitary Plan is denoted as a district plan land use rule pursuant to section 9(3) of the RMA. The authors study the difference in land prices between parcels located inside and outside of the urban boundary. Their results suggest that land inside the boundary is significantly more expensive than the land outside the boundary by a factor of between 7.9 and 13.2. As the authors noted, they do not have any information about the value of infrastructure that has been potentially capitalised into the land values inside the MUL.³⁴ Also, the timeframe of the available data does not provide them any information about land prices before and after any MUL expansion. This study provides valuable information about the price increases associated with limitations imposed by MUL regulation.

Martin & Norman (2020) study the price difference between the land inside the RUB and the farm land outside the boundary.³⁵ Consistent with the results of the previous studies, their study suggest the existence of a price premium for the land inside the urban boundary. However, their results suggest that converting farmland or lifestyle blocks into bulk-infrastructured residential sections would be unlikely to deliver land to the market substantially cheaper. Our review of the methodology of their study suggests that some of their assumptions have a significant impact on the results. For example, they refer to plans that suggest between 55 and 58 percent of land outside the urban boundary is unavailable for development, but they assume that only 35 percent of land outside the urban boundary is unavailable. This assumption is not justified and likely affects their results significantly. Our review of their methodology suggests that they have not used the location of the urban fringe in their analysis.

Zheng (2013) studied the impact of the MUL on land price. Consistent with earlier studies (for example, Grimes & Liang (2009)), his results suggest that the Auckland metropolitan urban limit results in upward pressure on residential land prices within the urban areas. His results show that the impact is uneven with a larger impact on land at the lower end of the price distribution. Also, when the supply of land on the urban periphery is restricted, the price of available residential land rises and new builds tend to be larger and more expensive houses.³⁶

For the review of the impacts of the RMA and other regulations, it is important to differentiate between the impact of the regulation and the costs imposed from implementation of the regulations. The RMA categorises the activities that may exceed the limitations introduced by discrete plans to six categories, namely permitted, controlled, restricted discretionary, discretionary, non-complying and prohibited. The ‘activity category’ is a policy setting that determines the degree to which each element of a plan binds. The permission required for undertaking the activities that may affect the environment is called a ‘resource consent’ or ‘planning permission’. There is a chance of a regulatory impact on the probability of granting a resource consent. Torshizian (2015) defined ‘permissibility’ as the effective permission level granted by an activity status category. He investigated the permissibility of Auckland Council’s activity statuses and its impact on regional development. His results indicate that, once the characteristics of the activities are taken into account, a difference remains between the likelihood of different activity categories, and that interpreted as the bias associated with RMA regulation. Accordingly, the activity category ‘restricted discretionary’ (which restricts the scope of consideration to planners) is approximately 20 percent less permissive than ‘discretionary’, which is meant to be a more involved affair. His results suggest that the change in permissibility of the restricted discretionary activity has happened after the amalgamation in 2010.³⁷ Also, after the amalgamation in 2010, the average likelihood of all activity statuses decreased by a factor of between -1.3 and -2.7 percent.³⁸

Bassett et al. (2013) reviewed New Zealand’s housing affordability problem and the development of housing in New Zealand since the early 1900s. They provide a comprehensive review of the role of central and local governments in planning regulations over time. They argue that the lengthy and costly process of releasing land outside the Metropolitan Urban Limit (MUL) in Hobsonville, Flat Bush, Papakura, Karaka and Silverdale, between 1989 and 2010 was very costly (both in terms of expensive hearings and the social and economic costs of slow regional development).

Fernandez et al. (2020) studied the impact of proximity to Wetlands on residential property prices in Auckland. Their results suggest that proximity to natural wetlands is associated with lower house prices, but the interaction of artificial wetlands with parks is associated with higher house prices. They mention the importance of school zones on house prices, but do not account for that in their estimations. Their study does not account for proximity to other amenities and facilities and does not investigate causal relationships.

Cooper & Namit (2021) investigated the impact of Auckland’s viewshaft policies on house prices. Accordingly, the CBD Viewshaft covers 1.7 million square meters of land. Their results suggest that land values per square meter within 75 meters of the viewshaft are $1,282 higher than those located farther away (between 75 and 175 meters from the viewshaft boundary). They conclude that the net cost from the viewshaft policy to the Auckland economy is $1.4 billion (per year). The study uses a robust regression discontinuity method and accounts for the difficult to quantify benefits of viewshafts.

In an international study, Kallergis et al. (2018) investigates housing affordability across 200 cities.³⁹ Their results suggest that a 10 percent increase in urban extent density leads to an 0.8 percent increase in price-to-income ratio. In the cities with enforced containment⁴⁰ the price-to-income ratio is 1.6 percent higher than average. The advantage of this study is the large sample of the cities that they have included in their analysis. However, the analysis controls for a very few related variables to urban limit regulation and the results may not be interpreted as causal effects.

Source: Grimes & Liang (2009); MBIE & MfE (2017); Zheng (2013); Productivity Commission (2012); Martin & Norman (2020).

The studies reviewed above have discussed the impact of regulation on house prices. The literature on the impact of regulation on house price growth is limited. Torshizian (2018) estimated the impact of MUL expansion on house price growth in the Auckland region. This is the only study that captures the impact on prices of houses located in different proximities to the expanded area, before and after the expansion. The study captures the impact for the suburbs located nearby the expanded area and compares that with the suburbs with similar opportunity to expand. For benchmarking, the study uses the price growth of houses compared to the houses with similar price range located in the central area (which are not directly affected by an urban expansion). Results suggest that:

• the price growth in expanded areas is similar to the central areas,

• the price growth in areas nearby the expanded area is similar to the other areas with similar features, and

• the price growth outside the urban boundary is significantly higher for areas located closer to the expanded area.

The author concludes that the reason for high price growth in the areas located outside the boundary and nearby the expanded area, is their expectation of future growth in their area.⁴¹

This expands the findings of the other studies by providing evidence for the lack of competition in the land market being a driver of house price growth. Accordingly, the lack of competition resulting from the MUL is associated with an average of 13 percent higher house price growth. This is equal to an average of 0.31 percent additional cost to the land values, which is equal to $4,730 (in 2021-dollar values).

Geographic constraints have not been the driver of HPG High agreement, Medium evidence, Medium certainty

As described, many studies of the regulation impacts do not directly account for the impact of geographic constraints. This is due to measurement issues. Given the robust economic assessment framework that these studies used and the high agreement across the studies, we conclude that the impact of geographic constraints on the findings of the studies of costs of regulation is insignificant.

Saiz (2010) investigated the impact of geographic constraints on urban development using GIS derived data (coastal areas and land steepness) for metropolitan centres in the U.S. over the period of 1970 – 2000. The findings show geographically constrained areas tended to be more expensive, with faster price growth. Furthermore, antigrowth local land policies are more likely occur in growing land-constrained areas.

Nunns (2019) provides an estimate of the likely costs imposed by geographic constraints. He defined geographic constraints as a lack of flat developable land.⁴² His results suggest that a 1 percent increase in geographic restrictions is associated with $39 per sqm higher land prices.

Source: Principal Economics.

The pattern of dwelling density in Auckland before and after the RMA effects come to existence in 1991 are shown on the left-hand side of Figure 22. As illustrated, the pattern has changed dramatically over time.⁴⁵ This is consistent with the changes in pattern of land values illustrated on the right-hand side of Figure 22. The comparisons between the two graphs shows the high correlation between patterns of dwelling density and land prices over time.⁴⁶

As shown in Figure 12, there has been many legislative changes over the 1980-2020 period that may have been associated with this change in the distribution of houses and prices. For example, the RMA and the local government reforms in 1989 led to widespread changes across New Zealand local governments. The 1989 reforms led to a decrease in the number of local governments by 90 percent (from 828 to 86 in 1989). The RMA played a complementary role to the 1989 local government reforms. There is a more careful assessment of the impact of the two reforms on providing enabling and effective development outcomes required.

Source: Principal Economics analysis (left hand side); Grimes & Liang, 2009 (right hand side).

The housing impacts of RM system and NPS-UD are complementary. The outcomes from NPS-UD, which is based on the RMA. This is shown in Table 4.

Source: Principal Economics based on Cabinet papers (MfE, 2021).

The RMA reduces the responsiveness of housing supply high agreement, medium evidence, medium certainty

A successful RM system provides:

• a regulatory framework that leads to higher certainty around zoning regulations.

• a more flexible housing supply through providing a more permissive regulation and allowing more flexibility in housing supply.

Given the complementary roles of the RM system and the NPS-UD, Resource Economics et al (2021) discuss that the total impact of the RM system and NPS-UD is to achieve a responsive housing supply. To distinguish between the impact of the RM system (and the reforms) and that of the NPS-UD, we can use estimates of the impacts of an improvement in the resource consent process and its impact on land values. Any impact on land values in addition to the estimated impacts of resource consent is likely driven by the NPS-UD.

The RM system (and the reform) provide a national level direction (or centralisation). This leads to a decrease in the chance of negative externalities from one region’s urban growth on other regions;⁴⁷ and an increase in certainties around permitted and prohibited activities (with less room for discretion). Also, one of the objectives the reforms is to decrease the number of Acts to resolve any potential inconsistencies across different pieces of legislation leading to a higher certainty/transparency level.

Torshizian (2015) estimated the likelihood of granting a consent to different activity statuses after accounting for the features of the applications. He argues that the likelihood of granting a consent should be similar across all activity statuses after accounting for the features of the application. After accounting for the likelihood of not applying for a consent due to its high processing cost (as a combination of time required for processing a consent and the associated fees), his results suggested that the activity category ‘restricted discretionary’ (which restricts the scope of consideration to planners) is approximately 20 percent less permissive than ‘discretionary’, which is meant to be a more involved affair. This figure provides an estimate of the impact of improved certainty on the likelihood of granting a consent.⁴⁸

The results of that study suggest that a 1 percent increase in the processing delays is associated with 1.4 percent lower likelihood of granting a consent. To capture the affordability impacts of lower consenting likelihood, we use the results of Nunns (2021), which suggests that a 1 percent higher likelihood of consent processing delays is associated with $24 per sqm increased costs (in term of land price distortions). Using these multipliers suggest that a 1 percent lower likelihood of granting a consent is associated with $17.1 per sqm higher prices. Figure 23 shows the relationship between the number of processing days of a resource consent application and the likelihood of granting a consent. Accordingly, the chance of granting a consent decreases significantly as the processing time increases.

Figure 23 Impact of processing delays on the likelihood of granting a resource consent

Source: Torshizian (2015); Nunns (2021); NZIER (2014); Grimes (2015); Principal Economics analysis.

MRCagney et al. (2016) referred to the complementary role of planning regulations and infrastructure planning, funding and provision. They emphasised on the importance of improved coordination between planning regulation and infrastructure planning, which are governed by separate legislation, to provide the infrastructure in the right place and at the right time. This has been recognised as an objective of the RM reform.

In addition to their discussion of the impact of urban growth boundaries and other regulations that we reviewed in the previous sections, also discussed development levies. They noted that the RMA sets strict rules about the relationship between development levies collected and how they were spent, but councils have sought the widest interpretation of the rules. They do not provide data but refer to some examples in Auckland. A successful RM reform will provide transparency and minimises the chance of any misinterpretation.

% change, Real PPI – Heavy and civil engineering construction

Source: Bank for International Settlements, Statistics NZ, Principal Economics 2021 analysis.

Note: We source s.a. real property price index from The Bank of International Settlements, PPI for heavy and civil engineering construction from Statistics NZ and adjust for inflation using the CPI from Statistics NZ. We then calculate the year-on-year annual change for each series. We highlight notable recessionary periods in New Zealand identified by Reddell et al., (2008). These include the First Oil Price Shock (1974 – 1977), The Second Oil Price Shock (1979 – 1982), The 91 – 92 Recession (attributable to the 1987 share market crash, subsequent monetary responses and impacts of the first Gulf War - 1991 – 1992), The Asian Crisis and drought (1997 – 1999), we add COVID-19 as an additional recessionary period (2020-).

Source: Statistics New Zealand, Bank for International Settlements, Principal Economics 2021 analysis.

_{Note: We source s.a. real property price index from The Bank of International Settlements, PPI for residential building construction from Statistics NZ and adjust for inflation using the CPI from Statistics NZ. We then calculate the year-on-year annual change for each series. We highlight notable recessionary periods in New Zealand identified by (Reddell et al., 2008). These include the First Oil Price Shock (1974 – 1977), The Second Oil Price Shock (1979 – 1982), The 91 – 92 Recession, (attributable to the 1987 share market crash, subsequent monetary responses and impacts of the first Gulf War - 1991 – 1992), The Asian Crisis and drought (1997 – 1999), we add COVID-19 as an additional recessionary period (2020-).}