Quantifying the costs of land use regulation: evidence from New Zealand

ABSTRACT

Land use regulations vary in the restrictions and enforcement that applies across time and space. That variation makes it difficult to determine when land use regulations hinder the flexibility of housing supply using a single time series method, so a range of approaches and country case studies may be most appropriate to test impacts. We use four methods to test for impacts of land use regulation in New Zealand utilising unit record data on house sales. We find: (i) house prices outstrip construction prices in many New Zealand cities; (ii) the extensive price of land is typically 5–6 times the intensive price of land; (iii) density and house prices are only weakly correlated; (iv) apartment and townhouses outstrip construction prices. All four results suggest land use regulations play a material role in constraining housing supply, driving up house prices.

KEYWORDS:

• Housing supply
• house prices
• land prices
• land use regulation

SUBJECT CLASSIFICATION CODES:

• R31
• R52

Notes

1. See New Zealand Productivity Commission (2012).

2. These restrictions may have been too weak initially to have much impact. The investor focussed limits were arguably more effective.

3. We refer to costly land use regulation in this paper, not because all land use regulation is costly but because we focus on regulation that could drive prices higher than they would otherwise need to. We do not examine potential benefits of land use regulation.

4. Gyourko et al. (2008) undertake a comprehensive study for the U.S. to build an index of regulation over time from detailed survey information from 2000 local authorities. But without recourse to such an index that provides time series information researchers have little information that might be used to inform the impact of land use regulation over time.

5. Here we are not particularly interested in the political economy of how land use regulation which impacts on prices might develop. Fischel (2015) provides useful context on this issue.

6. Glaeser and Gyourko (2018) argue that rather than comparing prices to income, comparing prices to these costs is the right gauge of whether house prices are too expensive – for all residents, not just families on low incomes.

7. The New Zealand Building Economist is available as a subscription service at: http://www.becon.co.nz/

8. One other method that could be used to compare the sale price of a leasehold property with that of a neighbouring freehold property. Leasehold properties sell for much less than freehold properties – a result consistent with house prices (of a freehold property) in Auckland largely comprising the land value.

9. This pedagogical example is highly stylised. The profit motive ensures these kinds of properties do not stick around over the long run (see Kendall & Tulip, 2018 for discussion). Implicitly we assume here an infinite supply of homogenous land (in the absence of land use regulation), when land is in fact heterogenous. This is important since Guthrie (2010) shows a development premium can exist even with an infinite supply of land and no land use regulations. Greenaway-McGrevy, Pacheco, and Sorensen (2018) also find an increase in the redevelopment premium after the Auckland Unitary Plan was announced. So in practice, some premium is likely to be present even in a laissez faire world absent of regulations on properties that are under developed.

10. We also experimented with a more restrictive control on houses of leaving out observations with a total floor area of more than 600 square metres. For Auckland, less than 0.07% of the observations lie within this range and in practice we find very similar results.

11. We need to transform our estimate of the land elasticity p’ into a price of land using the ratio of the mean home price to mean land area – the method in Glaeser and Gyourko (2003).

12. Glaeser and Gyourko (2003) work with densities in level terms. Alternatively, densities could be presented in changes over time and regressed against changes in house prices. Councils may also wish to monitor changes in densities over time to better reflect changes in market conditions.

13. The QV Cost Builder is a tool for providing construction cost estimates on subscription – available at: https://qvcostbuilder.co.nz/

14. Earlier unpublished work by Luen (2014) obtained construction costs for apartments from Levett Bucknall in May 2014. Rather than adopt these data as our benchmark, we use the difference in construction costs by floor as a robustness check on our core results that compare prices to construction costs.

15. See Beacon (2015) who estimate these costs as 13.7% of the cost of a new affordable home based on a sample of 69 new builds across Glen Innes, Avondale, Papatoetoe, Sunnyvale, New Lynn, Hobsonville, Mt Wellington, Papakura, Weymouth and West Auckland.

16. Glaeser and Gyourko (2005) argue that the durability of housing drives much of the population demographics in the U.S. where people remain in less productive regions where prices are below construction costs since housing depreciates only slowly.

17. Since we are primarily interested in cross-sectional analysis and do not estimate the hedonic regressions over an extended period, we do not include property price trends. Instead, trends in property prices are subsumed into period fixed effects. Future work could test whether fit is improved by incorporating trends rather than period fixed effects over longer time periods.

18. Since our data are spatial it is likely the errors contain spatial dependence. We could either model any spatial dependence or use robust standard errors to deal with any spatial dependence (see Conley, 1999 for example). Given that we are not doing statistical inference on the parameters that generate Figure 4, this is not critical to our results.

19. Saiz (2010) documents the role of geography on land prices for US cities.

20. In our context, the extensive margin might be considered the value of a vacant lot including the costs associated with development, that is, a ‘permitted vacant lot’.

21. We calculate the price-cost ratio for each sale within each area unit and present the mean price-cost ratio for each area unit.

22. As an alternative, future work might consider regressions of the change in density against the change in price.

23. We also considered a subsample restricted to value that lie between −0.005 and 0.005. We obtained very similar estimates – for example the coefficient on the fraction of suburbs greater than 140% is −0.00034 for Auckland but not quite significant at the 1% level.

24. We also use an F-test to see if the density relationship is similar across each of our territory authorities, allowing for the intercept terms to vary but enforcing the slope coefficients to be identical across the territory authorities. For each of the four regressions we reject the idea that the density–price relationship is the same – cities respond differently to demand pressures.

25. Since some cities contain many townhouses and small apartments not affected by this adjustment, the difference from the baseline estimate to the conservative case is not uniform across the cities we study.

Additional information

Funding

This work was supported by Superu under the Ministerial Social Sector Research Fund.