Tuesday, 7 August 2018

The Beeching Axe: the consequences of massive rail disinvestment

By Steve Gibbons, Stephan Heblich and Ted Pinchbeck


Did pulling up nearly half of Britain’s railways in the 1950s, 60s and 70s affect where people chose to live? Did it bring about the demise of rural communities and the ascendance of metropolitan areas? While some argue that it did, an alternative view is that these changes in population patterns were already occurring and that the cuts to rail lines and stations were simply an inevitable response to unprofitability in the face of changing patterns of demand. Our new research (Gibbons, Heblich and Pinchbeck, 2018, Centre for Economic Performance Discussion Paper 1563), on the consequences of the removal of the railways –the ‘Beeching Axe’, as the cuts in the 1960s came to be known – provides some answers to this long standing question.

The headline answer is that, populations in places losing their rail access declined relative to those where rail access was retained. While it is true that rail lines and stations were preserved in places where population was already growing before the 1950s, it is the loss of rail access, rather than any pre-existing population trends that explain subsequent population patterns in affected areas. These patterns had emerged by the 1980s and have persisted. If the cuts hadn’t happened, populations would have been considerably more evenly distributed across Britain at the end of the 20th Century. The London commuting area would have had 9% less population in 2001, but so would other major cities such as Birmingham, Manchester and Glasgow. Much of North West and central England gained population because of the rail cuts in the 1950s and 60s relative to areas in the South West, Wales and Scotland. The map below illustrates the what the populations of Britain’s travel to work areas would have looked like if the cuts hadn’t happened. The white and light grey areas indicate places where the population would be lower than it is now, the darker grey areas where it would be higher.

Figure 1: Counterfactual log population changes, without rail cuts, Travel to Work Areas, 2001

So what happened to rail in the mid-20th Century, and how did this affect places’ accessibility? There had been rapid and largely unregulated growth in the supply of rail infrastructure during the 19th Century, and by the mid-20th Century, with increasing use of roads, the rail network was losing money. Unprofitable lines began to close, a process accelerated by the infamous ‘Beeching report’  - The Reshaping of British Railways 1963  - which proposed a radical programme of cuts.


Figure 2 below illustrates the patterns of 1951-1981 changes in a rail accessibility index, at parish level – parish being the units on which historical census data is available. To measure rail access, we use a ‘closeness centrality’ index (sometimes called a market access index) which adds up how many destinations can be reached from an origin, with less weight on destinations with longer journey times. Bigger negative numbers mean a bigger cut. We have overlaid lines showing the rail lines that were cut from the network. The cuts were brutal. Around 40% of the lines in Britain went over this period. Even where lines were retained, many stations were closed - about 60% of them in all. Evidently, these cuts had the biggest effects in areas outside the cities, particularly outside London and the South East (the north of Scotland doesn’t lose much either, because it had little coverage to begin with).

Figure 2: Rail lines cut and changes in centrality/accessibility at parish level, 1951-198

It is these changes that we see played out in terms of population patterns in Figure 1 above, according to our statistical analysis. We also find changes in other demographic characteristics – populations in parishes losing rail access became less skilled and older. To carry out these analyses we link historical population census data at parish level to the centrality changes shown in Figure 1. We then estimate the statistical association between the change in centrality and the change in rail access, taking careful account of pre-existing population trends, and alternative explanations for subsequent population changes – like rural/urban differences, planning of new towns, and motorways. We also check that we don’t find comparable effects for stations that were proposed for closure in the infamous ‘Beeching report’, but were never actually closed.

One might think that the construction of the motorways and faster car journey would have compensated for these rail cuts. We look at this, finding that populations shifted with the construction of the motorway network too. Places that experienced improvements in accessibility through the motorway network were less affected by the rail cuts. Unfortunately, the places losing rail access were not those targeted by improvements in road access - the changes in rail and road centrality are unrelated to each other - so the motorway network in Britain would have done little to compensate the places worst affected by loss of rail.

So what are the lessons for today from these historical facts? The most general point is that transport shapes the spatial economy. This probably seems obvious, but empirical evidence on the way the spatial organisation of economic activity evolves in relation to changes in transport networks is only recently beginning to emerge. This evidence adds to that picture. The unique perspective it brings is that not only does activity reorganise when transport is improved, as shown by previous evidence, but it also reorganises when transport is destroyed.


Do the results mean that the opening of HS2 will cause everyone to move to the north, or would re-opening large swathes of disused railway relax the pressure on densely populated areas? This is probably far-fetched, given the shape of the modern day economy, the dominance of road and the promise of new transport technology. Though there has been a resurgence in rail use in the 21st century so the demand for rail is evidently there and, as our evidence shows, populations will shift to places with better transport access.




Wednesday, 11 July 2018

Turning Brownfields Around: the Role of Demand in Land Re-Development


Land demand is high in cities. Cities have access to good jobs, people and specialized consumption services. High demand and the limited supply of urban land translate into high land prices. In turn, high prices promote high density land uses. If a parcel in the square mile costs hundreds of millions, expect to find a tall office building there rather than a bucolic bungalow. Despite all this, it is common to find vacant or severely underused land in cities. Old factories, derelict dwellings, abandoned car parks; all are common images from urban life. But, for an economist, this is quite surprising. Is it that some land is simply unresponsive to demand conditions? 

Having a good answer to this question is important. Unused land makes our cities larger and our commutes longer. In stark distinction with parks, or undeveloped green-field land, abandoned previously-developed parcels are usually quite unpleasant (in econ-talk, they generate strong negative externalities). Planning authorities and local governments often have specific policies to promote land re-development, by offering tax breaks, grants or financing options to developers of infill land. Understanding how demand affects re-development helps us to anticipate whether these policies will be successful. Moreover, it also gives us a hint on whether or not market forces can themselves achieve the goal of re-developing these sites.

In a recent Centre for Economic Performance, Urban and Spatial Programme discussion paper, I address this question by using data on brownfields across England. As you may know, in the UK brownfield is the name usually given to land which was previously developed but is currently underused or vacant. Policies fostering redevelopment of brownfield land in priority over green-field (“brownfield-first”) have been a feature of the planning system at least since the 1990s and have seen some resurgence of interest in recent years. Whether these policies are actually effective in encouraging brownfield re-development in response to housing demand remains unclear.  In this paper, I use data on brownfield sites to test whether the prevalence of empty land in cities is indeed sensitive to demand conditions. In essence, I want to test whether high demand (or high price) areas are more likely to be re-developed than otherwise identical areas where demand is lower. The resulting estimate measures the price sensitiveness of brownfield re-development. If you are versed in economics, this is like estimating a price elasticity of land supply. There are several challenges when trying to estimate these parameters properly. I briefly discuss them below in case you are interested.

My findings indicate that brownfield re-development is indeed affected by local demand conditions. In the long run, re-development follows demand. Quantitatively, a 1% increase in prices leads to a 0.07% decrease in the probability of having brownfield land in a given hectare. This number looks very small but the baseline probability of finding brownfield land in my sample is also low, standing at roughly 1.5%. Hence, I conclude that price differences can induce substantial variation in the presence of empty land in our cities. Importantly, this is the case despite the tight planning restrictions that characterize the UK. This is not to say that planning restrictions cannot influence re-development. In my paper, I show that re-development of parcels in areas with tougher planning (such as the South East) is less sensitive to demand conditions.

To sum up, my results show that market forces do affect brownfield development in the long run.[1] According to my estimates, a large but plausible increase of prices of roughly 20% would lead to re-development of a large fraction of brownfields in English cities. It also suggests incentive based policies such as grants or tax breaks could, at least in principle, achieve substantial long-run re-development. Finally, the paper shows that equilibrium land supply is neither fixed nor determined exclusively by sprawl; it can also be shaped by development within the cities’ footprint. 

Technical Details
My analysis is based on data for previously-developed land sites in England, as recorded in the National Land Use Database of Previously Developed Land (NLUD-PDL). I use the 2007 version of this dataset, but newer versions can be found online. Data on prices are obtained from the land registry and data for land use changes are obtained from the Land Use Change Survey. 

 There are a few technical hurdles we have to overcome before we can obtain credible estimates of the price sensitivity of re-development. On the first place, because brownfield sites tend to generate negative externalities on nearby residents, they themselves affect prices. A growing literature has made this case over the last decade.[2] As a result, there is substantial scope for reverse causality, from vacant sites to prices. In addition, re-development costs may be correlated with amenities that affect land prices directly. For example, in a monocentric city, low amenity areas away from the city centre may be harder to re-develop because of reduced accessibility. Other confounders, such as differences in the type of sites (e.g. residential vs. industrial) in different areas could also bias na├»ve estimates.

In order to overcome this problem I need a demand shifter, a variable that affects demand without affecting supply conditions. In this study, I use school quality as my demand shifter. A long literature has documented price effects of school quality in residential markets and the UK is not an exception (see for example Gibbons, Machin and Silva, 2013). Combining data from school quality with detailed maps, I implement a two-stage boundary discontinuity design which uses school quality as an instrument in a regression of brownfield location variables on prices. The boundary discontinuity design uses school admission boundaries to ensure I am comparing locations which are very close to one another but have access to primary education of different quality. This method is what allows me to obtain credible estimates of the price sensitivity of re-development. 

 
Note: See Figure description in the text.

An illustration of the result is provided in the figure above. The horizontal axis represents distance to a school-admission (county) boundary, with negative distances corresponding to the side with low average school quality and positive distances corresponding to the side with high average school quality. The vertical axis measures the probability that a given hectare contains brownfield land. Fourth degree polynomials fitted on the raw data represented in solid lines. The discontinuity observed in the middle of the figure shows that brownfield land is less common in high demand locations. More details can be found in the paper.

References
Gamper-Rabindran, S., & Timmins, C. (2013). Does cleanup of hazardous waste sites raise housing values? Evidence of spatially localized benefits. Journal of Environmental Economics and Management, 65(3), 345-360.
Gibbons, S., Machin, S., & Silva, O. (2013). Valuing school quality using boundary discontinuities. Journal of Urban Economics, 75, 15-28.
Greenstone, M., & Gallagher, J. (2008). Does hazardous waste matter? Evidence from the housing market and the superfund program. The Quarterly Journal of Economics, 123(3), 951-1003.


[1] My estimates however, are based on very long run changes. Anecdotal evidence shows that re-development can take a long time. It took 50 years to re-develop the areas around the London Royal Docks or Battersea Power station.
[2] See for example the contributions in Greenstone and Gallagher (2008) and Gamper-Rabindran and Timmins (2013).

Thursday, 14 June 2018

Did the Blitz enhance London’s economy?

By Gerard Dericks & Hans Koster
The Blitz lasted from Sept 1940 to May 1941, during which the Luftwaffe dropped 18,291 tons of high explosives and countless incendiaries across Greater London. Although these attacks have now largely faded from living memory, our recent CEP Urban and Spatial Programme discussion paper ”The Billion Pound Drop” shows that the impact of the Blitz remains evident to this day in London in both its physical landscape and economy.

Figure 1: Greater and Inner London Blitz Bomb Density



Using recently digitised National Archive records on the locations of all bombs dropped during the Blitz (see Figure 1 above), we compare the locations of Blitz bomb strikes with local differences in London’s modern-day building heights, employment levels, and office rental prices. After controlling for the central concentration of bombs, we find that local areas which were more heavily bombed during the Blitz have more permissive development restrictions, more office space, and consequently higher worker densities today. For example, as Figure 2 below illustrates, the eastern core of the City of London was particularly heavily bombed, and is today one of the few areas in the City of London where tall buildings are permitted.

Figure 2: City of London Bomb Strikes and 10 Tallest Buildings



Consistent with considerable empirical evidence from other cities, the consequence of this higher worker density in London has been greater worker productivity (which we proxy with office rents). What is new about this research, however, is the magnitude of measured effects. Whereas previous research has primarily sampled secondary cities and has generally found that a doubling in worker density raises productivity by only about 5% (as measured by wages), even after extensive sensitivity tests our paper shows an increase in London of 25%. We argue that this difference is largely due to London’s unique position as perhaps the world’s foremost financial and commercial centre, and that the benefits of greater worker density here are likely to be exceptionally large.

City planners are tasked with controlling development in order to separate incompatible land uses and mitigate costs of congestion such as traffic. However, these restrictions (especially building height limits) entail various costs, for example, higher property prices and greater price volatility, but equally significant is the fact that constraining worker density damages the productivity of the economy. For many historical reasons London has one of the most restrictive planning regimes in the developed world. Based on back-of-the-envelope calculations, we estimate that the value of the Blitz to London in having reduced the restrictiveness of its planning regime is £4.5bn annually, equivalent to 1.2% of London’s GDP (assuming that, without the Blitz, London's density would have been constrained to what it is in non-bombed areas of the city).

Ideally, planners would calibrate the stringency of development controls to ensure that society makes the best trade-off between the costs and benefits of greater worker densities. However, in order to make this judgement, planners require accurate information on both these costs and benefits. What our research now shows is that for the case of London, and perhaps other global cities such as New York and Tokyo, the benefits of greater worker density appear to be much larger than anyone had previously surmised. Consequently, if welfare maximization is indeed city-planners’ primary goal, then, at least in those cities, planners should now be reviewing the stringency of their height restrictions and new development controls more generally.

The Blitz was a tragic episode in London’s history, the likes of which one only hopes will never be repeated. However, by locally relaxing the restrictive planning regime put in place after the war, for all its human cost, the Blitz has subsequently had an extremely positive effect on London’s present day economy. Furthermore, this lasting influence has now provided us with unique insights into our understanding of urban economics, and spotlights the exceptional dynamism of this enduring city.

Tuesday, 12 June 2018

Inclusive Growth in cities: Good intentions, difficult policy

Posted by Neil Lee, Department of Geography and Environment and SERC

Inclusive Growth - a concern with the pace and pattern of growth - has become a new mantra in local economic development. The phrase was barely used up until about 2009 (see figure 1). But interest has increased significantly since the financial crisis. The term first influenced development, with organisations such as the World Bank taking it up. But it has since spread into urban and regional policy in the developed world. The OECD have launched an ‘Inclusive Growth in Cities’ programme, the RSA launched a high profile report on Inclusive Growth, and an Inclusive Growth Analysis Unit (IGAU) has been established at the University of Manchester.

Figure 1. Google searches for Inclusive Growth and Pro-poor Growth

Note: Data from Google Trends. Height of each line gives indication of share of searches containing each term. 

It is hard not to be sympathetic to the basic idea of Inclusive Growth. It is a positive way to link two problems - declining living standards for many and low growth. It is more optimistic, and less politically loaded, than a focus on inequality. But how useful is it for policy? In a new paper published in Regional Studies, I argue that while there is much to like in this new policy agenda, there are significant problems operationalising Inclusive Growth at a city level. Inclusive Growth is a classic ‘fuzzy concept’ as described by Anne Markusen, with researchers and policymakers using the same term but often describing different concepts. It is a conceptual Rorschach (inkblot) test onto which people project their own particular interests.

The question is whether this fuzziness matters for policy. In some respects, it doesn’t. Efforts from cities to address problems of disadvantage and inequality are welcome, and Inclusive Growth provides cover for them to do that. While the precise definitions are fuzzy, the general direction is clear. If cities can do anything to help inclusion then that is better than nothing.

But definitions do matter for policy. ‘Growth’ is a clearly understood if highly imperfect indicator - politicians are judged on the performance of the national economy. When policymakers aim to hit precise targets they are more likely to do so (famously, New Labour hit virtually every social policy metric they aimed for - but doing so led to some unintended consequences). These measurement issues are important right now as civil servants try and work out how to replace European Funding. What should the targets be of the Shared Prosperity Fund: simply GVA or employment growth, or something which also measures inclusivity?

Precise targets also help policymakers make choices. Like economics, policymaking is often about tradeoffs, particularly given the stretched budgets and tough choices faced by local government. If Inclusive Growth becomes the target, a clear definition would help policymakers choose between competing priorities and focus investment. If the definition is too broad, Inclusive Growth is less useful for policymakers.

Even if they do have clear targets, there is little evidence on what the best interventions would be. The What Works Centre has highlighted the limited evidence for many general economic development interventions (although some argue they have set the bar high). There is even less evidence on the more specific target of Inclusive Growth. The JRF, OECD, IGAU and others are all developing frameworks and considering interventions right now (disclosure: I have a horse in this race, having produced several reports for the JRF). But it will take time for an evidence base to develop. The policy initiatives are outrunning the evidence.

The measurement issues matter. But even if they are solved, there is another, more fundamental problem with applying Inclusive Growth at the local level: local government often lacks the powers or ability to create growth, let alone shape it. The UK is both highly centralised and characterised by large regional disparities, which policymakers have found it hard to address. An Inclusive Growth strategy is unlikely to solve these problems.

But there is also a strong defence for the new focus on Inclusive Growth. Much economic development policy simply assumes benefits ‘trickle-down’ to disadvantaged groups. While imperfect, Inclusive Growth does at least focus attention on this. Rather than thinking about it as a specific concept, it might - as Ruth Lupton and Ceri Hughes argue - be better to think about it as a general policy agenda, which needs to be considered but does not necessarily form the sole focus of policy. While imperfect, the Inclusive Growth agenda is better than one which simply ignores distributional concerns.

Wednesday, 16 May 2018

The ‘Bedroom Tax’: How did families react? Did the policy achieve its objectives?

By Steve Gibbons


The ‘Bedroom Tax’ – or ‘under occupancy penalty’ or ‘removal of the spare room subsidy’ as it has been called officially – is a highly controversial part of the UK Government's recent social housing policy. The legislation was passed in April 2012 and came into effect in April 2013, and reduced housing benefits for social tenants – mainly council and housing association tenants - deemed to have a ‘spare’ bedroom.

The aim of the legislation was twofold. On the one hand, this was an attempt to curb increases in social housing expenditure. On the other hand, the Government was hoping to promote mobility and the reallocation of the limited social housing stock to better match households’ size and needs. However, the policy has been much criticised by housing charities and in the media for its draconian regulation of low income tenants’ entitlement to space, the penalty it imposed on tenants who were the least able to afford it and for its potential adverse impacts on their welfare. Typically, households would have ended up with a spare bedroom through no fault of their own, due to children leaving home, or due to a lack of availability of smaller accommodation when they were originally housed.

Our recently published CEP Urban and Spatial Programme discussion paper is the first to look directly at what impact this ‘bedroom tax’ actually had on the tenants it affected.  Whereas previous studies have simply asked a sample of tenants how they adjusted to the tax, we turn to an existing large scale survey of households who are followed year after year - the ‘Understanding Society’ longitudinal study. These data allow us to observe in detail how families actually reacted at the time the tax was introduced – in particular tracking whether they moved house.

The ‘bedroom tax’ legislation set out very specific rules regarding who in a household was entitled to their own bedroom and hence which households were deemed to have a spare bedroom. These rules were based number of adults and the age and gender of any children in the household. Anyone deemed as having one spare bedroom would face a 14% cut in housing benefit, while households with two spare bedrooms would face cuts of 25%.

These rules allow us to deduce which social tenant households in our data were affected by the policy and which weren’t. This means we can compare what happens to households who are affected by the policy because they had a spare bedroom, with very similar households who didn’t.  We can see, for example, whether there is any difference in what happens to a family with two adults and two children (a boy and girl) under 10 living in a flat with three bedrooms – who would be considered to have a spare room under the policy rules – with a household with two adults with a boy and girl aged over 10 – who wouldn’t.

So how did families react, and did the policy achieve what it set out to do?

Firstly, our results show very clearly that affected households did lose housing benefits – around £8 per week on average - and experienced a drop in overall income. However, we are unable to find precisely how tenants adjusted to these cuts.

The first thing that is clear is that the policy did not encourage households to move. We find no difference between affected and unaffected households in the likelihood of moving when the policy was introduced. One concern of the policy’s critics was that it would force moves, increase neighbourhood turnover, deprive poor children of a stable learning environment and push individuals already at the risk of being detached from the labour market to areas with even fewer employment opportunities. This evidently did not happen to any great extent.

We do find though that when social tenants do move after the introduction of the bedroom tax, they down size to smaller accommodation. So the policy was partly successful in one of its aims –rationalising the use of publicly-funded housing, albeit more slowly than might have been hoped. Although the policy didn’t encourage moves, it did encourage movers to downsize, so in the long run under-occupancy of social housing might be reduced. This change will however only occur in conjunction with natural turnover of occupants of social housing.

Households who didn’t move appear to have just taken the hit to their resources, presumably cutting back on other areas of expenditure, though we don’t detect precisely on what dimensions. We find no systematic falls in spending on food or savings. There is little evidence that individuals in affected households worked more or less. In line with what was predicted by its critics, the policy appears to have reduced well-being, as captured by measures of material deprivation and self-reported life satisfaction. However, these effects are not precisely estimated or large (they are not ‘statistically significant’). This evidence indicates that the policy did further strain the finances and standards of living of individuals who were already disadvantaged.

So did the policy save the Government money? It was expected that the policy would affect 660,000 households at the time it was introduced. Given the £8 per week benefits cut we observe in our data, this suggests direct savings of around £250 million per year – around half the Government’s own estimates of total savings. This simply amounts to a benefit cut for tenants who were unwilling or unable to move. These savings will also have been partly offset by the ‘discretionary payments’ that the government boosted in order to help support families adversely affected by the bedroom tax – around £60 million per year up to 2015/16.

So the bottom line is that the policy seems to have saved some public money – with the burden falling on the affected tenants - but will be slower than expected in achieving its aims of reducing social tenants’ use of bedroom space.

Thursday, 3 May 2018

Does gentrification displace low-income renters in Britain? In short: Yes!

By Sevrin Waights

Gentrification is an ambiguous term, which roughly speaking means the replacement of poor residents in a community by the rich, and a related change in the character of the community and its amenities. There are two broad mechanisms for gentrification – displacement and succession. Displacement is where the influx of rich residents actually increases the likelihood that poor residents move away (e.g. due to higher housing costs). Succession implies that rich households simply move in after poor residents that moved away for other reasons.

The distinction is important because displacement implies gentrification may be harmful whereas succession implies that it is a more benign process. My latest CEP Urban and Spatial Programme  discussion paper is the first study to provide empirical evidence that gentrification involves displacement of poor residents. While it’s true that several studies look at the question already, none of them find any evidence of displacement. Instead, these studies suggest that gentrification occurs through succession.

Displacement studies usually combine two types of data. Firstly, studies use data on the proportion of higher socioeconomic class households living in a neighbourhood (e.g. based on a Census). Neighbourhoods are then characterised as gentrifying or not according to whether there was a large increase in the share of high socioeconomic class residents over say ten years. Secondly, studies use data from longitudinal household surveys. Such datasets allow researchers to track individual households across all the different neighbourhoods they live in over the years. The usual approach is to link these data together in order to examine whether living in a gentrifying neighbourhood means households are more likely to move away. Previous studies find that poor households living in a neighbourhood characterised as gentrifying are no more likely to move away than poor households living in non-gentrifying neighbourhoods. This is interpreted as evidence that gentrification occurs through succession rather than displacement.

In my paper, however, I argue that previous estimates may be biased by the fact that different types of household (with different natural mobility rates) tend to live in different types of neighbourhood. This well documented phenomenon is called ‘sorting’ and means that previous studies might miss actual displacement. My approach makes use of year-to-year variation in winter temperatures in Great Britain. I argue that if displacement does happen, then it will be more pronounced in years with colder winters. The reason is that households will be less able to withstand rising rents resulting from gentrification if budgets are already stretched by higher fuel bills. This novel approach reveals a ‘causal’ effect because the type of household living in gentrifying neighbourhoods does not differ in cold years.

I use data from the UK Census to compute a measure of gentrification for every neighbourhood in Great Britain over two periods: the 1990s and the 2000s. Neighbourhoods are defined as gentrifying if there is an above-average increase in the share of residents with a university degree. Figure 1 illustrates my gentrification measure for London neighbourhoods in the 1990s (TTWA is the London Travel to Work Area, MSOAs are small census areas). Gentrification in the region, according to this definition, is evidently concentrated towards inner London but there are pockets elsewhere. I use this gentrification measure to estimate displacement effects for a sample of low-income private renter households from the British Household Panel Survey (BHPS). The BHPS is a survey of households that has been following a large sample of households since the 1990s, and so allows me to track which households move and when.

Figure 1: Gentrification index for London in the 1990s


I find that that gentrification does displace low-income households. In fact, my estimates show that you need to have a household income of more than 1.5 times the average for the city and year to have no chance of being displaced. My findings also indicate that displacement may be avoided if gentrification occurs slowly enough. Figure 2 illustrates the size of displacement effect (left axis) relative to the speed of gentrification (bottom axis). The figure shows that there are no significant displacement effects resulting from small increases (or decreases) in neighbourhood degree share, i.e. a slow pace of gentrification. Households only start to be displaced when the degree share increases by 10 percentage points more than average (which equates to 0.1 on the bottom axis). These findings suggest a need to rethink gentrification and its consequences.

Figure 2: Displacement effects at different levels of gentrification
A lot of place-based policies aim to encourage ‘mixed communities’ on the grounds of it being beneficial for existing low-income residents. While the evidence on whether mixed communities help is inconclusive, my findings suggest that such policies may end up displacing original residents altogether. If policymakers wish to improve outcomes for low-income private renters, it may be more effective to target housing assistance to households living in already gentrifying neighbourhoods.


Thursday, 29 March 2018

Housing: the happy self-delusion of ‘no shortage’

Posted by Paul Cheshire

The assertion that there is no actual shortage of houses seems to be gaining, if not traction, then at least supporters. Ian Mulheirn, of the consultants Oxford Economics, originated it. Matthew Parris on 10 Feb in The Spectator took up the cause of no housing shortage. It is true house prices have more or less doubled in real terms in every decade since the 1950s and continued to rise well ahead of inflation until this year. We know the young are getting squeezed out of owning houses. Similarly we know that the ONS measure of affordability shows houses are twice as unaffordable as in 1998 and are now less affordable relative to incomes than at the height of the 2007 boom. The measure may be imperfect but it is transparent - just the ratio of the median house price to median income.

The currency the ‘no shortage’ assertion has gained seems to be less the result of the persuasiveness of the evidence for it than the fact that it is a comforting narrative, appealing both to politicians and the CPRE/NIMBY brigade alike. It allows people to claim nothing uncomfortable (or effective) needs to be done about the crisis of housing affordability. It appeals to the NIMBY/CPRE brigade because the ‘solution’ doesn’t require us to build any more houses.

But the problem is that this claim of no ‘shortage of houses’ is based on no understanding of how housing markets work or even how one might usefully define a ‘shortage’. A shortage can only be usefully defined in terms of the balance of supply and demand. Basic economics is enough to give at least a hint that if prices are persistently rising in the long term, as house prices have, supply is less than demand. One of the additional paradoxes of our shortage, however, is that the constraints on supply imposed particularly by our planning system, cause prices to be more volatile too. So when demand does fall, all the adjustment is via price.

The evidence cited in support of the ‘no shortage’ assertion is that there were more houses per household in 2016 than there were in 1971. True: but there were more doctors per person in 2015 than in 1971 too. Far more. According to the World Bank the number of doctors per thousand people increased from 1 in 1971 to 2.8 2015. By comparison houses to households hardly increased at all: from 1 to 1.02 (England).

No one is asserting there is a surplus of doctors. As people get richer they demand more health care; that also happens as they get older. The ability of doctors to treat illness has greatly improved. It takes more doctors to treat cancer patients now than in 1971, partly because treatment can do so much more. The rising ratio of doctors to people reflects rising prosperity, the aging population and technical progress complementary to the demand for doctors. Much the same is true of houses. One of the inconvenient facts about the demand for houses is not just that as people get richer they demand more housing space but they do as they get older. Even after adjusting for income, education and other relevant factors, older people demand more housing space. As car ownership has grown people demand more space around their houses.

Not only do people buy bigger houses as they get richer, a few buy second houses. A second home is no more ‘needed’ than is a private pension or a new outfit. But incomes are not equally distributed and that is how markets work. Also, of course, since the real price of houses has risen so rapidly over the past two generations (and since 2007, other assets have performed so badly) houses are, increasingly, pensions; not just shelter. They may be bought to let using equity accumulated by older owner occupiers or just be a nest egg, even if vacant.

Houses are not barrels of Brent Crude – all the same. They all vary and one attribute on which they vary is space: both internal living space and space in gardens. Also location is a critical attribute of houses. As we have pointed out before houses in Barnsley – though buildable on brownfields – are not substitutes for houses in Oxford – where the high productivity and high paying jobs are.

As I and LSE colleagues recently showed, more restrictive local planning in fact increases the number of vacant houses as well as increasing commuting distances for workers. Because houses all vary, ‘house hunting’ involves search - for the best set of attributes you can afford, where you want to live. More restrictive local planning increases local house prices, creating an incentive to keep them occupied.

But tighter local planning also makes it more difficult to adapt the characteristics of the housing stock to what people want and where they want to live. The result is house hunting becomes less efficient, so more houses are empty. These two forces work against each other but when you carefully analyse the data over the past 30 years, it is clear that impaired search dominates. Local vacancies are 23 percent higher if local planning restrictiveness increases by one standard deviation. That is not all. Because it makes finding a suitable house locally more difficult it also increases the average distance people have to travel to work. The same increase in local restrictiveness causes a 6.1 percent rise in commuting distances.

If, in the 25 years to 2016, we had built in England at the rate we built in the 25 years to 1991, we would have built 2.2m more houses; that is we would have built 63 percent more houses than we did. New houses have also been smaller. The result is an aging stock of increasingly cramped housing. In 1967 62.1 percent of English houses were less than 50 years old: in 2015 that had shrunk to 38.8 percent: not much more than the proportion that were less than 25 years old in 1945 – despite almost no building during the 5 years of WWII. English houses are akin to Cuban cars: they are still in use but they are clapped out and polluting.

There are two other important points. The no housing shortage assertion tells us to look at figures for ‘net additions to the housing stock’ not at those for completions – houses actually built. Politicians increasingly do the same. For example, housing forecasts and targets in the latest London Plan are all in terms of ‘net additions’. On the face of it this sounds plausible but there are good reasons why the number of new houses actually built gives a far more reliable and useful figure. We have comparable data for a very long time – at least since 1946. So one can track house building over the long term. You can count houses but ‘net additions’ include conversions and changes of use as well as taking account of demolitions.

In the old days we used to knock down unfit houses, so ‘net additions’ were less than completions. But as the price of houses has gone up and up, instead of knocking them down we spruce them up, often subdividing them into two smaller units. Thus one house becomes two. In other words the worse the shortage of housing, the higher will be net additions relative to actual building. So measuring changes in the shortage of houses by comparing the number of households and the stock of homes over time will definitionally tend to underestimate the ‘shortage’. There may be ‘homeunits’ but, reflecting the growing shortage, they are increasingly small and less fit for purpose.

A similar argument applies to the denominator in the chosen measure of ‘shortage’: households. The increasing unaffordability of housing has generated an increase in young people living with, or returning to, their parents. Young couples put off having a family and live in a parent’s front room. Household formation is itself a function of houses prices and as they go up in real terms, household formation rates fall. Again Ian Mulheirn’s measure definitionally underestimates the shortage.

There is certainly a housing shortage in the sense that we have not been building enough houses to satisfy demand for at least 25 years. That there are fractionally more homeunits per household is irrelevant: incomes have risen so therefore demand has too – substantially; population has aged so (paradoxically to some) demand for housing has increased; the stock of houses, reflecting the falling rate of building, is aging and decreasing in average size; and because of the shortage, the formation of new households has been choked off.

Sorry but the politicians cannot just sit back comfortably. There is a housing shortage and it is causing a crisis of housing affordability. The only way to resolve the problem is to do something radical and uncomfortable. Build more houses.