Friday, 26 April 2019

Financial innovation in mortgage products spurred the rapid increase in credit and house price growth during the last housing boom

The dominance of the 30-year fixed rate mortgage is a defining feature of the United States’ housing market. For a brief period in the mid-2000s, however, this dominance was challenged by the popularity of non-traditional mortgage products that allowed borrowers easier access to credit through variable interest rates with teaser periods, extended terms, and interest only or negatively amortizing repayment schedules. In effect, borrowers could obtain a mortgage with lower monthly payments in the short-term than were available through the 30-year fixed rate mortgage.

As Figure 1 shows, the share of mortgages with at least one non-traditional feature grew sharply during the last decade. At their peak in 2005, about sixty percent of all purchase loans in the United States included at least one non-traditional feature. Their coincidence with rapid house price appreciation during the housing boom led many to conclude that they were used to speculate on the housing market and, thus, partly to blame for the boom. However, given that incomes rose little duringthat period, borrowers may have instead flocked to non-traditional mortgage products to maintain affordability in a time of increasing home prices.

Figure 1 – Share of mortgages with non-traditional features and national house prices

In a new CEP Discussion Paper, Affordability, Financial Innovation and the Start of the Housing Boom my coauthors and I study the relationship between the start of the housing boom and the use of non-traditional mortgages.

To do so, we first identify the starts of housing booms in individual US counties. Figure 2 shows substantial variation in the timing and size of local house price booms. Next, we use this variability to systematically track the use of non-traditional mortgage products around the local housing boom starts. To illustrate, the house price index for Clark County (containing Las Vegas) is shown in Figure 3(a). Our methodology estimates that the Clark County housing boom began in February 2004. In the figure, there is a clear difference in the house price path and appreciation on either side of the estimated housing boom start. In Clark County, we find that the rapid adoption of non-traditional mortgage products began earlier than the estimated start of the local housing boom (shown in Figure 3(b)).

Figure 2 – Distribution of House Price Breaks
Figure 3a - Clark County Estimated House Price Break and Non-traditional Mortgages, Estimated House Price Break
Figure 3b - Clark County Estimated House Price Break and Non-traditional Mortgages, Use of Alternative Mortgage Products

Like in the experience of Clark County, we find that the increased use of non-traditional mortgage products preceded the accelerated rise of house prices in markets with house price booms that began after the year 2000. In the year before, the share of new purchase mortgages with any non-traditional feature increased 5 percentage points on average, primarily due to the use of variable rate and interest-only mortgage products. Moreover, in late-booming markets, we find results consistent with other findings, that lenders altered their loan supply along dimensions that would be less frequently and consistently reported to investors, such as denial rates and the share of loans classified as subprime. Overall, these findings support the view that financial innovation in the 2000s contributed to rising house prices by reducing payment constraints.   

A key question regarding the county-level patterns we document is: what mechanism drove the expansion of non-traditional mortgage products in markets after 2000? To answer this question, we exploit the dramatic increase in Treasury rates in mid-2003 for plausibly externally influenced variation in lenders' financing constraints. Earlier work shows that in response to the higher Treasury rates and concurrent collapse of mortgage refinancings, lenders rapidly expanded their use of the private mortgage backed securities to finance new mortgage originations. This led to a sharper increase in mortgage supply and house prices in markets with higher shares of lenders connected to the private secondary market -- that is, those lenders that relied more on non-core deposits (funding outside deposits from retail customers) prior to the increase in Treasury rates.

In Figure 4, we show that, in addition to higher prices (panel (a)), counties exposed to a higher share of non-deposit lenders also experienced an immediate increase in the use of non-traditional mortgage products following the 2003 increase (panel (b)). Notably, while the growth in non-traditional mortgage products is instantaneous in mid-2003, house prices appreciate most sharply in the top three quartiles of the non-core deposit distribution of counties, but only after 6--12 months. In addition, across each quartile, growth in alternative mortgage products increased monotonically with the share of non-deposit lenders. This pattern suggests that an important mechanism during the 2000s was an external shock to lenders' cost of capital that drove them toward the secondary market, where non-traditional products flourished.

Figure 4a - Appreciation and Alternative Finance by Non-Core Lending Quartile, House Price Appreciation
Figure 4b - Appreciation and Alternative Finance by Non-Core Lending Quartile, Use of non-traditional mortgage products

However, we uncover a stark difference in the systematic relationship across markets with a house price boom that started before 2000.  In these markets, non-traditional mortgage products emerge as unlikely contributors to county-specific starts in booms. In addition, we find little evidence of credit supply growth in the years immediately before or after the start of these booms. We also find evidence that incomes in these markets grew faster than the national trend in the years before a local boom. These results suggest that the impetus for the housing booms of the late 1990s were driven more by economic fundamentals and that borrowers turned to these products to maintain affordability.

In the wake of the crisis, regulators have placed significant limitations on a number of non-traditional mortgage features by excluding them from the definition of a “Qualified Mortgage” for regulatory purposes. As laid out by the Consumer Financial Protection Bureau in their January 2013 report, lenders will no longer be able to underwrite a qualified mortgage loan based on a “teaser” rate in determining the ability to pay. Moreover, mortgages cannot contain interest-only, have lower payments than the interest due, or extended term features if lenders want to meet the legal presumption of complying with the qualified mortgage regulation. 

As we have shown, whether restricting these contract features will affect the formation and magnitude of housing price cycles in the future depends crucially on the context of the use of these features. Although these rules have only recently come into effect, the consequences of limiting these products is also likely to reduce credit access for some households, while guiding others into more standard contracts.
Additional info:

This article is based on the CEP Discussion Paper, ‘Affordability, Financial Innovation and the Start of the Housing Boom

About the author

Lindsay Relihan – LSE Geography and Environment

Lindsay Relihan is an Assistant Professor of Real Estate Economics and Finance at the London School of Economics. She is an applied microeconomist with interests in urban economics, household finance, housing, and real estate. Her research agenda is focused on understanding how the spatial relationships between firms and consumers shape outcomes in consumer credit markets.

Tuesday, 2 April 2019

Valuing the environmental benefits of canals using house prices

Britain has an extensive canal and navigable river network, which played a vital role in transporting goods from the Industrial Revolution through the 18th, 19th and early part of the 20th Century. Their use for transporting freight had all but disappeared by the mid-20th Century and many had fallen into disrepair or been abandoned. Since then, the canal and waterway network has been restored and developed into a potentially valuable environmental and recreational amenity, providing the venue for extensive range of tourism and leisure activities and a habitat for wildlife. Canals also provide transport corridors for walkers and cyclists along the towpaths formerly used by horses for drawing boats. Features of the canals are an attraction to those interested in industrial heritage and canal-side properties can have distinctive character with an outlook over green space and water.

Our recent research investigates the value of this resource to local residents in England and Wales, using house prices. Analysis of house prices is a well-established method within urban and environmental economics for establishing the value of amenities – such as good schools, transport, low crime or low pollution. This value is expressed as the monetary value of other types of consumption that people have to sacrifice in order to pay more for housing close to a desirable amenity (or away from an undesirable one). Part of our analysis looks prices close to canals across the whole of England and Wales, and part looks specifically at the change in prices induced by the restoration of the Droitwich Canals in the West Midlands after 2007.

We find that there is a quite a large house price premium for living close to a canal, but this is very localised (see Figure). On average, a buyer can expect to pay around 3-4% more for a property within 100m of a canal relative to prices elsewhere (in 2016 prices), but this premium falls to zero beyond 100m. The implication is that the price effect is driven predominantly by canal-side properties and others with a direct outlook on the canals or immediate access. There is no premium for living near a canal other than right up close to it. The premium is higher in dense urban areas, as we would expect if people are willing to pay more in housing markets where green space is scarce. We also find evidence that canal-side locations have been attractive for developers, with a much higher proportion of new-build sales within 100m of canals relative to elsewhere - a 5.9 percentage point increase on an 7.8% baseline.

Price premium  estimates and 95% confidence intervals for properties close to canals and waterways, 2002-2016 data. Distance scale in 100m. Vertical scale is prices in log points relative to properties between 1000m and 1500m from canals and waterways (0.01 = 1%).

Interestingly, the premium fell suddenly at the time of the last recession, from over 8% in 2007 to 4.4% in 2008. This step change suggests there was a structural shift in the demand for this environmental amenity at the time of the recession and the premium has not recovered since (up to the end of our data in 2016). A possible explanation is that demand shifted away from luxury aspects of property, including canal-side locations, as incomes fell and uncertainty about the housing market increased. 

Some back-of-the-envelope calculations indicate that the environmental benefits provide an uplift to land values within 100metres of canals in England and Wales that amounts to around around £0.8-£0.9 billion in 2016.

How we did the analysis

Although the idea of using house prices to value amenities is conceptually simple, there are challenges. The basic method is to use statistical techniques to estimate the average price difference between houses with a high level of an amenity (or dis-amenity) and similar houses with a lower level. Clearly, a key requirement is data on some variable that represents this exposure, in our setting, the indicators of distance from a property to its nearest canal. There are, however, potentially many ‘confounding factors’ which vary with distance to a canal and also affect the price directly – the physical characteristics of the housing, other amenities like distance to employment or distance to transport. Estimation methods must take account of these confounding factors so we are comparing houses on a like-for-like basis. Failure to do so might lead us to attribute differences in prices to proximity to canals, when in reality the price differences are caused by something else. For example, if canals in urban areas are predominantly in old industrial areas, and these industrial areas have older smaller houses and industrial buildings that are less attractive to residents, it might appear that proximity to canals reduces prices when in fact it is the average size of the houses or the industrial character of the environment which reduce prices. 

To avoid this type of bias, we adopt two strategies in our study. First, we use standard multiple regression techniques to estimate the association between canal proximity and housing prices, while adjusting for a rich set of structural housing characteristics and local area attributes on which we can obtain data (‘control variables). We control for a wide range of land use indicators, distance to geographical features, employment and demographic variables, and in our preferred versions of these specifications, we further control for ‘fixed effects’ at a small geographical scale – either Middle Layer Super Output Areas (MSOAS) or Lower Layer Super Output Areas (LSOAs) – and for differing price trends at Local Authority District level. This means we estimate the price effects from variation in the distance to canals, and associated variation in house prices, that occurs within these small geographical areas. Confounding factors that vary at a higher geographical level between LSOAs/MSOAs – such as access to labour markets – are eliminated.

Our second strategy focusses on a specific canal regeneration project, which restored an abandoned canal – the Droitwich Canal in the West Midlands of England. The Droitwich Canals were closed in 1939 and in the early 2000s were mostly overgrown, drained of water, non-navigable or completely destroyed. They underwent a major restoration from 2007 onwards and were re-opened in 2011. The restoration reopened them for boat navigation and recreation, improved the general environment and provided a habitat for aquatic life. In this case, we compare the price changes occurring in a ‘treatment group’ of properties close to the canal when the canal is restored, with price changes occurring at the same time in appropriate ‘control groups’. The assumption behind this method is that prices would have evolved in the treatment group close to the Droitwich canals in much the same way as in the control group, if the Droitwich canals had not been restored. As control groups, we use places further away from the Droitwich canal, and places close to an existing neighbouring canal – the Worcester and Birmingham canal – that has remained in continuous use, and where we would not expect to say any environmental amenity-related price changes at this time. These comparisons allow us to estimate the value of the restoration and the enhanced recreational and environmental amenities it provides, in so far as this value shows up in different price changes in the treatment and control groups. This type of ‘difference-in-difference’ estimator is widely used for estimating the impact of policies on economic outcomes in the policy evaluation literature. Both methods give similar findings, with a sharp increase in prices close to canals, with larger but less precisely measure effects from the analysis of the Droitwich canals restoration.

Disclosure: the research was funded by the Canal and River Trust, but carried out independently by researchers at the Centre for Economic Performance and Department of Geography and Environment.

Tuesday, 11 December 2018

Do foreign migrants ‘grease the wheels’ of the labour market?

[by Michael Amior]

Recent political developments in the US and Europe have led to renewed interest in the large and persistent regional disparities which plague our societies. These disparities have been partly driven by a secular decline in manufacturing employment, whose impact has been heavily concentrated geographically. In principle, these disparities should be eliminated by regional mobility. But at least in the US, fewer people are making long-distance moves than in the past.

In the face of these challenges, it has famously been argued that foreign migration can "grease the wheels" of the labour market. Given that new immigrants have already incurred the (fixed) costs of moving, they are very responsive to regional differences in economic opportunity - and therefore accelerate local population adjustment. If foreign migrants do indeed settle quickly in those regions where they are most needed, forcibly dispersing them within receiving countries (as several European countries do with refugees) may hurt natives as well as the migrants themselves.

In a recent CEP discussion paper I revisit this question using US census data spanning five decades (1960-2010) and 722 commuting zones. Remarkably, I find that new foreign migrants account for 30 to 60 percent of the average population response to local changes in labor demand. However, population is no more responsive in locations better supplied by new migrants: a bigger response from foreign workers is almost entirely offset by a reduced one from internal mobility. This is fundamentally a story of “crowding out”: I estimate that new foreign migrants to a commuting zone crowd out existing US residents one-for-one. This is entirely due to a reduction of internal moves in to the affected areas, rather than larger moves out.

The magnitude of the crowding out effect is puzzling. If Americans take time to adjust geographically to local declines in manufacturing employment, why do they appear so responsive to the location decisions of new immigrants? One plausible explanation is under-coverage of unauthorized migrants in the US census. This would overstate the crowding out effect (and also imply an even larger foreign contribution to local adjustment).

Even in the extreme case of complete crowd-out, a regionally flexible migrant workforce can save natives from having to incur potentially steep moving costs themselves. It is also worth stressing that the US population is generally considered to be relatively mobile. One might expect that foreign migration "greases the wheels" more effectively in parts of Europe where internal population adjustment is more sluggish.

Monday, 19 November 2018

The Economic Impacts of Constraining Second Home Investments

[by Christian Hilber]

Investment in second homes has been surging around the world. This surge has triggered a serious political backlash in many countries, especially in tourist areas and superstar cities. The backlash has at least in part been driven by legitimate concerns, such as ever more unaffordable housing, destruction of areas of natural beauty or creation of ghost towns during large parts of the year.

The crucial question is how politically to address these concerns. Some countries, such as the UK, and cities, such as Vancouver, have introduced substantive transaction taxes on the purchase of second homes.

Another policy that has become increasingly popular are constraints or outright bans on the construction of new second homes. The latest example in the UK is the Cornish seaside town of St. Ives. Other local communities in Cornwall and across the rest of the country have signalled interest in including similar policies in their own Neighbourhood Plans.

What are the economic impacts of such bans on local housing and labour markets? This is the question that my co-author, Olivier Schöni, and I explore in a recent CEP study.

In our empirical analysis, we exploit a unique quasi-natural experiment, the Swiss Second Home Initiative (SHI), to test theoretical predictions and identify causal effects of a ban on the construction of new second homes.

The SHI requested that construction of new second homes be banned in municipalities where such homes represent more than 20% of the total housing stock. The SHI was approved by the narrowest of margins – 50.6% of votes and 13.5 of 26 cantons – in March 2012. It came into force in January 2013.

Voters in tourist municipalities with very high shares of second homes were heavily opposed, presumably due to fears about adverse effects on the local economy. This contrasts with voters in the larger Swiss cities who favoured the initiative.

So what were the effects of banning the construction of new second homes in desirable Swiss tourist locations? The ban on the construction of new second homes lowered the price of primary homes, adversely affecting local homeowners, but increased the price of second homes, further raising the wealth of existing – typically already wealthy – second homeowners. We also find that the policy increased unemployment rates, thus harming the local labour force.

All in all, our findings suggest that the local economy effect (affecting primary house prices negatively) dominated the amenity-preservation effect (affecting primary house prices positively), resulting in an overall fall of the price of primary homes. They also suggest that, at least in the Swiss context, constraining the construction of new second homes reinforces rather than reduces wealth inequality.

Banning the construction of new second homes or imposing transaction taxes on second home purchases may be politically popular policies in the short run. But our research suggests that they may not do anything to cure the underlying causes of the problems.

[If you’d like to learn more about second homes, and the theoretical and empirical work we’re doing to look at the impact of constraints on investment then take a look at our longer piece in the latest edition of Centre Piece]

Tuesday, 13 November 2018

Measuring Urban Economic Density

[By Dzhamilya Nigmatulina]

What makes cities productive? For a long time, researchers have known that city size matters a lot – people who live in larger cities tend to earn more and be more productive. People (and firms) benefit from proximity to each other and these benefits are manifested in cheaper trade, improved hiring and learning, and a wider choice of inputs and products.

But does city size always generate proximity gains? A city that occupies a large area may also have very low density and low proximity, compared to a more compact city with the same population. In the same way, even a city’s high average density may not mean high proximity. Can we test if the shape of city density has economic implications? If so, how can we capture the meaningful dimensions of the city density?

In our recent CEP discussion paper we try to answer these questions using data from cities of sub-Saharan Africa.

A real world example

Cities with poor planning and land institutions may face issues of low proximity. In 1994 the urban scholar Lusugga Kironde living in Dar-es-Salaam noted about his city in his thesis:

"[…] Dar-es-Salaam is not systematically structured either in form of functional zones, or of income levels, or of types of developments. It does not reflect a structure responding to either a city well ordered by government, or, to the niceties of the market theory. It is this kind of unpatterned, irregular, and hotch potchy development, inconsistent with good land use planning or with the economic theories of land use structure, which engendered interest leading to this study."

Figure 1. Dar-es-Salaam, according to 1992 aerial photograph
Source: Kithakye 2009

Twenty years later and not much changed in Dar-es-Salaam. Even though the residential population distribution on Figure 3 tells us nothing about the location of firms and workers, it is evident that people reside far from each other: the highest density settlements are informal, and large residential areas are far away from the coastal centre of the city. The story of Dar-es-Salaam is not a unique one in Sub-Saharan Africa. Colonial history, imperfect transport systems, and lack of formal land has impelled cities to grow haphazardly, leaving many crammed in informal sector areas with poor access to the economic and social opportunities of urban life.

Figure 2. Dar-es-Salaam population distribution according to the 2012 population census.
Source: Tanzania National Bureau of Statistics, Population census 2012 and author’s calculations

Does shape matter?

In our study we use Landscan - a dataset the captures the distribution of ambient population, i.e. the average number of people in each square kilometre of the city over 24 hours. This measure captures not only residents, but also indirectly the location of firms. Understanding the actual proximity of people or workers within a city also requires a measure of the city extent, which defines a self-contained labor market. We choose population density to define city extents and use 1,500 people per km^2 as a simple cutoff.

Living Standard Measurement Study (LSMS) surveys from the World Bank give us income and wages for cities in six sub-Saharan Africa countries.

African cities are surprisingly dense and clustered

Comparing 599 large cities in Africa to other continents reveals that the African cities are denser, and more clustered, than those in the developed world. This is surprising, since the example of Dar-es-Salaam is often seen as representative of a more general problem of weaker land institutions in Africa (which would lead to lower clustering). If we look at differences of the cities in sub-Saharan Africa and the cities in other developing countries, we find that sub-Saharan Africa, Asia and North Africa are similar in terms of density and all clustering measures. It is Latin America that is less clustered.

Looking across African cities, density correlates with individual and household wages in that city, but clustering does not help explain differences in wages. This is puzzling. Especially when we see that for detailed firm-level data in Kampala higher worker density does matter for firm value added.


What explains the higher than expected clustering of African cities? One possible explanation is that our measure of “ambient” population captures the residential population more than the worker population. If higher density, or “cramming”, compensates for difficult commutes then the pattern of residential locations swamps the distribution of employment that matters in explaining productivity.

This could also explain why our clustering measures fail to explain wage premiums even though the Marshallian theories (and some additional findings for worker density in Kampala) highlight the benefits of proximity. Landscan only provides a proxy (e.g. built cover, building heights and other undisclosed data) measure for the things we care about. Comparing and contrasting novel spatial datasets, such as Landscan, with traditional survey and economic census data would help improve our understanding.

Adding transport costs can also paint a better picture on the true proximity between workers. See Akbar et al. (2017) for steps in this direction.

These questions and data limitations provide lots of opportunities for future research. As better data become within reach, we will be able to calculate our clustering indices and better test our proximity theories.