A tulip bulb produces flowers. Those flowers are what people actually enjoy consuming, not the bulb. Whilst that’s blindingly obvious for tulips, the equivalent is also true for housing. The physical dwelling is the asset, but it’s the actual living there (aka “housing services”) that people consume. The two things sound very similar and are often lumped together as “housing”. But in today’s post, we argue they are as different as bulbs and flowers. Sketching out a simplified framework of houses as assets we show how this can radically change how one views the “housing market”. Tomorrow, we use this to develop a toy model and bring it to the data to shed light on house price growth in England and Wales.
Apocalypse Now is widely regarded as a masterpiece of the new Hollywood era. Director Francis Ford Coppola displayed audacious vision and a willingness to take risks. But we don’t just mean artistic risk. Mr Coppola gambled financially too: he staked his Napa Valley house and vineyard on the film, pledging it order to get the $32 million in loans necessary to keep the production on the road. While his movie was exceptional, there is nothing unusual about Mr Coppola’s financial strategy. Small business owners worldwide use their personal assets, and often their house, to back loans to their firms: in a new paper, we use microdata for several thousand firms to show how important this can be for UK investment.
The interest-only product has undergone tremendous evolution, from its mass-market glory days in the run-up to the crisis, to its rebirth as a niche product. However, since reaching a low-point in 2016, the interest-only market is starting to show signs of life again as lenders re-enter the market.
Christopher Hackworth, Nicola Shadbolt and David Seaward.
While official housing market statistics are relatively timely and high frequency, they usually come with a lag of at least one month. So indicators that lead official estimates are helpful for identifying turning points, or any ‘shocks’ to the economy.
Angelina Carvalho, Chiranjit Chakraborty and Georgia Latsi.
Policy makers have access to more and more detailed datasets. These can be joined together to give an unprecedentedly rich description of the economy. But the data are often noisy and individual entries are not uniquely identifiable. This leads to a trade-off: very strict matching criteria may result in a limited and biased sample; making them too loose risks inaccurate data. The problem gets worse when joining large datasets as the potential number of matches increases exponentially. Even with today’s astonishing computer power, we need efficient techniques. In this post we describe a bipartite matching algorithm on such big data to deal with these issues. Similar algorithms are often used in online dating, closely modelled as the stable marriage problem.
Who is living in private rental properties, and why? The buy-to-let market has been headline news recently. Typically the story has been profit-hungry landlords squeezing out first-time buyers. But landlords are only half of the story. This post examines the rental market from the perspective of tenants. Our work suggests demand for private rental properties cannot explain all of the growth pre-crisis, but the case for over-exuberance is inconclusive. We think that factors driving tenant demand, including demographics, social housing and credit availability, accounts for around half of the growth in the Private Rental Sector (PRS) pre-crisis and over 80% post-crisis. The most important driver post-crisis has been tighter credit conditions, which generated demand for an additional 1 million PRS properties. Looking ahead, we project that tenant demand will drive the PRS to swell by up to an additional 1 million properties between 2014 and 2019. If tenant demand were the only factor in play this would translate to annual growth in the number of buy-to-let mortgages of 2-7%.
Ask most young Britons about the housing market and they’ll undoubtedly have a personal anecdote to share. They may tell you about their struggle to get on the ladder, or how they’ve had to make ever larger concessions such as moving to the fringes of town. Or, they may tell you of their plans to take on a mammoth mortgage because the alternative—waiting a little longer—means that what is in reach now will likely be out of reach soon enough. This post empirically underpins what has been anecdotally obvious for some time: that the burden of debt is disproportionately falling on the young, and much more so than any other time in the last 20 years.
Buy-to-Let (BTL) investors are taking on an increasingly relevant role in the UK housing market. In this post, I present some initial findings from my ongoing research on BTL. I use data from the England and Wales Land Registry and the Zoopla web portal to find properties that are advertised for rent shortly after being bought. I show that: 1) BTL investors prefer (a) London and (b) flats; 2) BTL investors are more likely to pay cash; 3) BTL transactions are faster; 4) BTL investors buy at a discount; and 5) BTL discounts are larger for (a) Northern regions and (b) big properties.
Good analysis requires new discoveries, creativity, even luck. But innovation is not just a matter of chance — it favours those who are ready for it, which in this case means having the right data. Utilising micro-data to answer new and different questions is a good start, but the next step is to link such item-level information from various sources together. That way we can create analytical opportunities beyond the sum of the parts. In this post I show how a unique linked dataset on the UK housing market reveals that buy-to-let buyers secure a greater discount from the asking price than other buyers.