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.
There are two ways people can make their resources go further when buying a home.
One is to increase the loan-to-value (LTV) ratio and hence increase the amount available to buy a house for a given deposit.
The other is to lengthen the term over which the mortgage is repaid, which increases the size of loan associated with a given level of monthly repayments.
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.
Mariana Gimpelewicz and Tom Stratton.
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.