Nik Lomax (p), Stephen Clark

This paper reports a mass appraisal exercise for the rental housing market in England using data on individual properties and their environment. Such mass market appraisals are common in the sales market, used primarily for the levying of local property taxes. Within the private rental sector there is less direct pressure for such mass market appraisals, although local property taxes are still usually levied on such properties so there is a need to ensure that such costs are covered through the rental charge. Instead there is the need to place a rental value on a property that reflects current market conditions. A rent too high and the property will remain on the market and not generate any income to the owner and a too low rent will provide a deflated income to the owner. Data available for appraising the rental market are limited from conventional sources.

The data used here are derived from a property listings web site. Techniques used are regression, machine learning and a pseudo practitioner based approach. From the regression analysis attributes that increase the rental listing price are: property type; the number of various types of rooms in the property, proximity to central London and proximity to railway stations, being located in more affluent neighbourhoods and being close to local amenities and better performing schools. There is also evidence of some seasonality, and the more popular a property was on the web site (measured through number of views), the higher the rental price. Of the machine learning algorithms used to predict rental price the two tree-based approaches were seen to outperform the regression based approaches. In terms of a simple measure of the median appraisal error, the practitioner based approach is seen to outperform the modelling approaches.

Ian Shuttleworth (p), John Osth , Marina Toger

One of the claims made about Big Data, such as that from mobile phones, is that it offers fresh insights into the behaviour of populations over and above those gained from traditional census and administrative sources. This contention is assessed in the presentation through the use of a year’s run of mobile phone data for 2016-2017 from a major Swedish provider. A cohort of phones, defined as those present and active in April 2016 and also in April 2017, is used to examine how far people are fixed in place and how often they change their (assumed) address at the spatial scale of the municipality. Each phone is recorded at its early morning location on the first Thursday of each month and these locations are used to estimate mobility patterns, flows, and circulations between municipalities. Additionally, estimates are made of leavers and re-entrants from and to Sweden. These estimates are compared with migration data provided by Statistics Sweden so as to judge their usefulness. The analysis also speaks to wider debates about mobility in (post-) modern societies and particularly whether there is evidence for some people that traditional fixed places of residence have declined in importance.