@evilal
Thanks for at l(e)ast engaging! And as I said at the time, I didn’t want to come across as bitchy or defensive or any other pejorative adjective you care to throw at me. I just really don’t have the buckets of free time that might allow me to engage in keyboard wars with someone who - at least until earlier today - was quick to cast asparagus but didn’t really have any meat on the bone (if you don’t mind mangled metaphors).
As it is, you’ve done a good job of impersonating a discussant at an academic conference (although they normally have at least one or two nice things to say - still, I got you to read the darn thing so I should probably be grateful for small miracles). Anyway, bearing in mind what’s on my plate aside from the pin, hopefully I can do a reasonably good job at responding:
(1) On 2006-2007 non-listings/duplicates, this was a tech issue, not a Daft report issue. As you can imagine, any serious outfit would be aware as best it can of every last property for sale in the country and how many it had versus hadn’t. I generally leave the tech guys to this - 7+ years of working with them convinces me these guys are definitely on top of their game - but in preparation for academic papers using this dataset, I checked up on this particular issue. It seems that whatever properties that have been put up for sale 2006-2012 have not been on Daft, the vast vast majority of them were pre-2008.
Given the nature of those listings in 2006-2007 (myhome agents principally, and your priors should be telling you something about where their properties lay in the bubble distribution), you would hopefully be among the first to criticise me for making conclusions about the change in distribution of prices in Ireland if I were missing an important chunk of that distribution.
(2) Speaking about 2008, yes, it could be argued that this is a relatively arbitrary exclusion. In fact, the research probably doesn’t need this exclusion as the right degree of interacted variables would allow distinction between bubble and crash periods (another paper of mine does this while tackling a different question). However, the purpose of the paper (and maps) was to compare bubble and crash periods so even if those observations hadn’t been excluded (which by the way would only give the model more observations to play with), boundaries have to be set between them and to prevent criticism in relation to arbitrary date choosing, the boundary was set at a year wide. The boundaries have been set bearing in mind the overall price indices for prices and rents respectively: give each segment a year to adjust to falling values before starting to look at the price structure as reflective of the crash period. You could include anything with falling values as crash but I think it’s hard to argue (particularly bearing in mind point (4) below) that the market goes overnight from bubble to crash, let alone if one did argue that how one might pick the day.
(3) This is based on addresses and GeoDirectory. As is obvious, and becoming annoying in relation to the house price register, if the seller/agent/whoever refuses to put in a meaningful address, it’s hard to do any analysis that is geographic in nature and relies on address. Fortunately, roughly two thirds of properties can be identified to their street or better, with most of the rest being identifiable at estate or village level, so while we would clearly want 100% accuracy, for a paper that is working with geographical units that are quite large, it is unlikely that this is a structural issue affecting the conclusions.
(4) I think you’re assuming that, in the eyes of the model, once a property is up, it’s up for ever. This is, in fairness, the myhome approach for their quarterly reports - every Leitrim property on the site, even those up since 2007 or 2009, go into the Q2 2012 figures reported. However, as is clear from the methodological notes on every Daft report, I’m not a fan of that approach. List prices have their uses and their limitations - their primary uses are as a measure of seller expectations and as a proxy for sales price. But, due to human behaviour, they are remarkably nominally rigid. Hence a McMansion listed for €1m in Q1 2007 and eventually revised down to €400,000 in Q3 2009 will make two appearances (and two only) in the model: once in Q1 2007 and again in Q3 2009, as these are the only times we can be clearest on what the seller’s expectations are. For a different study, one might want to focus in on these re-listings (and indeed I plan to study rigidities in list behaviour once my next working paper is complete) but for the purpose of this study, their re-inclusion is treatment enough.
Hopefully that answers your queries, gives 23rdbuchan something to watch and also addresses Devilsbit’s comment (I’m not sure which flaw he is referring to).
Given you did such a good job as being academic discussant, can I ask what you liked about the paper? Was there anything you would take away from it? Or would you still rather have a blank canvas in relation to what’s happened the structure of prices, rents and yields in Ireland, awaiting the day we get different data?
And now back to my analysis of Higgs’ Bosom. Yours haughtily from Level 7,
R
(PS. I very much doubt the above will be the last contribution on the subject. Unfortunately, I suspect this will be my last post on the Pin for at least a week and possibly a month.)
