PPR mix-aware analysis


The following uses a sample of 3147 3-bed Dublin properties.

I expect lots of noise in this data. The average number of 3-beds in the sample is 77/month, but number for Jan-May 2014 are only 36, 64, 48, 63, 51 respectively. However, they arguably agree with Mantissa’s mix-unaware analysis.



So the gap between asking and achieved troughed at about -15% in 2012 and most recently is up to +7%. The non-zero axes magnify this effect.

There is still a vast gulf between anecdotal (“everything is going 100k over asking and nothing is available under 400k”) and statistical data.


Wow, great work ps.

Quick questions

  • Are you removing outliers? Is there any need?
  • What % of the ads for 3-beds are you subsequently able to match to PPR entries, i.e. how complete do you think your coverage is?

For those not following the Great Index Debates, this is probably the best piece of analysis of Dublin 3-bed prices done by anyone, ever, across any medium (assuming it’s correct and I see no reason to doubt ps’s work). Puts MyHome/Daft/CSO to shame.


Great work!!

What is the average asking price over the whole time?

It looks like the asking price and selling has generally moved closer together over the period which is interesting. With the PPR data being available, this is what I would expect.


w.r.t asking prices, this is presumably last asking price.

Which isn’t as useful as might be expected if you’re looking at a house for sale, since you cannot know whether it will be re-listed higher or lower before it’s eventual sale.


The last 7 months have been bubblicious compared to the previous 24 months but have the CSO being reporting rises for much longer.

If you wish it, it will come true!!


whats with the sale price way above asking in the last month?


Would be interesting to get the data points so we can plot vs CSO, sunspots, etc. PS, would you be willing to post that in spreadsheet form?


What is your definition of Dublin?


Agreed, well done!

That’s a bit harsh - even allowing for the fact that the CSO haven’t yet been able to match BER records with stamp duty/PPR records, at least they’re on the case. In addition, the Daft.ie Report has included a fully mix-adjusted large-scale version of this (i.e. both asking price and RPPR analysis, for the whole country) in each report for the last 18 months. Indeed, the first mix-adjusted analysis of RPPR data came out within three weeks of the launch of the RPPR:
ronanlyons.com/2012/10/16/th … -register/

I would certainly expect robust discussion from someone such as yourself on the Daft.ie RPPR analysis, and indeed all house price analysis, but I wouldn’t expect you to ignore that it exists.


Interesting chart.

If the data are correct, then we can see that sale prices only matched and exceeded asking prices around the time the market hype suggesting shortage of supply started to be ratcheted up by the EA’s again. The question is … which led to which?

Blue Horseshoe


Ronan, would you care to link to your analysis of Dublin 3-beds? I have never seen such a thing and would be interested to read it.


Answers to questions:

The PPR data is based on a download from 08-Jun-2014; Myhome data based on 3-monthly snapshots since 2010, most recently 01-Apr-2014;

I’m going to say No. The standard deviations on price and on ratio of sale price to asking are not vast. Your question prompted me to look at extreme outliers – there were two properties where sale price was 4% and 5% of asking, both from 2011 and clearly spurious. I haven’t fixed this but won’t make a vast difference to results.

As you know, the only way to match the PPR and myhome data is by address. Matching addresses without house numbers is pointless, so I discard everything without one. I attempt some automated cleanup and standardisation on the addresses before matching, but the hit rate is poor. I have 630k myhome records, for 134k unique addresses of which 76k have house numbers. I only managed to match 12k of these against the PPR and when I limit it to houses for sale without any special conditions and with the PPR full price flag, it drops to under 11k. So the final match rate even after selecting only properties with house numbers is a measly one in seven.

The average asking for Dublin 3-bed houses in the period from Jan-2011 is €275,800. The average selling price is €251,100.
For anyone brave enough to take the data in relational database form, those sorts of queries are simplified by some views, with the two above satisfied by:

select avg(price) from houseaddrmatch where numbeds=3 and region like 'dublin%' and pprsaledate>='201101'; select avg(pprprice) from houseaddrmatch where numbeds=3 and region like 'dublin%' and pprsaledate>='201101';

I don’t think the PPR has done that. While prices were falling, selling prices were below asking. When prices stabilised/rose selling was above asking. Probably the same in any market.

It’s both better and worse than that. I have all the asking prices from my snapshots, so if I see a property more than once I have the data. But a) it’s not easy to decide what to do with multiple prices, b) I only have 3-monthly snapshots so very coarse granularity. So, as you surmise, I’m using the latest price seen.

It’s based on 51 individual matches. Scanning the raw data by eye I don’t see anything obviously spurious. It’s just that there are a lot of them going above asking. The breakdown of the ratio of selling to asking is: under 90% - 5, 90-100% - 9, 100-110% - 20, 110-120% - 7, 120-130% - 5, 130-140% - 3, 140-150% - 2.

You (or anyone) are welcome to the data. The format is twisty, though, and has multiple encoded fields for things like house type, sale type, tax type, price type etc. It makes much more sense as a relational database where it can be simplified using views. I suspect you’re probably comfortable using an RDBMS. I’m using the open source H2 database which takes about 5 minutes to download, install, and get running. I could give you the data as a H2 database along with some illustrative sample queries. Can also do it in CSV format, but will have to be three separate tables and you will be on your own trying to join them. H2 has good facilities for importing and exporting csv data, which allows the raw data or the results of queries to be exported for massaging in spreadsheets etc.

If you do a search on myhome.ie you can choose a region/postcode. In Dublin you have a choice of any of the Dublin postcodes, or just Dublin, or Dublin south, north, west or county. All of those fall within my definition of Dublin since I’m using the myhome data to drive this.


PS, thanks but I wasn’t even talking about the raw data; I just meant the two resulting data series charted in your graph above.

Thanks again for this analysis.


Sure – here’s the totals, you can derive the averages used in the chart. Not sure how the tabs will come across in HTML, probably as spaces – presume you can massage the text to recover tab data; if not gizza shout and I’ll stick something up on a drive. (Might be a bit slow with further info … I have a quantum physics exam on Thursday :open_mouth: )

Month Sample Count Total PPR price Total Asking price 201101 17 4,131,950 4,813,700 201102 29 8,306,700 9,079,750 201103 53 14,120,950 15,796,150 201104 49 13,755,500 15,680,850 201105 82 22,572,500 25,544,645 201106 78 21,844,750 24,780,295 201107 85 23,375,550 26,406,450 201108 77 21,430,477 24,343,650 201109 88 22,793,797 26,430,795 201110 97 23,648,316 27,688,650 201111 91 24,325,900 28,453,050 201112 122 28,599,200 33,109,200 201201 76 17,225,409 20,305,550 201202 79 16,530,578 19,520,300 201203 94 20,804,525 24,081,000 201204 95 22,958,050 26,787,000 201205 106 23,058,350 26,689,550 201206 114 24,550,550 27,368,500 201207 106 27,488,500 30,129,800 201208 103 24,303,963 27,134,500 201209 127 32,026,749 34,790,600 201210 129 28,853,658 32,016,050 201211 119 27,000,000 29,299,299 201212 164 42,420,717 45,007,900 201301 49 12,975,000 13,900,749 201302 57 13,358,100 14,320,050 201303 68 15,170,368 16,345,700 201304 58 14,699,000 15,695,200 201305 69 16,589,400 17,684,350 201306 73 17,068,000 18,993,850 201307 56 12,807,440 13,631,750 201308 42 9,713,950 10,717,800 201309 23 5,414,900 5,894,400 201310 42 9,923,450 10,449,450 201311 56 15,446,100 15,884,500 201312 112 34,957,500 35,159,950 201401 36 9,508,636 9,529,550 201402 64 20,252,400 19,877,300 201403 48 13,768,750 13,579,550 201404 63 17,683,200 17,442,950 201405 51 14,786,800 13,663,050


I also have further data on the breakdown of the house types within the 3-bed Dublin category:

However, slicing it even more ways than I did would result in very small samples.


Same again, for Dublin 4-beds. Sample sizes here are quite low, only 25 for that spike in Aug-13 and only 15 for May-14 so far, mostly around 50-60/month elsewhere. Total sample 1,599 4-bed Dublin houses. Interesting that average selling price has never overtaken average asking on 4-beds (apart from maybe last month). I thought that market was hotter than 3-beds, from anecdotal comments. I guess the moral is don’t get caught in a bidding war on a 4-bed.


Trojan work pa, fair play to you.


Wow! €200k for the extra bedroom.


Very good work PS!


Blue Horseshoe